Repository: Ecogenomics/GTDBTk (language: python)

Issue:
Prodigal Pointer Error, GTDBtk continues
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## Environment
- [ ] Installed via pip (include the output of `pip list`)
- [x] Using a conda environment (include the output of `conda list && conda list --revisions`)
- [ ] Using a Docker container (include the `IMAGE ID` of the container)

## Server information
- CPU: `model name	: Intel(R) Xeon(R) CPU E7-4850 v4 @ 2.10GHz 128`
- RAM: `MemTotal:       1056527808 kB`
- OS: `NAME="Red Hat Enterprise Linux Server" VERSION="7.9 (Maipo)"`

## Debugging information
- [ ] `gtdbtk.log` has been included (drag and drop the file to upload).
- [ ] Genomes have been included (if possible, and there are few).

## Additional comments
<!--- Include any additional comments which may help with debugging here. -->

Hi there

this is more a note on error recovery.

I ran gtdbtk (2.1) on a few thousand MAGs and got this issue:

```
*** Error in `prodigal': free(): invalid pointer: 0x00007f3992dca010 ***
======= Backtrace: =========
/lib64/libc.so.6(+0x81329)[0x7f3998862329]
prodigal(+0x356f)[0x56438f86256f]
/lib64/libc.so.6(__libc_start_main+0xf5)[0x7f3998803555]
prodigal(+0x4409)[0x56438f863409]
======= Memory map: ========
56438f85f000-56438f861000 r--p 00000000 00:32 358935210122               GTDBTK210/bin/prodigal
56438f861000-56438fa50000 r-xp 00002000 00:32 358935210122              GTDBTK210/bin/prodigal
56438fa50000-5643903f2000 r--p 001f1000 00:32 358935210122               GTDBTK210/bin/prodigal
5643903f3000-5643903f4000 r--p 00b93000 00:32 358935210122               GTDBTK210/bin/prodigal
5643903f4000-5643903f5000 rw-p 00b94000 00:32 358935210122               GTDBTK210/bin/prodigal
564390773000-564390794000 rw-p 00000000 00:00 0                          [heap]
7f398c000000-7f398c021000 rw-p 00000000 00:00 0
7f398c021000-7f3990000000 ---p 00000000 00:00 0
7f3992dca000-7f3992e53000 rw-p 00000000 00:00 0
7f39987e1000-7f39989a5000 r-xp 00000000 fd:00 536872223                  /usr/lib64/libc-2.17.so
7f39989a5000-7f3998ba4000 ---p 001c4000 fd:00 536872223                  /usr/lib64/libc-2.17.so
7f3998ba4000-7f3998ba8000 r--p 001c3000 fd:00 536872223                  /usr/lib64/libc-2.17.so
7f3998ba8000-7f3998baa000 rw-p 001c7000 fd:00 536872223                  /usr/lib64/libc-2.17.so
7f3998baa000-7f3998baf000 rw-p 00000000 00:00 0
7f3998baf000-7f3998cb0000 r-xp 00000000 fd:00 536947083                  /usr/lib64/libm-2.17.so
7f3998cb0000-7f3998eaf000 ---p 00101000 fd:00 536947083                  /usr/lib64/libm-2.17.so
7f3998eaf000-7f3998eb0000 r--p 00100000 fd:00 536947083                  /usr/lib64/libm-2.17.so
7f3998eb0000-7f3998eb1000 rw-p 00101000 fd:00 536947083                  /usr/lib64/libm-2.17.so
7f3998eb1000-7f3998ed3000 r-xp 00000000 fd:00 536872059                  /usr/lib64/ld-2.17.so
7f39990a1000-7f39990a4000 r--p 00000000 00:32 362997430686              GTDBTK210/lib/libgcc_s.so.1
7f39990a4000-7f39990b6000 r-xp 00003000 00:32 362997430686              GTDBTK210/lib/libgcc_s.so.1
7f39990b6000-7f39990b9000 r--p 00015000 00:32 362997430686               GTDBTK210/lib/libgcc_s.so.1
7f39990b9000-7f39990ba000 r--p 00017000 00:32 362997430686               GTDBTK210/lib/libgcc_s.so.1
7f39990ba000-7f39990bb000 rw-p 00018000 00:32 362997430686               GTDBTK210/lib/libgcc_s.so.1
7f39990bb000-7f39990be000 rw-p 00000000 00:00 0
7f39990cc000-7f39990d2000 rw-p 00000000 00:00 0
7f39990d2000-7f39990d3000 r--p 00021000 fd:00 536872059                  /usr/lib64/ld-2.17.so
7f39990d3000-7f39990d4000 rw-p 00022000 fd:00 536872059                  /usr/lib64/ld-2.17.so
7f39990d4000-7f39990d5000 rw-p 00000000 00:00 0
7fff6518c000-7fff6523f000 rw-p 00000000 00:00 0                          [stack]
7fff653a1000-7fff653a3000 r-xp 00000000 00:00 0                          [vdso]
ffffffffff600000-ffffffffff601000 r-xp 00000000 00:00 0                  [vsyscall]
```


and gtdbtk continued not seeing the error. I assume that checking the exit value of prodigal would solve this issue (see here: https://github.com/Ecogenomics/GTDBTk/blob/88ab52ec5eb569ef4809e9b4e267947d2b07787f/gtdbtk/biolib_lite/prodigal_biolib.py#L144) or run a subprocess with `check=True`.

best,
hans
de_novo_workflow fails to parse gtdb classify output
Hello,

I am trying to build a tree integrating my genomes with the GTDB refs. After the tree is built, the workflow fails with the error 

```
ERROR: Not a tab-separated line: user_genome	classification	fastani_reference	fastani_reference_radius	fastani_taxonomy	fastani_ani	fastani_af	closest_placement_reference	closest_placement_radius	closest_placement_taxonomy	closest_placement_ani	closest_placement_af	pplacer_taxonomy	classification_method	note	other_related_references(genome_id,species_name,radius,ANI,AF)	msa_percent	translation_table	red_value	warnings
```

I had provided the file produced by gtdb classify to the flag --gtdbtk_classification_file.
Merge Prescreening for 2.2.0

Round red_value column
Minor feature request: the `red_value` column in `*.summary.tsv` is currently reported to 16 decimals (e.g. 0.9958416172157424). Could this be rounded to 4 or 5 decimals?

Rationale: this would make diffing GTDB-Tk output easier. The smallest decimals in that column tend to flutter, causing spurious diffs between runs.

For perfect repeatability, the one other column to fix would be `other_related_references`. Putting a total sort on the internal fields in that column (including genome_id) would make it deterministic too. (Though perhaps not as minor to implement.)

I like to keep my analyses in git - ideal for keeping track across parameter changes and software / database updates. Don't underestimate `git diff --word-diff gtdbtk.bac120.summary.tsv` :nerd_face: 


Discussion / hints:

Fix patch:
diff --git a/README.md b/README.md
index 7abc589b..908f319a 100644
--- a/README.md
+++ b/README.md
@@ -7,8 +7,6 @@
 [![Docker Image Version (latest by date)](https://img.shields.io/docker/v/ecogenomic/gtdbtk?sort=date&color=299bec&label=docker)](https://hub.docker.com/r/ecogenomic/gtdbtk)
 [![Docker Pulls](https://img.shields.io/docker/pulls/ecogenomic/gtdbtk?color=299bec&label=pulls)](https://hub.docker.com/r/ecogenomic/gtdbtk)
 
-<b>GTDB-Tk v2.1.0+ requires an updated reference package ([R207_v2](https://data.gtdb.ecogenomic.org/releases/latest/auxillary_files/gtdbtk_v2_data.tar.gz)), [read more](https://ecogenomics.github.io/GTDBTk/installing/index.html#gtdb-tk-reference-data).</b>
-
 GTDB-Tk is a software toolkit for assigning objective taxonomic classifications to bacterial and archaeal genomes based 
 on the Genome Database Taxonomy ([GTDB](https://gtdb.ecogenomic.org/)). It is designed to work with recent advances that 
 allow hundreds or thousands of metagenome-assembled genomes (MAGs) to be obtained directly from environmental samples. 
@@ -39,13 +37,15 @@ Documentation for GTDB-Tk can be found [here](https://ecogenomics.github.io/GTDB
 
 ## ✨ New Features
 
-GTDB-Tk v2.1.0 includes the following new features:
-- GTDB-TK now uses a **divide-and-conquer** approach where the bacterial reference tree is split into multiple **class**-level subtrees. This reduces the memory requirements of GTDB-Tk from **320 GB** of RAM when using the full GTDB R07-RS207 reference tree to approximately **55 GB**. A manuscript describing this approach is in preparation. If you wish to continue using the full GTDB reference tree use the `--full-tree` flag.  
-This is the main change from v2.0.0. The split tree approach has been modified from order-level trees to class-level trees to resolve specific classification issues (See [#383](https://github.com/Ecogenomics/GTDBTk/issues/383)). 
-- Genomes that cannot be assigned to a domain (e.g. genomes with no bacterial or archaeal markers or genomes with no genes called by Prodigal) are now reported in the `gtdbtk.bac120.summary.tsv` as 'Unclassified'
-- Genomes filtered out during the alignment step are now reported in the `gtdbtk.bac120.summary.tsv` or `gtdbtk.ar53.summary.tsv` as 'Unclassified Bacteria/Archaea'
-- `--write_single_copy_genes` flag in now available in the `classify_wf` and `de_novo_wf` workflows.
+GTDB-Tk v2.2.0+ includes the following new features:
+- GTDB-TK `classify` and `classify_wf` have changed in version 2.2.0+. There is now an ANI classification stage (`ANI screen`) that precedes classification by placement in a reference tree.
+  - **This is now the default behavior for `classify` and `classify_wf`.**
+  - In `classify`, user genomes are first compared against a Mash database comprised of all GTDB representative genomes and genome pairs of sufficient similarity processed by FastANI. User genomes classified to a GTDB representative based on FastANI results are not run through pplacer. 
+  - In the `classify_wf` workflow, genomes are classified using Mash and FastANI before executing the identify step. User genomes classified with FastANI are not run through the remainder of the pipeline (identify, align, classify).
+  - To classify genomes without the additional `ani_screen` step, use the `--skip_ani_screen` flag.
 
+## 📈 Performance
+Using ANI screen "can" reduce computation by >50%, although it depends on the set of input genomes. A set of input genomes consisting primarily of new species will not benefit from ANI screen as much as a set of genomes that are largely assigned to GTDB species clusters. In the latter case, the ANI screen will reduce the number of genomes that need to be classified by pplacer which reduces computation time subsantially (between 25% and 60% in our testing).
 
 ## 📚 References
 
diff --git a/docs/assets/gtdbtk_logo.ai b/docs/assets/gtdbtk_logo.ai
new file mode 100644
index 00000000..f58911af
--- /dev/null
+++ b/docs/assets/gtdbtk_logo.ai
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+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>0</xmpG:green>
+                           <xmpG:blue>0</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>RGB Red</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>255</xmpG:red>
+                           <xmpG:green>0</xmpG:green>
+                           <xmpG:blue>0</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>RGB Yellow</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>255</xmpG:red>
+                           <xmpG:green>255</xmpG:green>
+                           <xmpG:blue>0</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>RGB Green</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>255</xmpG:green>
+                           <xmpG:blue>0</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>RGB Cyan</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>255</xmpG:green>
+                           <xmpG:blue>255</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>RGB Blue</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>0</xmpG:green>
+                           <xmpG:blue>255</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>RGB Magenta</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>255</xmpG:red>
+                           <xmpG:green>0</xmpG:green>
+                           <xmpG:blue>255</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=193 G=39 B=45</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>193</xmpG:red>
+                           <xmpG:green>39</xmpG:green>
+                           <xmpG:blue>45</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=237 G=28 B=36</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>237</xmpG:red>
+                           <xmpG:green>28</xmpG:green>
+                           <xmpG:blue>36</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=241 G=90 B=36</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>241</xmpG:red>
+                           <xmpG:green>90</xmpG:green>
+                           <xmpG:blue>36</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=247 G=147 B=30</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>247</xmpG:red>
+                           <xmpG:green>147</xmpG:green>
+                           <xmpG:blue>30</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=251 G=176 B=59</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>251</xmpG:red>
+                           <xmpG:green>176</xmpG:green>
+                           <xmpG:blue>59</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=252 G=238 B=33</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>252</xmpG:red>
+                           <xmpG:green>238</xmpG:green>
+                           <xmpG:blue>33</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=217 G=224 B=33</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>217</xmpG:red>
+                           <xmpG:green>224</xmpG:green>
+                           <xmpG:blue>33</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=140 G=198 B=63</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>140</xmpG:red>
+                           <xmpG:green>198</xmpG:green>
+                           <xmpG:blue>63</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=57 G=181 B=74</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>57</xmpG:red>
+                           <xmpG:green>181</xmpG:green>
+                           <xmpG:blue>74</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=0 G=146 B=69</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>146</xmpG:green>
+                           <xmpG:blue>69</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=0 G=104 B=55</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>104</xmpG:green>
+                           <xmpG:blue>55</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=34 G=181 B=115</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>34</xmpG:red>
+                           <xmpG:green>181</xmpG:green>
+                           <xmpG:blue>115</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=0 G=169 B=157</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>169</xmpG:green>
+                           <xmpG:blue>157</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=41 G=171 B=226</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>41</xmpG:red>
+                           <xmpG:green>171</xmpG:green>
+                           <xmpG:blue>226</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=0 G=113 B=188</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>113</xmpG:green>
+                           <xmpG:blue>188</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=46 G=49 B=146</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>46</xmpG:red>
+                           <xmpG:green>49</xmpG:green>
+                           <xmpG:blue>146</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=27 G=20 B=100</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>27</xmpG:red>
+                           <xmpG:green>20</xmpG:green>
+                           <xmpG:blue>100</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=102 G=45 B=145</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>102</xmpG:red>
+                           <xmpG:green>45</xmpG:green>
+                           <xmpG:blue>145</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=147 G=39 B=143</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>147</xmpG:red>
+                           <xmpG:green>39</xmpG:green>
+                           <xmpG:blue>143</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=158 G=0 B=93</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>158</xmpG:red>
+                           <xmpG:green>0</xmpG:green>
+                           <xmpG:blue>93</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=212 G=20 B=90</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>212</xmpG:red>
+                           <xmpG:green>20</xmpG:green>
+                           <xmpG:blue>90</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=237 G=30 B=121</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>237</xmpG:red>
+                           <xmpG:green>30</xmpG:green>
+                           <xmpG:blue>121</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=199 G=178 B=153</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>199</xmpG:red>
+                           <xmpG:green>178</xmpG:green>
+                           <xmpG:blue>153</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=153 G=134 B=117</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>153</xmpG:red>
+                           <xmpG:green>134</xmpG:green>
+                           <xmpG:blue>117</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=115 G=99 B=87</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>115</xmpG:red>
+                           <xmpG:green>99</xmpG:green>
+                           <xmpG:blue>87</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=83 G=71 B=65</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>83</xmpG:red>
+                           <xmpG:green>71</xmpG:green>
+                           <xmpG:blue>65</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=198 G=156 B=109</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>198</xmpG:red>
+                           <xmpG:green>156</xmpG:green>
+                           <xmpG:blue>109</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=166 G=124 B=82</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>166</xmpG:red>
+                           <xmpG:green>124</xmpG:green>
+                           <xmpG:blue>82</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=140 G=98 B=57</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>140</xmpG:red>
+                           <xmpG:green>98</xmpG:green>
+                           <xmpG:blue>57</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=117 G=76 B=36</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>117</xmpG:red>
+                           <xmpG:green>76</xmpG:green>
+                           <xmpG:blue>36</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=96 G=56 B=19</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>96</xmpG:red>
+                           <xmpG:green>56</xmpG:green>
+                           <xmpG:blue>19</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=66 G=33 B=11</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>66</xmpG:red>
+                           <xmpG:green>33</xmpG:green>
+                           <xmpG:blue>11</xmpG:blue>
+                        </rdf:li>
+                     </rdf:Seq>
+                  </xmpG:Colorants>
+               </rdf:li>
+               <rdf:li rdf:parseType="Resource">
+                  <xmpG:groupName>Grays</xmpG:groupName>
+                  <xmpG:groupType>1</xmpG:groupType>
+                  <xmpG:Colorants>
+                     <rdf:Seq>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=0 G=0 B=0</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>0</xmpG:red>
+                           <xmpG:green>0</xmpG:green>
+                           <xmpG:blue>0</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=26 G=26 B=26</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>26</xmpG:red>
+                           <xmpG:green>26</xmpG:green>
+                           <xmpG:blue>26</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=51 G=51 B=51</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>51</xmpG:red>
+                           <xmpG:green>51</xmpG:green>
+                           <xmpG:blue>51</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=77 G=77 B=77</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>77</xmpG:red>
+                           <xmpG:green>77</xmpG:green>
+                           <xmpG:blue>77</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=102 G=102 B=102</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>102</xmpG:red>
+                           <xmpG:green>102</xmpG:green>
+                           <xmpG:blue>102</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=128 G=128 B=128</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>128</xmpG:red>
+                           <xmpG:green>128</xmpG:green>
+                           <xmpG:blue>128</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=153 G=153 B=153</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>153</xmpG:red>
+                           <xmpG:green>153</xmpG:green>
+                           <xmpG:blue>153</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=179 G=179 B=179</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>179</xmpG:red>
+                           <xmpG:green>179</xmpG:green>
+                           <xmpG:blue>179</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=204 G=204 B=204</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>204</xmpG:red>
+                           <xmpG:green>204</xmpG:green>
+                           <xmpG:blue>204</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=230 G=230 B=230</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>230</xmpG:red>
+                           <xmpG:green>230</xmpG:green>
+                           <xmpG:blue>230</xmpG:blue>
+                        </rdf:li>
+                        <rdf:li rdf:parseType="Resource">
+                           <xmpG:swatchName>R=242 G=242 B=242</xmpG:swatchName>
+                           <xmpG:mode>RGB</xmpG:mode>
+                           <xmpG:type>PROCESS</xmpG:type>
+                           <xmpG:red>242</xmpG:red>
+                           <xmpG:green>242</xmpG:green>
+                           <xmpG:blue>242</xmpG:blue>
+                        </rdf:li>
+                     </rdf:Seq>
+                  </xmpG:Colorants>
+               </rdf:li>
+               <rdf:li rdf:parseType="Resource">
+                  <xmpG:groupName>Web Color Group</xmpG:groupName>
+                  <xmpG:groupType>1</xmpG:groupType>
+                  <xmpG:Colorants>
+                     <rdf:Seq>
+                        <rdf:li rdf:parseType="Resource">
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diff --git a/docs/src/announcements.rst b/docs/src/announcements.rst
index 7604e82c..8de8008b 100644
--- a/docs/src/announcements.rst
+++ b/docs/src/announcements.rst
@@ -1,6 +1,14 @@
 Announcements
 =============
 
+GTDB-Tk 2.2.0 available
+-----------------------
+
+*February 14, 2023*
+
+* GTDB-Tk version ``2.2.0`` is now available.
+* This version of GTDB-Tk **does not** require a new version of the GTDB-Tk reference package.
+
 
 GTDB-Tk 2.1.0 available
 -----------------------
diff --git a/docs/src/changelog.rst b/docs/src/changelog.rst
index 5db166e5..b3376b23 100644
--- a/docs/src/changelog.rst
+++ b/docs/src/changelog.rst
@@ -2,6 +2,38 @@
 Change log
 ==========
 
+2.2.0
+-----
+
+Minor changes:
+
+* (`#433 <https://github.com/Ecogenomics/GTDBTk/issues/433>`_) Added additional checks to ensure that the `--outgroup_taxon` cannot be set to a domain (`root`, `de_novo_wf`).
+* (`#459 <https://github.com/Ecogenomics/GTDBTk/issues/459>`_ / `#462 <https://github.com/Ecogenomics/GTDBTk/issues/462>`_ ) Fix deprecated np.bool in prodigal_biolib.py. Special thanks to @neoformit for his contribution.
+* (`#466 <http://github.com/Ecogenomics/GTDBTk/issues/466>`_) RED value has been rounded to 5 decimals after the comma.
+* (`#451 <http://github.com/Ecogenomics/GTDBTk/issues/451>`_) Extra checks have been added when Prodigal fails.
+* (`#448 <http://github.com/Ecogenomics/GTDBTk/issues/448>`_) Warning has been added when all the genomes are filtered out and not classified.
+
+Bug Fixes:
+
+* (`#420 <https://github.com/Ecogenomics/GTDBTk/issues/420>`_) Fixed an issue where GTDB-Tk might hang when classifying TIGRFAM markers (`identify`, `classify_wf`, `de_novo_wf`). Special thanks to @lfenske-93 and @sjaenick for their contribution.
+* (`#428 <https://github.com/Ecogenomics/GTDBTk/issues/428>`_) Fixed an issue where the `--gtdbtk_classification_file` would raise an error trying to read the `classify` summary (`root`, `de_novo_wf`).
+* (`#439 <https://github.com/Ecogenomics/GTDBTk/issues/439>`_) Fix the pipeline when using protein files instead of nucleotide files. symlink uses absolute path instead.
+
+
+
+
+2.1.1
+-----
+
+Documentation:
+
+* (`#410 <https://github.com/Ecogenomics/GTDBTk/issues/410>`_) Add documentation for `convert_to_itol`
+
+Bug Fixes:
+
+* (`#399 <https://github.com/Ecogenomics/GTDBTk/issues/399>`_) Fix `--genes` option attempting to create a directory.
+* (`#400 <https://github.com/Ecogenomics/GTDBTk/issues/400>`_) Updated contig.py to fix inconsistent pplacer paths causing the program to crash.
+
 
 2.1.0
 -----
diff --git a/docs/src/commands/align.rst b/docs/src/commands/align.rst
index b2f25151..8c730a54 100644
--- a/docs/src/commands/align.rst
+++ b/docs/src/commands/align.rst
@@ -22,12 +22,14 @@ Files output
 
 
 * :ref:`[prefix].log <files/gtdbtk.log>`
+* :ref:`[prefix].json <files/gtdbtk.json>`
 * :ref:`[prefix].warnings.log <files/gtdbtk.warnings.log>`
-* :ref:`align/[prefix].[domain].msa.fasta.gz <files/msa.fasta>`
-* :ref:`align/[prefix].[domain].user_msa.fasta.gz <files/user_msa.fasta>`
-* :ref:`align/[prefix].[domain].filtered.tsv <files/filtered.tsv>`
-* :ref:`align/intermediate_results/[prefix].[domain].marker_info.tsv <files/marker_info.tsv>`
-
+* align
+    * :ref:`[prefix].[domain].msa.fasta.gz <files/msa.fasta>`
+    * :ref:`[prefix].[domain].user_msa.fasta.gz <files/user_msa.fasta>`
+    * :ref:`[prefix].[domain].filtered.tsv <files/filtered.tsv>`
+    * intermediate_results
+        * :ref:`[prefix].[domain].marker_info.tsv <files/marker_info.tsv>`
 
 Example
 -------
diff --git a/docs/src/commands/classify.rst b/docs/src/commands/classify.rst
index 5a40b8da..caeec4e3 100644
--- a/docs/src/commands/classify.rst
+++ b/docs/src/commands/classify.rst
@@ -20,17 +20,33 @@ Files output
 ------------
 
 * classify
+    * :ref:`[prefix].[domain].summary.tsv <files/summary.tsv>`
+    * :ref:`[prefix].backbone.[domain].classify.tree <files/classify.tree>`
+    * :ref:`[prefix].[domain].tree.mapping.tsv <files/tree.mapping.tsv>`
+    * :ref:`[prefix].[domain].classify.tree.[index].tree <files/classify.tree>`
     * intermediate_results
-        * :ref:`[prefix].[domain].classification_pplacer.tsv <files/classification_pplacer.tsv>`
-        * :ref:`[prefix].[domain].classify.tree <files/classify.tree>`
+        * :ref:`[prefix].[domain].backbone.classification_pplacer.tsv <files/classification_pplacer.tsv>`
+        * :ref:`[prefix].[domain].class_level.classification_pplacer_tree_[index].tsv <files/classification_pplacer.tsv>`
+        * :ref:`[prefix].[domain].prescreened.msa.fasta <files/msa.fasta>`
+        * :ref:`[prefix].[domain].red_dictionary.tsv <files/red_dictionary.tsv>`
         * pplacer
-            * :ref:`pplacer.[domain].json <files/pplacer.domain.json>`
-            * :ref:`pplacer.[domain].out <files/pplacer.domain.out>`
-            * :ref:`[prefix].[domain].red_dictionary.tsv <files/red_dictionary.tsv>`
+            * :ref:`pplacer.backbone.[domain].json <files/pplacer.domain.json>`
+            * :ref:`pplacer.backbone.[domain].out <files/pplacer.domain.out>`
+            * tree_[index]
+                * :ref:`[prefix].[domain].user_msa.fasta <files/user_msa.fasta>`
+                * :ref:`pplacer.class_level.[domain].out <files/pplacer.domain.out>`
+                * :ref:`pplacer.class_level.[domain].json <files/pplacer.domain.json>`
+* ani_screen
+    * intermediate_results
+        * mash
+            * :ref:`[prefix].mash_distances.tsv <files/mash_distances.msh>`
+            * :ref:`[prefix].user_query_sketch.msh <files/user_query_sketch.msh>`
 * :ref:`[prefix].[domain].summary.tsv <files/summary.tsv>`
 * :ref:`[prefix].log <files/gtdbtk.log>`
+* :ref:`[prefix].json <files/gtdbtk.json>`
 * :ref:`[prefix].warnings.log <files/gtdbtk.warnings.log>`
 
+
 Example
 -------
 
@@ -51,16 +67,26 @@ Output
 
 .. code-block:: text
 
-    [2022-04-11 12:02:06] INFO: GTDB-Tk v2.0.0
-    [2022-04-11 12:02:06] INFO: gtdbtk classify --genome_dir /tmp/gtdbtk/genomes --align_dir /tmp/gtdbtk/align --out_dir /tmp/gtdbtk/classify -x gz --cpus 2
-    [2022-04-11 12:02:06] INFO: Using GTDB-Tk reference data version r207: /srv/db/gtdbtk/official/release207
-    [2022-04-11 12:02:07] TASK: Placing 2 archaeal genomes into reference tree with pplacer using 2 CPUs (be patient).
-    [2022-04-11 12:02:07] INFO: pplacer version: v1.1.alpha19-0-g807f6f3
-    [2022-04-11 12:07:06] INFO: Calculating RED values based on reference tree.
-    [2022-04-11 12:07:06] TASK: Traversing tree to determine classification method.
-    [2022-04-11 12:07:06] INFO: Completed 2 genomes in 0.00 seconds (18,558.87 genomes/second).
-    [2022-04-11 12:07:06] TASK: Calculating average nucleotide identity using FastANI (v1.32).
-    [2022-04-11 12:07:08] INFO: Completed 4 comparisons in 1.61 seconds (2.49 comparisons/second).
-    [2022-04-11 12:07:08] INFO: 2 genome(s) have been classified using FastANI and pplacer.
-    [2022-04-11 12:07:08] INFO: Note that Tk classification mode is insufficient for publication of new taxonomic designations. New designations should be based on one or more de novo trees, an example of which can be produced by Tk in de novo mode.
-    [2022-04-11 12:07:08] INFO: Done.
+    [2023-02-08 12:53:42] INFO: GTDB-Tk v2.2.0
+    [2023-02-08 12:53:42] INFO: gtdbtk classify --align_dir align_3lines/ --batchfile 3lines_batchfile.tsv --out_dir 3classify_ani --mash_db mash_db_dir/ --cpus 20
+    [2023-02-08 12:53:42] INFO: Using GTDB-Tk reference data version r207: /path/to/gtdbtk/database/release207_v2/
+    [2023-02-08 12:53:43] INFO: Loading reference genomes.
+    [2023-02-08 12:53:43] INFO: Using Mash version 2.2.2
+    [2023-02-08 12:53:43] INFO: Loading data from existing Mash sketch file: 3classify_ani/classify/ani_screen/intermediate_results/mash/gtdbtk.user_query_sketch.msh
+    [2023-02-08 12:53:43] INFO: Loading data from existing Mash sketch file: mash_db_dir/gtdb_ref_sketch.msh
+    [2023-02-08 12:53:46] INFO: Calculating Mash distances.
+    [2023-02-08 12:53:49] INFO: Calculating ANI with FastANI v1.3.
+    [2023-02-08 12:53:49] INFO: Completed 12 comparisons in 0.44 seconds (27.54 comparisons/second).
+    [2023-02-08 12:53:49] INFO: 2 genome(s) have been classified using the ANI pre-screening step.
+    [2023-02-08 12:53:49] TASK: Placing 1 bacterial genomes into backbone reference tree with pplacer using 20 CPUs (be patient).
+    [2023-02-08 12:53:49] INFO: pplacer version: v1.1.alpha19-0-g807f6f3
+    [2023-02-08 12:55:02] INFO: Calculating RED values based on reference tree.
+    [2023-02-08 12:55:03] INFO: 1 out of 1 have an class assignments. Those genomes will be reclassified.
+    [2023-02-08 12:55:03] TASK: Placing 1 bacterial genomes into class-level reference tree 5 (1/1) with pplacer using 20 CPUs (be patient).
+    [2023-02-08 12:57:38] INFO: Calculating RED values based on reference tree.
+    [2023-02-08 12:57:40] TASK: Traversing tree to determine classification method.
+    [2023-02-08 12:57:40] INFO: Completed 1 genome in 0.04 seconds (23.86 genomes/second).
+    [2023-02-08 12:57:40] INFO: 0 genome(s) have been classified using FastANI and pplacer.
+    [2023-02-08 12:57:40] WARNING: 1 of 3 genome has a warning (see summary file).
+    [2023-02-08 12:57:40] INFO: Note that Tk classification mode is insufficient for publication of new taxonomic designations. New designations should be based on one or more de novo trees, an example of which can be produced by Tk in de novo mode.
+    [2023-02-08 12:57:40] INFO: Done.
\ No newline at end of file
diff --git a/docs/src/commands/classify_wf.rst b/docs/src/commands/classify_wf.rst
index 02749902..a360c331 100644
--- a/docs/src/commands/classify_wf.rst
+++ b/docs/src/commands/classify_wf.rst
@@ -12,7 +12,11 @@ For arguments and output files, see each of the individual steps:
 * :ref:`commands/align`
 * :ref:`commands/classify`
 
-The classify workflow consists of three steps: ``identify``, ``align``, and ``classify``.
+The classify workflow consists of four steps: ``ani_screen``, ``identify``, ``align``, and ``classify``.
+
+The ``ani_screen`` step compares user genomes against a `Mash <https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0997-x>`_ database composed of all GTDB representative genomes,
+then verify the best mash hits using `FastANI <https://www.nature.com/articles/s41467-018-07641-9>`_. User genomes classified with FastANI are not run through the rest of the pipeline (``identify``, ``align``, ``classify``)
+and are reported in the summary file.
 
 The ``identify`` step calls genes using `Prodigal <http://compbio.ornl.gov/prodigal/>`_,
 and uses HMM models and the `HMMER <http://hmmer.org/>`_ package to identify the
diff --git a/docs/src/commands/identify.rst b/docs/src/commands/identify.rst
index 8ad6c7d6..d30b1aa7 100644
--- a/docs/src/commands/identify.rst
+++ b/docs/src/commands/identify.rst
@@ -19,11 +19,13 @@ Arguments
 ## Files output
 
 * :ref:`[prefix].log <files/gtdbtk.log>`
+* :ref:`[prefix].json <files/gtdbtk.json>`
 * :ref:`[prefix].warnings.log <files/gtdbtk.warnings.log>`
-* identify/
+* identify
     * :ref:`[prefix].[domain].markers_summary.tsv <files/markers_summary.tsv>`
     * :ref:`[prefix].translation_table_summary.tsv <files/translation_table_summary.tsv>`
-* identify/intermediate_results/marker_genes/[genome_id]/
+    * :ref:`[prefix].failed_genomes.tsv <files/failed_genomes.tsv>`
+    * intermediate_results/marker_genes/[genome_id]/
     * :ref:`[genome_id]_pfam_tophit.tsv <files/pfam_tophit.tsv>`
     * :ref:`[genome_id]_pfam.tsv <files/pfam.tsv>`
     * :ref:`[genome_id]_protein.faa <files/protein.faa>`
diff --git a/docs/src/files/failed_genomes.tsv.rst b/docs/src/files/failed_genomes.tsv.rst
new file mode 100644
index 00000000..e02a1f1e
--- /dev/null
+++ b/docs/src/files/failed_genomes.tsv.rst
@@ -0,0 +1,22 @@
+.. _files/failed_genomes.tsv:
+
+failed.genomes.tsv
+===================
+
+File reporting failed genomes which have been excluded from analysis due to Prodigal failing to call any genes.
+
+Produced by
+-----------
+ * :ref:`commands/identify`
+ * :ref:`commands/classify_wf`
+
+Example
+-------
+
+.. code-block:: text
+
+    GCA_000002165.1,No genes were called by Prodigal
+    GCA_000002175.1,No genes were called by Prodigal
+    GCA_000002185.1,No genes were called by Prodigal
+    GCA_000002195.1,No genes were called by Prodigal
+    GCA_000002205.1,No genes were called by Prodigal
\ No newline at end of file
diff --git a/docs/src/files/gtdbtk.json.rst b/docs/src/files/gtdbtk.json.rst
new file mode 100644
index 00000000..ca3939f2
--- /dev/null
+++ b/docs/src/files/gtdbtk.json.rst
@@ -0,0 +1,46 @@
+.. _files/gtdbtk.json:
+
+gtdbtk.json
+===========
+
+The console output of GTDB-Tk saved to disk in a JSON format.
+
+Produced by
+-----------
+
+* :ref:`commands/align`
+* :ref:`commands/align`
+* :ref:`commands/classify`
+* :ref:`commands/classify_wf`
+* :ref:`commands/de_novo_wf`
+* :ref:`commands/identify`
+* :ref:`commands/infer`
+
+Example
+-------
+
+.. code-block:: text
+
+    {
+        "version": "2.1.1",
+        "command_line": "gtdbtk classify_wf --batchfile /srv/projects/gtdbtk/test_new_features/gems_benchmark/3lines_batchfile.tsv --out_dir /srv/projects/gtdbtk/test_new_features/gems_benchmark/classify_wf_outdir_prescreen_3lines/ --keep_intermediates --cpus 20 --mash_db /srv/projects/gtdbtk/test_new_features/gems_benchmark/mash_sketch/cli/",
+        "database_version": "r207",
+        "database_path": "/srv/projects/gtdbtk/test_new_features/release207_v2/",
+        "steps": [
+            {
+                "name": "ANI screen",
+                "input": "/srv/projects/gtdbtk/test_new_features/gems_benchmark/3lines_batchfile.tsv",
+                "output_dir": "/srv/projects/gtdbtk/test_new_features/gems_benchmark/classify_wf_outdir_prescreen_3lines/",
+                "output_files": {
+                    "bac120": "/srv/projects/gtdbtk/test_new_features/gems_benchmark/classify_wf_outdir_prescreen_3lines/classify/ani_screen/gtdbtk.bac120.ani_summary.tsv"
+                },
+                "starts_at": "2023-02-01T08:02:17.814231",
+                "ends_at": "2023-02-01T08:02:27.782442",
+                "duration": "0:00:09",
+                "status": "completed",
+                "mash_k": 16,
+                "mash_s": 5000,
+                "mash_v": 1.0,
+                "mash_max_dist": 0.1,
+                "mash_db": "/srv/projects/gtdbtk/test_new_features/gems_benchmark/mash_sketch/cli/"
+            },
diff --git a/docs/src/files/index.rst b/docs/src/files/index.rst
index c5546e67..562e35b9 100644
--- a/docs/src/files/index.rst
+++ b/docs/src/files/index.rst
@@ -41,4 +41,7 @@ The following files are output by GTDB-Tk throughout various stages of the pipel
    unrooted.tree
    user_msa.fasta
    user_query_sketch.msh
+   tree.mapping.tsv
+   gtdbtk.json
+   failed_genomes.tsv
 
diff --git a/docs/src/files/mash_distances.msh.rst b/docs/src/files/mash_distances.msh.rst
index 42c13fc5..6d1ba7d2 100644
--- a/docs/src/files/mash_distances.msh.rst
+++ b/docs/src/files/mash_distances.msh.rst
@@ -8,6 +8,8 @@ The raw output of the distance from each genome to the GTDB-Tk reference genomes
 Produced by
 -----------
 * :ref:`commands/ani_rep`
+* :ref:`commands/classify`
+* :ref:`commands/classify_wf`
 
 Example
 -------
diff --git a/docs/src/files/tree.mapping.tsv.rst b/docs/src/files/tree.mapping.tsv.rst
new file mode 100644
index 00000000..16ec3060
--- /dev/null
+++ b/docs/src/files/tree.mapping.tsv.rst
@@ -0,0 +1,20 @@
+.. _files/tree.mapping.tsv:
+
+tree.mapping.tsv
+==================
+
+Map between genomes and the class_level taxonomy tree used to classify them.
+
+Produced by
+-----------
+ * :ref:`commands/classify_wf`
+ * :ref:`commands/classify`
+
+
+Example
+-------
+
+.. code-block:: text
+
+    user_genome is_ani_classification	class_tree_mapped	classification_rule
+    3300006853_26	False	5	Rule 3
\ No newline at end of file
diff --git a/docs/src/files/user_query_sketch.msh.rst b/docs/src/files/user_query_sketch.msh.rst
index 741431c4..db3412b3 100644
--- a/docs/src/files/user_query_sketch.msh.rst
+++ b/docs/src/files/user_query_sketch.msh.rst
@@ -9,3 +9,5 @@ Produced by
 -----------
 
 * :ref:`commands/ani_rep`
+* :ref:`commands/classify`
+* :ref:`commands/classify_wf`
diff --git a/docs/src/references.rst b/docs/src/references.rst
index f915248f..d59f960b 100644
--- a/docs/src/references.rst
+++ b/docs/src/references.rst
@@ -9,6 +9,7 @@ References
    :widths: 100
    :header-rows: 0
 
+   * - Chaumeil PA, et al. 2022. `GTDB-Tk2: memory friendly classification with the genome taxonomy database <https://academic.oup.com/bioinformatics/article/38/23/5315/6758240>`_. *Bioinformatics*, btac672.
    * - Chaumeil PA, et al. 2019. `GTDB-Tk: A toolkit to classify genomes with the Genome Taxonomy Database <https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btz848/5626182>`_. *Bioinformatics*, btz848.
 
 
diff --git a/gtdbtk/__init__.py b/gtdbtk/__init__.py
index 7b967e47..e7cf7b81 100644
--- a/gtdbtk/__init__.py
+++ b/gtdbtk/__init__.py
@@ -29,4 +29,4 @@
 __status__ = 'Production'
 __title__ = 'GTDB-Tk'
 __url__ = 'https://github.com/Ecogenomics/GTDBTk'
-__version__ = '2.1.1'
+__version__ = '2.2.0'
diff --git a/gtdbtk/__main__.py b/gtdbtk/__main__.py
index 35749469..457f9ae0 100644
--- a/gtdbtk/__main__.py
+++ b/gtdbtk/__main__.py
@@ -35,7 +35,7 @@ def print_help():
 
   Workflows:
     classify_wf -> Classify genomes by placement in GTDB reference tree
-                     (identify -> align -> classify)
+                     (ani_screening -> identify -> align -> classify)
     de_novo_wf  -> Infer de novo tree and decorate with GTDB taxonomy
                      (identify -> align -> infer -> root -> decorate)
 
@@ -91,10 +91,11 @@ def main():
                  hasattr(args, 'debug') and args.debug)
     logger = logging.getLogger('timestamp')
 
+
     # -------------------------------------------------
     # do what we came here to do
     try:
-        gt_parser = OptionsParser(__version__)
+        gt_parser = OptionsParser(__version__,args.out_dir if hasattr(args, 'out_dir') and args.out_dir else None)
         gt_parser.parse_options(args)
     except SystemExit:
         logger.error('Controlled exit resulting from early termination.')
diff --git a/gtdbtk/ani_rep.py b/gtdbtk/ani_rep.py
index d6548d21..d947ca0e 100644
--- a/gtdbtk/ani_rep.py
+++ b/gtdbtk/ani_rep.py
@@ -1,19 +1,19 @@
 import logging
 import os
 from collections import defaultdict
+from typing import List
 
 from gtdbtk.biolib_lite.common import canonical_gid
 from gtdbtk.biolib_lite.execute import check_dependencies
 from gtdbtk.biolib_lite.taxonomy import Taxonomy
-from gtdbtk.config.config import (FASTANI_DIR,
-                                  FASTANI_GENOMES_EXT,
-                                  FASTANI_GENOME_LIST,
-                                  TAXONOMY_FILE,
+from gtdbtk.config.config import (TAXONOMY_FILE,
                                   AF_THRESHOLD)
 from gtdbtk.config.output import DIR_ANI_REP_INT_MASH
+from gtdbtk.exceptions import GTDBTkExit
 from gtdbtk.external.fastani import FastANI
 from gtdbtk.external.mash import Mash
-from gtdbtk.io.gtdb_radii import GTDBRadiiFile
+from gtdbtk.files.gtdb_radii import GTDBRadiiFile
+from gtdbtk.tools import get_ref_genomes
 
 
 class ANIRep(object):
@@ -44,26 +44,52 @@ def check_dependencies(no_mash):
             dependencies.append('mash')
         check_dependencies(dependencies)
 
-    @staticmethod
-    def _get_ref_genomes():
-        """Returns a dictionary of genome accession to genome path.
 
-        Returns
-        -------
-        dict[str, str]
+    def run(self, genomes, no_mash, mash_d, out_dir, prefix, mash_k, mash_v, mash_s, min_af, mash_db):
+        """Runs the pipeline.
+
+        Parameters
+        ----------
+        genomes : dict[str, str]
             Dict[genome_id] = fasta_path
+        no_mash : bool
+            True if Mash should be used for pre-filtering, False otherwise.
+        mash_d : float
+             maximum distance to keep [0-1]
+        out_dir : str
+            The directory to write the output files to.
+        prefix : str
+            The prefix to use when writing output files.
+        mash_k : int
+            k-mer size [1-32]
+        mash_v : float
+            maximum p-value to keep [0-1]
+        mash_s : int
+            maximum number of non-redundant hashes
+        min_af : float
+            alignment fraction to consider the closest genomes
+        mash_db : Optional[str]
+            The path to read/write the pre-computed Mash reference sketch database.
         """
-        ref_genomes = dict()
-        with open(FASTANI_GENOME_LIST) as g_path_file:
-            for line in g_path_file:
-                (full_name, path) = line.strip().split()
-                if full_name.endswith(FASTANI_GENOMES_EXT):
-                    accession = full_name.split(FASTANI_GENOMES_EXT)[0]
-                ref_genomes[accession] = os.path.join(FASTANI_DIR, path, full_name)
-        return ref_genomes
-
-    def run(self, genomes, no_mash, max_d, out_dir, prefix, mash_k, mash_v, mash_s, min_af, mash_db):
-        """Runs the pipeline.
+        max_mash_dist = mash_d
+        fastani_results = self.run_mash_fastani(genomes, no_mash, mash_d, out_dir,
+                                                prefix, mash_k, mash_v,
+                                                mash_s, max_mash_dist, mash_db=mash_db)
+
+        taxonomy = Taxonomy().read(TAXONOMY_FILE, canonical_ids=True)
+        ani_summary_file = ANISummaryFile(out_dir, prefix, fastani_results, taxonomy)
+        ani_summary_file.write()
+        ANIClosestFile(out_dir,
+                       prefix,
+                       fastani_results,
+                       genomes,
+                       min_af,
+                       taxonomy)
+
+    def run_mash_fastani(self,genomes, no_mash, max_d, out_dir, prefix, mash_k, mash_v, mash_s, mash_max_dist=100, mash_db=None):
+        """Runs the mash and fastani pipeline.
+        This step is separated from the run function because it is called from 2 different
+        functions in the gtdbtk ( classify and ani_reps).
 
         Parameters
         ----------
@@ -84,24 +110,25 @@ def run(self, genomes, no_mash, max_d, out_dir, prefix, mash_k, mash_v, mash_s,
         mash_s : int
             maximum number of non-redundant hashes
         min_af : float
-            alignment fraction to consider closest genome
+            alignment fraction to consider the closest genome
         mash_db : Optional[str]
             The path to read/write the pre-computed Mash reference sketch database.
         """
         self.check_dependencies(no_mash)
 
         self.logger.info('Loading reference genomes.')
-        ref_genomes = self._get_ref_genomes()
+        ref_genomes = get_ref_genomes()
         d_compare = defaultdict(set)
         d_paths = {**genomes, **ref_genomes}
 
         # Pre-filter using Mash if specified.
         if not no_mash:
+
             dir_mash = os.path.join(out_dir, DIR_ANI_REP_INT_MASH)
 
             mash = Mash(self.cpus, dir_mash, prefix)
             self.logger.info(f'Using Mash version {mash.version()}')
-            mash_results = mash.run(genomes, ref_genomes, max_d, mash_k, mash_v, mash_s, mash_db)
+            mash_results = mash.run(genomes, ref_genomes, max_d, mash_k, mash_v, mash_s, mash_max_dist, mash_db)
             for qry_gid, ref_hits in mash_results.items():
                 d_compare[qry_gid] = d_compare[qry_gid].union(set(ref_hits.keys()))
 
@@ -113,21 +140,12 @@ def run(self, genomes, no_mash, max_d, out_dir, prefix, mash_k, mash_v, mash_s,
         self.logger.info(f'Calculating ANI with FastANI v{FastANI._get_version()}.')
         fastani = FastANI(self.cpus, force_single=True)
         fastani_results = fastani.run(d_compare, d_paths)
-
-        taxonomy = Taxonomy().read(TAXONOMY_FILE, canonical_ids=True)
-        ANISummaryFile(out_dir, prefix, fastani_results, taxonomy)
-        ANIClosestFile(out_dir,
-                       prefix,
-                       fastani_results,
-                       genomes,
-                       min_af,
-                       taxonomy)
-
+        return fastani_results
 
 class ANISummaryFile(object):
     name = 'ani_summary.tsv'
 
-    def __init__(self, root, prefix, results, taxonomy):
+    def __init__(self, root=None, prefix=None, results=None, taxonomy=None,marker_set_id=None):
         """Writes the ANI summary file generated by this pipeline.
 
         Parameters
@@ -141,24 +159,65 @@ def __init__(self, root, prefix, results, taxonomy):
         taxonomy : dict[str, tuple[str, str, str, str, str, str, str]]
             d[unique_id] -> [d__<taxon>, ..., s__<taxon>]
         """
-        self.path = os.path.join(root, f'{prefix}.{self.name}')
+        if marker_set_id:
+            self.name = f'{marker_set_id}.ani_summary.tsv'
+        if prefix is None:
+            self.path = root
+        else:
+            self.path = os.path.join(root, f'{prefix}.{self.name}')
         self.results = results
         self.taxonomy = taxonomy
         self.logger = logging.getLogger('timestamp')
-        self._write()
+        #self._write()
 
-    def _write(self):
+    @staticmethod
+    def get_col_order(ani_screen_step=False) -> List[str]:
+        """Return the column order that will be written. If a row is provided
+        then format the row in that specific order."""
+        cols = ['user_genome',
+                   'reference_genome',
+                   'fastani_ani',
+                   'fastani_af',
+                   'reference_taxonomy']
+        if ani_screen_step:
+            cols += ['other_related_references(genome_id,species_name,radius,ANI,AF)']
+        return cols
+
+    def write(self,ani_screen_step=False):
         with open(self.path, 'w') as fh:
-            fh.write('user_genome\treference_genome\tfastani_ani\tfastani_af\treference_taxonomy\n')
+            cols = self.get_col_order(ani_screen_step=ani_screen_step)
+            fh.write(f'\t'.join(cols) + '\n')
             for qry_gid, ref_hits in sorted(self.results.items()):
                 for ref_gid, ref_hit in sorted(ref_hits.items(), key=lambda x: (-x[1]['af'], -x[1]['ani'], x[0])):
                     canonical_rid = canonical_gid(ref_gid)
                     taxonomy_str = ';'.join(self.taxonomy[canonical_rid])
                     fh.write(f'{qry_gid}\t{ref_gid}')
                     fh.write(f'\t{ref_hit["ani"]}\t{ref_hit["af"]}')
-                    fh.write(f'\t{taxonomy_str}\n')
+                    fh.write(f'\t{taxonomy_str}')
+                    if ani_screen_step:
+                        fh.write(f'\t{ref_hit.get("other_related_refs","") or ""}')
+                    fh.write(f'\n')
         self.logger.info(f'Summary of results saved to: {self.path}')
 
+    def read(self):
+        """Read the ani_summary from disk."""
+        if not os.path.isfile(self.path):
+            raise GTDBTkExit(f'Error, ANI summary file not found: {self.path}')
+        with open(self.path) as fh:
+
+            # Load and verify the columns match the expected order.
+            cols_exp = self.get_col_order(True)
+            cols_cur = fh.readline().strip().split('\t')
+            if cols_exp != cols_cur:
+                raise GTDBTkExit(f'The classify summary file columns are inconsistent: {cols_cur}')
+
+            # Process the data.
+            results = {}
+            for line in fh.readlines():
+                qry_gid, ref_gid, ani, af, taxonomy_str,other_refs = line.strip('\n').split('\t')
+                results[qry_gid]={ref_gid:{'ani': float(ani), 'af': float(af), 'taxonomy': taxonomy_str,'other_refs':other_refs}}
+        return results
+
 
 class ANIClosestFile(object):
     name = 'ani_closest.tsv'
@@ -177,7 +236,7 @@ def __init__(self, root, prefix, results, genomes, min_af, taxonomy):
         genomes : dict[str, str]
             Dict[genome_id] = fasta_path
         min_af : float
-            alignment fraction to consider closest genome
+            alignment fraction to consider the closest genome
         taxonomy: dict[str, tuple[str, str, str, str, str, str, str]]
             d[unique_id] -> [d__<taxon>, ..., s__<taxon>]
         """
diff --git a/gtdbtk/ani_screen.py b/gtdbtk/ani_screen.py
new file mode 100644
index 00000000..a2240b4f
--- /dev/null
+++ b/gtdbtk/ani_screen.py
@@ -0,0 +1,112 @@
+import logging
+import os
+
+from gtdbtk.ani_rep import ANIRep, ANISummaryFile
+from gtdbtk.biolib_lite.common import make_sure_path_exists, canonical_gid
+
+from gtdbtk.biolib_lite.taxonomy import Taxonomy
+from gtdbtk.classify import Classify
+from gtdbtk.config.output import DIR_ANISCREEN
+
+from gtdbtk.config.config import (TAXONOMY_FILE, MASH_SKETCH_FILE, AF_THRESHOLD)
+from gtdbtk.files.gtdb_radii import GTDBRadiiFile
+
+
+class ANIScreener(object):
+    """Computes a list of genomes to a list of representatives."""
+
+    def __init__(self, cpus):
+        """Instantiate the ANI rep class.
+
+        Parameters
+        ----------
+        cpus : int
+            The maximum number of CPUs available to this workflow.
+        """
+        self.logger = logging.getLogger('timestamp')
+        self.cpus = cpus
+        self.gtdb_radii = GTDBRadiiFile()
+
+    def run_aniscreen(self,genomes, no_mash,out_dir,prefix, mash_k, mash_v, mash_s, mash_max_dist, mash_db):
+
+        # If prescreen is set to True, then we will first run all genomes against a mash database
+        # of all genomes in the reference package. The next step will be to classify those genomes with
+        # FastANI.
+        # All genomes classified with FastANI will be removed from the input genomes list for the
+        # rest of the pipeline.
+        mash_classified_user_genomes = {}
+        #if mash_db finishes with a backslash, it should be considered a directory
+        if mash_db.endswith('/'):
+            make_sure_path_exists(mash_db)
+        if os.path.isdir(mash_db):
+            mash_db = os.path.join(mash_db, MASH_SKETCH_FILE)
+
+        #we set mash_d == mash_max_dist to avoid user to run mash with impossible values
+        mash_d = mash_max_dist
+
+        ani_rep = ANIRep(self.cpus)
+        # we store all the mash information in the classify directory
+        fastani_results = ani_rep.run_mash_fastani(genomes, no_mash, mash_d, os.path.join(out_dir, DIR_ANISCREEN),
+                                                    prefix, mash_k, mash_v, mash_s, mash_max_dist, mash_db)
+
+        taxonomy = Taxonomy().read(TAXONOMY_FILE, canonical_ids=True)
+
+        mash_classified_user_genomes = self.sort_fastani_ani_screen(
+             fastani_results,taxonomy)
+
+        #We write the results in 2 different files for each domain
+        reports = {}
+        if mash_classified_user_genomes:
+            for domain,results in mash_classified_user_genomes.items():
+                ani_summary_file = ANISummaryFile(os.path.join(out_dir,DIR_ANISCREEN),prefix,results,taxonomy,domain)
+                ani_summary_file.write(ani_screen_step=True)
+                reports[domain] = os.path.join(out_dir,DIR_ANISCREEN,prefix + '.' + domain + '.ani_summary.tsv')
+        len_mash_classified_bac120 = len(mash_classified_user_genomes['bac120']) \
+            if 'bac120' in mash_classified_user_genomes else 0
+
+        len_mash_classified_ar53 = len(mash_classified_user_genomes['ar53']) \
+            if 'ar53' in mash_classified_user_genomes else 0
+
+        self.logger.info(f'{len_mash_classified_ar53 + len_mash_classified_bac120} genome(s) have '
+                         f'been classified using the ANI pre-screening step.')
+
+        return mash_classified_user_genomes,reports
+
+    def sort_fastani_ani_screen(self,fastani_results,taxonomy,bac_ar_diff=None):
+        """ When run mash/FastANI on all genomes before using pplacer, we need to sort those results and store them for
+        a later use
+
+        Parameters
+        ----------
+        fastani_results : dict
+            The results of the FastANI run
+        taxonomy : dict
+            The taxonomy of the reference genomes
+        """
+        classified_user_genomes = {}
+
+        # sort the dictionary by ani then af
+        for gid in fastani_results.keys():
+            thresh_results = [(ref_gid, hit) for (ref_gid, hit) in fastani_results[gid].items() if
+                              hit['af'] >= AF_THRESHOLD and hit['ani'] >= self.gtdb_radii.get_rep_ani(
+                                  canonical_gid(ref_gid))]
+            all_results = [(ref_gid, hit) for (ref_gid, hit) in fastani_results[gid].items()]
+            closest = sorted(thresh_results, key=lambda x: (-x[1]['ani'], -x[1]['af']))
+            all_closest = sorted(all_results, key=lambda x: (-x[1]['ani'], -x[1]['af']))
+            if len(closest) > 0:
+                ref_gid, hit = closest[0]
+                hit_taxonomy = taxonomy[canonical_gid(ref_gid)]
+                if len(all_closest) > 1:
+                    other_ref = '; '.join(Classify.formatnote(
+                        closest,taxonomy,Classify.parse_radius_file(), [ref_gid]))
+                    if len(other_ref) > 0:
+                        hit['other_related_refs'] = other_ref
+
+                if hit_taxonomy[0] == 'd__Bacteria':
+                    classified_user_genomes.setdefault('bac120', {})[gid]={ref_gid:hit}
+
+                elif hit_taxonomy[0] == 'd__Archaea':
+                    classified_user_genomes.setdefault('ar53', {})[gid]={ref_gid:hit}
+
+
+        return classified_user_genomes
\ No newline at end of file
diff --git a/gtdbtk/biolib_lite/prodigal_biolib.py b/gtdbtk/biolib_lite/prodigal_biolib.py
index 7cde477e..527409e2 100644
--- a/gtdbtk/biolib_lite/prodigal_biolib.py
+++ b/gtdbtk/biolib_lite/prodigal_biolib.py
@@ -26,6 +26,7 @@
 import ntpath
 import os
 import shutil
+import subprocess
 import tempfile
 from collections import defaultdict, namedtuple
 
@@ -134,14 +135,24 @@ def _producer(self, genome_file):
                     if self.closed_ends:
                         args += ' -c'
 
-                    cmd = 'prodigal %s -p %s -q -f gff -g %d -a %s -d %s -i %s > %s 2> /dev/null' % (args,
-                                                                                                     proc_str,
-                                                                                                     translation_table,
-                                                                                                     aa_gene_file_tmp,
-                                                                                                     nt_gene_file_tmp,
-                                                                                                     processed_prodigal_input,
-                                                                                                     gff_file_tmp)
-                    os.system(cmd)
+
+                    cmd = ['prodigal',args,'-p',proc_str,'-q',
+                           '-f','gff','-g',str(translation_table),
+                           '-a',aa_gene_file_tmp,'-d',nt_gene_file_tmp,
+                           '-i',processed_prodigal_input,'-o',gff_file_tmp]
+
+                    proc = subprocess.Popen(cmd, stdout=subprocess.PIPE,
+                                            stderr=subprocess.PIPE, encoding='utf-8')
+
+                    stdout, stderr = proc.communicate()
+                    #This extra step has been added for the issue 451 where Prodigal can return a free pointer error
+                    if proc.returncode != 0:
+                        self.logger.warning('Error running Prodigal on genome: '
+                                            '{}'.format(genome_file))
+                        self.logger.warning('Error message:')
+                        for line in stderr.splitlines():
+                            print(line)
+                        self.logger.warning('This genome is skipped.')
 
                     # determine coding density
                     prodigalParser = ProdigalGeneFeatureParser(gff_file_tmp)
@@ -375,7 +386,7 @@ def __build_coding_base_mask(self, seq_id):
 
         # safe way to calculate coding bases as it accounts
         # for the potential of overlapping genes
-        coding_base_mask = np.zeros(self.last_coding_base[seq_id], dtype=np.bool)
+        coding_base_mask = np.zeros(self.last_coding_base[seq_id], dtype=bool)
         for pos in self.genes[seq_id].values():
             coding_base_mask[pos[0]:pos[1] + 1] = True
 
diff --git a/gtdbtk/classify.py b/gtdbtk/classify.py
index 1ffb71c7..397d68ca 100644
--- a/gtdbtk/classify.py
+++ b/gtdbtk/classify.py
@@ -29,7 +29,8 @@
 from numpy import median as np_median
 
 import gtdbtk.config.config as Config
-from gtdbtk.biolib_lite.common import make_sure_path_exists
+from gtdbtk.ani_rep import ANIRep, ANISummaryFile
+from gtdbtk.biolib_lite.common import make_sure_path_exists,canonical_gid
 from gtdbtk.biolib_lite.execute import check_dependencies
 from gtdbtk.biolib_lite.newick import parse_label
 from gtdbtk.biolib_lite.seq_io import read_seq, read_fasta
@@ -38,19 +39,22 @@
 from gtdbtk.exceptions import GenomeMarkerSetUnknown, GTDBTkExit
 from gtdbtk.external.fastani import FastANI
 from gtdbtk.external.pplacer import Pplacer
-from gtdbtk.io.classify_summary import ClassifySummaryFileAR53, ClassifySummaryFileBAC120, ClassifySummaryFileRow
-from gtdbtk.io.marker.copy_number import CopyNumberFileAR53, CopyNumberFileBAC120
-from gtdbtk.io.pplacer_classification import PplacerClassifyFileBAC120, PplacerClassifyFileAR53, \
+
+from gtdbtk.files.classify_summary import ClassifySummaryFileAR53, ClassifySummaryFileBAC120, ClassifySummaryFileRow
+from gtdbtk.files.marker.copy_number import CopyNumberFileAR53, CopyNumberFileBAC120
+from gtdbtk.files.pplacer_classification import PplacerClassifyFileBAC120, PplacerClassifyFileAR53, \
     PplacerLowClassifyFileBAC120
-from gtdbtk.io.prodigal.tln_table_summary import TlnTableSummaryFile
-from gtdbtk.io.red_dict import REDDictFileAR53, REDDictFileBAC120
-from gtdbtk.io.missing_genomes import DisappearingGenomesFileAR53, DisappearingGenomesFileBAC120
-from gtdbtk.io.tree_mapping import GenomeMappingFile, GenomeMappingFileRow
+from gtdbtk.files.prodigal.tln_table_summary import TlnTableSummaryFile
+from gtdbtk.files.red_dict import REDDictFileAR53, REDDictFileBAC120
+from gtdbtk.files.gtdb_radii import GTDBRadiiFile
+from gtdbtk.files.missing_genomes import DisappearingGenomesFileAR53, DisappearingGenomesFileBAC120
+from gtdbtk.files.tree_mapping import GenomeMappingFile, GenomeMappingFileRow
 from gtdbtk.markers import Markers
 from gtdbtk.relative_distance import RelativeDistance
 from gtdbtk.split import Split
 from gtdbtk.tools import add_ncbi_prefix, symlink_f, get_memory_gb, get_reference_ids, TreeTraversal, \
-    calculate_patristic_distance, tqdm_log, truncate_taxonomy, standardise_taxonomy, limit_rank
+    calculate_patristic_distance, tqdm_log, standardise_taxonomy, limit_rank, aa_percent_msa
+
 
 sys.setrecursionlimit(15000)
 
@@ -80,6 +84,8 @@ def __init__(self, cpus=1, pplacer_cpus=None, af_threshold=None):
         self.cpus = max(cpus, 1)
         self.pplacer_cpus = max(pplacer_cpus if pplacer_cpus else cpus, 1)
 
+        self.gtdb_radii = GTDBRadiiFile()
+
         # pplacer seems to hang if more than 64 threads are given, warn the user
         # otherwise let them if they specify pplacer_cpus.
         if pplacer_cpus and pplacer_cpus > 64:
@@ -99,7 +105,8 @@ def __init__(self, cpus=1, pplacer_cpus=None, af_threshold=None):
         # lower classification
         self.rank_of_interest = "c__"
 
-    def parse_radius_file(self):
+    @staticmethod
+    def parse_radius_file():
         results = {}
         with open(Config.RADII_FILE) as f:
             for line in f:
@@ -112,7 +119,7 @@ def parse_radius_file(self):
 
     def parse_leaf_to_dir_path(self, genome_id):
         """ Convert a genome id to a path.
-         i.e GCA_123456789.0 would be converted to GCA/123/456/789/
+         i.e. GCA_123456789.0 would be converted to GCA/123/456/789/
 
         Parameters
         ----------
@@ -255,8 +262,8 @@ def place_genomes(self,
         pplacer = Pplacer()
         if levelopt is None or levelopt == 'high':
             self.logger.info(f'pplacer version: {pplacer.version}')
-        # #DEBUG line
-        run_pplacer = True
+        # #DEBUG: Skip pplacer
+        run_pplacer = False
         if run_pplacer:
             pplacer.run(self.pplacer_cpus, 'wag', pplacer_ref_pkg, pplacer_json_out,
                         user_msa_file, pplacer_out, pplacer_mmap_file)
@@ -318,13 +325,63 @@ def run(self,
             prefix,
             scratch_dir=None,
             debugopt=False,
-            fulltreeopt=False):
+            fulltreeopt=False,
+            skip_ani_screen=False,
+            no_mash=False,
+            mash_k=Config.MASH_K_VALUE,
+            mash_v=Config.MASH_V_VALUE,
+            mash_s=Config.MASH_S_VALUE,
+            mash_max_dist=Config.MASH_MAX_DISTANCE,
+            mash_db=None,
+            ani_summary_files=None):
         """Classify genomes based on position in reference tree."""
 
         _bac_gids, _ar_gids, bac_ar_diff = Markers().genome_domain(align_dir, prefix)
 
-        for marker_set_id in ('ar53', 'bac120'):
 
+        # If prescreen is set to True, then we will first run all genomes against a mash database
+        # of all genomes in the reference package. The next step will be to classify those genomes with
+        # FastANI.
+        # All genomes classified with FastANI will be removed from the input genomes list for the
+        # rest of the pipeline.
+        mash_classified_user_genomes = {}
+        if not skip_ani_screen:
+            if not no_mash:
+                # if mash_db finishes with a backslash, it should be considered a directory
+                if mash_db.endswith('/'):
+                    make_sure_path_exists(mash_db)
+                if os.path.isdir(mash_db):
+                    mash_db = os.path.join(mash_db, Config.MASH_SKETCH_FILE)
+
+            # we set mash_d == mash_max_dist to avoid user to run mash with impossible values
+            mash_d = mash_max_dist
+
+            ani_rep = ANIRep(self.cpus)
+            # we store all the mash information in the classify directory
+            fastani_results = ani_rep.run_mash_fastani(genomes, no_mash, mash_d, os.path.join(out_dir, DIR_ANISCREEN), prefix, mash_k, mash_v, mash_s,mash_max_dist, mash_db )
+
+            mash_classified_user_genomes = self._sort_fastani_results_pre_pplacer(
+                fastani_results,bac_ar_diff)
+
+            len_mash_classified_bac120 = len(mash_classified_user_genomes['bac120']) \
+                if 'bac120' in mash_classified_user_genomes else 0
+
+            len_mash_classified_ar53 = len(mash_classified_user_genomes['ar53']) \
+                if 'ar53' in mash_classified_user_genomes else 0
+
+
+            self.logger.info(f'{len_mash_classified_ar53+len_mash_classified_bac120} genome(s) have '
+                             f'been classified using the ANI pre-screening step.')
+
+        if skip_ani_screen and ani_summary_files is not None:
+            # if the ani_Screen step was run, we need to load the results from the ani_summary_files
+            # and use them to generate the final taxonomy file
+            mash_classified_user_genomes = self.load_fastani_results_pre_pplacer(ani_summary_files)
+
+        output_files = {}
+
+        for marker_set_id in ('ar53', 'bac120'):
+            warning_counter, prodigal_fail_counter = 0, 0
             if marker_set_id == 'ar53':
                 marker_summary_fh = CopyNumberFileAR53(align_dir, prefix)
                 marker_summary_fh.read()
@@ -365,31 +422,85 @@ def run(self,
                 # they still have to be written in the bac120 summary file:
                 if marker_set_id == 'bac120':
                     # Add failed genomes from prodigal and genomes with no markers in the bac120 summary file
-                    # This is a executive direction: failed prodigal and genomes with no markers are nit bacterial or archaeal
+                    # This is an executive direction: failed prodigal and genomes with no markers are not bacterial or archaeal
                     # but they need to be included in one of the summary file
-                    self.add_failed_genomes_to_summary(align_dir, summary_file, prefix)
+                    prodigal_failed_counter = self.add_failed_genomes_to_summary(align_dir, summary_file, prefix)
                     if summary_file.has_row():
                         summary_file.write()
+                        output_files.setdefault(marker_set_id, []).append(summary_file.path)
                         # Symlink to the summary file from the root
                         symlink_f(PATH_BAC120_SUMMARY_OUT.format(prefix=prefix),
                                   os.path.join(out_dir, os.path.basename(PATH_BAC120_SUMMARY_OUT.format(prefix=prefix))))
-                continue
+                        if prodigal_failed_counter > 0:
+                            self.logger.warning(f"{prodigal_failed_counter} of {len(genomes)} "
+                                                f"genome{'' if prodigal_failed_counter == 1 else 's'} "
+                                                f"ha{'s' if prodigal_failed_counter == 1 else 've'} been labeled as 'Unclassified'.")
 
-            # Write the RED dictionary to disk (intermediate file).
-            red_dict_file.write()
+                continue
 
-            percent_multihit_dict = self.parser_marker_summary_file(marker_summary_fh)
+            msa_dict = read_fasta(user_msa_file)
 
             # Read the translation table summary file (identify).
             tln_table_summary_file = TlnTableSummaryFile(align_dir, prefix)
             tln_table_summary_file.read()
 
-            msa_dict = read_fasta(user_msa_file)
+            percent_multihit_dict = self.parser_marker_summary_file(marker_summary_fh)
+
+            # run pplacer to place bins in reference genome tree
+            genomes_to_process = [seq_id for seq_id, _seq in read_seq(user_msa_file)]
+
+            # if mash_classified_user_genomes is has key marker_set_id, we
+            # need to add those genomes to the summary file and remove those genomes to the list of genomes
+            # to process with pplacer
+            if mash_classified_user_genomes and marker_set_id in mash_classified_user_genomes:
+                list_summary_rows = mash_classified_user_genomes.get(marker_set_id)
+                for row in list_summary_rows:
+                    row,warning_counter = self._add_warning_to_row(row,
+                                                   msa_dict,
+                                                   tln_table_summary_file.genomes,
+                                                   percent_multihit_dict,
+                                                   warning_counter)
+                    summary_file.add_row(row)
+                    if row.gid in genomes_to_process:
+                        genomes_to_process.remove(row.gid)
+
+                # if genomes are classified with pre-screen, they need to be removed from the user_msa_file
+                prescreened_msa_file_path = os.path.join(
+                    out_dir, PATH_BAC120_PRESCREEN_MSA.format(prefix=prefix))
+                #makes sure the path exists
+                make_sure_path_exists(os.path.dirname(prescreened_msa_file_path))
+
+                prescreened_msa_file = open(prescreened_msa_file_path, 'w')
+
+                for gid in genomes_to_process:
+                    prescreened_msa_file.write('>{}\n{}\n'.format(gid, msa_dict.get(gid)))
+                prescreened_msa_file.close()
+                user_msa_file = prescreened_msa_file_path
+
+
+
+
+            # Write the RED dictionary to disk (intermediate file).
+            red_dict_file.write()
+
+            # if the new user_msa_file is empty, we need to skip the rest of the loop
+            if not os.path.exists(user_msa_file) or os.path.getsize(user_msa_file) < 30:
+                if summary_file.has_row():
+                    summary_file.write()
+                    # Symlink to the summary file from the root
+                    if marker_set_id == 'bac120':
+                        symlink_f(PATH_BAC120_SUMMARY_OUT.format(prefix=prefix),
+                                  os.path.join(out_dir,
+                                               os.path.basename(PATH_BAC120_SUMMARY_OUT.format(prefix=prefix))))
+                    elif marker_set_id == 'ar53':
+                        symlink_f(PATH_AR53_SUMMARY_OUT.format(prefix=prefix),
+                                  os.path.join(out_dir, os.path.basename(PATH_AR53_SUMMARY_OUT.format(prefix=prefix))))
+                continue
+
 
             if not fulltreeopt and marker_set_id == 'bac120':
                 splitter = Split(self.order_rank, self.gtdb_taxonomy, self.reference_ids)
-                # run pplacer to place bins in reference genome tree
-                genomes_to_process = [seq_id for seq_id, _seq in read_seq(user_msa_file)]
+
                 debug_file = self._generate_summary_file(
                     marker_set_id, prefix, out_dir, debugopt, fulltreeopt)
 
@@ -399,6 +510,8 @@ def run(self,
                                                         prefix,
                                                         scratch_dir,
                                                         'high')
+                output_files.setdefault(marker_set_id, []).append(high_classify_tree)
+
                 tree = self._assign_mrca_red(
                     high_classify_tree, marker_set_id, 'high')
 
@@ -419,8 +532,9 @@ def run(self,
                         tree_mapping_dict_reverse.setdefault(v, []).append(k)
 
                 splitter = Split(self.order_rank, self.gtdb_taxonomy, self.reference_ids)
-                sorted_high_taxonomy, len_sorted_genomes, high_taxonomy_used = splitter.map_high_taxonomy(
-                    high_classification, tree_mapping_dict, summary_file, tree_mapping_file)
+                sorted_high_taxonomy,warning_counter, len_sorted_genomes, high_taxonomy_used = splitter.map_high_taxonomy(
+                    high_classification, tree_mapping_dict, summary_file, tree_mapping_file,msa_dict,
+                    tln_table_summary_file.genomes,percent_multihit_dict,bac_ar_diff,warning_counter)
 
                 if debugopt:
                     with open(out_dir + '/' + prefix + '_backbone_classification.txt', 'w') as htu:
@@ -437,17 +551,18 @@ def run(self,
                     low_classify_tree, submsa_file_path = self._place_in_low_tree(
                         tree_iter, len(sorted_high_taxonomy), idx + 1, listg, msa_dict, marker_set_id, prefix,
                         scratch_dir, out_dir)
-                    genomes_to_process = [seq_id for seq_id, _seq in read_seq(submsa_file_path)]
+                    output_files.setdefault(marker_set_id, []).append(low_classify_tree)
+                    genomes_to_process_subtree = [seq_id for seq_id, _seq in read_seq(submsa_file_path)]
                     mrca_lowtree = self._assign_mrca_red(
                         low_classify_tree, marker_set_id, 'low', tree_iter)
                     pplacer_classify_file = PplacerLowClassifyFileBAC120(out_dir, prefix, tree_iter)
                     pplacer_taxonomy_dict = self._get_pplacer_taxonomy(pplacer_classify_file,
                                                                        marker_set_id, user_msa_file, mrca_lowtree)
-                    disappearing_genomes = [seq_id for seq_id in genomes_to_process if
+                    disappearing_genomes = [seq_id for seq_id in genomes_to_process_subtree if
                                             seq_id not in pplacer_taxonomy_dict]
                     if disappearing_genomes:
                         self.logger.warning(
-                            f"{len(disappearing_genomes)} out of {len(genomes_to_process)} have not been"
+                            f"{len(disappearing_genomes)} out of {len(genomes_to_process_subtree)} have not been"
                             f" properly placed by pplacer. This is a known issue with pplacer but we do "
                             f"not have a solution currently. Those missing genomes are written to "
                             f"the {disappearing_genomes_file.file_name} file. We recommend rerunning "
@@ -455,11 +570,11 @@ def run(self,
                         for disappearing_genome in disappearing_genomes:
                             disappearing_genomes_file.add_genome(disappearing_genome, tree_iter)
 
-                    class_level_classification, classified_user_genomes = self._parse_tree(mrca_lowtree, genomes, msa_dict,
+                    class_level_classification, classified_user_genomes,warning_counter = self._parse_tree(mrca_lowtree, genomes, msa_dict,
                                                                   percent_multihit_dict, tln_table_summary_file.genomes,
                                                                   bac_ar_diff, submsa_file_path, red_dict_file.data,
-                                                                  summary_file, pplacer_taxonomy_dict,
-                                                                  high_classification, debug_file, debugopt,
+                                                                  summary_file, pplacer_taxonomy_dict,warning_counter,
+                                                                  high_classification, debug_file,skip_ani_screen, debugopt,
                                                                   tree_mapping_file, tree_iter,
                                                                   tree_mapping_dict_reverse)
 
@@ -470,14 +585,16 @@ def run(self,
                             for l, b in classified_user_genomes.items():
                                 olf.write(l + '\t' + str(b) + '\n')
 
+
                 # add filtered genomes to the summary file
-                self.add_filtered_genomes_to_summary(align_dir, summary_file, marker_set_id, prefix)
+                warning_counter = self.add_filtered_genomes_to_summary(align_dir,warning_counter, summary_file, marker_set_id, prefix)
 
                 # Add failed genomes from prodigal and genomes with no markers in the bac120 summary file
                 # This is a executive direction: failed prodigal and genomes with
                 # no markers are not bacterial or archaeal
                 # but they need to be included in one of the summary file
-                self.add_failed_genomes_to_summary(align_dir, summary_file, prefix)
+                prodigal_failed_counter = self.add_failed_genomes_to_summary(align_dir, summary_file, prefix)
+                warning_counter = warning_counter + prodigal_failed_counter
 
                 # Symlink to the summary file from the root
                 symlink_f(PATH_BAC120_SUMMARY_OUT.format(prefix=prefix),
@@ -493,34 +610,33 @@ def run(self,
                                                    prefix,
                                                    scratch_dir)
 
-                genomes_to_process = [seq_id for seq_id, _seq in read_seq(user_msa_file)]
+                #genomes_to_process = [seq_id for seq_id, _seq in read_seq(user_msa_file)]
 
                 # get taxonomic classification of each user genome
                 debug_file = self._generate_summary_file(
                     marker_set_id, prefix, out_dir, debugopt, fulltreeopt)
 
                 tree_to_process = self._assign_mrca_red(classify_tree, marker_set_id)
-
                 pplacer_taxonomy_dict = self._get_pplacer_taxonomy(pplacer_classify_file,
                                                                    marker_set_id,
                                                                    user_msa_file,
                                                                    tree_to_process)
 
                 disappearing_genomes = [seq_id for seq_id in genomes_to_process if seq_id not in pplacer_taxonomy_dict]
-
-                self._parse_tree(tree_to_process, genomes, msa_dict, percent_multihit_dict,
+                class_level_classification, classified_user_genomes,warning_counter = self._parse_tree(tree_to_process, genomes, msa_dict, percent_multihit_dict,
                                  tln_table_summary_file.genomes,
                                  bac_ar_diff, user_msa_file, red_dict_file.data, summary_file,
-                                 pplacer_taxonomy_dict, None,
-                                 debug_file, debugopt, None, None, None)
+                                 pplacer_taxonomy_dict,warning_counter, None,
+                                 debug_file,skip_ani_screen, debugopt, None, None, None)
                 # add filtered genomes to the summary file
-                self.add_filtered_genomes_to_summary(align_dir, summary_file, marker_set_id, prefix)
+                warning_counter = self.add_filtered_genomes_to_summary(align_dir,warning_counter, summary_file, marker_set_id, prefix)
 
                 # Add failed genomes from prodigal and genomes with no markers in the bac120 summary file
                 # This is a executive direction: failed prodigal and genomes with no markers are nit bacterial or archaeal
                 # but they need to be included in one of the summary file
                 if marker_set_id == 'bac120':
-                    self.add_failed_genomes_to_summary(align_dir, summary_file, prefix)
+                    prodigal_fail_counter = self.add_failed_genomes_to_summary(align_dir, summary_file, prefix)
+                    warning_counter = warning_counter + prodigal_fail_counter
 
                 # Symlink to the summary file from the root
                 if marker_set_id == 'bac120':
@@ -541,13 +657,47 @@ def run(self,
 
             if not fulltreeopt and marker_set_id == 'bac120':
                 tree_mapping_file.write()
+                output_files.setdefault(marker_set_id, []).append(tree_mapping_file.path)
 
 
+            if warning_counter > 0:
+                sum_of_genomes =  len(genomes_to_process)+prodigal_failed_counter
+                if mash_classified_user_genomes and marker_set_id in mash_classified_user_genomes:
+                    sum_of_genomes +=len(mash_classified_user_genomes.get(marker_set_id))
+                self.logger.warning(f"{warning_counter} of {sum_of_genomes} "
+                                    f"genome{'' if warning_counter==1 else 's'}"
+                                    f" ha{'s' if warning_counter==1 else 've'} a warning (see summary file).")
 
             # Write the summary file to disk.
             if disappearing_genomes_file.data:
                 disappearing_genomes_file.write()
+                output_files.setdefault(marker_set_id, []).append(disappearing_genomes_file.path)
             summary_file.write()
+            output_files.setdefault(marker_set_id, []).append(summary_file.path)
+        return output_files
+
+    def _add_warning_to_row(self,row,msa_dict,
+                            trans_table_dict,
+                            percent_multihit_dict,
+                            warning_counter):
+
+        if row.gid in msa_dict:
+            row.msa_percent = aa_percent_msa(msa_dict.get(row.gid))
+            row.tln_table = trans_table_dict.get(row.gid)
+
+        warnings = row.warnings
+        if row.gid in percent_multihit_dict:
+            if warnings is not None:
+                warnings.append('Genome has more than {}% of markers with multiple hits'.format(
+                    percent_multihit_dict.get(row.gid)))
+            else:
+                warnings = ['Genome has more than {}% of markers with multiple hits'.format(
+                    percent_multihit_dict.get(row.gid))]
+        if warnings:
+            row.warnings = ';'.join(set(warnings))
+            warning_counter += 1
+
+        return row,warning_counter
 
     def _generate_summary_file(self, marker_set_id, prefix, out_dir, debugopt=None, fulltreeopt=None):
         debug_file = None
@@ -659,7 +809,7 @@ def _get_fastani_verification(tree, reference_ids, tt):
         return out, qry_nodes
 
     def _classify_red_topology(self, tree, msa_dict, percent_multihit_dict, trans_table_dict, bac_ar_diff,
-                               user_msa_file, red_dict, summary_file, pplacer_taxonomy_dict,
+                               user_msa_file, red_dict,warning_counter, summary_file, pplacer_taxonomy_dict,
                                high_classification, debug_file, debugopt, classified_user_genomes,
                                unclassified_user_genomes, tt,tree_iter,tree_mapping_file, valid_classes,valid_phyla):
         user_genome_ids = set(read_fasta(user_msa_file).keys())
@@ -914,7 +1064,7 @@ def _classify_red_topology(self, tree, msa_dict, percent_multihit_dict, trans_ta
                 summary_row.red_value = red_value_to_report
                 if summary_row.classification_method is None:
                     summary_row.classification_method = detection
-                summary_row.msa_percent = self.aa_percent_msa(msa_dict.get(summary_row.gid))
+                summary_row.msa_percent = aa_percent_msa(msa_dict.get(summary_row.gid))
                 summary_row.tln_table = trans_table_dict.get(summary_row.gid)
 
                 tree_mapping_file.add_row(mapping_row)
@@ -931,7 +1081,7 @@ def _classify_red_topology(self, tree, msa_dict, percent_multihit_dict, trans_ta
                 summary_row.pplacer_tax = pplacer_taxonomy_dict.get(leaf.taxon.label)
                 if summary_row.classification_method is None:
                     summary_row.classification_method = detection
-                summary_row.msa_percent = self.aa_percent_msa(msa_dict.get(summary_row.gid))
+                summary_row.msa_percent = aa_percent_msa(msa_dict.get(summary_row.gid))
                 summary_row.tln_table = trans_table_dict.get(summary_row.gid)
                 summary_row.red_value = current_rel_list
 
@@ -947,6 +1097,7 @@ def _classify_red_topology(self, tree, msa_dict, percent_multihit_dict, trans_ta
                 if summary_row.warnings is not None:
                     warnings.extend(summary_row.warnings.split(';'))
                 summary_row.warnings = ';'.join(set(warnings))
+                warning_counter += 1
             if len(notes) > 0:
                 if summary_row.note is not None:
                     notes.extend(summary_row.note.split(';'))
@@ -958,11 +1109,11 @@ def _classify_red_topology(self, tree, msa_dict, percent_multihit_dict, trans_ta
                     leaf.taxon.label, current_rel_list, '\t'.join(str(x) for x in debug_info), detection))
 
 
-        return class_level_classification
+        return class_level_classification,warning_counter
 
     def _parse_tree(self, tree, genomes, msa_dict, percent_multihit_dict, trans_table_dict, bac_ar_diff,
-                    user_msa_file, red_dict, summary_file, pplacer_taxonomy_dict, high_classification,
-                    debug_file, debugopt, tree_mapping_file, tree_iter, tree_mapping_dict_reverse):
+                    user_msa_file, red_dict, summary_file, pplacer_taxonomy_dict,warning_counter, high_classification,
+                    debug_file,prescreening, debugopt, tree_mapping_file, tree_iter, tree_mapping_dict_reverse):
         # Genomes can be classified by using FastANI or RED values
         # We go through all leaves of the tree. if the leaf is a user
         # genome we take its parent node and look at all the leaves
@@ -974,11 +1125,13 @@ def _parse_tree(self, tree, genomes, msa_dict, percent_multihit_dict, trans_tabl
         self.logger.log(Config.LOG_TASK, 'Traversing tree to determine classification method.')
         fastani_verification, qury_nodes = self._get_fastani_verification(tree, self.reference_ids, tt)
 
+
         #DEBUG: Skip FastANI step
         #fastani_verification = {}
 
         # we run a fastani comparison for each user genomes against the
         # selected genomes in the same genus
+        ##if not prescreening and len(fastani_verification) > 0:
         if len(fastani_verification) > 0:
             fastani = FastANI(cpus=self.cpus, force_single=True)
             d_ani_compare, d_paths = self._get_fastani_genome_path(
@@ -992,11 +1145,12 @@ def _parse_tree(self, tree, genomes, msa_dict, percent_multihit_dict, trans_tabl
 
 
 
-        classified_user_genomes, unclassified_user_genomes = self._sort_fastani_results(
+        classified_user_genomes, unclassified_user_genomes,warning_counter = self._sort_fastani_results(
             fastani_verification, pplacer_taxonomy_dict, all_fastani_dict, msa_dict, percent_multihit_dict,
-            trans_table_dict, bac_ar_diff, summary_file)
+            trans_table_dict, bac_ar_diff,warning_counter, summary_file)
+        #if not prescreening:
         self.logger.info(f'{len(classified_user_genomes):,} genome(s) have '
-                         f'been classified using FastANI and pplacer.')
+                             f'been classified using FastANI and pplacer.')
 
         if tree_mapping_file:
             for genome in classified_user_genomes.keys():
@@ -1015,15 +1169,15 @@ def _parse_tree(self, tree, genomes, msa_dict, percent_multihit_dict, trans_tabl
             phyla_in_spe_tree = self.get_authorised_rank(class_in_spe_tree,self.PHYLUM_IDX)
 
 
-        order_level_classification = self._classify_red_topology(tree, msa_dict, percent_multihit_dict,
+        class_level_classification,warning_counter = self._classify_red_topology(tree, msa_dict, percent_multihit_dict,
                                                                  trans_table_dict, bac_ar_diff, user_msa_file,
-                                                                 red_dict, summary_file,
+                                                                 red_dict,warning_counter, summary_file,
                                                                  pplacer_taxonomy_dict, high_classification,
                                                                  debug_file, debugopt, classified_user_genomes,
                                                                  unclassified_user_genomes, tt,tree_iter,tree_mapping_file,
                                                                  class_in_spe_tree, phyla_in_spe_tree)
 
-        return order_level_classification,classified_user_genomes
+        return class_level_classification,classified_user_genomes,warning_counter
 
     def _assign_mrca_red(self, input_tree, marker_set_id, levelopt=None, tree_iter=None):
         """Parse the pplacer tree and write the partial taxonomy for each user genome based on their placements
@@ -1162,7 +1316,7 @@ def _get_pplacer_taxonomy(self, pplacer_classify_file, marker_set_id, user_msa_f
 
         return pplacer_classify_file.data
 
-    def add_filtered_genomes_to_summary(self, align_dir, summary_file, marker_set_id,prefix):
+    def add_filtered_genomes_to_summary(self, align_dir,warning_counter, summary_file, marker_set_id,prefix):
         if marker_set_id == 'bac120':
             filtered_file = os.path.join(align_dir,PATH_BAC120_FILTERED_GENOMES.format(prefix=prefix))
             domain = 'Bacteria'
@@ -1178,8 +1332,11 @@ def add_filtered_genomes_to_summary(self, align_dir, summary_file, marker_set_id
                 summary_row.classification = f'Unclassified {domain}'
                 summary_row.warnings = infos[1]
                 summary_file.add_row(summary_row)
+                warning_counter += 1
+        return warning_counter
 
     def add_failed_genomes_to_summary(self, align_dir, summary_file, prefix):
+        warning_counter = 0
         prodigal_failed_file = os.path.join(align_dir, PATH_FAILS.format(prefix=prefix))
         align_failed_file = os.path.join(align_dir, PATH_FAILED_ALIGN_GENOMES.format(prefix=prefix))
         for failfile in (prodigal_failed_file, align_failed_file):
@@ -1192,8 +1349,11 @@ def add_failed_genomes_to_summary(self, align_dir, summary_file, prefix):
                         summary_row.classification = f'Unclassified'
                         summary_row.warnings = infos[1]
                         summary_file.add_row(summary_row)
+                        warning_counter += 1
+        return warning_counter
 
-    def _formatnote(self, sorted_dict, labels):
+    @staticmethod
+    def formatnote(sorted_dict,gtdb_taxonomy,species_radius, labels):
         """Format the note field by concatenating all information in a sorted dictionary
 
         Parameters
@@ -1208,28 +1368,93 @@ def _formatnote(self, sorted_dict, labels):
             note field
 
         """
+        gtdb_taxonomy = {canonical_gid(k): v for k, v in gtdb_taxonomy.items()}
         note_list = []
         for element in sorted_dict:
             if element[0] not in labels:
                 note_str = "{}, {}, {}, {}, {}".format(element[0],
-                                                       self.gtdb_taxonomy.get(
-                                                           add_ncbi_prefix(element[0]))[6],
-                                                       self.species_radius.get(
+                                                       gtdb_taxonomy.get(
+                                                           add_ncbi_prefix(canonical_gid(element[0])))[6],
+                                                       species_radius.get(
                                                            element[0]),
                                                        round(
                                                            element[1].get('ani'), 2),
-                                                       element[1].get('af'))
+                                                       round(element[1].get('af'),3))
                 note_list.append(note_str)
         return note_list
 
-    def aa_percent_msa(self, aa_string):
-        aa_len = sum([1 for c in aa_string if c.isalpha()])
-        aa_perc = float(aa_len) / len(aa_string)
-        return round(aa_perc * 100, 2)
+
+    def _sort_fastani_results_pre_pplacer(self,fastani_results,bac_ar_diff):
+        """ When run mash/FastANI on all genomes before using pplacer, we need to sort those results and store them for
+        a later use
+
+        Parameters
+        ----------
+        fastani_results : dictionary listing the fastani ANI for each user genomes against the potential genomes
+
+        """
+        classified_user_genomes = {}
+
+        # sort the dictionary by ani then af
+        for gid in fastani_results.keys():
+            thresh_results = [(ref_gid, hit) for (ref_gid, hit) in fastani_results[gid].items() if
+                              hit['af'] >= Config.AF_THRESHOLD and hit['ani'] >= self.gtdb_radii.get_rep_ani(
+                                  canonical_gid(ref_gid))]
+            closest = sorted(thresh_results, key=lambda x: (-x[1]['ani'], -x[1]['af']))
+            if len(closest) > 0:
+                summary_row = ClassifySummaryFileRow()
+                summary_row.gid = gid
+                summary_row.classification_method = 'ani_screen'
+                if len(closest) > 1:
+                    other_ref = '; '.join(self.formatnote(
+                        closest,self.gtdb_taxonomy,self.species_radius, [gid]))
+                    if len(other_ref) == 0:
+                        summary_row.other_related_refs = None
+                    else:
+                        summary_row.other_related_refs = other_ref
+                summary_row.note = 'classification based on ANI only'
+
+                fastani_matching_reference = closest[0][0]
+                current_ani = fastani_results.get(gid).get(
+                    fastani_matching_reference).get('ani')
+                current_af = fastani_results.get(gid).get(
+                    fastani_matching_reference).get('af')
+
+                summary_row.fastani_ref = fastani_matching_reference
+                summary_row.fastani_ref_radius = str(
+                    self.species_radius.get(fastani_matching_reference))
+                summary_row.fastani_tax = ";".join(self.gtdb_taxonomy.get(
+                    add_ncbi_prefix(fastani_matching_reference)))
+                summary_row.fastani_ani = round(current_ani, 2)
+                summary_row.fastani_af = round(current_af, 3)
+                taxa_str = ";".join(self.gtdb_taxonomy.get(
+                    add_ncbi_prefix(fastani_matching_reference)))
+                summary_row.classification = standardise_taxonomy(
+                    taxa_str)
+
+                warnings = []
+                if gid in bac_ar_diff:
+                    warnings.append('Genome domain questionable ( {}% Bacterial, {}% Archaeal)'.format(
+                        bac_ar_diff.get(gid).get('bac120'),
+                        bac_ar_diff.get(gid).get('ar53')))
+
+                if len(warnings) > 0:
+                    summary_row.warnings = warnings
+
+                if (taxa_str.split(';')[0]).split('__')[1] == 'Bacteria':
+                    domain = 'bac120'
+                else:
+                    domain = 'ar53'
+                classified_user_genomes.setdefault(domain, []).append(summary_row)
+
+        return classified_user_genomes
+
+
+
 
     def _sort_fastani_results(self, fastani_verification, pplacer_taxonomy_dict,
                               all_fastani_dict, msa_dict, percent_multihit_dict,
-                              trans_table_dict, bac_ar_diff, summary_file):
+                              trans_table_dict, bac_ar_diff,warning_counter, summary_file):
         """Format the note field by concatenating all information in a sorted dictionary
 
         Parameters
@@ -1300,7 +1525,7 @@ def _sort_fastani_results(self, fastani_verification, pplacer_taxonomy_dict,
 
                     summary_row.pplacer_tax = pplacer_taxonomy_dict.get(userleaf.taxon.label)
                     summary_row.classification_method = 'taxonomic classification defined by topology and ANI'
-                    summary_row.msa_percent = self.aa_percent_msa(
+                    summary_row.msa_percent = aa_percent_msa(
                         msa_dict.get(summary_row.gid))
                     summary_row.tln_table = trans_table_dict.get(summary_row.gid)
                     if len(warnings) > 0:
@@ -1313,7 +1538,7 @@ def _sort_fastani_results(self, fastani_verification, pplacer_taxonomy_dict,
                         summary_row.fastani_tax = ";".join(self.gtdb_taxonomy.get(
                             add_ncbi_prefix(fastani_matching_reference)))
                         summary_row.fastani_ani = round(current_ani, 2)
-                        summary_row.fastani_af = current_af
+                        summary_row.fastani_af = round(current_af,3)
                         if pplacer_leafnode == fastani_matching_reference:
                             if taxa_str.endswith("s__"):
                                 taxa_str = taxa_str + pplacer_leafnode
@@ -1326,8 +1551,8 @@ def _sort_fastani_results(self, fastani_verification, pplacer_taxonomy_dict,
                             summary_row.closest_placement_af = summary_row.fastani_af
                             summary_row.note = 'topological placement and ANI have congruent species assignments'
                             if len(sorted_dict) > 0:
-                                other_ref = '; '.join(self._formatnote(
-                                    sorted_dict, [fastani_matching_reference]))
+                                other_ref = '; '.join(self.formatnote(
+                                    sorted_dict,self.gtdb_taxonomy,self.species_radius, [fastani_matching_reference]))
                                 if len(other_ref) == 0:
                                     summary_row.other_related_refs = None
                                 else:
@@ -1346,14 +1571,13 @@ def _sort_fastani_results(self, fastani_verification, pplacer_taxonomy_dict,
                             if pplacer_leafnode in all_fastani_dict.get(userleaf.taxon.label):
                                 summary_row.closest_placement_ani = round(all_fastani_dict.get(
                                     userleaf.taxon.label).get(pplacer_leafnode).get('ani'), 2)
-                                summary_row.closest_placement_af = all_fastani_dict.get(
-                                    userleaf.taxon.label).get(pplacer_leafnode).get('af')
-                            summary_row.note = 'topological placement and ANI have incongruent species assignments'
+                                summary_row.closest_placement_af = round(all_fastani_dict.get(
+                                    userleaf.taxon.label).get(pplacer_leafnode).get('af'),3)
                             summary_row.classification_method = 'ANI'
 
                             if len(sorted_dict) > 0:
-                                other_ref = '; '.join(self._formatnote(
-                                    sorted_dict, [fastani_matching_reference, pplacer_leafnode]))
+                                other_ref = '; '.join(self.formatnote(
+                                    sorted_dict,self.gtdb_taxonomy,self.species_radius, [fastani_matching_reference, pplacer_leafnode]))
                                 if len(other_ref) == 0:
                                     summary_row.other_related_refs = None
                                 else:
@@ -1369,12 +1593,12 @@ def _sort_fastani_results(self, fastani_verification, pplacer_taxonomy_dict,
                         if pplacer_leafnode in all_fastani_dict.get(userleaf.taxon.label):
                             summary_row.closest_placement_ani = round(all_fastani_dict.get(
                                 userleaf.taxon.label).get(pplacer_leafnode).get('ani'), 2)
-                            summary_row.closest_placement_af = all_fastani_dict.get(
-                                userleaf.taxon.label).get(pplacer_leafnode).get('af')
+                            summary_row.closest_placement_af = round(all_fastani_dict.get(
+                                userleaf.taxon.label).get(pplacer_leafnode).get('af'),3)
 
                         if len(sorted_dict) > 0:
-                            other_ref = '; '.join(self._formatnote(
-                                sorted_dict, [pplacer_leafnode]))
+                            other_ref = '; '.join(self.formatnote(
+                                sorted_dict,self.gtdb_taxonomy,self.species_radius, [pplacer_leafnode]))
                             if len(other_ref) == 0:
                                 summary_row.other_related_refs = None
                             else:
@@ -1394,7 +1618,7 @@ def _sort_fastani_results(self, fastani_verification, pplacer_taxonomy_dict,
                 summary_row.pplacer_tax = pplacer_taxonomy_dict.get(
                     userleaf.taxon.label)
                 summary_row.classification_method = 'ANI'
-                summary_row.msa_percent = self.aa_percent_msa(
+                summary_row.msa_percent = aa_percent_msa(
                     msa_dict.get(summary_row.gid))
                 summary_row.tln_table = trans_table_dict.get(summary_row.gid)
 
@@ -1402,8 +1626,8 @@ def _sort_fastani_results(self, fastani_verification, pplacer_taxonomy_dict,
                 if len(sorted_prefilter_af_dict) > 0:
 
                     if len(sorted_dict) > 0:
-                        other_ref = '; '.join(self._formatnote(
-                            sorted_dict, exception_genomes))
+                        other_ref = '; '.join(self.formatnote(
+                            sorted_dict,self.gtdb_taxonomy,self.species_radius, exception_genomes))
                         if len(other_ref) == 0:
                             summary_row.other_related_refs = None
                         else:
@@ -1411,7 +1635,7 @@ def _sort_fastani_results(self, fastani_verification, pplacer_taxonomy_dict,
 
                     if len(warnings) > 0:
                         summary_row.warnings = ';'.join(warnings)
-
+                        warning_counter += 1
                     if sorted_prefilter_af_dict[0][1].get('ani') >= self.species_radius.get(
                             sorted_prefilter_af_dict[0][0]):
                         fastani_matching_reference = sorted_prefilter_af_dict[0][0]
@@ -1432,7 +1656,7 @@ def _sort_fastani_results(self, fastani_verification, pplacer_taxonomy_dict,
                         summary_row.fastani_ani = round(current_ani, 2)
                         current_af = all_fastani_dict.get(userleaf.taxon.label).get(
                             fastani_matching_reference).get('af')
-                        summary_row.fastani_af = current_af
+                        summary_row.fastani_af = round(current_af,3)
                         summary_row.note = 'topological placement and ANI have incongruent species assignments'
                         if len(warnings) > 0:
                             summary_row.warnings = ';'.join(warnings)
@@ -1448,14 +1672,14 @@ def _sort_fastani_results(self, fastani_verification, pplacer_taxonomy_dict,
 
                 else:
                     if len(sorted_dict) > 0:
-                        other_ref = '; '.join(self._formatnote(
-                            sorted_dict, exception_genomes))
+                        other_ref = '; '.join(self.formatnote(
+                            sorted_dict,self.gtdb_taxonomy,self.species_radius, exception_genomes))
                         if len(other_ref) == 0:
                             summary_row.other_related_refs = None
                         else:
                             summary_row.other_related_refs = other_ref
                     unclassified_user_genomes[userleaf.taxon.label] = summary_row
-        return classified_user_genomes, unclassified_user_genomes
+        return classified_user_genomes, unclassified_user_genomes,warning_counter
 
     def _get_redtax(self, list_subnode, closest_rank):
         """
@@ -1909,5 +2133,63 @@ def get_authorised_rank(self, order_in_spe_tree,rank_index):
                 results.append(v[rank_index])
         return list(set(results))
 
+    def load_fastani_results_pre_pplacer(self,ani_summary_files):
+        """Load the FastANI results for the genomes classified with ANI Screen."""
+        fastani_results={}
+        for domain,ani_summary_file_path in ani_summary_files.items():
+            ani_summary_file = ANISummaryFile(ani_summary_file_path)
+            fastani_results[domain]=ani_summary_file.read()
+
+        classified_user_genomes = {}
+
+        # sort the dictionary by ani then af
+        for domain,fastani_results in fastani_results.items():
+            for gid,ani_results in fastani_results.items():
+                summary_row = ClassifySummaryFileRow()
+                summary_row.gid = gid
+                summary_row.classification_method = 'ani_screen'
+                summary_row.note = 'classification based on ANI only'
+
+                fastani_matching_reference = list(ani_results)[0]
+
+                if 'other_refs' in fastani_results.get(gid).get(
+                    fastani_matching_reference):
+                    summary_row.other_related_refs = fastani_results.get(gid).get(
+                    fastani_matching_reference).get('other_refs')
+                current_ani = fastani_results.get(gid).get(
+                        fastani_matching_reference).get('ani')
+                current_af = fastani_results.get(gid).get(
+                        fastani_matching_reference).get('af')
+
+                summary_row.fastani_ref = fastani_matching_reference
+                summary_row.fastani_ref_radius = str(
+                        self.species_radius.get(fastani_matching_reference))
+                summary_row.fastani_tax = fastani_results.get(gid).get(
+                        fastani_matching_reference).get('taxonomy')
+                summary_row.fastani_ani = round(current_ani, 2)
+                summary_row.fastani_af = round(current_af,3)
+                taxa_str = ";".join(self.gtdb_taxonomy.get(
+                        add_ncbi_prefix(fastani_matching_reference)))
+                summary_row.classification = standardise_taxonomy(
+                        taxa_str)
+
+                classified_user_genomes.setdefault(domain, []).append(summary_row)
+        return classified_user_genomes
+
+    def convert_rows_to_dict(self,list_of_summary_rows):
+        """Convert a list of rows to a dictionary."""
+        dict_of_rows = {}
+        for domain,rows in list_of_summary_rows.items():
+            for row in rows:
+                if row.other_related_refs:
+                    infos ={row.gid:{row.fastani_ref: {'ani': row.fastani_ani,
+                                                 'af': row.fastani_af,
+                                                 'other_refs': row.other_related_refs}}}
+                else:
+                    infos ={row.gid:{row.fastani_ref: {'ani': row.fastani_ani, 'af': row.fastani_af}}}
+                dict_of_rows.setdefault(domain, {}).update(infos)
+
+        return dict_of_rows
+
 
 
diff --git a/gtdbtk/cli.py b/gtdbtk/cli.py
index 4b62f487..7d7aab64 100644
--- a/gtdbtk/cli.py
+++ b/gtdbtk/cli.py
@@ -4,7 +4,8 @@
 
 from gtdbtk.biolib_lite.custom_help_formatter import ChangeTempAction
 from gtdbtk.biolib_lite.custom_help_formatter import CustomHelpFormatter
-from gtdbtk.config.config import AF_THRESHOLD, PPLACER_MIN_RAM_BAC_FULL
+from gtdbtk.config.config import AF_THRESHOLD, PPLACER_MIN_RAM_BAC_FULL, MASH_K_VALUE, MASH_S_VALUE, MASH_D_VALUE, \
+    MASH_V_VALUE, MASH_MAX_DISTANCE
 
 
 @contextmanager
@@ -73,6 +74,9 @@ def __extension(group):
     group.add_argument('-x', '--extension', type=str, default='fna',
                        help='extension of files to process, ``gz`` = gzipped')
 
+def __skip_ani_screen(group):
+    group.add_argument('--skip_ani_screen', action="store_true", default=False,
+                       help="Skip the ani_screening step to classify genomes using mash and FastANI ")
 
 def __skip_gtdb_refs(group):
     group.add_argument('--skip_gtdb_refs', action="store_true", default=False,
@@ -245,24 +249,28 @@ def __no_mash(group):
 
 
 def __mash_k(group):
-    group.add_argument('--mash_k', default=16, type=int,
+    group.add_argument('--mash_k', default=MASH_K_VALUE, type=int,
                        help='k-mer size [1-32]')
 
 
 def __mash_s(group):
-    group.add_argument('--mash_s', default=5000, type=int,
+    group.add_argument('--mash_s', default=MASH_S_VALUE, type=int,
                        help='maximum number of non-redundant hashes')
 
 
 def __mash_d(group):
-    group.add_argument('--mash_d', default=0.1, type=float,
+    group.add_argument('--mash_d', default=MASH_D_VALUE, type=float,
                        help='maximum distance to keep [0-1]')
 
 
 def __mash_v(group):
-    group.add_argument('--mash_v', default=1.0, type=float,
+    group.add_argument('--mash_v', default=MASH_V_VALUE, type=float,
                        help='maximum p-value to keep [0-1]')
 
+def __mash_max_distance(group):
+    group.add_argument('--mash_max_distance', default=MASH_MAX_DISTANCE, type=float,
+                       help='Maximum Mash distance to select a potential GTDB genome as representative '
+                            'of a user genome.')
 
 def __mash_db(group):
     group.add_argument('--mash_db', default=None, type=str,
@@ -310,6 +318,10 @@ def __write_single_copy_genes(group):
                        help='output unaligned single-copy marker genes')
 
 
+def __prescreen(grp):
+    pass
+
+
 def get_main_parser():
     # Setup the main, and sub parsers.
     main_parser = argparse.ArgumentParser(
@@ -360,6 +372,15 @@ def get_main_parser():
             __batchfile(grp)
         with arg_group(parser, 'required named arguments') as grp:
             __out_dir(grp, required=True)
+        with mutex_group(parser, required=True) as grp:
+            __skip_ani_screen(grp)
+            __mash_db(grp)
+        with arg_group(parser, 'optional Mash arguments') as grp:
+            __no_mash(grp)
+            __mash_k(grp)
+            __mash_s(grp)
+            __mash_v(grp)
+            __mash_max_distance(grp)
         with arg_group(parser, 'optional arguments') as grp:
             __full_tree(grp)
             __extension(grp)
@@ -441,6 +462,15 @@ def get_main_parser():
         with arg_group(parser, 'required named arguments') as grp:
             __align_dir(grp, required=True)
             __out_dir(grp, required=True)
+        with mutex_group(parser, required=True) as grp:
+            __skip_ani_screen(grp)
+            __mash_db(grp)
+        with arg_group(parser, 'optional Mash arguments') as grp:
+            __no_mash(grp)
+            __mash_k(grp)
+            __mash_s(grp)
+            __mash_v(grp)
+            __mash_max_distance(grp)
         with arg_group(parser, 'optional arguments') as grp:
             __extension(grp)
             __prefix(grp)
diff --git a/gtdbtk/config/config.py b/gtdbtk/config/config.py
index 9e6fde63..1ade7b31 100644
--- a/gtdbtk/config/config.py
+++ b/gtdbtk/config/config.py
@@ -27,6 +27,8 @@
 MASK_DIR = os.path.join(GENERIC_PATH, "masks/")
 PPLACER_DIR = os.path.join(GENERIC_PATH, "pplacer/")
 FASTANI_DIR = os.path.join(GENERIC_PATH, "fastani/")
+MASH_DIR = os.path.join(GENERIC_PATH, "mash/")
+
 TAX_FOLDER = os.path.join(GENERIC_PATH, "taxonomy/")
 RADII_DIR = os.path.join(GENERIC_PATH, "radii/")
 METADATA_DIR = os.path.join(GENERIC_PATH, "metadata/")
@@ -124,67 +126,23 @@
                               "TIGR03625.HMM", "TIGR03632.HMM", "TIGR03654.HMM",
                               "TIGR03723.HMM", "TIGR03725.HMM", "TIGR03953.HMM"]}
 
-#
-#New Version of AR53_MARKERS
-# AR53_MARKERS = {"PFAM": ["PF01868.17.hmm", "PF01282.20.hmm", "PF01655.19.hmm",
-#                           "PF01092.20.hmm", "PF01000.27.hmm", "PF00368.19.hmm",
-#                           "PF00827.18.hmm", "PF01269.18.hmm", "PF00466.21.hmm",
-#                           "PF01015.19.hmm", "PF13685.7.hmm", "PF02978.20.hmm",
-#                           "PF04919.13.hmm", "PF01984.21.hmm", "PF04104.15.hmm",
-#                           "PF00410.20.hmm", "PF01798.19.hmm", "PF01864.18.hmm",
-#                           "PF01990.18.hmm", "PF07541.13.hmm", "PF04019.13.hmm",
-#                           "PF00900.21.hmm", "PF01090.20.hmm", "PF02006.17.hmm",
-#                           "PF01157.19.hmm", "PF01191.20.hmm", "PF01866.18.hmm",
-#                           "PF01198.20.hmm", "PF01496.20.hmm", "PF00687.22.hmm",
-#                           "PF03874.17.hmm", "PF01194.18.hmm", "PF01200.19.hmm",
-#                           "PF13656.7.hmm", "PF01280.21.hmm"],
-#                  "TIGRFAM": ["TIGR00468.HMM", "TIGR01060.HMM", "TIGR03627.HMM",
-#                              "TIGR01020.HMM", "TIGR02258.HMM", "TIGR00293.HMM",
-#                              "TIGR00389.HMM", "TIGR01012.HMM", "TIGR00490.HMM",
-#                              "TIGR03677.HMM", "TIGR03636.HMM", "TIGR03722.HMM",
-#                              "TIGR00458.HMM", "TIGR00291.HMM", "TIGR00670.HMM",
-#                              "TIGR00064.HMM", "TIGR03629.HMM", "TIGR00021.HMM",
-#                              "TIGR03672.HMM", "TIGR00111.HMM", "TIGR03684.HMM",
-#                              "TIGR01077.HMM", "TIGR01213.HMM", "TIGR01080.HMM",
-#                              "TIGR00501.HMM", "TIGR00729.HMM", "TIGR01038.HMM",
-#                              "TIGR00270.HMM", "TIGR03628.HMM", "TIGR01028.HMM",
-#                              "TIGR00521.HMM", "TIGR03671.HMM", "TIGR00240.HMM",
-#                              "TIGR02390.HMM", "TIGR02338.HMM", "TIGR00037.HMM",
-#                              "TIGR02076.HMM", "TIGR00335.HMM", "TIGR01025.HMM",
-#                              "TIGR00471.HMM", "TIGR00336.HMM", "TIGR00522.HMM",
-#                              "TIGR02153.HMM", "TIGR02651.HMM", "TIGR03674.HMM",
-#                              "TIGR00323.HMM", "TIGR00134.HMM", "TIGR02236.HMM",
-#                              "TIGR03683.HMM", "TIGR00491.HMM", "TIGR00658.HMM",
-#                              "TIGR03680.HMM", "TIGR00392.HMM", "TIGR00422.HMM",
-#                              "TIGR00279.HMM", "TIGR01052.HMM", "TIGR00442.HMM",
-#                              "TIGR00308.HMM", "TIGR00398.HMM", "TIGR00456.HMM",
-#                              "TIGR00549.HMM", "TIGR00408.HMM", "TIGR00432.HMM",
-#                              "TIGR00264.HMM", "TIGR00982.HMM", "TIGR00324.HMM",
-#                              "TIGR01952.HMM", "TIGR03626.HMM", "TIGR03670.HMM",
-#                              "TIGR00337.HMM", "TIGR01046.HMM", "TIGR01018.HMM",
-#                              "TIGR00936.HMM", "TIGR00463.HMM", "TIGR01309.HMM",
-#                              "TIGR03653.HMM", "TIGR00042.HMM", "TIGR02389.HMM",
-#                              "TIGR00307.HMM", "TIGR03673.HMM", "TIGR00373.HMM",
-#                              "TIGR01008.HMM", "TIGR00283.HMM", "TIGR00425.HMM",
-#                              "TIGR00405.HMM", "TIGR03665.HMM", "TIGR00448.HMM"]}
+
 
 #New Version of AR53_MARKERS
 AR53_MARKERS = {"PFAM": ["PF04919.13.hmm","PF07541.13.hmm","PF01000.27.hmm",
-"PF00687.22.hmm","PF00466.21.hmm","PF00827.18.hmm","PF01280.21.hmm","PF01090.20.hmm",
-"PF01200.19.hmm","PF01015.19.hmm","PF00900.21.hmm","PF00410.20.hmm"],
-"TIGRFAM":["TIGR00037.HMM","TIGR00064.HMM","TIGR00111.HMM",
-"TIGR00134.HMM","TIGR00279.HMM","TIGR00291.HMM","TIGR00323.HMM",
-"TIGR00335.HMM","TIGR00373.HMM","TIGR00405.HMM","TIGR00448.HMM",
-"TIGR00483.HMM","TIGR00491.HMM","TIGR00522.HMM","TIGR00967.HMM",
-"TIGR00982.HMM","TIGR01008.HMM","TIGR01012.HMM","TIGR01018.HMM",
-"TIGR01020.HMM","TIGR01028.HMM","TIGR01046.HMM","TIGR01052.HMM",
-"TIGR01171.HMM","TIGR01213.HMM","TIGR01952.HMM","TIGR02236.HMM",
-"TIGR02338.HMM","TIGR02389.HMM","TIGR02390.HMM","TIGR03626.HMM",
-"TIGR03627.HMM","TIGR03628.HMM","TIGR03629.HMM","TIGR03670.HMM",
-"TIGR03671.HMM","TIGR03672.HMM","TIGR03673.HMM","TIGR03674.HMM",
-"TIGR03676.HMM","TIGR03680.HMM"]}
-
-
+                        "PF00687.22.hmm","PF00466.21.hmm","PF00827.18.hmm","PF01280.21.hmm","PF01090.20.hmm",
+                        "PF01200.19.hmm","PF01015.19.hmm","PF00900.21.hmm","PF00410.20.hmm"],
+                "TIGRFAM":["TIGR00037.HMM","TIGR00064.HMM","TIGR00111.HMM",
+                            "TIGR00134.HMM","TIGR00279.HMM","TIGR00291.HMM","TIGR00323.HMM",
+                            "TIGR00335.HMM","TIGR00373.HMM","TIGR00405.HMM","TIGR00448.HMM",
+                            "TIGR00483.HMM","TIGR00491.HMM","TIGR00522.HMM","TIGR00967.HMM",
+                            "TIGR00982.HMM","TIGR01008.HMM","TIGR01012.HMM","TIGR01018.HMM",
+                            "TIGR01020.HMM","TIGR01028.HMM","TIGR01046.HMM","TIGR01052.HMM",
+                            "TIGR01171.HMM","TIGR01213.HMM","TIGR01952.HMM","TIGR02236.HMM",
+                            "TIGR02338.HMM","TIGR02389.HMM","TIGR02390.HMM","TIGR03626.HMM",
+                            "TIGR03627.HMM","TIGR03628.HMM","TIGR03629.HMM","TIGR03670.HMM",
+                            "TIGR03671.HMM","TIGR03672.HMM","TIGR03673.HMM","TIGR03674.HMM",
+                            "TIGR03676.HMM","TIGR03680.HMM"]}
 
 
 # Information for Multiple hits markers:
@@ -227,6 +185,15 @@
 FASTANI_GENOME_LIST = os.path.join(FASTANI_DIR, "genome_paths.tsv")
 FASTANI_GENOMES_EXT = "_genomic.fna.gz"
 
+# Mash configuration
+MASH_SKETCH_FILE = 'gtdb_ref_sketch.msh'
+MASH_K_VALUE = 16
+MASH_S_VALUE = 5000
+MASH_MAX_DISTANCE = 0.1
+MASH_D_VALUE = MASH_MAX_DISTANCE
+MASH_V_VALUE = 1.0
+
+
 # MRCA RED VALUE
 MRCA_RED_BAC120 = os.path.join(RED_DIR, f"gtdbtk_{VERSION_DATA}_bac120.tsv")
 MRCA_RED_AR53 = os.path.join(RED_DIR, f"gtdbtk_{VERSION_DATA}_ar53.tsv")
@@ -248,3 +215,50 @@
 
 # Internal settings used for logging.
 LOG_TASK = 21
+
+
+
+
+#
+#New Version of AR53_MARKERS
+# AR53_MARKERS = {"PFAM": ["PF01868.17.hmm", "PF01282.20.hmm", "PF01655.19.hmm",
+#                           "PF01092.20.hmm", "PF01000.27.hmm", "PF00368.19.hmm",
+#                           "PF00827.18.hmm", "PF01269.18.hmm", "PF00466.21.hmm",
+#                           "PF01015.19.hmm", "PF13685.7.hmm", "PF02978.20.hmm",
+#                           "PF04919.13.hmm", "PF01984.21.hmm", "PF04104.15.hmm",
+#                           "PF00410.20.hmm", "PF01798.19.hmm", "PF01864.18.hmm",
+#                           "PF01990.18.hmm", "PF07541.13.hmm", "PF04019.13.hmm",
+#                           "PF00900.21.hmm", "PF01090.20.hmm", "PF02006.17.hmm",
+#                           "PF01157.19.hmm", "PF01191.20.hmm", "PF01866.18.hmm",
+#                           "PF01198.20.hmm", "PF01496.20.hmm", "PF00687.22.hmm",
+#                           "PF03874.17.hmm", "PF01194.18.hmm", "PF01200.19.hmm",
+#                           "PF13656.7.hmm", "PF01280.21.hmm"],
+#                  "TIGRFAM": ["TIGR00468.HMM", "TIGR01060.HMM", "TIGR03627.HMM",
+#                              "TIGR01020.HMM", "TIGR02258.HMM", "TIGR00293.HMM",
+#                              "TIGR00389.HMM", "TIGR01012.HMM", "TIGR00490.HMM",
+#                              "TIGR03677.HMM", "TIGR03636.HMM", "TIGR03722.HMM",
+#                              "TIGR00458.HMM", "TIGR00291.HMM", "TIGR00670.HMM",
+#                              "TIGR00064.HMM", "TIGR03629.HMM", "TIGR00021.HMM",
+#                              "TIGR03672.HMM", "TIGR00111.HMM", "TIGR03684.HMM",
+#                              "TIGR01077.HMM", "TIGR01213.HMM", "TIGR01080.HMM",
+#                              "TIGR00501.HMM", "TIGR00729.HMM", "TIGR01038.HMM",
+#                              "TIGR00270.HMM", "TIGR03628.HMM", "TIGR01028.HMM",
+#                              "TIGR00521.HMM", "TIGR03671.HMM", "TIGR00240.HMM",
+#                              "TIGR02390.HMM", "TIGR02338.HMM", "TIGR00037.HMM",
+#                              "TIGR02076.HMM", "TIGR00335.HMM", "TIGR01025.HMM",
+#                              "TIGR00471.HMM", "TIGR00336.HMM", "TIGR00522.HMM",
+#                              "TIGR02153.HMM", "TIGR02651.HMM", "TIGR03674.HMM",
+#                              "TIGR00323.HMM", "TIGR00134.HMM", "TIGR02236.HMM",
+#                              "TIGR03683.HMM", "TIGR00491.HMM", "TIGR00658.HMM",
+#                              "TIGR03680.HMM", "TIGR00392.HMM", "TIGR00422.HMM",
+#                              "TIGR00279.HMM", "TIGR01052.HMM", "TIGR00442.HMM",
+#                              "TIGR00308.HMM", "TIGR00398.HMM", "TIGR00456.HMM",
+#                              "TIGR00549.HMM", "TIGR00408.HMM", "TIGR00432.HMM",
+#                              "TIGR00264.HMM", "TIGR00982.HMM", "TIGR00324.HMM",
+#                              "TIGR01952.HMM", "TIGR03626.HMM", "TIGR03670.HMM",
+#                              "TIGR00337.HMM", "TIGR01046.HMM", "TIGR01018.HMM",
+#                              "TIGR00936.HMM", "TIGR00463.HMM", "TIGR01309.HMM",
+#                              "TIGR03653.HMM", "TIGR00042.HMM", "TIGR02389.HMM",
+#                              "TIGR00307.HMM", "TIGR03673.HMM", "TIGR00373.HMM",
+#                              "TIGR01008.HMM", "TIGR00283.HMM", "TIGR00425.HMM",
+#                              "TIGR00405.HMM", "TIGR03665.HMM", "TIGR00448.HMM"]}
diff --git a/gtdbtk/config/output.py b/gtdbtk/config/output.py
index 79e1458f..00205fdb 100644
--- a/gtdbtk/config/output.py
+++ b/gtdbtk/config/output.py
@@ -57,8 +57,14 @@
 PATH_AR53_PPLACER_CLASS = join(DIR_CLASSIFY_INTERMEDIATE, '{prefix}.ar53.classification_pplacer.tsv')
 PATH_BAC120_BACKBONE_PPLACER_CLASS = join(DIR_CLASSIFY_INTERMEDIATE, '{prefix}.bac120.backbone.classification_pplacer.tsv')
 PATH_BAC120_CLASS_LEVEL_PPLACER_CLASS = join(DIR_CLASSIFY_INTERMEDIATE, '{prefix}.bac120.class_level.classification_pplacer_tree_{iter}.tsv')
+PATH_BAC120_PRESCREEN_MSA = join(DIR_CLASSIFY_INTERMEDIATE, '{prefix}.bac120.prescreened.msa.fasta')
+PATH_AR53_PRESCREEN_MSA = join(DIR_CLASSIFY_INTERMEDIATE, '{prefix}.ar53.prescreened.msa.fasta')
 
-
+#Command: ani_screen
+DIR_ANISCREEN = join(DIR_CLASSIFY, 'ani_screen')
+DIR_ANISCREEN_INTERMEDIATE = join(DIR_ANISCREEN, 'intermediate_results')
+PATH_BAC120_ANI_FASTANI = join(DIR_ANISCREEN, '{prefix}.bac120_fastani.summary.tsv')
+PATH_AR53_ANI_FASTANI = join(DIR_ANISCREEN, '{prefix}.ar53_fastani.summary.tsv')
 
 
 DIR_PPLACER = join(DIR_CLASSIFY_INTERMEDIATE, 'pplacer')
@@ -97,5 +103,4 @@
 DIR_ANI_REP_INT_MASH = join(DIR_ANI_REP_INT, 'mash')
 
 # General files
-PATH_WARNINGS = '{prefix}.warnings.log'
-
+PATH_WARNINGS = '{prefix}.warnings.log'
\ No newline at end of file
diff --git a/gtdbtk/decorate.py b/gtdbtk/decorate.py
index 7a3c0cce..5bd9e88b 100644
--- a/gtdbtk/decorate.py
+++ b/gtdbtk/decorate.py
@@ -384,3 +384,5 @@ def run(self, input_tree, input_taxonomy, output_tree):
                            schema='newick',
                            suppress_rooting=True,
                            unquoted_underscores=True)
+
+        return {'all':[out_table,out_taxonomy,output_tree]}
diff --git a/gtdbtk/external/fastani.py b/gtdbtk/external/fastani.py
index 6bc9038d..8f269afa 100644
--- a/gtdbtk/external/fastani.py
+++ b/gtdbtk/external/fastani.py
@@ -316,7 +316,7 @@ def parse_output_file(self, path_out):
                                 self.logger.warning('You are using FastANI v1.0, it is recommended '
                                                     'that you update to a more recent version.')
                                 self._suppress_v1_warning = True
-                        af = round(float(frac1) / float(frac2), 2)
+                        af = float(frac1) / float(frac2)
                         if path_qry not in out:
                             out[path_qry] = {path_ref: (float(ani), af)}
                         elif path_ref not in out[path_qry]:
diff --git a/gtdbtk/external/hmm_aligner.py b/gtdbtk/external/hmm_aligner.py
index d3e91b51..5faf4a25 100644
--- a/gtdbtk/external/hmm_aligner.py
+++ b/gtdbtk/external/hmm_aligner.py
@@ -24,8 +24,8 @@
 
 from gtdbtk.biolib_lite.execute import check_dependencies
 from gtdbtk.exceptions import GTDBTkException
-from gtdbtk.io.marker.copy_number import CopyNumberFileAR53, CopyNumberFileBAC120
-from gtdbtk.io.marker.tophit import TopHitPfamFile, TopHitTigrFile
+from gtdbtk.files.marker.copy_number import CopyNumberFileAR53, CopyNumberFileBAC120
+from gtdbtk.files.marker.tophit import TopHitPfamFile, TopHitTigrFile
 from gtdbtk.tools import tqdm_log
 
 
diff --git a/gtdbtk/external/mash.py b/gtdbtk/external/mash.py
index c25e9c79..0359887a 100644
--- a/gtdbtk/external/mash.py
+++ b/gtdbtk/external/mash.py
@@ -8,6 +8,7 @@
 from gtdbtk.biolib_lite.common import make_sure_path_exists
 from gtdbtk.exceptions import GTDBTkExit
 from gtdbtk.tools import tqdm_log
+import gtdbtk.config.config as Config
 
 
 class Mash(object):
@@ -44,7 +45,7 @@ def version():
         except Exception:
             return 'unknown'
 
-    def run(self, qry, ref, mash_d, mash_k, mash_v, mash_s, mash_db) -> Dict[str, Dict[str, Tuple[float, float, int, int]]]:
+    def run(self, qry, ref, mash_d, mash_k, mash_v, mash_s, mash_max_dist, mash_db) -> Dict[str, Dict[str, Tuple[float, float, int, int]]]:
         """Run Mash on a set of reference and query genomes.
 
         Parameters
@@ -61,6 +62,8 @@ def run(self, qry, ref, mash_d, mash_k, mash_v, mash_s, mash_db) -> Dict[str, Di
             Maximum p-value to report.
         mash_s: int
             Maximum number of non-redundant hashes.
+        max_mash_dist : float
+            The maximum Mash distance to consider a genome as a close match.
         mash_db : Optional[str]
             The path to read/write the pre-computed Mash reference sketch database.
 
@@ -74,7 +77,7 @@ def run(self, qry, ref, mash_d, mash_k, mash_v, mash_s, mash_db) -> Dict[str, Di
         # Generate an output file comparing the distances between these genomes.
         mash_dists = DistanceFile(qry_sketch, ref_sketch, self.out_dir, self.prefix,
                                   self.cpus, max_d=mash_d, mash_v=mash_v)
-        results = mash_dists.read()
+        results = mash_dists.read(mash_max_dist)
 
         # Convert the results back to the accession
         path_to_qry = {v: k for (k, v) in qry.items()}
@@ -90,7 +93,7 @@ class DistanceFile(object):
     """The resulting distance file from the mash dist command."""
     name = 'mash_distances.tsv'
 
-    def __init__(self, qry_sketch, ref_sketch, root, prefix, cpus, max_d, mash_v):
+    def __init__(self, qry_sketch, ref_sketch, root, prefix, cpus, max_d, mash_v ,):
         """Create a new Mash distance file using these arguments.
 
         Parameters
@@ -132,7 +135,7 @@ def _calculate(self):
         if proc.returncode != 0:
             raise GTDBTkExit(f'Error running Mash dist: {proc.stderr.read()}')
 
-    def read(self) -> Dict[str, Dict[str, Tuple[float, float, int, int]]]:
+    def read(self,max_mash_dist=100) -> Dict[str, Dict[str, Tuple[float, float, int, int]]]:
         """Reads the results of the distance file.
 
         Returns
@@ -144,8 +147,9 @@ def read(self) -> Dict[str, Dict[str, Tuple[float, float, int, int]]]:
             hits = re.findall(r'(.+)\t(.+)\t(.+)\t(.+)\t(\d+)\/(\d+)\n', fh.read())
         for ref_id, qry_id, dist, p_val, shared_n, shared_d in hits:
             dist, p_val = float(dist), float(p_val)
-            shared_num, shared_den = int(shared_n), int(shared_d)
-            out[qry_id][ref_id] = (dist, p_val, shared_num, shared_den)
+            if dist <= max_mash_dist:
+                shared_num, shared_den = int(shared_n), int(shared_d)
+                out[qry_id][ref_id] = (dist, p_val, shared_num, shared_den)
         return out
 
 
@@ -258,7 +262,7 @@ def __init__(self, genomes, root, prefix, cpus, k, s):
 
 
 class RefSketchFile(SketchFile):
-    name = 'gtdb_ref_sketch.msh'
+    name = Config.MASH_SKETCH_FILE
 
     def __init__(self, genomes, root, prefix, cpus, k, s, mash_db=None):
         """Create a query file for a given set of genomes.
diff --git a/gtdbtk/external/pfam_search.py b/gtdbtk/external/pfam_search.py
index 5bb59a7d..64a0307d 100644
--- a/gtdbtk/external/pfam_search.py
+++ b/gtdbtk/external/pfam_search.py
@@ -20,7 +20,7 @@
 
 from gtdbtk.exceptions import GTDBTkExit
 from gtdbtk.external.pypfam.Scan.PfamScan import PfamScan
-from gtdbtk.io.marker.tophit import TopHitPfamFile
+from gtdbtk.files.marker.tophit import TopHitPfamFile
 from gtdbtk.tools import sha256, file_has_checksum, tqdm_log
 
 
diff --git a/gtdbtk/external/prodigal.py b/gtdbtk/external/prodigal.py
index 7b967ec7..28d6c9ae 100644
--- a/gtdbtk/external/prodigal.py
+++ b/gtdbtk/external/prodigal.py
@@ -24,7 +24,7 @@
 from gtdbtk.biolib_lite.prodigal_biolib import Prodigal as BioLibProdigal
 from gtdbtk.config.output import CHECKSUM_SUFFIX
 from gtdbtk.exceptions import ProdigalException
-from gtdbtk.io.prodigal.tln_table import TlnTableFile
+from gtdbtk.files.prodigal.tln_table import TlnTableFile
 from gtdbtk.tools import sha256, file_has_checksum, tqdm_log
 
 
diff --git a/gtdbtk/external/tigrfam_search.py b/gtdbtk/external/tigrfam_search.py
index 8d483bb7..4c0229ac 100644
--- a/gtdbtk/external/tigrfam_search.py
+++ b/gtdbtk/external/tigrfam_search.py
@@ -22,7 +22,7 @@
 
 from gtdbtk.biolib_lite.common import make_sure_path_exists
 from gtdbtk.exceptions import GTDBTkExit
-from gtdbtk.io.marker.tophit import TopHitTigrFile
+from gtdbtk.files.marker.tophit import TopHitTigrFile
 from gtdbtk.tools import sha256, file_has_checksum, tqdm_log
 
 
@@ -138,7 +138,7 @@ def _workerThread(self, queueIn, queueOut, n_skipped):
                 args = ['hmmsearch', '-o', hmmsearch_out, '--tblout', output_hit_file,
                         '--noali', '--notextw', '--cut_nc', '--cpu',
                         str(self.cpus_per_genome), self.tigrfam_hmms, gene_file]
-                p = subprocess.Popen(args, stdout=subprocess.PIPE, encoding='utf-8')
+                p = subprocess.Popen(args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
                 stdout, stderr = p.communicate()
 
                 if p.returncode != 0:
diff --git a/gtdbtk/io/__init__.py b/gtdbtk/files/__init__.py
similarity index 100%
rename from gtdbtk/io/__init__.py
rename to gtdbtk/files/__init__.py
diff --git a/gtdbtk/io/batchfile.py b/gtdbtk/files/batchfile.py
similarity index 100%
rename from gtdbtk/io/batchfile.py
rename to gtdbtk/files/batchfile.py
diff --git a/gtdbtk/io/classify_summary.py b/gtdbtk/files/classify_summary.py
similarity index 67%
rename from gtdbtk/io/classify_summary.py
rename to gtdbtk/files/classify_summary.py
index db563d5d..78c86a0a 100644
--- a/gtdbtk/io/classify_summary.py
+++ b/gtdbtk/files/classify_summary.py
@@ -17,50 +17,61 @@
 
 import logging
 import os
+from typing import Dict, List, Tuple, Optional, Union
 
+from gtdbtk.biolib_lite.common import make_sure_path_exists
 from gtdbtk.config.output import PATH_AR53_SUMMARY_OUT, PATH_BAC120_SUMMARY_OUT
 from gtdbtk.exceptions import GTDBTkExit
 
 
-class ClassifySummaryFileRow(object):
+class ClassifySummaryFileRow:
     """A row contained within the ClassifySummaryFile object."""
 
+    __slots__ = ('gid', 'classification', 'fastani_ref', 'fastani_ref_radius',
+                 'fastani_tax', 'fastani_ani', 'fastani_af', 'closest_placement_ref',
+                 'closest_placement_radius', 'closest_placement_tax', 'closest_placement_ani',
+                 'closest_placement_af', 'pplacer_tax', 'classification_method',
+                 'note', 'other_related_refs', 'msa_percent', 'tln_table',
+                 'red_value', 'warnings')
+
     def __init__(self):
-        """Initialise the row, default all of the values to None."""
-        self.gid = None
-        self.classification = None
-        self.fastani_ref = None
-        self.fastani_ref_radius = None
-        self.fastani_tax = None
-        self.fastani_ani = None
-        self.fastani_af = None
-        self.closest_placement_ref = None
-        self.closest_placement_radius = None
-        self.closest_placement_tax = None
-        self.closest_placement_ani = None
-        self.closest_placement_af = None
-        self.pplacer_tax = None
-        self.classification_method = None
-        self.note = None
-        self.other_related_refs = None
-        self.msa_percent = None
-        self.tln_table = None
-        self.red_value = None
-        self.warnings = None
-
-
-class ClassifySummaryFile(object):
+        """Initialise the row, default all the values to None."""
+        self.gid: Optional[str] = None
+        self.classification: Optional[str] = None
+        self.fastani_ref: Optional[str] = None
+        self.fastani_ref_radius: Optional[float] = None
+        self.fastani_tax: Optional[str] = None
+        self.fastani_ani: Optional[float] = None
+        self.fastani_af: Optional[float] = None
+        self.closest_placement_ref: Optional[str] = None
+        self.closest_placement_radius: Optional[float] = None
+        self.closest_placement_tax: Optional[str] = None
+        self.closest_placement_ani: Optional[float] = None
+        self.closest_placement_af: Optional[float] = None
+        self.pplacer_tax: Optional[str] = None
+        self.classification_method: Optional[str] = None
+        self.note: Optional[str] = None
+        self.other_related_refs: Optional[str] = None
+        self.msa_percent: Optional[float] = None
+        self.tln_table: Optional[int] = None
+        self.red_value: Optional[float] = None
+        self.warnings: Optional[str] = None
+
+
+class ClassifySummaryFile:
     """Store the GTDB-Tk classify summary output."""
 
-    def __init__(self, path: str, marker_set: str):
+    __slots__ = ('logger', 'path', 'marker_set', 'rows', 'none_value')
+
+    def __init__(self, path: str, marker_set: Optional[str] = None):
         self.logger = logging.getLogger('timestamp')
-        self.path = path
-        self.marker_set = marker_set
-        self.rows = dict()  # keyed by user_genome
-        self.none_value = 'N/A'
+        self.path: str = path
+        self.marker_set: Optional[str] = marker_set
+        self.rows: Dict[str, ClassifySummaryFileRow] = dict()  # keyed by user_genome
+        self.none_value: str = 'N/A'
 
     @staticmethod
-    def get_col_order(row: ClassifySummaryFileRow = None):
+    def get_col_order(row: ClassifySummaryFileRow = None) -> Tuple[List[str], List[Union[str, float, int]]]:
         """Return the column order that will be written. If a row is provided
         then format the row in that specific order."""
         if row is None:
@@ -80,8 +91,7 @@ def get_col_order(row: ClassifySummaryFileRow = None):
                    ('pplacer_taxonomy', row.pplacer_tax),
                    ('classification_method', row.classification_method),
                    ('note', row.note),
-                   ('other_related_references(genome_id,species_name,radius,ANI,AF)',
-                    row.other_related_refs),
+                   ('other_related_references(genome_id,species_name,radius,ANI,AF)', row.other_related_refs),
                    ('msa_percent', row.msa_percent),
                    ('translation_table', row.tln_table),
                    ('red_value', row.red_value),
@@ -97,18 +107,31 @@ def add_row(self, row: ClassifySummaryFileRow):
             raise GTDBTkExit(f'Attempting to add duplicate row: {row.gid}')
         self.rows[row.gid] = row
 
-    def has_row(self):
+    def has_row(self) -> bool:
         if self.rows.items():
             return True
         return False
 
+    def get_gid_taxonomy(self) -> Dict[str, List[str]]:
+        out = dict()
+        for gid, row in self.rows.items():
+            split_tax = row.classification.split(';')
+            if len(split_tax) != 7:
+                raise GTDBTkExit(f'Expected a 7-rank taxonomy for {gid} but got {row.classification}')
+            out[gid] = split_tax
+        return out
+
     def write(self):
         """Writes the summary file to disk. None will be replaced with N/A"""
+        make_sure_path_exists(os.path.dirname(self.path))
         with open(self.path, 'w') as fh:
             fh.write('\t'.join(self.get_col_order()[0]) + '\n')
             for gid, row in sorted(self.rows.items()):
                 buf = list()
-                for data in self.get_col_order(row)[1]:
+                for idx,data in enumerate(self.get_col_order(row)[1]):
+                    # for the red_value field, we want to round the data to 5 decimals after the comma if the value is not None
+                    if idx==self.get_col_order()[0].index('red_value') and data is not None:
+                        data = round(data,5)
                     buf.append(self.none_value if data is None else str(data))
                 fh.write('\t'.join(buf) + '\n')
 
@@ -165,5 +188,3 @@ class ClassifySummaryFileBAC120(ClassifySummaryFile):
     def __init__(self, out_dir: str, prefix: str):
         path = os.path.join(out_dir, PATH_BAC120_SUMMARY_OUT.format(prefix=prefix))
         super().__init__(path, 'bac120')
-
-
diff --git a/gtdbtk/io/gtdb_radii.py b/gtdbtk/files/gtdb_radii.py
similarity index 100%
rename from gtdbtk/io/gtdb_radii.py
rename to gtdbtk/files/gtdb_radii.py
diff --git a/gtdbtk/io/marker/__init__.py b/gtdbtk/files/marker/__init__.py
similarity index 100%
rename from gtdbtk/io/marker/__init__.py
rename to gtdbtk/files/marker/__init__.py
diff --git a/gtdbtk/io/marker/copy_number.py b/gtdbtk/files/marker/copy_number.py
similarity index 99%
rename from gtdbtk/io/marker/copy_number.py
rename to gtdbtk/files/marker/copy_number.py
index 7eb8d131..b0323f83 100644
--- a/gtdbtk/io/marker/copy_number.py
+++ b/gtdbtk/files/marker/copy_number.py
@@ -24,7 +24,7 @@
 from gtdbtk.config.config import BAC120_MARKERS, AR53_MARKERS
 from gtdbtk.config.output import PATH_BAC120_MARKER_SUMMARY, PATH_AR53_MARKER_SUMMARY
 from gtdbtk.exceptions import GTDBTkExit
-from gtdbtk.io.marker.tophit import TopHitPfamFile, TopHitTigrFile, Hit
+from gtdbtk.files.marker.tophit import TopHitPfamFile, TopHitTigrFile, Hit
 
 
 class CopyNumberFile(object):
diff --git a/gtdbtk/io/marker/tophit.py b/gtdbtk/files/marker/tophit.py
similarity index 100%
rename from gtdbtk/io/marker/tophit.py
rename to gtdbtk/files/marker/tophit.py
diff --git a/gtdbtk/io/marker_info.py b/gtdbtk/files/marker_info.py
similarity index 100%
rename from gtdbtk/io/marker_info.py
rename to gtdbtk/files/marker_info.py
diff --git a/gtdbtk/io/missing_genomes.py b/gtdbtk/files/missing_genomes.py
similarity index 100%
rename from gtdbtk/io/missing_genomes.py
rename to gtdbtk/files/missing_genomes.py
diff --git a/gtdbtk/io/pplacer_classification.py b/gtdbtk/files/pplacer_classification.py
similarity index 100%
rename from gtdbtk/io/pplacer_classification.py
rename to gtdbtk/files/pplacer_classification.py
diff --git a/gtdbtk/io/prodigal/__init__.py b/gtdbtk/files/prodigal/__init__.py
similarity index 100%
rename from gtdbtk/io/prodigal/__init__.py
rename to gtdbtk/files/prodigal/__init__.py
diff --git a/gtdbtk/io/prodigal/tln_table.py b/gtdbtk/files/prodigal/tln_table.py
similarity index 100%
rename from gtdbtk/io/prodigal/tln_table.py
rename to gtdbtk/files/prodigal/tln_table.py
diff --git a/gtdbtk/io/prodigal/tln_table_summary.py b/gtdbtk/files/prodigal/tln_table_summary.py
similarity index 100%
rename from gtdbtk/io/prodigal/tln_table_summary.py
rename to gtdbtk/files/prodigal/tln_table_summary.py
diff --git a/gtdbtk/io/red_dict.py b/gtdbtk/files/red_dict.py
similarity index 100%
rename from gtdbtk/io/red_dict.py
rename to gtdbtk/files/red_dict.py
diff --git a/gtdbtk/files/stage_logger.py b/gtdbtk/files/stage_logger.py
new file mode 100644
index 00000000..315fda64
--- /dev/null
+++ b/gtdbtk/files/stage_logger.py
@@ -0,0 +1,158 @@
+import os
+from typing import List, Optional, Dict
+
+from pydantic import BaseModel
+
+
+class Steps(BaseModel):
+    """ Information about a specific step of GTDB-Tk"""
+    name: str
+    output_dir: Optional[str]
+    starts_at: Optional[str]
+    ends_at: Optional[str]
+    duration: Optional[str]
+    status: Optional[str]
+
+    def is_complete(self) -> bool:
+        if self.status == "completed":
+            return True
+        else:
+            return False
+
+class ANIScreenStep(Steps):
+    """ Information about the ANI screen step of GTDB-Tk"""
+    name: str = "ANI screen"
+    genome_dir: Optional[str]
+    batchfile: Optional[str]
+    mash_k: Optional[str]
+    mash_s: Optional[str]
+    mash_v: Optional[str]
+    mash_max_dist: Optional[str]
+    mash_db: Optional[str]
+    output_files: Optional[Dict]
+
+class IdentifyStep(Steps):
+    """ Information about the Identify step of GTDB-Tk"""
+    name: str = "identify"
+    genes: Optional[bool]
+    extension: Optional[str]
+    write_single_copy_genes: Optional[bool]
+    genome_dir: Optional[str]
+    batchfile: Optional[str]
+    output_files: Optional[Dict]
+
+class AlignStep(Steps):
+    """ Information about the Align step of GTDB-Tk"""
+    name: str = "align"
+    identify_dir: Optional[str]
+    skip_gtdb_refs: Optional[bool]
+    taxa_filter: Optional[str]
+    min_perc_aa: Optional[str]
+    custom_msa_filters: Optional[bool]
+    skip_trimming: Optional[bool]
+    rnd_seed: Optional[str]
+    cols_per_gene: Optional[str]
+    min_consensus: Optional[str]
+    max_consensus: Optional[str]
+    min_perc_taxa: Optional[str]
+    outgroup_taxon: Optional[str]
+    output_files: Optional[Dict]
+
+class ClassifyStep(Steps):
+    """ Information about the Classify step of GTDB-Tk"""
+    name: str = "classify"
+    align_dir: Optional[str]
+    genome_dir: Optional[str]
+    batchfile: Optional[str]
+    scratch_dir: Optional[str]
+    debug_option: Optional[bool]
+    full_tree: Optional[bool]
+    skip_ani_screen: Optional[bool]
+    no_mash: Optional[bool]
+    mash_k: Optional[str]
+    mash_v: Optional[str]
+    mash_s: Optional[str]
+    mash_db: Optional[str]
+    mash_max_dist: Optional[str]
+    output_files: Optional[Dict]
+
+class InferStep(Steps):
+    """ Information about the Infer step of GTDB-Tk"""
+    name: str = "infer"
+    msa_file: Optional[str]
+    prot_model: Optional[str]
+    no_support: Optional[bool]
+    gamma: Optional[bool]
+
+class RootStep(Steps):
+    """ Information about the Root step of GTDB-Tk"""
+    name: str = "root"
+    input_tree: Optional[str]
+    gtdbtk_classification_file: Optional[str]
+    custom_taxonomy_file: Optional[str]
+    outgroup_taxon: Optional[str]
+
+class DecorateStep(Steps):
+    """ Information about the Decorate step of GTDB-Tk"""
+    name: str = "decorate"
+    input_tree: Optional[str]
+    gtdbtk_classification_file: Optional[str]
+    custom_taxonomy_file: Optional[str]
+    suffix: Optional[str]
+    output_files: Optional[Dict]
+
+class StageLogger(BaseModel):
+    version : str
+    command_line: str
+    database_version: str
+    database_path: str
+    steps: List[Steps]
+
+    def has_stage(self, stage_object: object) -> bool:
+        processed_steps = [x for x in self.steps if isinstance(x,stage_object)]
+        if len(processed_steps) > 0:
+            return True
+        else:
+            return False
+
+    def get_stage(self, stage_object: object) -> object:
+        processed_steps = [x for x in self.steps if isinstance(x,stage_object)]
+        if len(processed_steps) > 0:
+            return processed_steps[0]
+        else:
+            return None
+
+class StageLoggerFile:
+    def __init__(self,version:str, command_line: str,
+                 database_version: str, database_path: str,
+                 output_dir: str):
+        self.output_dir = output_dir
+        self.path = os.path.join(self.output_dir, "gtdbtk.json")
+        self.stage_logger = StageLogger(version = version,
+                                        command_line=command_line,
+                                        database_version=database_version,
+                                        database_path=database_path,
+                                        steps=[])
+    def write(self):
+        with open(self.path, "w") as f:
+            f.write(self.stage_logger.json(indent=4))
+
+    def read(self):
+        with open(self.path, "r") as f:
+            self.stage_logger = StageLogger.parse_raw(f.read())
+            steps = []
+            for step in self.stage_logger.steps:
+                if step.name == "ANI screen":
+                    step_object = ANI_Screen_Step(**step.dict())
+                elif step.name == "identify":
+                    step_object = Identify_Step(**step.dict())
+                elif step.name == "align":
+                    step_object = Align_Step(**step.dict())
+                elif step.name == "classify":
+                    step_object = Classify_Step(**step.dict())
+                elif step.name == "infer":
+                    step_object = InferStep(**step.dict())
+                else:
+                    raise Exception(f"Unknown step name {step.name}")
+                steps.append(step_object)
+            self.stage_logger.steps = steps
\ No newline at end of file
diff --git a/gtdbtk/io/tree_mapping.py b/gtdbtk/files/tree_mapping.py
similarity index 100%
rename from gtdbtk/io/tree_mapping.py
rename to gtdbtk/files/tree_mapping.py
diff --git a/gtdbtk/main.py b/gtdbtk/main.py
index 9ccb18da..e48b5c7c 100644
--- a/gtdbtk/main.py
+++ b/gtdbtk/main.py
@@ -14,19 +14,23 @@
 #    along with this program. If not, see <http://www.gnu.org/licenses/>.     #
 #                                                                             #
 ###############################################################################
-
+import argparse
 import logging
+import ntpath
 import os
 import shutil
 import subprocess
 import sys
 import tempfile
+import time
+from datetime import datetime, timedelta
 from typing import Dict, Tuple
 
 from tqdm import tqdm
 
 import gtdbtk.config.config as Config
 from gtdbtk.ani_rep import ANIRep
+from gtdbtk.ani_screen import ANIScreener
 from gtdbtk.biolib_lite.common import (check_dir_exists,
                                        check_file_exists,
                                        make_sure_path_exists,
@@ -39,20 +43,22 @@
 from gtdbtk.decorate import Decorate
 from gtdbtk.exceptions import *
 from gtdbtk.external.fasttree import FastTree
+from gtdbtk.files.stage_logger import StageLoggerFile, ANIScreenStep, IdentifyStep, ClassifyStep, AlignStep, \
+    InferStep, RootStep, DecorateStep
 from gtdbtk.infer_ranks import InferRanks
-from gtdbtk.io.batchfile import Batchfile
-from gtdbtk.io.classify_summary import ClassifySummaryFileAR53
+from gtdbtk.files.batchfile import Batchfile
+from gtdbtk.files.classify_summary import ClassifySummaryFileAR53, ClassifySummaryFile
 from gtdbtk.markers import Markers
 from gtdbtk.misc import Misc
 from gtdbtk.model.enum import Domain
 from gtdbtk.pipeline.export_msa import export_msa
 from gtdbtk.reroot_tree import RerootTree
-from gtdbtk.tools import symlink_f, get_reference_ids, confirm
+from gtdbtk.tools import symlink_f, get_reference_ids, confirm, assert_outgroup_taxon_valid
 
 
 class OptionsParser(object):
 
-    def __init__(self, version):
+    def __init__(self, version,output_dir=None):
         """Initialization.
 
         Parameters
@@ -67,6 +73,21 @@ def __init__(self, version):
 
         self.genomes_to_process = None
 
+        #Setup the Stage Logger File
+        if output_dir is not None:
+            base_name = ntpath.basename(sys.argv[0])
+            if base_name == '__main__.py':
+                prog_name = __name__.split('.')[0]
+            else:
+                prog_name = base_name
+            #timestamp_logger.info(f'{prog_name} {" ".join(sys.argv[1:])}')
+            self.stage_logger_file = StageLoggerFile(output_dir=output_dir,
+                                                    version=self.version,
+                                                    command_line=f'{prog_name} {" ".join(sys.argv[1:])}',
+                                                    database_version = Config.VERSION_DATA,
+                                                    database_path=Config.GENERIC_PATH)
+            self.stage_logger = self.stage_logger_file.stage_logger
+
     def _check_package_compatibility(self):
         """Check that GTDB-Tk is using the most up-to-date reference package."""
         pkg_ver = float(Config.VERSION_DATA.replace('r', ''))
@@ -204,16 +225,24 @@ def _read_taxonomy_files(self, options) -> Dict[str, Tuple[str, str, str, str, s
             check_file_exists(options.gtdbtk_classification_file)
 
             self.logger.info('Reading GTDB-Tk classification file.')
-            gtdbtk_taxonomy = Taxonomy().read(options.gtdbtk_classification_file)
-            del gtdbtk_taxonomy['user_genome']
-            num_reassigned = 0
+            gtdbtk_classify_file = ClassifySummaryFile(path=options.gtdbtk_classification_file)
+            gtdbtk_classify_file.read()
+            gtdbtk_taxonomy = gtdbtk_classify_file.get_gid_taxonomy()
+            if len(gtdbtk_taxonomy) == 0:
+                raise GTDBTkExit(f'No genomes found in GTDB-Tk classification file: {options.gtdbtk_classification_file}')
+
+            num_rep_reassigned = 0
+            num_usr_reassigned = 0
             for gid, taxa in gtdbtk_taxonomy.items():
                 if gid in taxonomy:
-                    num_reassigned += 1
+                    num_rep_reassigned += 1
+                else:
+                    num_usr_reassigned += 1
                 taxonomy[gid] = taxa
 
             self.logger.info(f'Read GTDB-Tk classifications for {len(gtdbtk_taxonomy):,} genomes.')
-            self.logger.info(f'Reassigned taxonomy for {num_reassigned:,} GTDB representative genomes.')
+            self.logger.info(f'Reassigned taxonomy for {num_rep_reassigned:,} GTDB representative '
+                             f'genomes, and {num_usr_reassigned:,} query genomes.')
 
         if options.custom_taxonomy_file:
             # add and overwrite taxonomy for genomes specified in the
@@ -245,7 +274,7 @@ def _read_taxonomy_files(self, options) -> Dict[str, Tuple[str, str, str, str, s
 
         return taxonomy
 
-    def identify(self, options):
+    def identify(self, options , classified_genomes = None):
         """Identify marker genes in genomes.
 
         Parameters
@@ -253,22 +282,38 @@ def identify(self, options):
         options : argparse.Namespace
             The CLI arguments input by the user.
         """
+        identify_step = IdentifyStep()
+        identify_step.starts_at = datetime.now()
+        identify_step.output_dir = options.out_dir
+        identify_step.genes = options.genes
+        identify_step.extension = options.extension
+        identify_step.write_single_copy_genes = options.write_single_copy_genes
 
         if options.genome_dir:
             check_dir_exists(options.genome_dir)
+            identify_step.genome_dir = options.genome_dir
 
         if options.batchfile:
             check_file_exists(options.batchfile)
+            identify_step.batchfile = options.batchfile
 
         make_sure_path_exists(options.out_dir)
 
         genomes, tln_tables = self._genomes_to_process(options.genome_dir,
                                                        options.batchfile,
                                                        options.extension)
-        self.genomes_to_process = genomes
+        #if self.genomes_to_process does not exist, create it
+        if not hasattr(self, 'genomes_to_process'):
+            self.genomes_to_process = genomes
+
+        if classified_genomes is not None:
+            for marker_set_id in ['bac120', 'ar53']:
+                if marker_set_id in classified_genomes:
+                    for classified_genome in classified_genomes.get(marker_set_id).keys():
+                        genomes.pop(classified_genome, None)
 
         markers = Markers(options.cpus)
-        markers.identify(genomes,
+        reports = markers.identify(genomes,
                          tln_tables,
                          options.out_dir,
                          options.prefix,
@@ -276,6 +321,15 @@ def identify(self, options):
                          options.genes,
                          options.write_single_copy_genes)
 
+        identify_step.output_files = reports
+
+        identify_step.ends_at = datetime.now()
+        duration = identify_step.ends_at - identify_step.starts_at
+        #we round the duration to the nearest second
+        identify_step.duration = str(duration - timedelta(microseconds=duration.microseconds))
+        identify_step.status = 'completed'
+
+        self.stage_logger.steps.append(identify_step)
         self.logger.info('Done.')
 
     def align(self, options):
@@ -287,11 +341,27 @@ def align(self, options):
             The CLI arguments input by the user.
         """
 
+        align_step = AlignStep()
+        align_step.starts_at = datetime.now()
+        align_step.output_dir = options.out_dir
+        align_step.skip_gtdb_refs = options.skip_gtdb_refs
+        align_step.taxa_filter = options.taxa_filter
+        align_step.min_perc_aa = options.min_perc_aa
+        align_step.custom_msa_filters = options.custom_msa_filters
+        align_step.skip_trimming = options.skip_trimming
+        align_step.rnd_seed = options.rnd_seed
+        align_step.cols_per_gene = options.cols_per_gene
+        align_step.min_consensus = options.min_consensus
+        align_step.max_consensus = options.max_consensus
+        align_step.min_perc_taxa = options.min_perc_taxa
+        align_step.outgroup_taxon = options.outgroup_taxon if hasattr(options, 'outgroup_taxon') else None
+
         check_dir_exists(options.identify_dir)
+        align_step.identify_dir = options.identify_dir
         make_sure_path_exists(options.out_dir)
 
         markers = Markers(options.cpus, options.debug)
-        markers.align(options.identify_dir,
+        reports = markers.align(options.identify_dir,
                       options.skip_gtdb_refs,
                       options.taxa_filter,
                       options.min_perc_aa,
@@ -307,8 +377,18 @@ def align(self, options):
                       options.outgroup_taxon if hasattr(options, 'outgroup_taxon') else None,
                       self.genomes_to_process)
 
+        align_step.ends_at = datetime.now()
+        duration = align_step.ends_at - align_step.starts_at
+        #we round the duration to the nearest second
+        align_step.duration = str(duration - timedelta(microseconds=duration.microseconds))
+        align_step.output_files = reports
+        align_step.status = 'completed'
+
+        self.stage_logger.steps.append(align_step)
+
         self.logger.info('Done.')
 
+
     def infer(self, options):
         """Infer a tree from a user specified MSA.
 
@@ -318,6 +398,15 @@ def infer(self, options):
             The CLI arguments input by the user.
         """
 
+        infer_step = InferStep()
+        infer_step.starts_at = datetime.now()
+        infer_step.output_dir = options.out_dir
+        infer_step.msa_file = options.msa_file
+        infer_step.prot_model = options.prot_model
+        infer_step.no_support = options.no_support
+        infer_step.gamma = options.gamma
+
+
         check_file_exists(options.msa_file)
         make_sure_path_exists(options.out_dir)
 
@@ -343,15 +432,23 @@ def infer(self, options):
 
         fasttree = FastTree()
         fasttree.run(output_tree, tree_log, fasttree_log, options.prot_model,
-                     options.no_support, options.gamma, options.msa_file,
-                     options.cpus)
+                      options.no_support, options.gamma, options.msa_file,
+                      options.cpus)
         self.logger.info(f'FastTree version: {fasttree.version}')
 
         if hasattr(options, 'subparser_name') and options.subparser_name == 'infer':
-            symlink_f(output_tree[len(options.out_dir) + 1:],
+            symlink_f(output_tree[len(options.out_dir.rstrip('/')) + 1:],
                       os.path.join(options.out_dir,
                                    os.path.basename(output_tree)))
 
+        infer_step.ends_at = datetime.now()
+        duration = infer_step.ends_at - infer_step.starts_at
+        #we round the duration to the nearest second
+        infer_step.duration = str(duration - timedelta(microseconds=duration.microseconds))
+        infer_step.status = 'completed'
+
+        self.stage_logger.steps.append(infer_step)
+
         self.logger.info('Done.')
 
     def run_test(self, options):
@@ -445,23 +542,70 @@ def classify(self, options):
             The CLI arguments input by the user.
         """
 
+        classify_step = ClassifyStep()
+        classify_step.starts_at = datetime.now()
+        classify_step.output_dir = options.out_dir
+        classify_step.debug_option = options.debug
+        classify_step.full_tree = options.full_tree
+        classify_step.skip_ani_screen = options.skip_ani_screen
+        classify_step.no_mash = options.no_mash
+        classify_step.mash_k = options.mash_k
+        classify_step.mash_v = options.mash_v
+        classify_step.mash_s = options.mash_s
+        classify_step.mash_db = options.mash_db
+        classify_step.mash_max_dist = options.mash_max_distance
+
+        ani_summary_files = {}
+        if self.stage_logger_file.stage_logger.has_stage(ANIScreenStep):
+            previous_ani_step = self.stage_logger_file.stage_logger.get_stage(ANIScreenStep)
+            ani_summary_files = previous_ani_step.output_files
+
+
         check_dir_exists(options.align_dir)
+        classify_step.align_dir = options.align_dir
         make_sure_path_exists(options.out_dir)
         if options.scratch_dir:
             make_sure_path_exists(options.scratch_dir)
+            classify_step.scratch_dir = options.scratch_dir
+
+        if options.genome_dir:
+            check_dir_exists(options.genome_dir)
+            classify_step.genome_dir = options.genome_dir
+
+        if options.batchfile:
+            check_file_exists(options.batchfile)
+            classify_step.batchfile = options.batchfile
 
         genomes, _ = self._genomes_to_process(options.genome_dir,
                                               options.batchfile,
                                               options.extension)
 
         classify = Classify(options.cpus, options.pplacer_cpus, options.min_af)
-        classify.run(genomes=genomes,
+        reports = classify.run(genomes=genomes,
                      align_dir=options.align_dir,
                      out_dir=options.out_dir,
                      prefix=options.prefix,
                      scratch_dir=options.scratch_dir,
                      debugopt=options.debug,
-                     fulltreeopt=options.full_tree)
+                     fulltreeopt=options.full_tree,
+                     skip_ani_screen=options.skip_ani_screen,
+                     no_mash=options.no_mash,
+                     mash_k=options.mash_k,
+                     mash_v=options.mash_v,
+                     mash_s=options.mash_s,
+                     mash_db=options.mash_db,
+                     mash_max_dist=options.mash_max_distance,
+                     ani_summary_files=ani_summary_files
+                     )
+
+        classify_step.ends_at = datetime.now()
+        duration = classify_step.ends_at - classify_step.starts_at
+        classify_step.output_files = reports
+        #we round the duration to the nearest second
+        classify_step.duration = str(duration - timedelta(microseconds=duration.microseconds))
+        classify_step.status = 'completed'
+
+        self.stage_logger.steps.append(classify_step)
 
         self.logger.info('Note that Tk classification mode is insufficient for publication of new taxonomic '
                          'designations. New designations should be based on one or more de novo trees, an '
@@ -469,6 +613,72 @@ def classify(self, options):
 
         self.logger.info('Done.')
 
+    def ani_screen(self, options ):
+        """Run a mash/FastANI screen of all user genomes
+        against the reference genomes.
+
+        Parameters
+        ----------
+        options : argparse.Namespace
+            The CLI arguments input by the user.
+        """
+
+        ani_step = ANIScreenStep()
+        ani_step.starts_at = datetime.now()
+        ani_step.output_dir = options.out_dir
+        ani_step.mash_db = options.mash_db
+        ani_step.mash_k = options.mash_k
+        ani_step.mash_v = options.mash_v
+        ani_step.mash_s = options.mash_s
+        ani_step.mash_max_dist = options.mash_max_distance
+
+        if options.genome_dir:
+            check_dir_exists(options.genome_dir)
+            ani_step.genome_dir = options.genome_dir
+
+        if options.batchfile:
+            check_file_exists(options.batchfile)
+            ani_step.batchfile = options.batchfile
+
+        make_sure_path_exists(options.out_dir)
+
+        genomes, tln_tables = self._genomes_to_process(options.genome_dir,
+                                                       options.batchfile,
+                                                       options.extension)
+        self.genomes_to_process = genomes
+
+        make_sure_path_exists(options.out_dir)
+
+        genomes, _ = self._genomes_to_process(options.genome_dir,
+                                              options.batchfile,
+                                              options.extension)
+
+        aniscreener = ANIScreener(options.cpus)
+        classified_genomes,reports = aniscreener.run_aniscreen(
+            genomes=genomes,
+            no_mash=options.no_mash,
+            out_dir=options.out_dir,
+            prefix=options.prefix,
+            mash_k=options.mash_k,
+            mash_v=options.mash_v,
+            mash_s=options.mash_s,
+            mash_max_dist=options.mash_max_distance,
+            mash_db=options.mash_db)
+
+
+        self.logger.info('Done.')
+
+        ani_step.ends_at = datetime.now()
+        duration = ani_step.ends_at - ani_step.starts_at
+        #we round the duration to the nearest second
+        ani_step.duration = str(duration - timedelta(microseconds=duration.microseconds))
+        ani_step.status = 'completed'
+        ani_step.output_files=reports
+
+        self.stage_logger.steps.append(ani_step)
+
+        return classified_genomes
+
     def trim_msa(self, options):
         """ Trim an untrimmed archaea or bacterial MSA file.
 
@@ -514,7 +724,7 @@ def export_msa(self, options):
         export_msa(domain=domain, output_file=user_output)
         self.logger.info('Done.')
 
-    def root(self, options):
+    def root(self, options: argparse.Namespace):
         """Root tree using outgroup.
 
         Parameters
@@ -523,7 +733,19 @@ def root(self, options):
             The CLI arguments input by the user.
         """
 
+        root_step = RootStep()
+        root_step.starts_at = datetime.now()
+
+
         check_file_exists(options.input_tree)
+        assert_outgroup_taxon_valid(options.outgroup_taxon)
+
+        root_step.outgroup_taxon = options.outgroup_taxon
+
+        root_step.input_tree = options.input_tree
+        # set gtdbtk_classification_file exists in options we can use it
+        root_step.gtdbtk_classification_file=options.gtdbtk_classification_file if hasattr(options, 'gtdbtk_classification_file') else None
+        root_step.custom_taxonomy_file=options.custom_taxonomy_file if hasattr(options, 'custom_taxonomy_file') else None
 
         taxonomy = self._read_taxonomy_files(options)
 
@@ -534,10 +756,20 @@ def root(self, options):
                 outgroup.add(genome_id)
 
         reroot = RerootTree()
-        reroot.root_with_outgroup(options.input_tree,
+        reports = reroot.root_with_outgroup(options.input_tree,
                                   options.output_tree,
                                   outgroup)
 
+        root_step.ends_at = datetime.now()
+        duration = root_step.ends_at - root_step.starts_at
+        #we round the duration to the nearest second
+        root_step.duration = str(duration - timedelta(microseconds=duration.microseconds))
+        root_step.status = 'completed'
+
+        # if self.stage_logger exists, we add the step to the stage_logger
+        if hasattr(self, 'stage_logger'):
+            self.stage_logger.steps.append(root_step)
+
         self.logger.info('Done.')
 
     def decorate(self, options):
@@ -549,12 +781,21 @@ def decorate(self, options):
             The CLI arguments input by the user.
         """
 
+        decorate_step = DecorateStep()
+        decorate_step.starts_at = datetime.now()
+
         check_file_exists(options.input_tree)
 
         taxonomy = self._read_taxonomy_files(options)
 
+        decorate_step.input_tree = options.input_tree
+        # set gtdbtk_classification_file exists in options we can use it
+        decorate_step.gtdbtk_classification_file=options.gtdbtk_classification_file if hasattr(options, 'gtdbtk_classification_file') else None
+        decorate_step.custom_taxonomy_file=options.custom_taxonomy_file if hasattr(options, 'custom_taxonomy_file') else None
+        decorate_step.suffix=options.suffix if hasattr(options, 'suffix') else None
+
         d = Decorate()
-        d.run(options.input_tree,
+        reports = d.run(options.input_tree,
               taxonomy,
               options.output_tree)
 
@@ -582,6 +823,18 @@ def decorate(self, options):
                 raise GenomeMarkerSetUnknown(
                     'There was an error determining the marker set.')
 
+        decorate_step.ends_at = datetime.now()
+        duration = decorate_step.ends_at - decorate_step.starts_at
+        #we round the duration to the nearest second
+        decorate_step.duration = str(duration - timedelta(microseconds=duration.microseconds))
+        decorate_step.status = 'completed'
+        decorate_step.output_files=reports
+
+        if hasattr(self, 'stage_logger'):
+            self.stage_logger.steps.append(decorate_step)
+
+        self.logger.info('Done.')
+
     def check_install(self):
         """ Verify all GTDB-Tk data files are present.
 
@@ -694,6 +947,7 @@ def parse_options(self, options):
         if options.subparser_name == 'de_novo_wf':
             check_dependencies(['prodigal', 'hmmalign'])
             check_dependencies(['FastTree' + ('MP' if options.cpus > 1 else '')])
+            assert_outgroup_taxon_valid(options.outgroup_taxon)
 
             if options.skip_gtdb_refs and options.custom_taxonomy_file is None:
                 raise GTDBTkExit("When running de_novo_wf, The '--skip_gtdb_refs' flag requires"
@@ -800,8 +1054,6 @@ def parse_options(self, options):
             check_dependencies(['prodigal', 'hmmalign', 'pplacer', 'guppy',
                                 'fastANI'])
 
-
-
             if options.write_single_copy_genes and not options.keep_intermediates:
                 self.logger.warning('--write_single_copy_genes flag is set to True,'
                                     ' but --keep_intermediates is set to False. '
@@ -810,16 +1062,56 @@ def parse_options(self, options):
                     self.logger.info('Exiting workflow.')
                     sys.exit(0)
 
+            # if options.skip_aniscreen is false,
+            # we need to make sure the options.mash_db is selected too to point to the folder
+            # where the sketch file is.
+            if not options.skip_ani_screen and not options.no_mash and not options.mash_db:
+                self.logger.error('You must specify a path to the mash database with --mash_db')
+                sys.exit(1)
 
             #options.write_single_copy_genes = False
 
-            self.identify(options)
+            classified_genomes = None
+            #We ned to check if the ani screen has already be ran, if so, we need to skip it.
+            #Check is the gtdbtk.json file exists in the output folder
+            if os.path.isfile(self.stage_logger_file.path) and 1==2:
+                #If the file exists, we need to check if the ani_screen step has been ran
+                #If the ani_screen step has been ran, we need to skip it.
+                self.stage_logger_file.read()
+                stage_logger = self.stage_logger_file.stage_logger
+                if stage_logger.has_stage(ANIScreenStep):
+                    # we get the genomes already classified by the ani_screen step
+                    previous_ani_step = stage_logger.get_stage(ANIScreenStep)
+                    if previous_ani_step.is_complete():
+                        self.logger.warning('The ani_screen step has already been completed, we load existing results.')
+                        ani_summary_files = previous_ani_step.output_files
+                        classify_method = Classify()
+                        classified_genomes=classify_method.load_fastani_results_pre_pplacer(ani_summary_files)
+                        classified_genomes = classify_method.convert_rows_to_dict(classified_genomes)
+                        len_mash_classified_bac120 = len(classified_genomes['bac120']) \
+                            if 'bac120' in classified_genomes else 0
+
+                        len_mash_classified_ar53 = len(classified_genomes['ar53']) \
+                            if 'ar53' in classified_genomes else 0
+
+                        self.logger.info(f'{len_mash_classified_ar53 + len_mash_classified_bac120} genome(s) have '
+                                         f'been classified using the ANI pre-screening step.')
+
+                        options.skip_ani_screen = True
+
+            #Before identify step, we run the ani_screen step, these genomes classify with this step will not continue
+            # in the identify step.
+
+            if not options.skip_ani_screen:
+                classified_genomes = self.ani_screen(options)
+
+            self.identify(options,classified_genomes)
 
             options.identify_dir = options.out_dir
             options.align_dir = options.out_dir
             options.taxa_filter = None
             options.custom_msa_filters = False
-            # Added here due to the other mutex argument being include above.
+            # Added here due to the other mutex argument being included above.
             options.skip_trimming = False
             options.min_consensus = None
             options.min_perc_taxa = None
@@ -831,10 +1123,15 @@ def parse_options(self, options):
 
             self.align(options)
 
+            # because we run ani_screen before the identify step, we do not need to rerun the
+            #ani step again, so we set the skip_aniscreen to True
+            options.skip_ani_screen = True
+
             self.classify(options)
             if not options.keep_intermediates:
                 self.remove_intermediate_files(options.out_dir,'classify_wf')
 
+
         elif options.subparser_name == 'identify':
             self.identify(options)
         elif options.subparser_name == 'align':
@@ -842,16 +1139,15 @@ def parse_options(self, options):
         elif options.subparser_name == 'infer':
             self.infer(options)
         elif options.subparser_name == 'classify':
-
-            # TODO: Remove this block once the split_tree function is implemented.
-            # if hasattr(options, 'split_tree') and options.split_tree:
-            #     self.logger.warning('The split tree option is not yet '
-            #                         ' supported, overriding value to False.')
-            # options.split_tree = False
-
-            # if options.recalculate_red and options.split_tree:
-            #     raise GTDBTkExit('--split_tree and --recalculate_red are mutually exclusive.')
+            # if options.skip_ani_screen is not selected,
+            # we need to make sure the options.mash_db is selected too to point to the folder
+            # where the sketch file is.
+            if not options.skip_ani_screen and not options.no_mash and not options.mash_db:
+                print(options.skip_ani_screen, options.no_mash, options.mash_db)
+                self.logger.error('You must specify a path to the mash database with --mash_db')
             self.classify(options)
+            self.stage_logger_file.write()
+
         elif options.subparser_name == 'root':
             self.root(options)
         elif options.subparser_name == 'decorate':
@@ -879,4 +1175,7 @@ def parse_options(self, options):
                               options.subparser_name + '"\n')
             sys.exit(1)
 
+        if hasattr(self,'stage_logger' ) and self.stage_logger.steps:
+            self.stage_logger_file.write()
+
         return 0
diff --git a/gtdbtk/markers.py b/gtdbtk/markers.py
index dc985b8d..fcb3bc1c 100644
--- a/gtdbtk/markers.py
+++ b/gtdbtk/markers.py
@@ -35,11 +35,11 @@
 from gtdbtk.external.pfam_search import PfamSearch
 from gtdbtk.external.prodigal import Prodigal
 from gtdbtk.external.tigrfam_search import TigrfamSearch
-from gtdbtk.io.marker.copy_number import CopyNumberFileAR53, CopyNumberFileBAC120
-from gtdbtk.io.marker.tophit import TopHitPfamFile, TopHitTigrFile
-from gtdbtk.io.marker_info import MarkerInfoFileAR53, MarkerInfoFileBAC120
-from gtdbtk.io.prodigal.tln_table import TlnTableFile
-from gtdbtk.io.prodigal.tln_table_summary import TlnTableSummaryFile
+from gtdbtk.files.marker.copy_number import CopyNumberFileAR53, CopyNumberFileBAC120
+from gtdbtk.files.marker.tophit import TopHitPfamFile, TopHitTigrFile
+from gtdbtk.files.marker_info import MarkerInfoFileAR53, MarkerInfoFileBAC120
+from gtdbtk.files.prodigal.tln_table import TlnTableFile
+from gtdbtk.files.prodigal.tln_table_summary import TlnTableSummaryFile
 from gtdbtk.pipeline import align
 from gtdbtk.tools import merge_two_dicts, symlink_f, tqdm_log
 from gtdbtk.trim_msa import TrimMSA
@@ -76,7 +76,7 @@ def __init__(self, cpus=1, debug=False):
         self.tigrfam_top_hit_suffix = TIGRFAM_TOP_HIT_SUFFIX
 
     def _report_identified_marker_genes(self, gene_dict, outdir, prefix,
-                                        write_single_copy_genes):
+                                        write_single_copy_genes,reports):
         """Report statistics for identified marker genes."""
 
         # Summarise the copy number of each AR53 and BAC120 markers.
@@ -104,16 +104,13 @@ def _report_identified_marker_genes(self, gene_dict, outdir, prefix,
 
         # Write each of the summary files to disk.
         ar53_copy_number_file.write()
+        reports.setdefault('ar53',[]).append(ar53_copy_number_file.path)
         bac120_copy_number_file.write()
+        reports.setdefault('bac120',[]).append(bac120_copy_number_file.path)
         tln_summary_file.write()
+        reports.setdefault('all',[]).append(tln_summary_file.path)
 
         # Create a symlink to store the summary files in the root.
-        # symlink_f(PATH_BAC120_MARKER_SUMMARY.format(prefix=prefix),
-        #           os.path.join(outdir, os.path.basename(PATH_BAC120_MARKER_SUMMARY.format(prefix=prefix))))
-        # symlink_f(PATH_AR53_MARKER_SUMMARY.format(prefix=prefix),
-        #           os.path.join(outdir, os.path.basename(PATH_AR53_MARKER_SUMMARY.format(prefix=prefix))))
-        # symlink_f(PATH_TLN_TABLE_SUMMARY.format(prefix=prefix),
-        #           os.path.join(outdir, os.path.basename(PATH_TLN_TABLE_SUMMARY.format(prefix=prefix))))
         if os.path.exists(os.path.join(outdir, os.path.basename(PATH_FAILS.format(prefix=prefix)))):
             symlink_f(PATH_FAILS.format(prefix=prefix),
                       os.path.join(outdir, os.path.basename(PATH_FAILS.format(prefix=prefix))))
@@ -155,6 +152,8 @@ def _report_identified_marker_genes(self, gene_dict, outdir, prefix,
                         with open(marker_path, 'w') as fh:
                             fh.write('\n'.join(to_write))
 
+        return reports
+
     def identify(self, genomes, tln_tables, out_dir, prefix, force, genes, write_single_copy_genes):
         """Identify marker genes in genomes.
 
@@ -183,12 +182,15 @@ def identify(self, genomes, tln_tables, out_dir, prefix, force, genes, write_sin
         """
         check_dependencies(['prodigal', 'hmmsearch'])
 
+        reports = {}
+
         self.logger.info(f'Identifying markers in {len(genomes):,} genomes with '
                          f'{self.cpus} threads.')
 
         self.marker_gene_dir = os.path.join(out_dir, DIR_MARKER_GENE)
         self.failed_genomes = os.path.join(
             out_dir, PATH_FAILS.format(prefix=prefix))
+        reports.setdefault('all',[]).append(self.failed_genomes)
 
         if not genes:
             prodigal = Prodigal(self.cpus,
@@ -207,6 +209,7 @@ def identify(self, genomes, tln_tables, out_dir, prefix, force, genes, write_sin
                 'Using supplied genomes as called genes, skipping Prodigal.')
             genome_dictionary = dict()
             for gid, gpath in genomes.items():
+
                 genome_dictionary[gid] = {'aa_gene_path': gpath,
                                           'translation_table_path': None,
                                           'nt_gene_path': None,
@@ -216,7 +219,7 @@ def identify(self, genomes, tln_tables, out_dir, prefix, force, genes, write_sin
                 # so we can use this symlink in the align step
                 symlink_protein_dir = os.path.join(self.marker_gene_dir, gid)
                 make_sure_path_exists(symlink_protein_dir)
-                symlink_f(gpath, os.path.join(symlink_protein_dir,gid+self.protein_file_suffix))
+                symlink_f(os.path.abspath(gpath), os.path.join(symlink_protein_dir,gid+self.protein_file_suffix))
 
         # annotated genes against TIGRFAM and Pfam databases
         self.logger.log(Config.LOG_TASK,
@@ -246,8 +249,10 @@ def identify(self, genomes, tln_tables, out_dir, prefix, force, genes, write_sin
 
         self.logger.log(Config.LOG_TASK,
                         'Summarising identified marker genes.')
-        self._report_identified_marker_genes(genome_dictionary, out_dir, prefix,
-                                             write_single_copy_genes)
+        reports = self._report_identified_marker_genes(genome_dictionary, out_dir, prefix,
+                                             write_single_copy_genes,reports)
+
+        return reports
 
     def _path_to_identify_data(self, identity_dir, warn=True):
         """Get path to genome data produced by 'identify' command."""
@@ -465,6 +470,8 @@ def align(self,
               genomes_to_process=None):
         """Align marker genes in genomes."""
 
+        reports = {}
+
         # read genomes that failed identify steps to skip them
         failed_genomes_file = os.path.join(os.path.join(
             identify_dir, PATH_FAILS.format(prefix=prefix)))
@@ -637,9 +644,9 @@ def align(self,
                 self.logger.log(Config.LOG_TASK,
                                 f'Masking columns of {domain_str} multiple sequence alignment using canonical mask.')
                 trimmed_seqs, pruned_seqs = self._apply_mask(gtdb_msa,
-                                                             user_msa,
-                                                             gtdb_msa_mask,
-                                                             min_perc_aa / 100.0)
+                                                            user_msa,
+                                                           gtdb_msa_mask,
+                                                            min_perc_aa / 100.0)
                 self.logger.info('Masked {} alignment from {:,} to {:,} AAs.'.format(
                     domain_str,
                     len(list(user_msa.values())[0]),
@@ -662,6 +669,8 @@ def align(self,
                         perc_alignment = valid_bases * 100.0 / len(pruned_seq)
                     fout.write(
                         f'{pruned_seq_id}\tInsufficient number of amino acids in MSA ({perc_alignment:.1f}%)\n')
+            reports.setdefault(marker_set_id, []).append(marker_filtered_genomes)
+
 
             # write out MSAs
             if not skip_gtdb_refs:
@@ -669,6 +678,7 @@ def align(self,
                                  f'{domain_str} GTDB and user genomes.')
                 self._write_msa(trimmed_seqs, marker_msa_path,
                                 gtdb_taxonomy, zip_output=True)
+                reports.setdefault(marker_set_id, []).append(marker_msa_path)
 
             trimmed_user_msa = {k: v for k, v in trimmed_seqs.items()
                                 if k in user_msa}
@@ -677,6 +687,7 @@ def align(self,
                                  f'{domain_str} user genomes.')
                 self._write_msa(trimmed_user_msa,
                                 marker_user_msa_path, gtdb_taxonomy, zip_output=True)
+                reports.setdefault(marker_set_id, []).append(marker_user_msa_path)
             else:
                 self.logger.info(
                     f'All {domain_str} user genomes have been filtered out.')
@@ -687,6 +698,9 @@ def align(self,
             with open(no_marker_filtered_genomes, 'w') as fout:
                 for no_marker_gid in no_marker_gids:
                     fout.write(f'{no_marker_gid}\tNo bacterial or archaeal marker\n')
+            reports.setdefault('all', []).append(no_marker_filtered_genomes)
+
+        return reports
 
     def _write_individual_markers(self, user_msa, marker_set_id, marker_list, out_dir, prefix):
         marker_dir = join(out_dir, DIR_ALIGN_MARKERS)
diff --git a/gtdbtk/pipeline/align.py b/gtdbtk/pipeline/align.py
index af16cc0d..e64d214a 100644
--- a/gtdbtk/pipeline/align.py
+++ b/gtdbtk/pipeline/align.py
@@ -8,9 +8,9 @@
 from gtdbtk.config.config import LOG_TASK
 from gtdbtk.exceptions import GTDBTkExit
 from gtdbtk.external.hmm_aligner import HmmAligner
-from gtdbtk.io.marker.copy_number import CopyNumberFile
-from gtdbtk.io.marker.tophit import TopHitPfamFile, TopHitTigrFile
-from gtdbtk.io.marker_info import MarkerInfoFile
+from gtdbtk.files.marker.copy_number import CopyNumberFile
+from gtdbtk.files.marker.tophit import TopHitPfamFile, TopHitTigrFile
+from gtdbtk.files.marker_info import MarkerInfoFile
 from gtdbtk.tools import tqdm_log
 
 
diff --git a/gtdbtk/reroot_tree.py b/gtdbtk/reroot_tree.py
index c3aa08db..cd8a30ad 100644
--- a/gtdbtk/reroot_tree.py
+++ b/gtdbtk/reroot_tree.py
@@ -117,6 +117,8 @@ def root_with_outgroup(self, input_tree: str, output_tree: str, outgroup: Set[st
                                suppress_rooting=True, unquoted_underscores=True)
             self.logger.info(f'Rerooted tree written to: {output_tree}')
 
+        return {'all':[output_tree]}
+
     def midpoint(self, input_tree, output_tree):
         """Reroot tree bat midpoint.
 
diff --git a/gtdbtk/split.py b/gtdbtk/split.py
index b01bb8c6..ee0c90db 100644
--- a/gtdbtk/split.py
+++ b/gtdbtk/split.py
@@ -24,11 +24,10 @@
 from gtdbtk.biolib_lite.seq_io import read_fasta
 from gtdbtk.config.output import *
 from gtdbtk.exceptions import GenomeMarkerSetUnknown, GTDBTkExit
-from gtdbtk.io.classify_summary import ClassifySummaryFileRow
-from gtdbtk.io.pplacer_classification import PplacerHighClassifyRow, PplacerHighClassifyFile
-from gtdbtk.io.tree_mapping import GenomeMappingFileRow
-from gtdbtk.tools import TreeTraversal, standardise_taxonomy
-
+from gtdbtk.files.classify_summary import ClassifySummaryFileRow
+from gtdbtk.files.pplacer_classification import PplacerHighClassifyRow, PplacerHighClassifyFile
+from gtdbtk.files.tree_mapping import GenomeMappingFileRow
+from gtdbtk.tools import TreeTraversal, standardise_taxonomy, aa_percent_msa
 
 class Split(object):
     """Determine taxonomic classification of genomes by ML placement using the Split Methods."""
@@ -339,7 +338,8 @@ def _classify_on_terminal_branch(self, list_leaf_ranks, current_rel_list, parent
 
         return ';'.join(term_branch_taxonomy[1:self.order_rank.index(closest_rank) + 1])
 
-    def map_high_taxonomy(self,high_classification, mapping_dict, summary_file,tree_mapping_file):
+    def map_high_taxonomy(self,high_classification, mapping_dict, summary_file,
+                          tree_mapping_file,msa_dict,trans_table_dict,percent_multihit_dict,bac_ar_diff,warning_counter):
         mapped_rank = {}
         counter = 0
         high_taxonomy_used = {}
@@ -367,6 +367,23 @@ def map_high_taxonomy(self,high_classification, mapping_dict, summary_file,tree_
                     summary_row.pplacer_tax = v.get('pplacer_tax')
                     summary_row.red_value = v.get('rel_dist')
                     summary_row.note = 'classification based on placement in backbone tree'
+                    summary_row.msa_percent = aa_percent_msa(msa_dict.get(summary_row.gid))
+                    summary_row.tln_table = trans_table_dict.get(summary_row.gid)
+
+                    warnings = []
+                    if summary_row.gid in percent_multihit_dict:
+                        warnings.append('Genome has more than {}% of markers with multiple hits'.format(
+                            percent_multihit_dict.get(summary_row.gid)))
+                    if summary_row.gid in bac_ar_diff:
+                        warnings.append('Genome domain questionable ( {}% Bacterial, {}% Archaeal)'.format(
+                            bac_ar_diff.get(summary_row.gid).get('bac120'),
+                            bac_ar_diff.get(summary_row.gid).get('ar53')))
+                    if len(warnings) > 0:
+                        if summary_row.warnings is not None:
+                            warnings.extend(summary_row.warnings.split(';'))
+                        summary_row.warnings = ';'.join(set(warnings))
+                        warning_counter += 1
+
 
                     mapping_row = GenomeMappingFileRow()
                     mapping_row.gid = k
@@ -377,4 +394,4 @@ def map_high_taxonomy(self,high_classification, mapping_dict, summary_file,tree_
                     tree_mapping_file.add_row(mapping_row)
                     summary_file.add_row(summary_row)
 
-        return mapped_rank, counter , high_taxonomy_used
+        return mapped_rank,warning_counter, counter , high_taxonomy_used
diff --git a/gtdbtk/tools.py b/gtdbtk/tools.py
index da269f1a..9022eb5a 100644
--- a/gtdbtk/tools.py
+++ b/gtdbtk/tools.py
@@ -1,6 +1,8 @@
 import hashlib
 import json
 import logging
+from typing import Optional
+
 import math
 import os
 import random
@@ -42,6 +44,28 @@ def get_reference_ids():
                 results.add(raw_id[3:])
     return frozenset(results)
 
+def get_ref_genomes():
+    """Returns a dictionary of genome accession to genome path.
+
+    Returns
+    -------
+    dict[str, str]
+        Dict[genome_id] = fasta_path
+    """
+    ref_genomes = dict()
+    with open(Config.FASTANI_GENOME_LIST) as g_path_file:
+        for line in g_path_file:
+            (full_name, path) = line.strip().split()
+            if full_name.endswith(Config.FASTANI_GENOMES_EXT):
+                accession = full_name.split(Config.FASTANI_GENOMES_EXT)[0]
+            ref_genomes[accession] = os.path.join(Config.FASTANI_DIR, path, full_name)
+    return ref_genomes
+
+def aa_percent_msa(aa_string):
+        aa_len = sum([1 for c in aa_string if c.isalpha()])
+        aa_perc = float(aa_len) / len(aa_string)
+        return round(aa_perc * 100, 2)
+
 def truncate_taxonomy(taxonomy, rank):
     taxonomy_list = taxonomy.split(';')
     taxonomy_list = taxonomy_list[0:order_rank.index(rank)]
@@ -470,3 +494,23 @@ def __exit__(self, exc_type, exc_val, exc_tb):
 
     def __del__(self):
         self.tqdm.close()
+
+def assert_outgroup_taxon_valid(outgroup_taxon: Optional[str]):
+    """Check that the outgroup taxon is a valid string, no checks are made
+    to ensure that it is a real taxon. If an invalid string is provided, an
+    exception is raised."""
+    if not outgroup_taxon:
+        raise GTDBTkExit('The outgroup taxon cannot be empty, please specify a '
+                         'valid taxon (e.g. p__Proteobacteria).')
+    if len(outgroup_taxon) < 4:
+        raise GTDBTkExit('The outgroup taxon seems suspiciously short and is '
+                         'not a valid GTDB taxon, please specify a valid '
+                         'taxon (e.g. p__Proteobacteria).')
+    if outgroup_taxon.startswith('d__'):
+        raise GTDBTkExit('You cannot specify a domain as an outgroup, please '
+                         'select a lower rank (i.e. phylum, class, order, '
+                         'family, genus, species).')
+    if outgroup_taxon[:3] not in {'p__', 'c__', 'o__', 'f__', 'g__', 's__'}:
+        raise GTDBTkExit('You must specify the outgroup taxon in '
+                         'Greengenes-style format (e.g. p__Proteobacteria).')
+    return
diff --git a/scripts/gtdb_to_ncbi_majority_vote.py b/scripts/gtdb_to_ncbi_majority_vote.py
index 5d674254..be577664 100755
--- a/scripts/gtdb_to_ncbi_majority_vote.py
+++ b/scripts/gtdb_to_ncbi_majority_vote.py
@@ -366,6 +366,9 @@ def get_ncbi_majority_vote(self,
                 ncbi_classification = ncbi_lineages[mv_taxon]
                 break
 
+        if not ncbi_classification:
+            ncbi_classification = self.rank_prefix
+
         return ';'.join(ncbi_classification)
 
     def ncbi_majority_vote(self,


Test patch:
diff --git a/tests/test_classify.py b/tests/test_classify.py
index 41c99c2f..2bfaec0f 100644
--- a/tests/test_classify.py
+++ b/tests/test_classify.py
@@ -26,7 +26,7 @@
 from gtdbtk.biolib_lite.taxonomy import Taxonomy
 from gtdbtk.classify import Classify
 from gtdbtk.config.output import *
-from gtdbtk.io.pplacer_classification import PplacerClassifyFileAR53
+from gtdbtk.files.pplacer_classification import PplacerClassifyFileAR53
 
 
 class TestClassify(unittest.TestCase):
diff --git a/tests/test_gtdbtk/test__main__.py b/tests/test_gtdbtk/test__main__.py
index cdf543d1..1f9dfd73 100644
--- a/tests/test_gtdbtk/test__main__.py
+++ b/tests/test_gtdbtk/test__main__.py
@@ -21,7 +21,7 @@
 import unittest
 
 from gtdbtk.config.config import CONCAT_AR53, CONCAT_BAC120, MASK_AR53, MASK_DIR, MASK_BAC120
-from gtdbtk.io.classify_summary import ClassifySummaryFileAR53
+from gtdbtk.files.classify_summary import ClassifySummaryFileAR53
 from gtdbtk.tools import sha256
 
 
diff --git a/tests/test_gtdbtk/test_io/__init__.py b/tests/test_gtdbtk/test_files/__init__.py
similarity index 100%
rename from tests/test_gtdbtk/test_io/__init__.py
rename to tests/test_gtdbtk/test_files/__init__.py
diff --git a/tests/test_gtdbtk/test_io/test_marker/__init__.py b/tests/test_gtdbtk/test_files/test_marker/__init__.py
similarity index 100%
rename from tests/test_gtdbtk/test_io/test_marker/__init__.py
rename to tests/test_gtdbtk/test_files/test_marker/__init__.py
diff --git a/tests/test_gtdbtk/test_io/test_marker/test_copy_number.py b/tests/test_gtdbtk/test_files/test_marker/test_copy_number.py
similarity index 98%
rename from tests/test_gtdbtk/test_io/test_marker/test_copy_number.py
rename to tests/test_gtdbtk/test_files/test_marker/test_copy_number.py
index ed1d42f3..156428db 100644
--- a/tests/test_gtdbtk/test_io/test_marker/test_copy_number.py
+++ b/tests/test_gtdbtk/test_files/test_marker/test_copy_number.py
@@ -21,8 +21,8 @@
 import unittest
 
 from gtdbtk.config.output import PATH_BAC120_MARKER_SUMMARY, PATH_AR53_MARKER_SUMMARY
-from gtdbtk.io.marker.copy_number import CopyNumberFile, CopyNumberFileAR53, CopyNumberFileBAC120
-from gtdbtk.io.marker.tophit import TopHitPfamFile, TopHitTigrFile, Hit
+from gtdbtk.files.marker.copy_number import CopyNumberFile, CopyNumberFileAR53, CopyNumberFileBAC120
+from gtdbtk.files.marker.tophit import TopHitPfamFile, TopHitTigrFile, Hit
 
 
 class TestCopyNumberFile(unittest.TestCase):
diff --git a/tests/test_gtdbtk/test_io/test_marker/test_tophit.py b/tests/test_gtdbtk/test_files/test_marker/test_tophit.py
similarity index 99%
rename from tests/test_gtdbtk/test_io/test_marker/test_tophit.py
rename to tests/test_gtdbtk/test_files/test_marker/test_tophit.py
index daa354b5..2f4e8421 100644
--- a/tests/test_gtdbtk/test_io/test_marker/test_tophit.py
+++ b/tests/test_gtdbtk/test_files/test_marker/test_tophit.py
@@ -21,7 +21,7 @@
 import unittest
 
 from gtdbtk.config.output import PFAM_TOP_HIT_SUFFIX, TIGRFAM_TOP_HIT_SUFFIX
-from gtdbtk.io.marker.tophit import Hit, TopHitPfamFile, TopHitFile, TopHitTigrFile
+from gtdbtk.files.marker.tophit import Hit, TopHitPfamFile, TopHitFile, TopHitTigrFile
 
 
 class TestHit(unittest.TestCase):
diff --git a/tests/test_gtdbtk/test_io/test_prodigal/__init__.py b/tests/test_gtdbtk/test_files/test_prodigal/__init__.py
similarity index 100%
rename from tests/test_gtdbtk/test_io/test_prodigal/__init__.py
rename to tests/test_gtdbtk/test_files/test_prodigal/__init__.py
diff --git a/tests/test_gtdbtk/test_io/test_prodigal/test_tln_table_summary.py b/tests/test_gtdbtk/test_files/test_prodigal/test_tln_table_summary.py
similarity index 97%
rename from tests/test_gtdbtk/test_io/test_prodigal/test_tln_table_summary.py
rename to tests/test_gtdbtk/test_files/test_prodigal/test_tln_table_summary.py
index ee016b06..5b27a12b 100644
--- a/tests/test_gtdbtk/test_io/test_prodigal/test_tln_table_summary.py
+++ b/tests/test_gtdbtk/test_files/test_prodigal/test_tln_table_summary.py
@@ -22,7 +22,7 @@
 
 from gtdbtk.config.output import PATH_TLN_TABLE_SUMMARY
 from gtdbtk.exceptions import GTDBTkExit
-from gtdbtk.io.prodigal.tln_table_summary import TlnTableSummaryFile
+from gtdbtk.files.prodigal.tln_table_summary import TlnTableSummaryFile
 
 
 class TestTlnTableSummaryFile(unittest.TestCase):