SASpector 0.0.5
Short-read Assembly inSpector
Requires Python: >=3.4


# Table of Contents

1.  [SASpector: a tool for analysis of missing regions in (bacterial) draft genomes"](#orgc954b8f)
2.  [Introduction](#orgfc8146a)
3.  [System Requirements](#org1d89a66)
    1.  [General dependencies](#org36ea480)
    2.  [Third-party tools (available on bioconda)](#org88d3159)
    3.  [Installation](#org51dbcec)
    4.  [Command line options](#org9550907)
    5.  [Usage](#orgab8c9a6)
    6.  [Output](#orgfb6aafb)
4.  [Contact](#org86ccb18)
5.  [References](#org7083ee4)


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# SASpector: a tool for analysis of missing regions in (bacterial) draft genomes"

A bioinformatics tool to extract and analyze missing regions of
short-read assemblies by mapping the contigs to a reference genome.

Publication: to be added (doi)

![Image](https://github.com/LoGT-KULeuven/SASpector/blob/dev/SASPector-logo.png?raw=true)

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# Introduction

SASpector is a tool that compares a short-reads assembly with a
reference bacterial genome (for example obtained via hybrid assembly)
by extracting missing (unmapped) regions from the reference and
analyzing them to see functional and compositional pattern. The aim of
the analysis is to explain why these regions are missed by the
short-read assembly and if important parts of the genome are missed
when a resolved genome is lacking.

The tool takes as global inputs the reference genome and a short-read
assembly as contigs/draft genome, both in FASTA format. This
repository contains a command-line tool \`SASpector\` to obtain missing
regions and several script for evaluation and analysis for those
missing regions:

-   *mapper.py*: mapping of the short-reads assembly against the
    reference assembly using progressiveMauve (Mauve aligner).
-   *summary.py*: extraction of the mapped, unmapped (missing) and
    conflict regions to FASTA files. Also, creates summary statistic for
    the missing regions and reference which are written to separate
    tab-delimited files.
-   *gene_predict.py*: prediction of genes in the missing regions using
    Prokka. Optionally, run BLASTX to the predicted genes with an
    user-defined protein database in FASTA file. In this repository you
    can find protein database retrieved from UniprotKB for bacterial
    species: *Escherichia coli*, *Pseudomonas aeruginosa*, *Klebsiella
    pneumoniae*, *Bacillus subtillis* and *Staphylococcus aureus*.

Optionally, some scripts provide additional analysis:

-   *kmer.py*: tandem repeats in the missing regions and pairwise
    comparison between kmers of missing and mapped regions using
    Sourmash.
-   *coverage.py*: calculates the average coverage of the missing and
    mapped regions with a provided BAM file.
-   *quastunmap.py*: performs a genome quality assessment for the
    missing regions to the reference and provides a genome viewer using
    QUAST and Icarus.


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# System Requirements

Linux 64-bit and OS X are supported.


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## General dependencies

-   Python3 (3.4+)
-   shutil
-   shlex
-   Seaborn
-   BioPython
-   matplotlib
-   Pandas
-   numpy
-   sourmash

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## Third-party tools (available on bioconda)

-   [progressiveMauve](https://darlinglab.org/mauve/mauve.html)
-   [prokka](https://github.com/tseemann/prokka)
-   [BLAST+](https://blast.ncbi.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs&DOC_TYPE=Download)
-   [QUAST](https://github.com/ablab/quast)
-   [SAMtools](http://samtools.sourceforge.net/)
-   [Tandem repeats finder](https://tandem.bu.edu/trf/trf.download.html)
-   [refseq\_masher](https://github.com/phac-nml/refseq_masher)

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## Installation
First you need to ensure that the above tools are available in your
environment. This can be achieved by means of conda virtual
environments as follows: 

	$ conda install mamba -n base -c conda-forge
	$ mamba create -n SASpector 
	$ mamba activate SASpector
	$ mamba install -c conda-forge -c bioconda -c defaults blast emboss progressivemauve prokka quast samtools sourmash trf mash pyani

We recommend to install SASpector as follows:

	$ git clone https://github.com/LoGT-KULeuven/SASpector
	$ cd SASpector
	$ python setup.py install --user

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## Command line options

For basic functionalities, run \`SASpector\`:

    usage: SASpector - Short-read Assembly inSpector [-h] [-p PREFIX]
    						 [-dir OUTDIR] [-f [LENGTH]]
    						 [-db [PROTEIN-DB]] [-k [K]]
    						 [-q] [-msh] [-c [BAMFILE]]
    						 --draft draft_assembly.fasta
                             --reference reference_assembly.fasta
    Required arguments:
      -draft, --draft        Short-read assembly FASTA file as contigs/draft genome.

    optional arguments:
	  -ref, --reference      Reference assembly FASTA file. If this is not provided, you can
	            try to use the -msh option to automatically serach refseq
      -h, --help            show this help message and exit
      -p PREFIX, --prefix PREFIX
    			Genome ID
      -dir OUTDIR, --outdir OUTDIR
    			Output directory
      -f [LENGTH], --flanking [LENGTH]
    			Add flanking regions to the extracted missing regions
    			[Default = 100 bp]
      -db [PROTEIN-DB], --proteindb [PROTEIN-DB]
    			BLAST protein database FASTA file to use for checking
    			the Prokka gene prediction
      -k [K], --kmers [K]   Choose k to calculate kmer frequencies
      -q, --quast           Run QUAST with unmapped regions against reference
    			assembly
      -c [BAMFILE], --coverage [BAMFILE]
    			Run SAMtools bedcov to look at short-read coverage in
    			the missing regions. Needs alignment of reads to the
    			reference genome in BAM format
      -msh [PATH], --mash_selection [PATH]
		        run automated reference selection (experimental feature)
				needs a sketch file (provided in SASpector/assets)


First, Mauve performs an alignment of both genomes with the
progressiveMauve algorithm. This generates the subdirectory
*prefix.alignment* with several output files and importantly the
backbone file with coordinates of the mapped and unmapped regions in
the reference genome.

This backbone file is parsed and we extract the sequences that are not
covered in the short-read assembly from the reference genome. They are
written to a multi-fasta file with the prefix and coordinates in the
headers, which is done equally for the mapped and conflict regions
(regions that didn't map correctly due to gaps or indels). Two
tab-delimited summary files are generated in a subdirectory called
summary. One for the reference, with the amount of gapped and ungapped
regions, the fraction of the reference genome that they represent, the
GC content and the length. The other one for the unmapped regions,
with for each region the GC content and length and then for each amino
acid the occurence frequency averaged over all 6 reading frames. As an
optional input, the user can add flanking regions to the extracted
missing regions.

Finally, SASpector predicts genes that are in the missing regions
using Prokka and if a protein FASTA file database is provided,
SASpector will BLAST the output sequences from Prokka to the database
generating a tab-delimited summary with the hits of the sequences. You
can use our defined database *saspector_proteindb.fasta*.

As optional analysis:

-   kmer analysis and tandem repeats: SASpector runs Tandem Repeats
    Finder and will generate HTML reports for the missing regions with
    tandem repeats. Finally, SASpector will perform a pairwise
    comparison between kmers of missing regions and mapped regions
    (k-size = 31) for comparative studies, using sourmash.

-   Coverage analysis: if a BAM file is provided, SASpector will
    calculate the average coverage of the missing and the mapped
    regions, using SAMtools. It will generate a sorted BAM file and
    tab-delimited reports of the coverage for both regions.

-   QUAST: SASpector will run QUAST for the missing regions against the
    reference genome for genome quality assessment and will provide
    Icarus as genome viewer.


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## Usage

    SASpector [Reference genome].fasta [Contigs].fasta -p [Genome ID] -dir [Output directory] -f [Length] -db [Protein database].fasta -k [kmer size] -c [reference genome].bam -q


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## Output

    [Output directory]/
      [Genome ID]_unmappedregions.fasta    FASTA file of the missing regions
      [Genome ID]_mappedregions.fasta      FASTA file of the mapped regions
      [Genome ID]_conflictregions.fasta    FASTA file of regions that did not map correctly
      [Genome ID]_referencesummary.tsv     tab-delimited summary report of the reference genome
      [Genome ID]_unmapsummary.tsv         tab-delimited summary report of the missing regions
      [Genome ID]_length_missing.jpg       Distribution plot of the length of the missing missing regions
      [Genome ID]_gc_content_missing.jpg   Distribution plot of the GC content of the missing regions
      [Genome ID]_codons_missing.jpg       Boxplot of the averaged frequency for each amino acid (for all 6 reading frames) of the missing regions
      alignment/
        [Genome ID].alignment              Alignment output from progressiveMauve
        [Genome ID].bbcols                 Coordinates of the mapped and unmapped regions from Mauve (not used)
        [Genome ID].backbone               Coordinates of the mapped, unmapped and conflicts regions from progressiveMauve
        [Genome ID].sslist                 SSlists of short-reads assembly and reference genome
      genesprediction/
        [Genome ID].predictedgenes.gff                    Genes annotation GFF3 file
        [Genome ID].predictedgenes.gbk                    Genbank file
        [Genome ID].predictedgenes.fna                    Nucleotide FASTA file of the missing regions
        [Genome ID].predictedgenes.faa                    Protein FASTA file of the translated CDS sequences
        [Genome ID].predictedgenes.ffn                    Nucleotide FASTA file of all the prediction transcripts
        [Genome ID].predictedgenes.sqn                    Sequin file for submission to Genbank
        [Genome ID].predictedgenes.fsa                    Nucleotide FASTA file of the missing regions, used by 'tbl2asn' for the .sqn file
        [Genome ID].predictedgenes.tbl                    Feature table file, used by 'tbl2asn' for the .sqn file
        [Genome ID].predictedgenes.err                    NCBI discrepancy report
        [Genome ID].predictedgenes.log                    Output report of Prokka during its run
        [Genome ID].predictedgenes.txt                    Statistics of the annotated features
        [Genome ID].predictedgenes.tsv                    tab-delimited report of all features
        [Genome ID]_blastxresults.tsv      tab-delimited report of BLASTX
      kmer/
        [Genome ID]_sourmash               tab-delimited output of pairwise comparison between missing regions and mapped regions
        _distances.tsv
        sourmash                Clustermap of pairwise comparison
        _clustermap.jpg
      trf/
        *.html                  Tandem Repeat Finder HTML interactive reports
      coverage/
        [Genome ID].sorted.bam             Sorted BAM file of the reference genome
        [Genome ID].sorted.bam.bai         Sorted BAM index file
        [Genome ID]_mappedregions.bed      BED file of the mapped regions
        [Genome ID]_unmappedregions.bed    BED file of the missing regions
        [Genome ID]_mapcvg.tsv             tab-delimited report of the average coverage of the mapped regions
        [Genome ID]_unmappedcvg.tsv        tab-delimited report of the average coverage of the missing regions
        [Genome ID]_coverageresults.tsv    tab-delimited summary report of the average coverage, total depth per base and locations for both regions
        coverage_boxplots.jpg      Boxplot comparison of the average coverage for both regions
      quast/
        report.txt              QUAST summary table
        report.tsv              tab-delimited summary report
        report.tex              LaTex summary report
        report.html             HTML interactive report, includes all tables and plots for statistics
        report.pdf              PDF report
        icarus.html             Icarus genome viewer


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# Contact

Laboratory of Computational Systems Biology, KU Leuven


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# References

-   Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403-410.
-   Darling, A. C. E. (2004). Mauve: Multiple Alignment of Conserved Genomic Sequence With Rearrangements. Genome Research, 14(7), 1394-1403.$
-   Brown and Irber (2016), sourmash: a library for MinHash sketching of DNA Journal of Open Source Software, 1(5), 27.
-   Pierce NT, Irber L, Reiter T et al. Large-scale sequence comparisons with sourmash [version 1; peer review: 2 approved]. F1000Research 2019, 8:1006
-   Gurevich, A., Saveliev, V., Vyahhi, N., & Tesler, G. (2013). QUAST: Quality assessment tool for genome assemblies. Bioinformatics, 29(8), 1072-1075.
-   Li H.\*, Handsaker B.\*, Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G., Durbin R. and 1000 Genome Project Data Processing Subgroup (2009) The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics, 25, 2078-9.
-   Torsten Seemann, Prokka: rapid prokaryotic genome annotation, Bioinformatics, Volume 30, Issue 14, 15 July 2014, Pages 2068-2069.
-   Benson G. (1999). Tandem repeats finder: a program to analyze DNA sequences. Nucleic acids research, 27(2), 573-580.