dataesr/scientific-paragraphs-categorization huggingface dataset:
1). The BLAST output is in the form of a 13-column tabular file, including the key information from query name to percentage identity. 7. Once users acquire the BLAST hits, they can pull out the FASTA sequences in one of two ways (online or locally). a. For example, users can first pull out a list of protein ID from the file (e. g. , BLASTP_clps. tsv) like Table  1. b. Users can then use the Batch Entrez (https://www. ncbi. nlm. nih. gov/sites/batchentrez) to retrieve the FASTA sequences corresponding to the list of protein IDs acquired in step 7a. c. There is also a way to retrieve these FASTA sequences via the protein ID locally. Users can use the SeqKit  (Shen et al. , 2016)  to quickly pull out the amino acid sequences from the NCBI database. 8. Finally, by using a different approach via local BLAST, users can acquire the two necessary documents (e.
g. , BLASTP_clps. tsv and clps_hits. fasta) for the TreeTuner pipeline. These are files comparable to the ones generated by the NCBI server. CRITICAL: Make sure to remove the header of the BLAST hits file (e. g. , BLASTP_clps. tsv) obtained from the online BLAST. Otherwise, the annotations will confuse the scripts in the following step. Minimizing the redundancy and complexity of large phylogenetic datasets via coarse tuning Timing: 6 h (Depending on the size of the data, computing power, and Internet speed) The point of coarse tuning is to use TreeTrimmer  (Maruyama et al. , 2013)  to do some preliminary trimming of the phylogeny to get a rough sense of the minimal tree diversity. CRITICAL: Make sure to download the latest NCBI taxonomy database which is usually updated weekly. Even so, users might still be presented with a list of unmatched protein IDs (e.
12 (or trimAl v1. 4)  (Criscuolo and Gribaldo, 2010)  and FastTree v2. 1  (Price et al. , 2010)  to build a preliminary tree. 11. With the input files available, users can test the TreeTrimmer tool with their data. The example files can be found via the GitHub link (https://github. com/zx0223winner/TreeTuner). a. TreeTrimmer  (Maruyama et al. , 2013)   b. There are three necessary input files for TreeTrimmer  (Maruyama et al. , 2013). Sample files can be found in the GitHub ''tutorial'' directory under the name ''TreeTuner_file_examples''. Users can first prepare the TreeTrimmer parameter input file (clps_parameter_input. in). As described in the help document, TreeTrimmer will identify and remove user-defined 'redundant' sequences via the taxa information and branch cut-off parameters. If so, the orthologous sequences from closely related organisms and 'recently' evolved lineage-specific paralogs will be greatly reduced.
Users can also adjust the number of representative OTUs to be retained in the TreeTrimmer parameter input file. c. The example file can be found from the GitHub. Here, the 'query_tag' was set to 564937 for the purposes of retaining the reference A. thaliana ClpS protein (GenBank ID: NP_564937; TAIR ID: AT1G68660. 1). Step 10b and 9d, respectively. However, there is an additional step needed to format the input document ''clps_taxonomic_info. txt'' so that it can be recognized by TreeTrimmer. e. Once all the necessary files are prepared, users can run the command as follows: f. Two output documents will be produced (e. g. , ''clps_treetrimmer. out'' and ''clps_aligned_ trimmed. newick__clps_parameter_input. in. tt0. 0. tre''). The first file contains the number of OTUs to be trimmed via user-defined taxa and branch cut-off.
The second file is detailed in the next step. g. The trimmed tree file is in Newick format with a long name (e. g. , ''clps_aligned_trimmed. ne-wick__clps_parameter_input. in. tt0. 0. tre''). The output also displays the difference before and after running TreeTrimmer (Figures  3A  and 3B ). In the case of our ClpS test dataset, the number of OTUs decreased from 1157 to 44. Coloring the Newick tree after coarse tuning based on the taxonomic categories Timing: 30 min Coloring the tree based on the taxonomic categories is a common way to help visualize phylogenetic diversity. Users will create the image file of a trimmed tree in steps 12-14. 13. The script will require Python version 3 and ETE3 toolkit  (Huerta-Cepas et al. , 2016)  to be preinstalled. The Python script and the relevant example files can be downloaded from the GitHub (https://github.
Minimizing the redundancy and complexity of large phylogenetic datasets via fine tuning Timing: 12 h Users will have a rough sense of the taxonomic diversity contained in their phylogenetic dataset based on the results of steps 9-14. However, they may wish to proceed to some more curated, in-depth analyses requiring more sophisticated tree-building methods and optimized taxon sampling. Here we will use the custom scripts to further resolve the complexity of the tree. Users will acquire the trimmed files in Newick format (e. g. , renamed_clps_aligned_trimmed. fasta. fasttree) in steps 15-20. 15. In the same way as was done with coarse tuning, users will need to prepare two input files (e. g. ,  ''clps_hits. fasta'' and ''clps_acc2tax_prot_all. txt''). The first file contains the FASTA sequences for the NCBI BLAST protein homologs of the A.
thaliana ClpS protein, which was already generated in step 7c. For the second file, users will need to do some polishing on the file acc2tax_pro-t_all. txt generated at step 9c. Note: Due to the size of acc2tax_prot_all. txt file (40 Gb), it might take several hours to run this step. 16. With the two files ready, users can run the Python script called ''rename_ncbi_blastdb. py'' downloaded from GitHub (https://github. com/zx0223winner/TreeTuner).
Note: The reason for renaming the FASTA file header is to insert the hierarchical taxonomic terms. The Python script will connect the terms with an underscore ('_'), so the header is treated as a single term. Note: It is also important to remove special characters such as '/' and '()' in the header, since they can interfere with the proper running of the FastTree and Perl scripts. 17. With the renamed FASTA file, users can repeat the tree building in step 10b and change the input file name ''clps_hits_trimmed. fasta'' to the renamed FASTA file (e. g. , ''new_renamed_ clps_hits. fasta''). # Header of the FASTA file before and after renaming. > mafft -auto new_renamed_clps_hits. fasta > renamed_clps_aligned. fasta ##2## Users will find the BMGE v1. 12 package (BMGE-1. 12. tar. gz) from ftp://ftp.
CRITICAL: The reason for repeating the tree building step is due to the different header formats for coarse versus fine tuning. For example, the coarse-tuning pipeline requires the header without version information (e. g. , NP_564937 without '. 1') to be recognized by a third-party tool (i. e. , acc2tax) in step 9. Similarly, the fine-tuning pipeline requires the header containing hierarchical taxonomic terms to be pulled out by the custom scripts via taxa rank. Users should know that the tree files created by the coarse-tuning pipeline can help optimize the taxon sampling for downstream fine-tuning. Troubleshooting 5 18. The final output file from the previous step will yield a Newick tree file named ''renamed_clps_a-ligned_trimmed. newick''. We will then use two custom Perl scripts (''perl rm_inparal_rank.
pl'' and ''trim2untrim. pl'' downloaded via GitHub at https://github. com/zx0223winner/TreeTuner) to fine-tune the tree and then apply sophisticated tree-building methods and optimized taxon sampling. CRITICAL: While users can acquire the first two input files from the previous steps (i. e. , the Newick tree file and aligned FASTA file), the remaining two parameter files will need to be carefully prepared. The output tree file created by the coarse-tuning pipeline at step 11f can be a useful guide. # This file will contain information on how to reduce specific genera/phyla/kingdoms. It will reduce specifically at the taxonomic terms (0 = domain (e. g. , Eukaryota), 1 = kingdom (e. g. , Viridiplantae), 2 = phyla (e. g. , Streptophyta), 3 = class (Klebsormidiophyceae), etc. ) (space-delimited).
19. The taxa rank file is determined based on specific header formats as follows. 20. The previous Perl script will yield three output files (e. g. , ''renamed_clps_aligned_trimmed. fasta_removedSeq'', ''renamed_clps_aligned_trimmed. fasta. fasttree'', ''renamed_clps_ aligned_trimmed. fasta. genus_trimmed''). Then users can run the following script to acquire the trimmed sequence alignment (e. g. , renamed_clps_aligned_trimmed. fasta_sub). 21. With the fine-tuning alignment available from the previous step, users can see the difference before and after running the Perl scripts (Figures  5A  and 5B ). In the case of our ClpS test dataset, the number of OTUs decreased from 1144 to 66. Users can also proceed with more rigorous tree-building methods (e. g. , IQ-TREE) to recreate a Newick tree file (Figure  5C ).
Coloring the Newick tree after fine-tuning based on the taxonomic categories Timing: 30 min After generating the Newick tree in the previous step, a different Python script is used to plot the tree based on the taxonomic categories, due to the fact that different headers are used by the coarseand fine-tuning processes. Users will create the image file of a trimmed tree at steps 22-24. 22. As was the case for coloring the Newick tree after coarse-tuning, some pre-installed packages are needed. 24. Users can then visualize the tree in the ETE tree browser. The different colors reflect the taxa categories (Figure  6 ).
Users can then apply the fine-tuning scripts to pull out sequences from lineages of particular interest to proceed with more rigorous and computer-intensive downstream analyses, such as Bayesian and maximum likelihood tree reconstructions  (Maruyama et al. , 2013). Users can also set different cut-offs for the branch length values. This is useful for reducing sequence redundancy from closely related organisms or the presence of lineage-specific paralogs. In order to acquire the appropriate OTU diversity for the phylogenetic question at hand, users will need to be familiar (B) Tree of 66 OTUs retained after using fine-tuning. The phylogeny was created by FastTree  (Price et al. , 2010)  under the maximum likelihood method model (JTT + CAT). Fine-tuning parameters: branch length cut-off for non-removed taxa: 1.
LIMITATIONS Phylogeny-based OTU reduction methods consider the phylogenetic relationship between sequences in an alignment. For example, one can set the threshold for bootstrap branch support to reflect the robustness of the tree topology and/or consider branch lengths to measure the extent of sequence divergence via measuring the average number of nucleotide or protein substitutions per site. At the same time, taxonomy-based dataset trimming can result in biased OTU retention because highly diverse clades may be represented by more leaves than less diverse ones. Also, the TreeTuner pipeline is not fully automated and relies on user-defined OTUs. It also should be noted that there are many reasons why gene trees do not equal 'species trees', including endosymbiosis (and endosymbiotic gene transfer), lateral gene transfer, gene duplication and differential gene loss, and incomplete lineage sorting.
If we also consider contamination and gene/protein misannotation, then it can be very difficult to decide how to flag anomalies in a way that is consistent and useful. Nevertheless, we provide a step-by-step solution to guide users who need to trim down their large phylogenetic datasets for more rigorous downstream analysis. In the future, it might be possible for TreeTuner to be updated to work with other types of sequences, such as non-coding RNAs (ncRNAs), though such RNAs evolve extremely quickly and thus are not likely to retain useful phylogenetic information over large evolutionary timescales.
Potential solution Make sure to remove the top annotated lines ('#') in BLAST hits file (e. g. , BLASTP_clps. tsv) generated via the online BLAST. Otherwise, the annotations will not be recognized by the subsequent scripts. Problem 3 Do I have to update the NCBI taxonomy database for each run? Why do some unmatched protein IDs pop up even with the latest NCBI taxonomy database? (step 9).
Potential solution If users download the original ReadMe file and script of TreeTrimmer (TreeTrimmer. v130413. zip) from the link (https://code. google. com/archive/p/treetrimmer/), there might be an error after running, which might be due to the issue of version compatibility. So, we suggest users download the updated TreeTrimmer script from GitHub to use this TreeTuner pipeline. Make sure to have the pre-installation packages (e. g. , Ruby v2. 5. 1 and BioRuby v2. 0. 3) before running the TreeTrimmer.
Potential solution The reason for repeating the tree building step is due to the different header format between coarse and fine tuning. For example, the coarse-tuning pipeline requires the header without version (e. g. , NP_564937 without '. 1') to be recognized by the third-party tool (i. e. , acc2tax) at step 9. Similarly, the fine-tuning pipeline requires the header containing hierarchical taxonomic terms to be pulled out by the custom scripts via taxa rank. Users should know that tree files created by coarse-tuning pipeline can help the fine-tuning to optimize the taxon sampling.
perl update_blastdb. pl -blastdb_version 5 -showall # This will give the results like this:. pl -blastdb_version 5 nr -decompress # This will bring the results like this: # Connected to NCBI; downloading BLASTDBv5 # Downloading nr (55 volumes). # Downloading nr. 00. tar. gz. # Downloading nr. 00. tar. gz. md5 #. # Note: makeblastdb file is from the BLAST package and the following command can be run in the terminal. >. /makeblastdb -in MMETSP. fasta -dbtype prot -title MMETSP_prot_database -out MMETSP_db >. /blastp -query clps. fasta -db nr -out BLASTP_clps. tsv -evalue 1e-10 -outfmt "6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore ppos" -max_tar-get_seqs 10000.
This will remove the description in the header of FASTA file. >awk '{print $1}' clps_hits. fasta > clps_hits_no_description. fasta # The command is to trim the large acc2tax_prot_all. txt file into smaller size with clps hits only via using the formerly generated ''clps_hits_id. txt'' file (Step 7a). There are two ways to do that. >grep -wf clps_hits_id. txt acc2tax_prot_all. txt > clps_acc2tax_prot_all. txt # or >awk 'NR==FNR{a[$2]=$0;next}{print a[$1]}' acc2tax_prot_all. txt clps_hits_id. txt >clps_acc2tax_prot_all. txt # Usage: python3 rename_ncbi_blastdb. py <FASTA File> <Taxon Id FILE> <Renamed FASTA File> # Note: The first time running the python script will update your local NCBI taxa database which will take around 5 mins. # Users can comment the two lines below (Line:86 and Line:87) to avoid the updating each time.
trim2untrim. pl <Previous_output. genus_trimmed> <Aligned_FASTA> > perl trim2untrim. pl renamed_clps_aligned_trimmed. fasta. genus_trimmed renamed_clps_aligned_trimmed. fasta # Usage: trim2untrim. pl <Previous_output. genus_trimmed> iqtree -s renamed_clps_aligned_trimmed. fasta_sub -alrt 1000 -bb 1000 -nt AUTO 23. The Python script will require Python version 3 and ETE3 toolkit (Huerta-Cepas et al. , 2016) to be pre-installed. The Python script and the relevant example files can be downloaded from the Gi-tHub (https://github. com/zx0223winner/TreeTuner).
> perl update_blastdb. pl -blastdb_version 5 -showall # This will give the results like this:. pl -blastdb_version 5 nr -decompress # This will bring the results like this: # Connected to NCBI; downloading BLASTDBv5 # Downloading nr (55 volumes). # Downloading nr. 00. tar. gz. # Downloading nr. 00. tar. gz. md5 #. # Note: makeblastdb file is from the BLAST package and the following command can be run in the terminal. >. /makeblastdb -in MMETSP. fasta -dbtype prot -title MMETSP_prot_database -out MMETSP_db >. /blastp -query clps. fasta -db nr -out BLASTP_clps. tsv -evalue 1e-10 -outfmt "6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore ppos" -max_tar-get_seqs 10000.
# This will remove the description in the header of FASTA file. >awk '{print $1}' clps_hits. fasta > clps_hits_no_description. fasta # The command is to trim the large acc2tax_prot_all. txt file into smaller size with clps hits only via using the formerly generated ''clps_hits_id. txt'' file (Step 7a). There are two ways to do that. >grep -wf clps_hits_id. txt acc2tax_prot_all. txt > clps_acc2tax_prot_all. txt # or >awk 'NR==FNR{a[$2]=$0;next}{print a[$1]}' acc2tax_prot_all. txt clps_hits_id. txt >clps_acc2tax_prot_all. txt # Usage: python3 rename_ncbi_blastdb. py <FASTA File> <Taxon Id FILE> <Renamed FASTA File> # Note: The first time running the python script will update your local NCBI taxa database which will take around 5 mins. # Users can comment the two lines below (Line:86 and Line:87) to avoid the updating each time.
nodes. dmp and names. dmpfiles are in the taxdump. tar. gzfile after decompressing. c. acc2tax_nucl_all. txtand acc2tax_nucl_all. txtfiles will need to be created by merging files in the link (https://ftp. ncbi. nih. gov/pub/taxonomy/accession2taxid/). d. The acc2tax package needs to be compiled first before running. Note that acc2tax requires the NCBI protein ID to be trimmed to remove the version (e. g. , NP_564937. 1 was trimmed to NP_564937). Users can follow the commands below: # RID: PVFT67SX016 # Database: nr # Fields: query acc. ver, subject acc. ver, % identity, alignment length, mismatches, gap opens, q. start, q. end, s. start, s. end, evalue, bit score, % positives # 1667 hits found > zcat nucl_gb. accession2taxid. gz nucl_wgs. accession2taxid. gz dead_nucl. accession2tax- id. gz dead_wgs.
accession2taxid. gz |sort > acc2tax_nucl_all. txt > zcat prot. accession2taxid. gz dead_prot. accession2taxid. gz | sort > acc2tax_prot_all. txt # Compile the source code > cc -o acc2tax acc2tax. c # Trim the protein version >sed 's/\(. *\)\. */\1/g' clps_hits_id. txt > clps_hits_id_trimmed. txt # Four taxa files need to be put in the same directory (i. e. , directory name: /your/directory/ Acc2tax_092021/) >chmod +x acc2tax Users will acquire the trimmed tree file in Newick format (e. g. ,''clps_aligned_trimmed. newick__clps_parameter_input. in. tt0. 0. tre'')in steps 9-11. Since the redundant species will be trimmed based on the taxon information, users will need to use acc2tax (https://github. com/richardmleggett/acc2tax) to first pull out the hierarchical taxonomic terms for each ClpS homolog.
9. Users can first download the package acc2tax via the GitHub link (https://github. com/ richardmleggett/acc2tax) and then prepare the other necessary taxa information files downloaded from the link (https://ftp. ncbi. nih. gov/pub/taxonomy/). a. Four necessary taxonomic information files can be acquired from the NCBI taxonomy database which is updated weekly: nodes. dmp, names. dmp, acc2tax_nucl_all. txt, and acc2tax_-prot_all. txt. b>. /acc2tax -i clps_hits_id_trimmed. txt -p -d /your/directory/Acc2tax_092021 -o clps_taxonomic_info. txt.
This article summarizes the state of the art of ν μ and νμ CC0π cross-section measurements on carbon and argon and discusses the relevant nuclear models, parametrizations and uncertainties in GENIE v3. The CC0π event topology is common in experiments at a few-GeV energy range. Although its main contribution comes from quasielastic interactions, this topology is still not well understood. The GENIE global analysis framework is exploited to analyze CC0π datasets from MiniBooNE, T2K and MINERνA. A partial tune for each experiment is performed, providing a common base for the discussion of tensions between datasets. The results offer an improved description of nuclear CC0π datasets as well as data-driven uncertainties for each experiment. This work is a step towards a GENIE global tune that improves our understanding of neutrino interactions on nuclei.
I. INTRODUCTION A major experimental program aims to measure neutrino-nucleus interactions over the few-GeV region. MiniBooNE was the first neutrino experiment to provide a double-differential flux-integrated CC0π cross-section measurement with high statistics on carbon  [1]. Since then T2K  [2] , MicroBooNE  [3]  and MINERνA  [4]  have produced a large body of measurements on different nuclei, such as carbon or argon. However, a detailed quantitative understanding of neutrino-nucleus interactions is still missing. In order to avoid biases in cross-section measurements due to theory assumptions, neutrino experiments focus on the study of specific topologies instead of interaction processes like quasielastic (QEL) scattering. The most dominant event topology below the 1 GeV region is CC0π, which is usually defined as an event with one muon and no pions in the final state.
The present work extends this analysis campaign to a second stage; an explicit tune of nuclear model parameters to recent nuclear data. This work is further necessitated by outstanding discrepancies between GENIE predictions and more recent datasets, which use heavy nuclei as targets. Several neutrino collaborations, such as MicroBooNE and MINERνA, tried to address these discrepancies by tuning GENIE against the ν μ CC0π T2K and inclusive ν μ CC MINERνA datasets, respectively  [10] [11] [12]. All these tunes simulate 2p2h interactions with the Valencia model  [13]. In both cases, the results suggest an enhancement of the 2p2h cross section. These tunes are not available for wider use within GENIE, and in some cases, these were performed with obsolete GENIE versions which differ substantially from the latest one.
In this paper, we describe the GENIE analysis of the available ν μ and νμ CC0π datasets from MiniBooNE, T2K, MINERνA and MicroBooNE. The main goal is to provide improved simulations tuned to nuclear data and quantify the major sources of uncertainties in CC0π measurements. In order to do so, new degrees of freedom are developed within the GENIE Monte Carlo (MC) event generator in order to quantify the effect of variation away from the nominal models. Most of the new degrees of freedom can be used to tune other available comprehensive model configurations (CMCs) in GENIE. In this analysis we focus on the 'retuning' of the G18_10a_02_11b tune against ν μ -12 C CC0π data from MiniBooNE, T2K and MINERνA. The G18_10a_02_11b was previously tuned against free-nucleon data  [5]. In this paper, we refer to G18_10a_02_11b as the nominal tune.
Other CMCs will be considered in future iterations of this work. The GENIE global analysis software  [5]  is used to perform a partial tune for each experiment using doubledifferential flux-integrated CC0π cross-section measurements as a function of muon kinematics. We further note that only carbon datasets are considered in this work. While a more expansive study of the nuclear A dependence will be a valuable aspect of future work, this choice carries the advantage of providing a consistent basis for the exploration of statistical tensions. This work is a step closer to a global tune with neutrino-nucleus cross-section data, which can be performed using the same analysis strategy once all the tensions are well understood. Future iterations of this work will also incorporate measurements on different topologies, such as CC1 π AE.
This work is organized as follows: Sec. II provides an overview of the available CC0π data to date. The newly developed GENIE parameters are discussed in Sec. III. This is followed by a description of the tuning procedure in Sec. IV and a discussion of the tune results and tensions between CC0π and CCNp0π datasets in Sec. V. In addition, Sec. VI describes some modeling aspects relevant for the exploration of the CC0π and CCNp0π tension. The main conclusions of this paper are highlighted in Sec. VII.