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Merge pull request nf-core#343 from Darcy220606/add_mmseqs2_taxonomy
Add mmseqs2 taxonomy
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| Original file line number | Diff line number | Diff line change |
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| #!/usr/bin/env python3 | ||
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| # Written by Anan Ibrahim and released under the MIT license. | ||
| # See git repository (https://github.com/Darcy220606/AMPcombi) for full license text. | ||
| # Date: March 2024 | ||
| # Version: 0.1.0 | ||
|
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||
| # Required modules | ||
| import sys | ||
| import os | ||
| import pandas as pd | ||
| import numpy as np | ||
| import argparse | ||
|
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| tool_version = "0.1.0" | ||
| ######################################### | ||
| # TOP LEVEL: AMPCOMBI | ||
| ######################################### | ||
| parser = argparse.ArgumentParser(prog = 'merge_taxonomy', formatter_class=argparse.RawDescriptionHelpFormatter, | ||
| usage='%(prog)s [options]', | ||
| description=('''\ | ||
| ............................................................................. | ||
| *merge_taxonomy* | ||
| ............................................................................. | ||
| This script merges all three funcscan workflows with | ||
| MMseqs2 taxonomy results. This is done in three submodules that can be | ||
| activated seperately. | ||
| .............................................................................'''), | ||
| epilog='''Thank you for running taxonomy_merge!''', | ||
| add_help=True) | ||
| parser.add_argument('--version', action='version', version='merge_taxonomy ' + tool_version) | ||
|
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| ######################################### | ||
| # SUBPARSERS | ||
| ######################################### | ||
| subparsers = parser.add_subparsers(required=True) | ||
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| ######################################### | ||
| # SUBPARSER: AMPCOMBI | ||
| ######################################### | ||
| ampcombi_parser = subparsers.add_parser('ampcombi_taxa') | ||
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| ampcombi_parser.add_argument("--ampcombi", dest="amp", nargs='?', help="Enter the path to the ampcombi_complete_summary.tsv' \n (default: %(default)s)", | ||
| type=str, default='ampcombi_complete_summary.csv') | ||
| ampcombi_parser.add_argument("--taxonomy", dest="taxa1", nargs='+', help="Enter the list of taxonomy files for all samples. ") | ||
|
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| ######################################### | ||
| # SUBPARSER: COMBGC | ||
| ######################################### | ||
| combgc_parser = subparsers.add_parser('combgc_taxa') | ||
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| combgc_parser.add_argument("--combgc", dest="bgc", nargs='?', help="Enter the path to the combgc_complete_summary.tsv' \n (default: %(default)s)", | ||
| type=str, default='combgc_complete_summary.csv') | ||
| combgc_parser.add_argument("--taxonomy", dest="taxa2", nargs='+', help="Enter the list of taxonomy files for all samples. ") | ||
|
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| ######################################### | ||
| # SUBPARSER: HAMRONIZATION | ||
| ######################################### | ||
| hamronization_parser = subparsers.add_parser('hamronization_taxa') | ||
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| hamronization_parser.add_argument("--hamronization", dest="arg", nargs='?', help="Enter the path to the hamronization_complete_summary.tsv' \n (default: %(default)s)", | ||
| type=str, default='hamronization_complete_summary.csv') | ||
| hamronization_parser.add_argument("--taxonomy", dest="taxa3",nargs='+', help="Enter the list of taxonomy files for all samples. ") | ||
|
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||
| ######################################### | ||
| # TAXONOMY | ||
| ######################################### | ||
| def reformat_mmseqs_taxonomy(mmseqs_taxonomy): | ||
| mmseqs2_df = pd.read_csv(mmseqs_taxonomy, sep='\t', header=None, names=['contig_id', 'taxid', 'rank_label', 'scientific_name', 'lineage', 'mmseqs_lineage_contig']) | ||
| # remove the lineage column | ||
| mmseqs2_df.drop('lineage', axis=1, inplace=True) | ||
| mmseqs2_df['mmseqs_lineage_contig'].unique() | ||
| # convert any classification that has Eukaryota/root to NaN as funcscan targets bacteria ONLY ** | ||
| for i, row in mmseqs2_df.iterrows(): | ||
| lineage = str(row['mmseqs_lineage_contig']) | ||
| if 'Eukaryota' in lineage or 'root' in lineage: | ||
| mmseqs2_df.at[i, 'mmseqs_lineage_contig'] = np.nan | ||
| # insert the sample name in the first column according to the file basename | ||
| file_basename = os.path.basename(mmseqs_taxonomy) | ||
| filename = os.path.splitext(file_basename)[0] | ||
| mmseqs2_df.insert(0, 'sample_id', filename) | ||
| return mmseqs2_df | ||
|
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||
| ######################################### | ||
| # FUNCTION: AMPCOMBI | ||
| ######################################### | ||
| def ampcombi_taxa(args): | ||
| merged_df = pd.DataFrame() | ||
|
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||
| # assign input args to variables | ||
| ampcombi = args.amp | ||
| taxa_list = args.taxa1 | ||
|
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| # prepare the taxonomy files | ||
| taxa_df = pd.DataFrame() | ||
| # append the dfs to the taxonomy_files_combined | ||
| for file in taxa_list: # list of taxa files ['',''] | ||
| df = reformat_mmseqs_taxonomy(file) | ||
| taxa_df = pd.concat([taxa_df, df]) | ||
|
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| # filter the tool df | ||
| tool_df = pd.read_csv(ampcombi, sep=',') #current ampcombi version is comma sep. CHANGE WITH VERSION 0.2.0 | ||
| # make sure 1st and 2nd column have the same column labels | ||
| tool_df.rename(columns={tool_df.columns[0]: 'sample_id'}, inplace=True) | ||
| tool_df.rename(columns={tool_df.columns[1]: 'contig_id'}, inplace=True) | ||
| # grab the real contig id in another column copy for merging | ||
| tool_df['contig_id_merge'] = tool_df['contig_id'].str.rsplit('_', 1).str[0] | ||
|
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| # merge rows from taxa to ampcombi_df based on substring match in sample_id | ||
| # grab the unique sample names from the taxonomy table | ||
| samples_taxa = taxa_df['sample_id'].unique() | ||
| # for every sampleID in taxadf merge the results | ||
| for sampleID in samples_taxa: | ||
| # subset ampcombi | ||
| subset_tool = tool_df.loc[tool_df['sample_id'].str.contains(sampleID)] | ||
| # subset taxa | ||
| subset_taxa = taxa_df.loc[taxa_df['sample_id'].str.contains(sampleID)] | ||
| # merge | ||
| subset_df = pd.merge(subset_tool, subset_taxa, left_on = 'contig_id_merge', right_on='contig_id', how='left') | ||
| # cleanup the table | ||
| columnsremove = ['contig_id_merge','contig_id_y', 'sample_id_y'] | ||
| subset_df.drop(columnsremove, axis=1, inplace=True) | ||
| subset_df.rename(columns={'contig_id_x': 'contig_id', 'sample_id_x':'sample_id'},inplace=True) | ||
| # append in the combined_df | ||
| merged_df = merged_df.append(subset_df, ignore_index=True) | ||
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| # write to file | ||
| merged_df.to_csv('ampcombi_complete_summary_taxonomy.tsv', sep='\t', index=False) | ||
|
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| ######################################### | ||
| # FUNCTION: COMBGC | ||
| ######################################### | ||
| def combgc_taxa(args): | ||
| merged_df = pd.DataFrame() | ||
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| # assign input args to variables | ||
| combgc = args.bgc | ||
| taxa_list = args.taxa2 | ||
|
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| # prepare the taxonomy files | ||
| taxa_df = pd.DataFrame() | ||
| # append the dfs to the taxonomy_files_combined | ||
| for file in taxa_list: # list of taxa files ['',''] | ||
| df = reformat_mmseqs_taxonomy(file) | ||
| taxa_df = pd.concat([taxa_df, df]) | ||
|
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| # filter the tool df | ||
| tool_df = pd.read_csv(combgc, sep='\t') | ||
| # make sure 1st and 2nd column have the same column labels | ||
| tool_df.rename(columns={tool_df.columns[0]: 'sample_id'}, inplace=True) | ||
| tool_df.rename(columns={tool_df.columns[1]: 'contig_id'}, inplace=True) | ||
|
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| # merge rows from taxa to ampcombi_df based on substring match in sample_id | ||
| # grab the unique sample names from the taxonomy table | ||
| samples_taxa = taxa_df['sample_id'].unique() | ||
| # for every sampleID in taxadf merge the results | ||
| for sampleID in samples_taxa: | ||
| # subset ampcombi | ||
| subset_tool = tool_df.loc[tool_df['sample_id'].str.contains(sampleID)] | ||
| # subset taxa | ||
| subset_taxa = taxa_df.loc[taxa_df['sample_id'].str.contains(sampleID)] | ||
| # merge | ||
| subset_df = pd.merge(subset_tool, subset_taxa, left_on = 'contig_id', right_on='contig_id', how='left') | ||
| # cleanup the table | ||
| columnsremove = ['sample_id_y'] | ||
| subset_df.drop(columnsremove, axis=1, inplace=True) | ||
| subset_df.rename(columns={'sample_id_x':'sample_id'},inplace=True) | ||
| # append in the combined_df | ||
| merged_df = merged_df.append(subset_df, ignore_index=True) | ||
|
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| # write to file | ||
| merged_df.to_csv('combgc_complete_summary_taxonomy.tsv', sep='\t', index=False) | ||
|
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| ######################################### | ||
| # FUNCTION: HAMRONIZATION | ||
| ######################################### | ||
| def hamronization_taxa(args): | ||
| merged_df = pd.DataFrame() | ||
|
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| # assign input args to variables | ||
| hamronization = args.arg | ||
| taxa_list = args.taxa3 | ||
|
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| # prepare the taxonomy files | ||
| taxa_df = pd.DataFrame() | ||
| # append the dfs to the taxonomy_files_combined | ||
| for file in taxa_list: # list of taxa files ['',''] | ||
| df = reformat_mmseqs_taxonomy(file) | ||
| taxa_df = pd.concat([taxa_df, df]) | ||
|
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| # filter the tool df | ||
| tool_df = pd.read_csv(hamronization, sep='\t') | ||
| # rename the columns | ||
| tool_df.rename(columns={'input_file_name':'sample_id', 'input_sequence_id':'contig_id'}, inplace=True) | ||
| # reorder the columns | ||
| new_order = ['sample_id', 'contig_id'] + [col for col in tool_df.columns if col not in ['sample_id', 'contig_id']] | ||
| tool_df = tool_df.reindex(columns=new_order) | ||
| # grab the real contig id in another column copy for merging | ||
| tool_df['contig_id_merge'] = tool_df['contig_id'].str.rsplit('_', 1).str[0] | ||
|
|
||
| # merge rows from taxa to ampcombi_df based on substring match in sample_id | ||
| # grab the unique sample names from the taxonomy table | ||
| samples_taxa = taxa_df['sample_id'].unique() | ||
| # for every sampleID in taxadf merge the results | ||
| for sampleID in samples_taxa: | ||
| # subset ampcombi | ||
| subset_tool = tool_df.loc[tool_df['sample_id'].str.contains(sampleID)] | ||
| # subset taxa | ||
| subset_taxa = taxa_df.loc[taxa_df['sample_id'].str.contains(sampleID)] | ||
| # merge | ||
| subset_df = pd.merge(subset_tool, subset_taxa, left_on = 'contig_id_merge', right_on='contig_id', how='left') | ||
| # cleanup the table | ||
| columnsremove = ['contig_id_merge','contig_id_y', 'sample_id_y'] | ||
| subset_df.drop(columnsremove, axis=1, inplace=True) | ||
| subset_df.rename(columns={'contig_id_x': 'contig_id', 'sample_id_x':'sample_id'},inplace=True) | ||
| # append in the combined_df | ||
| merged_df = merged_df.append(subset_df, ignore_index=True) | ||
|
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| # write to file | ||
| merged_df.to_csv('hamronization_complete_summary_taxonomy.tsv', sep='\t', index=False) | ||
|
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| ######################################### | ||
| # SUBPARSERS: DEFAULT | ||
| ######################################### | ||
| ampcombi_parser.set_defaults(func=ampcombi_taxa) | ||
| combgc_parser.set_defaults(func=combgc_taxa) | ||
| hamronization_parser.set_defaults(func=hamronization_taxa) | ||
|
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| if __name__ == '__main__': | ||
| args = parser.parse_args() | ||
| args.func(args) # call the default function |
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