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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,2 +1,2 @@ | ||
| 1.4.1 | ||
| 1.4.2 | ||
| VDB_v6 |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -11,7 +11,7 @@ | |
| pd.options.display.max_colwidth = 100 | ||
| # from tqdm import tqdm | ||
| __program__ = os.path.split(sys.argv[0])[-1] | ||
| __version__ = "2023.12.15" | ||
| __version__ = "2023.12.20" | ||
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| def main(args=None): | ||
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@@ -21,23 +21,27 @@ def main(args=None): | |
| # Path info | ||
| description = """ | ||
| Running: {} v{} via Python v{} | {}""".format(__program__, __version__, sys.version.split(" ")[0], sys.executable) | ||
| usage = "{} -i <identifier_mapping> -a <annotations> -t <taxnomy> -o <output_table>".format(__program__) | ||
| usage = "{} -i <identifier_mapping> -a <annotations> -t <taxonomy> -o <output_table>".format(__program__) | ||
| epilog = "Copyright 2021 Josh L. Espinoza ([email protected])" | ||
|
|
||
| # Parser | ||
| parser = argparse.ArgumentParser(description=description, usage=usage, epilog=epilog, formatter_class=argparse.RawTextHelpFormatter) | ||
| # Pipeline | ||
| parser.add_argument("-i","--identifier_mapping", default="stdin", type=str, help = "path/to/identifier_mapping.tsv[.gz] [id_protein]<tab>[id_genome] (No header) [Default: stdin]") | ||
| parser.add_argument("-a","--annotations", type=str, required=True, help = "path/to/annotations.tsv[.gz] Output from annotations.py. Multi-level header that contains (UniRef, sseqid)") | ||
| parser.add_argument("-a","--annotations", type=str, required=True, help = "path/to/annotations.tsv[.gz] Output from annotations.py. First column must be qseqid. See --annotation_header_mode") | ||
| parser.add_argument("-t","--taxonomy", type=str, required=True, help = "path/to/taxonomy.tsv[.gz] [id_genome]<tab>[classification] (No header). Use output from `merge_taxonomy_classifications.py` with --no_header and --no_domain") | ||
| parser.add_argument("-s","--sequences", type=str, required=True, help = "path/to/proteins.fasta[.gz]") | ||
| parser.add_argument("-o","--output", type=str, default="stdout", help = "path/to/humann_uniref_annotations.tsv[.gz] (veba_output/profiling/databases/) is recommended [Default: stdout]") | ||
| parser.add_argument("-o","--output_table", type=str, default="stdout", help = "path/to/humann_uniref_annotations.tsv[.gz] (veba_output/profiling/databases/) is recommended [Default: stdout]") | ||
| parser.add_argument("--output_fasta", type=str, help = "path/to/humann_uniref.fasta[.gz] (Recommended to add to veba_output/tmp/ and then build the diamond base in veba_output/profiling/databases/ )") | ||
| parser.add_argument("--sep", default=";", help = "Separator for taxonomic levels [Default: ;]") | ||
| # parser.add_argument("--mandatory_taxonomy_prefixes", help = "Comma-separated values for mandatory prefix levels. (e.g., 'c__,f__,g__,s__')") | ||
| # parser.add_argument("--discarded_file", help = "Proteins that have been discarded due to incomplete lineage") | ||
| parser.add_argument("-g", "--no_append_genome_identifier", action="store_true", help = "Don't add genome to taxonomic lineage") | ||
| parser.add_argument("--genome_prefix", type=str, default="t__", help = "Taxonomic level prefix for genome") | ||
| parser.add_argument("--header", action="store_true", help = "Write header") | ||
| parser.add_argument("-m", "--annotation_header_mode", default="multilevel", choices={"header", "multilevel", "no_header"}, help = "If --annotation_header_mode == 'multiindex' header assumes that contains (UniRef, sseqid), --annotation_header_mode == 'header' assumes one-level header that contains `sseqid`, --annotation_header_mode == 'no_header' assumes there is no header [Default: multilevel]") | ||
| parser.add_argument("--sseqid_index", type=int, default=1, help = "Python indexing for sseqid position if --annotation_header_mode == no_header. Assumes qseqid is index=0. [Default: 1]") | ||
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| # Options | ||
| opts = parser.parse_args() | ||
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@@ -46,49 +50,102 @@ def main(args=None): | |
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| if opts.identifier_mapping == "stdin": | ||
| opts.identifier_mapping = sys.stdin | ||
| else: | ||
| if opts.identifier_mapping.endswith(".gz"): | ||
| opts.identifier_mapping = gzip.open(opts.identifier_mapping, "rt") | ||
| else: | ||
| opts.identifier_mapping = open(opts.identifier_mapping, "r") | ||
|
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||
| if opts.output_table == "stdout": | ||
| opts.output_table = sys.stdout | ||
|
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| if opts.output_fasta is not None: | ||
| if opts.output_fasta.endswith(".gz"): | ||
| opts.output_fasta = gzip.open(opts.output_fasta, "wt") | ||
| else: | ||
| opts.output_fasta = open(opts.output_fasta, "w") | ||
|
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| if opts.output == "stdout": | ||
| opts.output = sys.stdout | ||
|
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| # Proteins to genomes | ||
| protein_to_genome = pd.read_csv(opts.identifier_mapping, sep="\t", index_col=0, header=None).iloc[:,0] | ||
| A1 = set(protein_to_genome.index) | ||
| A2= set(protein_to_genome.values) | ||
| # protein_to_genome = pd.read_csv(opts.identifier_mapping, sep="\t", index_col=0, header=None).iloc[:,0] | ||
| protein_to_genome = dict() | ||
| for line in tqdm(opts.identifier_mapping, desc="Getting genome for each protein: {}".format(opts.identifier_mapping), unit=" Proteins"): | ||
| line = line.strip() | ||
| if line: | ||
| id_protein, id_genome = line.split("\t") | ||
| protein_to_genome[id_protein] = id_genome | ||
|
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| A1 = set(protein_to_genome.keys()) | ||
| A2= set(protein_to_genome.values()) | ||
| print("--identifier_mapping", opts.identifier_mapping, file=sys.stderr) | ||
| print(" * {} proteins".format(len(A1)), file=sys.stderr) | ||
| print(" * {} genomes".format(len(A2)), file=sys.stderr) | ||
|
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| # Annotations | ||
| df_annotations = pd.read_csv(opts.annotations, sep="\t", index_col=0, header=[0,1]) | ||
| assert "UniRef" in df_annotations.columns.get_level_values(0), "--annotations must have a 2 level header (i.e., Pandas MultiIndex with 2 levels) where the first level has 'UniRef' as created by `annotate.py`" | ||
| df_annotations = df_annotations["UniRef"] | ||
| protein_to_uniref = df_annotations["sseqid"].dropna() | ||
| if opts.annotation_header_mode == "no_header": | ||
| df_annotations = pd.read_csv(opts.annotations, sep="\t", index_col=0, header=None) | ||
| protein_to_uniref = df_annotations.iloc[:,opts.sseqid_index - 1].dropna() | ||
| else: | ||
| if opts.annotation_header_mode == "multilevel": | ||
| df_annotations = pd.read_csv(opts.annotations, sep="\t", index_col=0, header=[0,1]) | ||
| assert "UniRef" in df_annotations.columns.get_level_values(0), "--annotations must have a 2 level header (i.e., Pandas MultiIndex with 2 levels) where the first level has 'UniRef' as created by `annotate.py`" | ||
| df_annotations = df_annotations["UniRef"] | ||
| else: | ||
| # Annotations | ||
| df_annotations = pd.read_csv(opts.annotations, sep="\t", index_col=0) | ||
| protein_to_uniref = df_annotations["sseqid"].dropna() | ||
|
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| B1 = set(protein_to_uniref.index) | ||
| B2 = set(protein_to_uniref.values) | ||
|
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| print("--annotations", opts.annotations, file=sys.stderr) | ||
| print(" * {} proteins".format(len(B1)), file=sys.stderr) | ||
| print(" * {} UniRef hits".format(len(B2)), file=sys.stderr) | ||
|
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||
| # Taxonomy | ||
| genome_to_taxonomy = pd.read_csv(opts.taxonomy, sep="\t", index_col=0, header=None).iloc[:,0] | ||
| C1 = set(genome_to_taxonomy.index) | ||
| C2 = set(genome_to_taxonomy.values) | ||
| # genome_to_taxonomy = pd.read_csv(opts.taxonomy, sep="\t", index_col=0, header=None).iloc[:,0] | ||
| if opts.taxonomy.endswith(".gz"): | ||
| f_taxonomy = gzip.open(opts.taxonomy, "rt") | ||
| else: | ||
| f_taxonomy = open(opts.taxonomy, "r") | ||
|
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| genome_to_taxonomy = dict() | ||
| for line in tqdm(f_taxonomy, desc="Getting taxonomy for each genome: {}".format(opts.taxonomy), unit=" Genomes"): | ||
| line = line.strip() | ||
| if line: | ||
| id_genome, taxonomy = line.split("\t") | ||
| genome_to_taxonomy[id_genome] = taxonomy | ||
| f_taxonomy.close() | ||
|
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| C1 = set(genome_to_taxonomy.keys()) | ||
| C2 = set(genome_to_taxonomy.values()) | ||
|
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| print("--taxonomy", opts.taxonomy, file=sys.stderr) | ||
| print(" * {} genomes".format(len(C1)), file=sys.stderr) | ||
| print(" * {} taxonomic classifications".format(len(C2)), file=sys.stderr) | ||
|
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| if opts.sequences.endswith(".gz"): | ||
| f = gzip.open(opts.sequences, "rt") | ||
| f_sequences = gzip.open(opts.sequences, "rt") | ||
| else: | ||
| f = open(opts.sequences, "r") | ||
| f_sequences = open(opts.sequences, "r") | ||
|
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| protein_to_length = dict() | ||
| for header, seq in tqdm(SimpleFastaParser(f), "Calculating length of proteins: {}".format(opts.sequences), unit=" Proteins"): | ||
| id = header.split(" ")[0] | ||
| protein_to_length[id] = len(seq) | ||
| protein_to_length = pd.Series(protein_to_length) | ||
| D = set(protein_to_length.index) | ||
| if opts.output_fasta is None: | ||
| protein_to_length = dict() | ||
| for header, seq in tqdm(SimpleFastaParser(f_sequences), "Calculating length of proteins: {}".format(opts.sequences), unit=" Proteins"): | ||
| id = header.split(" ")[0] | ||
| if id in B1: | ||
| protein_to_length[id] = len(seq) | ||
| else: | ||
| protein_to_length = dict() | ||
| for header, seq in tqdm(SimpleFastaParser(f_sequences), "Calculating length of proteins: {}".format(opts.sequences), unit=" Proteins"): | ||
| id = header.split(" ")[0] | ||
| if id in B1: | ||
| protein_to_length[id] = len(seq) | ||
| print(">{}\n{}".format(id, seq), file=opts.output_fasta) | ||
| opts.output_fasta.close() | ||
| f_sequences.close() | ||
|
|
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| D = set(protein_to_length.keys()) | ||
|
|
||
| # Checks | ||
| assert A1 >= B1, "Not all proteins in --annotations are in --identifier_mapping." | ||
|
|
@@ -98,22 +155,19 @@ def main(args=None): | |
| # Append genome to taxonomy | ||
| if not opts.no_append_genome_identifier: | ||
| tmp = dict() | ||
| for id_genome, taxonomy in genome_to_taxonomy.items(): | ||
| for id_genome, taxonomy in tqdm(genome_to_taxonomy.items(), desc="Adding genome identifiers"): | ||
| tmp[id_genome] = "{}{}{}{}".format(taxonomy, opts.sep, opts.genome_prefix, id_genome) | ||
| genome_to_taxonomy = pd.Series(tmp) | ||
| genome_to_taxonomy = tmp | ||
|
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| # id_protein, uniref_hit, len, lineage | ||
| df_output = protein_to_uniref.to_frame("UniRef") | ||
| df_output["Length"] = protein_to_length[protein_to_uniref.index] | ||
| df_output["Taxonomy"] = protein_to_uniref.index.map(lambda id_protein: genome_to_taxonomy[protein_to_genome[id_protein]]) | ||
| df_output["Length"] = df_output.index.map(lambda id_protein: protein_to_length[id_protein]) | ||
| df_output["Taxonomy"] = df_output.index.map(lambda id_protein: genome_to_taxonomy[protein_to_genome[id_protein]]) | ||
| df_output.index.name = "id_protein" | ||
|
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| # if opts.discarded_file: | ||
|
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| df_output.to_csv(opts.output, sep="\t", header=bool(opts.header)) | ||
| df_output.to_csv(opts.output_table, sep="\t", header=bool(opts.header)) | ||
|
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| if __name__ == "__main__": | ||
| main() | ||