Mercurial > repos > cpt > cpt_putative_isp
changeset 1:4e02e6e9e77d draft
planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
| author | cpt |
|---|---|
| date | Mon, 05 Jun 2023 02:51:35 +0000 |
| parents | 10d13d0c37d3 |
| children | 0a02ef22ce17 |
| files | cpt-macros.xml cpt.py cpt_putative_isp/cpt-macros.xml cpt_putative_isp/cpt.py cpt_putative_isp/generate-putative-isp.py cpt_putative_isp/generate-putative-isp.xml cpt_putative_isp/macros.xml cpt_putative_isp/spaninFuncs.py generate-putative-isp.py generate-putative-isp.xml macros.xml spaninFuncs.py |
| diffstat | 12 files changed, 1455 insertions(+), 1387 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cpt-macros.xml Mon Jun 05 02:51:35 2023 +0000 @@ -0,0 +1,115 @@ +<macros> + <xml name="gff_requirements"> + <requirements> + <requirement type="package" version="2.7">python</requirement> + <requirement type="package" version="1.65">biopython</requirement> + <requirement type="package" version="2.12.1">requests</requirement> + <requirement type="package" version="1.2.2">cpt_gffparser</requirement> + <yield/> + </requirements> + <version_command> + <![CDATA[ + cd '$__tool_directory__' && git rev-parse HEAD + ]]> + </version_command> + </xml> + <xml name="citation/mijalisrasche"> + <citation type="doi">10.1371/journal.pcbi.1008214</citation> + <citation type="bibtex">@unpublished{galaxyTools, + author = {E. Mijalis, H. Rasche}, + title = {CPT Galaxy Tools}, + year = {2013-2017}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + </xml> + <xml name="citations"> + <citations> + <citation type="doi">10.1371/journal.pcbi.1008214</citation> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {E. Mijalis, H. Rasche}, + title = {CPT Galaxy Tools}, + year = {2013-2017}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + <yield/> + </citations> + </xml> + <xml name="citations-crr"> + <citations> + <citation type="doi">10.1371/journal.pcbi.1008214</citation> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {C. Ross}, + title = {CPT Galaxy Tools}, + year = {2020-}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + <yield/> + </citations> + </xml> + <xml name="citations-2020"> + <citations> + <citation type="doi">10.1371/journal.pcbi.1008214</citation> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {E. Mijalis, H. Rasche}, + title = {CPT Galaxy Tools}, + year = {2013-2017}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {A. Criscione}, + title = {CPT Galaxy Tools}, + year = {2019-2021}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + <yield/> + </citations> + </xml> + <xml name="citations-2020-AJC-solo"> + <citations> + <citation type="doi">10.1371/journal.pcbi.1008214</citation> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {A. Criscione}, + title = {CPT Galaxy Tools}, + year = {2019-2021}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + <yield/> + </citations> + </xml> + <xml name="citations-clm"> + <citations> + <citation type="doi">10.1371/journal.pcbi.1008214</citation> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {C. Maughmer}, + title = {CPT Galaxy Tools}, + year = {2017-2020}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + <yield/> + </citations> + </xml> + <xml name="sl-citations-clm"> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {C. Maughmer}, + title = {CPT Galaxy Tools}, + year = {2017-2020}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + <yield/> + </xml> +</macros>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cpt.py Mon Jun 05 02:51:35 2023 +0000 @@ -0,0 +1,341 @@ +#!/usr/bin/env python +from Bio.Seq import Seq, reverse_complement, translate +from Bio.SeqRecord import SeqRecord +from Bio import SeqIO +from Bio.Data import CodonTable +import logging + +logging.basicConfig() +log = logging.getLogger() + +PHAGE_IN_MIDDLE = re.compile("^(?P<host>.*)\s*phage (?P<phage>.*)$") +BACTERIOPHAGE_IN_MIDDLE = re.compile("^(?P<host>.*)\s*bacteriophage (?P<phage>.*)$") +STARTS_WITH_PHAGE = re.compile( + "^(bacterio|vibrio|Bacterio|Vibrio|)?[Pp]hage (?P<phage>.*)$" +) +NEW_STYLE_NAMES = re.compile("(?P<phage>v[A-Z]_[A-Z][a-z]{2}_.*)") + + +def phage_name_parser(name): + host = None + phage = None + name = name.replace(", complete genome.", "") + name = name.replace(", complete genome", "") + + m = BACTERIOPHAGE_IN_MIDDLE.match(name) + if m: + host = m.group("host") + phage = m.group("phage") + return (host, phage) + + m = PHAGE_IN_MIDDLE.match(name) + if m: + host = m.group("host") + phage = m.group("phage") + return (host, phage) + + m = STARTS_WITH_PHAGE.match(name) + if m: + phage = m.group("phage") + return (host, phage) + + m = NEW_STYLE_NAMES.match(name) + if m: + phage = m.group("phage") + return (host, phage) + + return (host, phage) + + +class OrfFinder(object): + def __init__(self, table, ftype, ends, min_len, strand): + self.table = table + self.table_obj = CodonTable.ambiguous_generic_by_id[table] + self.ends = ends + self.ftype = ftype + self.min_len = min_len + self.starts = sorted(self.table_obj.start_codons) + self.stops = sorted(self.table_obj.stop_codons) + self.re_starts = re.compile("|".join(self.starts)) + self.re_stops = re.compile("|".join(self.stops)) + self.strand = strand + + def locate(self, fasta_file, out_nuc, out_prot, out_bed, out_gff3): + seq_format = "fasta" + log.debug("Genetic code table %i" % self.table) + log.debug("Minimum length %i aa" % self.min_len) + + out_count = 0 + + out_gff3.write("##gff-version 3\n") + + for idx, record in enumerate(SeqIO.parse(fasta_file, seq_format)): + for i, (f_start, f_end, f_strand, n, t) in enumerate( + self.get_all_peptides(str(record.seq).upper()) + ): + out_count += 1 + + descr = "length %i aa, %i bp, from %s..%s[%s] of %s" % ( + len(t), + len(n), + f_start, + f_end, + f_strand, + record.description, + ) + fid = record.id + "|%s%i" % (self.ftype, i + 1) + + r = SeqRecord(Seq(n), id=fid, name="", description=descr) + t = SeqRecord(Seq(t), id=fid, name="", description=descr) + + SeqIO.write(r, out_nuc, "fasta") + SeqIO.write(t, out_prot, "fasta") + + nice_strand = "+" if f_strand == +1 else "-" + + out_bed.write( + "\t".join( + map(str, [record.id, f_start, f_end, fid, 0, nice_strand]) + ) + + "\n" + ) + + out_gff3.write( + "\t".join( + map( + str, + [ + record.id, + "getOrfsOrCds", + "CDS", + f_start + 1, + f_end, + ".", + nice_strand, + 0, + "ID=%s.%s.%s" % (self.ftype, idx, i + 1), + ], + ) + ) + + "\n" + ) + log.info("Found %i %ss", out_count, self.ftype) + + def start_chop_and_trans(self, s, strict=True): + """Returns offset, trimmed nuc, protein.""" + if strict: + assert s[-3:] in self.stops, s + assert len(s) % 3 == 0 + for match in self.re_starts.finditer(s, overlapped=True): + # Must check the start is in frame + start = match.start() + if start % 3 == 0: + n = s[start:] + assert len(n) % 3 == 0, "%s is len %i" % (n, len(n)) + if strict: + t = translate(n, self.table) + else: + # Use when missing stop codon, + t = "M" + translate(n[3:], self.table, to_stop=True) + yield start, n, t # Edited by CPT to be a generator + + def break_up_frame(self, s): + """Returns offset, nuc, protein.""" + start = 0 + for match in self.re_stops.finditer(s, overlapped=True): + index = match.start() + 3 + if index % 3 != 0: + continue + n = s[start:index] + for (offset, n, t) in self.start_chop_and_trans(n): + if n and len(t) >= self.min_len: + yield start + offset, n, t + start = index + + def putative_genes_in_sequence(self, nuc_seq): + """Returns start, end, strand, nucleotides, protein. + Co-ordinates are Python style zero-based. + """ + nuc_seq = nuc_seq.upper() + # TODO - Refactor to use a generator function (in start order) + # rather than making a list and sorting? + answer = [] + full_len = len(nuc_seq) + + for frame in range(0, 3): + for offset, n, t in self.break_up_frame(nuc_seq[frame:]): + start = frame + offset # zero based + answer.append((start, start + len(n), +1, n, t)) + + rc = reverse_complement(nuc_seq) + for frame in range(0, 3): + for offset, n, t in self.break_up_frame(rc[frame:]): + start = full_len - frame - offset # zero based + answer.append((start, start - len(n), -1, n, t)) + answer.sort() + return answer + + def get_all_peptides(self, nuc_seq): + """Returns start, end, strand, nucleotides, protein. + + Co-ordinates are Python style zero-based. + """ + # Refactored into generator by CPT + full_len = len(nuc_seq) + if self.strand != "reverse": + for frame in range(0, 3): + for offset, n, t in self.break_up_frame(nuc_seq[frame:]): + start = frame + offset # zero based + yield (start, start + len(n), +1, n, t) + if self.strand != "forward": + rc = reverse_complement(nuc_seq) + for frame in range(0, 3): + for offset, n, t in self.break_up_frame(rc[frame:]): + start = full_len - frame - offset # zero based + yield (start - len(n), start, -1, n, t) + + +class MGAFinder(object): + def __init__(self, table, ftype, ends, min_len): + self.table = table + self.table_obj = CodonTable.ambiguous_generic_by_id[table] + self.ends = ends + self.ftype = ftype + self.min_len = min_len + self.starts = sorted(self.table_obj.start_codons) + self.stops = sorted(self.table_obj.stop_codons) + self.re_starts = re.compile("|".join(self.starts)) + self.re_stops = re.compile("|".join(self.stops)) + + def locate(self, fasta_file, out_nuc, out_prot, out_bed, out_gff3): + seq_format = "fasta" + log.debug("Genetic code table %i" % self.table) + log.debug("Minimum length %i aa" % self.min_len) + + out_count = 0 + + out_gff3.write("##gff-version 3\n") + + for idx, record in enumerate(SeqIO.parse(fasta_file, seq_format)): + for i, (f_start, f_end, f_strand, n, t) in enumerate( + self.get_all_peptides(str(record.seq).upper()) + ): + out_count += 1 + + descr = "length %i aa, %i bp, from %s..%s[%s] of %s" % ( + len(t), + len(n), + f_start, + f_end, + f_strand, + record.description, + ) + fid = record.id + "|%s%i" % (self.ftype, i + 1) + + r = SeqRecord(Seq(n), id=fid, name="", description=descr) + t = SeqRecord(Seq(t), id=fid, name="", description=descr) + + SeqIO.write(r, out_nuc, "fasta") + SeqIO.write(t, out_prot, "fasta") + + nice_strand = "+" if f_strand == +1 else "-" + + out_bed.write( + "\t".join( + map(str, [record.id, f_start, f_end, fid, 0, nice_strand]) + ) + + "\n" + ) + + out_gff3.write( + "\t".join( + map( + str, + [ + record.id, + "getOrfsOrCds", + "CDS", + f_start + 1, + f_end, + ".", + nice_strand, + 0, + "ID=%s.%s.%s" % (self.ftype, idx, i + 1), + ], + ) + ) + + "\n" + ) + log.info("Found %i %ss", out_count, self.ftype) + + def start_chop_and_trans(self, s, strict=True): + """Returns offset, trimmed nuc, protein.""" + if strict: + assert s[-3:] in self.stops, s + assert len(s) % 3 == 0 + for match in self.re_starts.finditer(s, overlapped=True): + # Must check the start is in frame + start = match.start() + if start % 3 == 0: + n = s[start:] + assert len(n) % 3 == 0, "%s is len %i" % (n, len(n)) + if strict: + t = translate(n, self.table) + else: + # Use when missing stop codon, + t = "M" + translate(n[3:], self.table, to_stop=True) + yield start, n, t + + def break_up_frame(self, s): + """Returns offset, nuc, protein.""" + start = 0 + for match in self.re_stops.finditer(s, overlapped=True): + index = match.start() + 3 + if index % 3 != 0: + continue + n = s[start:index] + for (offset, n, t) in self.start_chop_and_trans(n): + if n and len(t) >= self.min_len: + yield start + offset, n, t + start = index + + def putative_genes_in_sequence(self, nuc_seq): + """Returns start, end, strand, nucleotides, protein. + Co-ordinates are Python style zero-based. + """ + nuc_seq = nuc_seq.upper() + # TODO - Refactor to use a generator function (in start order) + # rather than making a list and sorting? + answer = [] + full_len = len(nuc_seq) + + for frame in range(0, 3): + for offset, n, t in self.break_up_frame(nuc_seq[frame:]): + start = frame + offset # zero based + answer.append((start, start + len(n), +1, n, t)) + + rc = reverse_complement(nuc_seq) + for frame in range(0, 3): + for offset, n, t in self.break_up_frame(rc[frame:]): + start = full_len - frame - offset # zero based + answer.append((start, start - len(n), -1, n, t)) + answer.sort() + return answer + + def get_all_peptides(self, nuc_seq): + """Returns start, end, strand, nucleotides, protein. + + Co-ordinates are Python style zero-based. + """ + # Refactored into generator by CPT + + full_len = len(nuc_seq) + for frame in range(0, 3): + for offset, n, t in self.break_up_frame(nuc_seq[frame:]): + start = frame + offset # zero based + yield (start, start + len(n), +1, n, t) + rc = reverse_complement(nuc_seq) + for frame in range(0, 3): + for offset, n, t in self.break_up_frame(rc[frame:]): + start = full_len - frame - offset # zero based + yield (start - len(n), start, -1, n, t)
--- a/cpt_putative_isp/cpt-macros.xml Fri Jun 17 13:07:15 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,115 +0,0 @@ -<?xml version="1.0"?> -<macros> - <xml name="gff_requirements"> - <requirements> - <requirement type="package" version="2.7">python</requirement> - <requirement type="package" version="1.65">biopython</requirement> - <requirement type="package" version="2.12.1">requests</requirement> - <yield/> - </requirements> - <version_command> - <![CDATA[ - cd $__tool_directory__ && git rev-parse HEAD - ]]> - </version_command> - </xml> - <xml name="citation/mijalisrasche"> - <citation type="doi">10.1371/journal.pcbi.1008214</citation> - <citation type="bibtex">@unpublished{galaxyTools, - author = {E. Mijalis, H. Rasche}, - title = {CPT Galaxy Tools}, - year = {2013-2017}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - </xml> - <xml name="citations"> - <citations> - <citation type="doi">10.1371/journal.pcbi.1008214</citation> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {E. Mijalis, H. Rasche}, - title = {CPT Galaxy Tools}, - year = {2013-2017}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - <yield/> - </citations> - </xml> - <xml name="citations-crr"> - <citations> - <citation type="doi">10.1371/journal.pcbi.1008214</citation> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {C. Ross}, - title = {CPT Galaxy Tools}, - year = {2020-}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - <yield/> - </citations> - </xml> - <xml name="citations-2020"> - <citations> - <citation type="doi">10.1371/journal.pcbi.1008214</citation> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {E. Mijalis, H. Rasche}, - title = {CPT Galaxy Tools}, - year = {2013-2017}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {A. Criscione}, - title = {CPT Galaxy Tools}, - year = {2019-2021}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - <yield/> - </citations> - </xml> - <xml name="citations-2020-AJC-solo"> - <citations> - <citation type="doi">10.1371/journal.pcbi.1008214</citation> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {A. Criscione}, - title = {CPT Galaxy Tools}, - year = {2019-2021}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - <yield/> - </citations> - </xml> - <xml name="citations-clm"> - <citations> - <citation type="doi">10.1371/journal.pcbi.1008214</citation> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {C. Maughmer}, - title = {CPT Galaxy Tools}, - year = {2017-2020}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - <yield/> - </citations> - </xml> - <xml name="sl-citations-clm"> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {C. Maughmer}, - title = {CPT Galaxy Tools}, - year = {2017-2020}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - <yield/> - </xml> -</macros>
--- a/cpt_putative_isp/cpt.py Fri Jun 17 13:07:15 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,341 +0,0 @@ -#!/usr/bin/env python -from Bio.Seq import Seq, reverse_complement, translate -from Bio.SeqRecord import SeqRecord -from Bio import SeqIO -from Bio.Data import CodonTable -import logging - -logging.basicConfig() -log = logging.getLogger() - -PHAGE_IN_MIDDLE = re.compile("^(?P<host>.*)\s*phage (?P<phage>.*)$") -BACTERIOPHAGE_IN_MIDDLE = re.compile("^(?P<host>.*)\s*bacteriophage (?P<phage>.*)$") -STARTS_WITH_PHAGE = re.compile( - "^(bacterio|vibrio|Bacterio|Vibrio|)?[Pp]hage (?P<phage>.*)$" -) -NEW_STYLE_NAMES = re.compile("(?P<phage>v[A-Z]_[A-Z][a-z]{2}_.*)") - - -def phage_name_parser(name): - host = None - phage = None - name = name.replace(", complete genome.", "") - name = name.replace(", complete genome", "") - - m = BACTERIOPHAGE_IN_MIDDLE.match(name) - if m: - host = m.group("host") - phage = m.group("phage") - return (host, phage) - - m = PHAGE_IN_MIDDLE.match(name) - if m: - host = m.group("host") - phage = m.group("phage") - return (host, phage) - - m = STARTS_WITH_PHAGE.match(name) - if m: - phage = m.group("phage") - return (host, phage) - - m = NEW_STYLE_NAMES.match(name) - if m: - phage = m.group("phage") - return (host, phage) - - return (host, phage) - - -class OrfFinder(object): - def __init__(self, table, ftype, ends, min_len, strand): - self.table = table - self.table_obj = CodonTable.ambiguous_generic_by_id[table] - self.ends = ends - self.ftype = ftype - self.min_len = min_len - self.starts = sorted(self.table_obj.start_codons) - self.stops = sorted(self.table_obj.stop_codons) - self.re_starts = re.compile("|".join(self.starts)) - self.re_stops = re.compile("|".join(self.stops)) - self.strand = strand - - def locate(self, fasta_file, out_nuc, out_prot, out_bed, out_gff3): - seq_format = "fasta" - log.debug("Genetic code table %i" % self.table) - log.debug("Minimum length %i aa" % self.min_len) - - out_count = 0 - - out_gff3.write("##gff-version 3\n") - - for idx, record in enumerate(SeqIO.parse(fasta_file, seq_format)): - for i, (f_start, f_end, f_strand, n, t) in enumerate( - self.get_all_peptides(str(record.seq).upper()) - ): - out_count += 1 - - descr = "length %i aa, %i bp, from %s..%s[%s] of %s" % ( - len(t), - len(n), - f_start, - f_end, - f_strand, - record.description, - ) - fid = record.id + "|%s%i" % (self.ftype, i + 1) - - r = SeqRecord(Seq(n), id=fid, name="", description=descr) - t = SeqRecord(Seq(t), id=fid, name="", description=descr) - - SeqIO.write(r, out_nuc, "fasta") - SeqIO.write(t, out_prot, "fasta") - - nice_strand = "+" if f_strand == +1 else "-" - - out_bed.write( - "\t".join( - map(str, [record.id, f_start, f_end, fid, 0, nice_strand]) - ) - + "\n" - ) - - out_gff3.write( - "\t".join( - map( - str, - [ - record.id, - "getOrfsOrCds", - "CDS", - f_start + 1, - f_end, - ".", - nice_strand, - 0, - "ID=%s.%s.%s" % (self.ftype, idx, i + 1), - ], - ) - ) - + "\n" - ) - log.info("Found %i %ss", out_count, self.ftype) - - def start_chop_and_trans(self, s, strict=True): - """Returns offset, trimmed nuc, protein.""" - if strict: - assert s[-3:] in self.stops, s - assert len(s) % 3 == 0 - for match in self.re_starts.finditer(s, overlapped=True): - # Must check the start is in frame - start = match.start() - if start % 3 == 0: - n = s[start:] - assert len(n) % 3 == 0, "%s is len %i" % (n, len(n)) - if strict: - t = translate(n, self.table) - else: - # Use when missing stop codon, - t = "M" + translate(n[3:], self.table, to_stop=True) - yield start, n, t # Edited by CPT to be a generator - - def break_up_frame(self, s): - """Returns offset, nuc, protein.""" - start = 0 - for match in self.re_stops.finditer(s, overlapped=True): - index = match.start() + 3 - if index % 3 != 0: - continue - n = s[start:index] - for (offset, n, t) in self.start_chop_and_trans(n): - if n and len(t) >= self.min_len: - yield start + offset, n, t - start = index - - def putative_genes_in_sequence(self, nuc_seq): - """Returns start, end, strand, nucleotides, protein. - Co-ordinates are Python style zero-based. - """ - nuc_seq = nuc_seq.upper() - # TODO - Refactor to use a generator function (in start order) - # rather than making a list and sorting? - answer = [] - full_len = len(nuc_seq) - - for frame in range(0, 3): - for offset, n, t in self.break_up_frame(nuc_seq[frame:]): - start = frame + offset # zero based - answer.append((start, start + len(n), +1, n, t)) - - rc = reverse_complement(nuc_seq) - for frame in range(0, 3): - for offset, n, t in self.break_up_frame(rc[frame:]): - start = full_len - frame - offset # zero based - answer.append((start, start - len(n), -1, n, t)) - answer.sort() - return answer - - def get_all_peptides(self, nuc_seq): - """Returns start, end, strand, nucleotides, protein. - - Co-ordinates are Python style zero-based. - """ - # Refactored into generator by CPT - full_len = len(nuc_seq) - if self.strand != "reverse": - for frame in range(0, 3): - for offset, n, t in self.break_up_frame(nuc_seq[frame:]): - start = frame + offset # zero based - yield (start, start + len(n), +1, n, t) - if self.strand != "forward": - rc = reverse_complement(nuc_seq) - for frame in range(0, 3): - for offset, n, t in self.break_up_frame(rc[frame:]): - start = full_len - frame - offset # zero based - yield (start - len(n), start, -1, n, t) - - -class MGAFinder(object): - def __init__(self, table, ftype, ends, min_len): - self.table = table - self.table_obj = CodonTable.ambiguous_generic_by_id[table] - self.ends = ends - self.ftype = ftype - self.min_len = min_len - self.starts = sorted(self.table_obj.start_codons) - self.stops = sorted(self.table_obj.stop_codons) - self.re_starts = re.compile("|".join(self.starts)) - self.re_stops = re.compile("|".join(self.stops)) - - def locate(self, fasta_file, out_nuc, out_prot, out_bed, out_gff3): - seq_format = "fasta" - log.debug("Genetic code table %i" % self.table) - log.debug("Minimum length %i aa" % self.min_len) - - out_count = 0 - - out_gff3.write("##gff-version 3\n") - - for idx, record in enumerate(SeqIO.parse(fasta_file, seq_format)): - for i, (f_start, f_end, f_strand, n, t) in enumerate( - self.get_all_peptides(str(record.seq).upper()) - ): - out_count += 1 - - descr = "length %i aa, %i bp, from %s..%s[%s] of %s" % ( - len(t), - len(n), - f_start, - f_end, - f_strand, - record.description, - ) - fid = record.id + "|%s%i" % (self.ftype, i + 1) - - r = SeqRecord(Seq(n), id=fid, name="", description=descr) - t = SeqRecord(Seq(t), id=fid, name="", description=descr) - - SeqIO.write(r, out_nuc, "fasta") - SeqIO.write(t, out_prot, "fasta") - - nice_strand = "+" if f_strand == +1 else "-" - - out_bed.write( - "\t".join( - map(str, [record.id, f_start, f_end, fid, 0, nice_strand]) - ) - + "\n" - ) - - out_gff3.write( - "\t".join( - map( - str, - [ - record.id, - "getOrfsOrCds", - "CDS", - f_start + 1, - f_end, - ".", - nice_strand, - 0, - "ID=%s.%s.%s" % (self.ftype, idx, i + 1), - ], - ) - ) - + "\n" - ) - log.info("Found %i %ss", out_count, self.ftype) - - def start_chop_and_trans(self, s, strict=True): - """Returns offset, trimmed nuc, protein.""" - if strict: - assert s[-3:] in self.stops, s - assert len(s) % 3 == 0 - for match in self.re_starts.finditer(s, overlapped=True): - # Must check the start is in frame - start = match.start() - if start % 3 == 0: - n = s[start:] - assert len(n) % 3 == 0, "%s is len %i" % (n, len(n)) - if strict: - t = translate(n, self.table) - else: - # Use when missing stop codon, - t = "M" + translate(n[3:], self.table, to_stop=True) - yield start, n, t - - def break_up_frame(self, s): - """Returns offset, nuc, protein.""" - start = 0 - for match in self.re_stops.finditer(s, overlapped=True): - index = match.start() + 3 - if index % 3 != 0: - continue - n = s[start:index] - for (offset, n, t) in self.start_chop_and_trans(n): - if n and len(t) >= self.min_len: - yield start + offset, n, t - start = index - - def putative_genes_in_sequence(self, nuc_seq): - """Returns start, end, strand, nucleotides, protein. - Co-ordinates are Python style zero-based. - """ - nuc_seq = nuc_seq.upper() - # TODO - Refactor to use a generator function (in start order) - # rather than making a list and sorting? - answer = [] - full_len = len(nuc_seq) - - for frame in range(0, 3): - for offset, n, t in self.break_up_frame(nuc_seq[frame:]): - start = frame + offset # zero based - answer.append((start, start + len(n), +1, n, t)) - - rc = reverse_complement(nuc_seq) - for frame in range(0, 3): - for offset, n, t in self.break_up_frame(rc[frame:]): - start = full_len - frame - offset # zero based - answer.append((start, start - len(n), -1, n, t)) - answer.sort() - return answer - - def get_all_peptides(self, nuc_seq): - """Returns start, end, strand, nucleotides, protein. - - Co-ordinates are Python style zero-based. - """ - # Refactored into generator by CPT - - full_len = len(nuc_seq) - for frame in range(0, 3): - for offset, n, t in self.break_up_frame(nuc_seq[frame:]): - start = frame + offset # zero based - yield (start, start + len(n), +1, n, t) - rc = reverse_complement(nuc_seq) - for frame in range(0, 3): - for offset, n, t in self.break_up_frame(rc[frame:]): - start = full_len - frame - offset # zero based - yield (start - len(n), start, -1, n, t)
--- a/cpt_putative_isp/generate-putative-isp.py Fri Jun 17 13:07:15 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,317 +0,0 @@ -#!/usr/bin/env python - -##### findSpanin.pl --> findSpanin.py -######### Incooperated from the findSpanin.pl script, but better and more snakey. - -import argparse -from cpt import OrfFinder -from Bio import SeqIO -from Bio import Seq -import re -from spaninFuncs import * -import os - -# if __name__ == '__main__': -# pass -############################################################################### - -if __name__ == "__main__": - - # Common parameters for both ISP / OSP portion of script - - parser = argparse.ArgumentParser( - description="Get putative protein candidates for spanins" - ) - parser.add_argument( - "fasta_file", type=argparse.FileType("r"), help="Fasta file" - ) # the "input" argument - - parser.add_argument( - "-f", - "--format", - dest="seq_format", - default="fasta", - help="Sequence format (e.g. fasta, fastq, sff)", - ) # optional formats for input, currently just going to do ntFASTA - - parser.add_argument( - "--strand", - dest="strand", - choices=("both", "forward", "reverse"), - default="both", - help="select strand", - ) # Selection of +, -, or both strands - - parser.add_argument( - "--table", dest="table", default=11, help="NCBI Translation table", type=int - ) # Uses "default" NCBI codon table. This should always (afaik) be what we want... - - parser.add_argument( - "-t", - "--ftype", - dest="ftype", - choices=("CDS", "ORF"), - default="ORF", - help="Find ORF or CDSs", - ) # "functional type(?)" --> Finds ORF or CDS, for this we want just the ORF - - parser.add_argument( - "-e", - "--ends", - dest="ends", - choices=("open", "closed"), - default="closed", - help="Open or closed. Closed ensures start/stop codons are present", - ) # includes the start and stop codon - - parser.add_argument( - "-m", - "--mode", - dest="mode", - choices=("all", "top", "one"), - default="all", # I think we want this to JUST be all...nearly always - help="Output all ORFs/CDSs from sequence, all ORFs/CDSs with max length, or first with maximum length", - ) - - parser.add_argument( - "--switch", - dest="switch", - default="all", - help="switch between ALL putative osps, or a range. If not all, insert a range of two integers separated by a colon (:). Eg: 1234:4321", - ) - # isp parameters - parser.add_argument( - "--isp_min_len", - dest="isp_min_len", - default=60, - help="Minimum ORF length, measured in codons", - type=int, - ) - parser.add_argument( - "--isp_on", - dest="out_isp_nuc", - type=argparse.FileType("w"), - default="_out_isp.fna", - help="Output nucleotide sequences, FASTA", - ) - parser.add_argument( - "--isp_op", - dest="out_isp_prot", - type=argparse.FileType("w"), - default="_out_isp.fa", - help="Output protein sequences, FASTA", - ) - parser.add_argument( - "--isp_ob", - dest="out_isp_bed", - type=argparse.FileType("w"), - default="_out_isp.bed", - help="Output BED file", - ) - parser.add_argument( - "--isp_og", - dest="out_isp_gff3", - type=argparse.FileType("w"), - default="_out_isp.gff3", - help="Output GFF3 file", - ) - parser.add_argument( - "--isp_min_dist", - dest="isp_min_dist", - default=10, - help="Minimal distance to first AA of TMD, measured in AA", - type=int, - ) - parser.add_argument( - "--isp_max_dist", - dest="isp_max_dist", - default=30, - help="Maximum distance to first AA of TMD, measured in AA", - type=int, - ) - parser.add_argument( - "--putative_isp", - dest="putative_isp_fa", - type=argparse.FileType("w"), - default="_putative_isp.fa", - help="Output of putative FASTA file", - ) - parser.add_argument( - "--min_tmd_size", - dest="min_tmd_size", - default=10, - help="Minimal size of the TMD domain", - type=int, - ) - parser.add_argument( - "--max_tmd_size", - dest="max_tmd_size", - default=20, - help="Maximum size of the TMD domain", - type=int, - ) - parser.add_argument( - "--summary_isp_txt", - dest="summary_isp_txt", - type=argparse.FileType("w"), - default="_summary_isp.txt", - help="Summary statistics on putative i-spanins", - ) - parser.add_argument( - "--putative_isp_gff", - dest="putative_isp_gff", - type=argparse.FileType("w"), - default="_putative_isp.gff3", - help="gff3 output for putative i-spanins", - ) - - parser.add_argument( - "--max_isp", - dest="max_isp", - default=230, - help="Maximum size of the ISP", - type=int, - ) - - parser.add_argument( - "--isp_mode", - action="store_true", - default=True - ) - - parser.add_argument( - "--peri_min", - type=int, - default=18, - help="amount of residues after TMD is found min" - ) - - parser.add_argument( - "--peri_max", - type=int, - default=206, - help="amount of residues after TMD is found max" - ) - # parser.add_argument('-v', action='version', version='0.3.0') # Is this manually updated? - args = parser.parse_args() - the_args = vars(parser.parse_args()) - - ### isp output, naive ORF finding: - isps = OrfFinder(args.table, args.ftype, args.ends, args.isp_min_len, args.strand) - isps.locate( - args.fasta_file, - args.out_isp_nuc, - args.out_isp_prot, - args.out_isp_bed, - args.out_isp_gff3, - ) - """ - >T7_EIS MLEFLRKLIPWVLVGMLFGLGWHLGSDSMDAKWKQEVHNEYVKRVEAAKSTQRAIGAVSAKYQEDLAALEGSTDRIISDLRSDNKRLRVRVKTTGISDGQCGFEPDGRAELDDRDAKRILAVTQKGDAWIRALQDTIRELQRK - >lambda_EIS MSRVTAIISALVICIIVCLSWAVNHYRDNAITYKAQRDKNARELKLANAAITDMQMRQRDVAALDAKYTKELADAKAENDALRDDVAAGRRRLHIKAVCQSVREATTASGVDNAASPRLADTAERDYFTLRERLITMQKQLEGTQKYINEQCR - """ - args.fasta_file.close() - args.fasta_file = open(args.fasta_file.name, "r") - args.out_isp_prot.close() - args.out_isp_prot = open(args.out_isp_prot.name, "r") - - pairs = tuple_fasta(fasta_file=args.out_isp_prot) - - # print(pairs) - - have_tmd = [] # empty candidates list to be passed through the user input criteria - - for ( - each_pair - ) in ( - pairs - ): # grab transmembrane domains from spaninFuncts (queries for lysin snorkels # and a range of hydrophobic regions that could be TMDs) - if len(each_pair[1]) <= args.max_isp: - try: - have_tmd += find_tmd( - pair=each_pair, - minimum=args.isp_min_dist, - maximum=args.isp_max_dist, - TMDmin=args.min_tmd_size, - TMDmax=args.max_tmd_size, - isp_mode=args.isp_mode, - peri_min=args.peri_min, - peri_max=args.peri_max, - ) - except TypeError: - continue - - if args.switch == "all": - pass - else: - # for each_pair in have_lipo: - range_of = args.switch - range_of = re.search(("[\d]+:[\d]+"), range_of).group(0) - start = int(range_of.split(":")[0]) - end = int(range_of.split(":")[1]) - have_tmd = parse_a_range(pair=have_tmd, start=start, end=end) - - total_isp = len(have_tmd) - - # ORF = [] # mightttttttttttt use eventually - length = [] # grabbing length of the sequences - candidate_dict = {k: v for k, v in have_tmd} - with args.putative_isp_fa as f: - for desc, s in candidate_dict.items(): # description / sequence - f.write(">" + str(desc)) - f.write("\n" + lineWrapper(str(s).replace("*","")) + "\n") - length.append(len(s)) - # ORF.append(desc) - if not length: - raise Exception("Parameters yielded no candidates.") - bot_size = min(length) - top_size = max(length) - avg = (sum(length)) / total_isp - n = len(length) - if n == 0: - raise Exception("no median for empty data") - if n % 2 == 1: - med = length[n // 2] - else: - i = n // 2 - med = (length[i - 1] + length[i]) / 2 - - #### Extra statistics - args.out_isp_prot.close() - all_orfs = open(args.out_isp_prot.name, "r") - all_isps = open(args.putative_isp_fa.name, "r") - #record = SeqIO.read(all_orfs, "fasta") - #print(len(record)) - n = 0 - for line in all_orfs: - if line.startswith(">"): - n += 1 - all_orfs_counts = n - - c = 0 - for line in all_isps: - if line.startswith(">"): - c += 1 - all_isps_counts = c - - #print(f"{n} -> {c}") - #count = 0 - #for feature in record.features: - # count += 1 - #print(count) - - - with args.summary_isp_txt as f: - f.write("total potential o-spanins: " + str(total_isp) + "\n") - f.write("average length (AA): " + str(avg) + "\n") - f.write("median length (AA): " + str(med) + "\n") - f.write("maximum orf in size (AA): " + str(top_size) + "\n") - f.write("minimum orf in size (AA): " + str(bot_size) + "\n") - f.write("ratio of isps found from naive orfs: " + str(c) + "/" +str(n)) - - # Output the putative list in gff3 format - args.putative_isp_fa = open(args.putative_isp_fa.name, "r") - gff_data = prep_a_gff3(fa=args.putative_isp_fa, spanin_type="isp",org=args.fasta_file) - write_gff3(data=gff_data, output=args.putative_isp_gff) - - """https://docs.python.org/3.4/library/subprocess.html""" - """https://github.tamu.edu/CPT/Galaxy-Tools/blob/f0bf4a4b8e5124d4f3082d21b738dfaa8e1a3cf6/tools/phage/transmembrane.py"""
--- a/cpt_putative_isp/generate-putative-isp.xml Fri Jun 17 13:07:15 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,82 +0,0 @@ -<?xml version="1.1"?> -<tool id="edu.tamu.cpt2.spanin.generate-putative-isp" name="ISP candidates" version="1.0"> - <description>constructs a putative list of potential i-spanin from an input genomic FASTA</description> - <macros> - <import>macros.xml</import> - <import>cpt-macros.xml</import> - </macros> - <expand macro="requirements"> - </expand> - <command detect_errors="aggressive"><![CDATA[ -python $__tool_directory__/generate-putative-isp.py -$fasta_file ---strand $strand ---switch $switch ---isp_on $isp_on ---isp_op $isp_op ---isp_ob $isp_ob ---isp_og $isp_og ---isp_min_len $isp_min_len ---isp_min_dist $isp_min_dist ---isp_max_dist $isp_max_dist ---min_tmd_size $min_tmd_size ---max_tmd_size $max_tmd_size ---putative_isp $putative_isp ---summary_isp_txt $summary_isp ---putative_isp_gff $putative_isp_gff ---isp_max $isp_max ---peri_min $peri_min ---peri_max $peri_max - -]]></command> - <inputs> - <param type="select" label="Strand Choice" name="strand"> - <option value="both">both</option> - <option value="forward">+</option> - <option value="reverse">-</option> - </param> - <param label="Single Genome FASTA" name="fasta_file" type="data" format="fasta" /> - <param label="i-spanin minimal length" name="isp_min_len" type="integer" value="60" /> - <param label="i-spanin maximum length" name="isp_max" type="integer" value="230" /> - <param label="Range Selection; default is all; for a specific range to check for a spanin input integers separated by a colon (eg. 1000:2000)" type="text" name="switch" value="all" /> - <param label="TMD minimal distance from start codon" name="isp_min_dist" type="integer" value="10" /> - <param label="TMD maximum distance from start codon" name="isp_max_dist" type="integer" value="35" help="Searches for a TMD between TMDmin and TMDmax ie [TMDmin,TMDmax]" /> - <param label="TMD minimal size" name="min_tmd_size" type="integer" value="10" /> - <param label="TMD maximum size" name="max_tmd_size" type="integer" value="25" /> - <param label="Periplasmic minimal residue amount" name="peri_min" type="integer" value="16" /> - <param label="Periplasmic maximum residue amount" name="peri_max" type="integer" value="206" /> - </inputs> - <outputs> - <data format="fasta" name="isp_on" label="NucSequences.fa" hidden = "true"/> - <data format="fasta" name="isp_op" label="ProtSequences.fa" hidden = "true"/> - <data format="bed" name="isp_ob" label="BED_Output.bed" hidden = "true"/> - <data format="gff3" name="isp_og" label="GFF_Output.gff" hidden = "true"/> - <data format="fasta" name="putative_isp" label="putative_isp.fa"/> - <data format="txt" name="summary_isp" label="summary_isp.txt"/> - <data format="gff3" name="putative_isp_gff" label="putative_isp.gff3"/> - </outputs> - <help><![CDATA[ - -**What it does** -Searches a genome for candidate i-spanins (ISPs), a phage protein involved in outer membrane disruption during Gram-negative bacterial host cell lysis. - -**METHODOLOGY** - -Locates ALL potential start sequences, based on TTG / ATG / GTG (M / L / V). This list is pared down to those within the user-set min/max lengths. That filtered list generates a set of files with the ORFs in FASTA (nt and aa), BED, and GFF3 file formats. - -With the protein FASTA, the tool next reads in each potential sequence and determines if it has a putative transmembrane domain (TMD) with the following criteria: - - 1. Presence of snorkeling Lysine residues surrounded by hydrophobic residues described for TMD below, within the range the user specifies. - 2. A putative transmembrane domain, or TMD, defined as a repeated hydrophobic region within the sequence ([FIWLVMYCATGSP]), of length and position within the range the user inputs. - 3. Length of expected periplasmic region. User defines minimum and maximum thresholds for required number of residues after TMD. - -**INPUT** --> Genomic FASTA -*NOTE: This tool only takes a SINGLE genomic fasta. It does not work with multiFASTAs.* - -**OUTPUT** --> putative_isp.fa (FASTA) file, putative_isp.gff3, and basic summary statistics as summary_isp.txt. - -Protein sequences which passed the above filters are returned as the candidate ISPs. - -]]></help> - <expand macro="citations-crr" /> -</tool>
--- a/cpt_putative_isp/macros.xml Fri Jun 17 13:07:15 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,63 +0,0 @@ -<?xml version="1.0"?> -<macros> - <xml name="requirements"> - <requirements> - <requirement type="package" version="2019.06.05">regex</requirement> - <requirement type="package" version="3.6">python</requirement> - <requirement type="package" version="1.77">biopython</requirement> - <requirement type="package" version="1.1.7">cpt_gffparser</requirement> - <yield/> - </requirements> - </xml> - <token name="@BLAST_TSV@"> - "$blast_tsv" - </token> - <xml name="blast_tsv"> - <param label="Blast Results" help="TSV/tabular (25 Column)" - name="blast_tsv" type="data" format="tabular" /> - </xml> - - <token name="@BLAST_XML@"> - "$blast_xml" - </token> - <xml name="blast_xml"> - <param label="Blast Results" help="XML format" - name="blast_xml" type="data" format="blastxml" /> - </xml> - <xml name="gff3_with_fasta"> - <param label="Genome Sequences" name="fasta" type="data" format="fasta" /> - <param label="Genome Annotations" name="gff3" type="data" format="gff3" /> - </xml> - <xml name="genome_selector"> - <param name="genome_fasta" type="data" format="fasta" label="Source FASTA Sequence"/> - </xml> - <xml name="gff3_input"> - <param label="GFF3 Annotations" name="gff3_data" type="data" format="gff3"/> - </xml> - <xml name="input/gff3+fasta"> - <expand macro="gff3_input" /> - <expand macro="genome_selector" /> - </xml> - <token name="@INPUT_GFF@"> - "$gff3_data" - </token> - <token name="@INPUT_FASTA@"> - genomeref.fa - </token> - <token name="@GENOME_SELECTOR_PRE@"> - ln -s $genome_fasta genomeref.fa; - </token> - <token name="@GENOME_SELECTOR@"> - genomeref.fa - </token> - <xml name="input/fasta"> - <param label="Fasta file" name="sequences" type="data" format="fasta"/> - </xml> - - <token name="@SEQUENCE@"> - "$sequences" - </token> - <xml name="input/fasta/protein"> - <param label="Protein fasta file" name="sequences" type="data" format="fasta"/> - </xml> -</macros>
--- a/cpt_putative_isp/spaninFuncs.py Fri Jun 17 13:07:15 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,469 +0,0 @@ -""" -PREMISE -### Functions/Classes that are used in both generate-putative-osp.py and generate-putative-isp.py -###### Main premise here is to make the above scripts a little more DRY, as well as easily readable for execution. -###### Documentation will ATTEMPT to be thourough here -""" - -import re -from Bio import SeqIO -from Bio import Seq -from collections import OrderedDict - -# Not written in OOP for a LITTLE bit of trying to keep the complication down in case adjustments are needed by someone else. -# Much of the manipulation is string based; so it should be straightforward as well as moderately quick -################## GLobal Variables -Lys = "K" - - -def check_back_end_snorkels(seq, tmsize): - """ - Searches through the backend of a potential TMD snorkel. This is the 2nd part of a TMD snorkel lysine match. - --> seq : should be the sequence fed from the "search_region" portion of the sequence - --> tmsize : size of the potential TMD being investigated - """ - found = [] - if seq[tmsize - 4] == Lys and re.search(("[FIWLVMYCATGS]"), seq[tmsize - 5]): - found = "match" - return found - elif seq[tmsize - 3] == Lys and re.search(("[FIWLVMYCATGS]"), seq[tmsize - 4]): - found = "match" - return found - elif seq[tmsize - 2] == Lys and re.search(("[FIWLVMYCATGS]"), seq[tmsize - 3]): - found = "match" - return found - elif seq[tmsize - 1] == Lys and re.search(("[FIWLVMYCATGS]"), seq[tmsize - 2]): - found = "match" - return found - else: - found = "NOTmatch" - return found - - -def prep_a_gff3(fa, spanin_type, org): - """ - Function parses an input detailed 'fa' file and outputs a 'gff3' file - ---> fa = input .fa file - ---> output = output a returned list of data, easily portable to a gff3 next - ---> spanin_type = 'isp' or 'osp' - """ - with org as f: - header = f.readline() - orgacc = header.split(" ") - orgacc = orgacc[0].split(">")[1].strip() - fa_zip = tuple_fasta(fa) - data = [] - for a_pair in fa_zip: - # print(a_pair) - if re.search(("(\[1\])"), a_pair[0]): - strand = "+" - elif re.search(("(\[-1\])"), a_pair[0]): - strand = "-" # column 7 - start = re.search(("[\d]+\.\."), a_pair[0]).group(0).split("..")[0] # column 4 - end = re.search(("\.\.[\d]+"), a_pair[0]).group(0).split("..")[1] # column 5 - orfid = re.search(("(ORF)[\d]+"), a_pair[0]).group(0) # column 1 - if spanin_type == "isp": - methodtype = "CDS" # column 3 - spanin = "isp" - elif spanin_type == "osp": - methodtype = "CDS" # column 3 - spanin = "osp" - elif spanin_type == "usp": - methodtype = "CDS" - spanin = "usp" - else: - raise "need to input spanin type" - source = "cpt.py|putative-*.py" # column 2 - score = "." # column 6 - phase = "." # column 8 - attributes = "ID=" +orgacc+ "|"+ orfid + ";ALIAS=" + spanin + ";SEQ="+a_pair[1] # column 9 - sequence = [[orgacc, source, methodtype, start, end, score, strand, phase, attributes]] - data += sequence - return data - - -def write_gff3(data, output="results.gff3"): - """ - Parses results from prep_a_gff3 into a gff3 file - ---> input : list from prep_a_gff3 - ---> output : gff3 file - """ - data = data - filename = output - with filename as f: - f.write("#gff-version 3\n") - for value in data: - f.write( - "{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format( - value[0], - value[1], - value[2], - value[3], - value[4], - value[5], - value[6], - value[7], - value[8], - ) - ) - f.close() - - -def find_tmd(pair, minimum=10, maximum=30, TMDmin=10, TMDmax=20, isp_mode=False, peri_min=18, peri_max=206): - """ - Function that searches for lysine snorkels and then for a spanning hydrophobic region that indicates a potential TMD - ---> pair : Input of tuple with description and AA sequence (str) - ---> minimum : How close from the initial start codon a TMD can be within - ---> maximum : How far from the initial start codon a TMD can be within - ---> TMDmin : The minimum size that a transmembrane can be (default = 10) - ---> TMDmax : The maximum size tha ta transmembrane can be (default = 20) - """ - # hydrophobicAAs = ['P', 'F', 'I', 'W', 'L', 'V', 'M', 'Y', 'C', 'A', 'T', 'G', 'S'] - tmd = [] - s = str(pair[1]) # sequence being analyzed - # print(s) # for trouble shooting - if maximum > len(s): - maximum = len(s) - search_region = s[minimum - 1 : maximum + 1] - #print(f"this is the search region: {search_region}") - # print(search_region) # for trouble shooting - - for tmsize in range(TMDmin, TMDmax+1, 1): - #print(f"this is the current tmsize we're trying: {tmsize}") - # print('==============='+str(tmsize)+'================') # print for troubleshooting - pattern = "[PFIWLVMYCATGS]{"+str(tmsize)+"}" # searches for these hydrophobic residues tmsize total times - #print(pattern) - #print(f"sending to regex: {search_region}") - if re.search( - ("[K]"), search_region[1:8]): # grabbing one below with search region, so I want to grab one ahead here when I query. - store_search = re.search(("[K]"), search_region[1:8]) # storing regex object - where_we_are = store_search.start() # finding where we got the hit - if re.search( - ("[PFIWLVMYCATGS]"), search_region[where_we_are + 1] - ) and re.search( - ("[PFIWLVMYCATGS]"), search_region[where_we_are - 1] - ): # hydrophobic neighbor - #try: - g = re.search(("[PFIWLVMYCATGS]"), search_region[where_we_are + 1]).group() - backend = check_back_end_snorkels(search_region, tmsize) - if backend == "match": - if isp_mode: - g = re.search((pattern), search_region).group() - end_of_tmd = re.search((g), s).end()+1 - amt_peri = len(s) - end_of_tmd - if peri_min <= amt_peri <= peri_max: - pair_desc = pair[0] + ", peri_count~="+str(amt_peri) - new_pair = (pair_desc,pair[1]) - tmd.append(new_pair) - else: - tmd.append(pair) - else: - continue - #else: - #print("I'm continuing out of snorkel loop") - #print(f"{search_region}") - #continue - if re.search((pattern), search_region): - #print(f"found match: {}") - #print("I AM HEREEEEEEEEEEEEEEEEEEEEEEE") - #try: - if isp_mode: - g = re.search((pattern), search_region).group() - end_of_tmd = re.search((g), s).end()+1 - amt_peri = len(s) - end_of_tmd - if peri_min <= amt_peri <= peri_max: - pair_desc = pair[0] + ", peri_count~="+str(amt_peri) - new_pair = (pair_desc,pair[1]) - tmd.append(new_pair) - else: - tmd.append(pair) - else: - continue - - return tmd - - -def find_lipobox(pair, minimum=10, maximum=50, min_after=30, max_after=185, regex=1, osp_mode=False): - """ - Function that takes an input tuple, and will return pairs of sequences to their description that have a lipoobox - ---> minimum - min distance from start codon to first AA of lipobox - ---> maximum - max distance from start codon to first AA of lipobox - ---> regex - option 1 (default) => more strict regular expression ; option 2 => looser selection, imported from LipoRy - - """ - if regex == 1: - pattern = "[ILMFTV][^REKD][GAS]C" # regex for Lipobox from findSpanin.pl - elif regex == 2: - pattern = "[ACGSILMFTV][^REKD][GAS]C" # regex for Lipobox from LipoRy - - candidates = [] - s = str(pair[1]) - # print(s) # trouble shooting - search_region = s[minimum-1 : maximum + 5] # properly slice the input... add 4 to catch if it hangs off at max input - # print(search_region) # trouble shooting - patterns = ["[ILMFTV][^REKD][GAS]C","AW[AGS]C"] - for pattern in patterns: - #print(pattern) # trouble shooting - if re.search((pattern), search_region): # lipobox must be WITHIN the range... - # searches the sequence with the input RegEx AND omits if - g = re.search((pattern), search_region).group() # find the exact group match - amt_peri = len(s) - re.search((g), s).end() + 1 - if min_after <= amt_peri <= max_after: # find the lipobox end region - if osp_mode: - pair_desc = pair[0] + ", peri_count~="+str(amt_peri) - new_pair = (pair_desc,pair[1]) - candidates.append(new_pair) - else: - candidates.append(pair) - - return candidates - - -def tuple_fasta(fasta_file): - """ - #### INPUT: Fasta File - #### OUTPUT: zipped (zip) : pairwise relationship of description to sequence - #### - """ - fasta = SeqIO.parse(fasta_file, "fasta") - descriptions = [] - sequences = [] - for r in fasta: # iterates and stores each description and sequence - description = r.description - sequence = str(r.seq) - if ( - sequence[0] != "I" - ): # the translation table currently has I as a potential start codon ==> this will remove all ORFs that start with I - descriptions.append(description) - sequences.append(sequence) - else: - continue - - return zip(descriptions, sequences) - - -def lineWrapper(text, charactersize=60): - - if len(text) <= charactersize: - return text - else: - return ( - text[:charactersize] - + "\n" - + lineWrapper(text[charactersize:], charactersize) - ) - - -def getDescriptions(fasta): - """ - Takes an output FASTA file, and parses retrieves the description headers. These headers contain information needed - for finding locations of a potential i-spanin and o-spanin proximity to one another. - """ - desc = [] - with fasta as f: - for line in f: - if line.startswith(">"): - desc.append(line) - return desc - - -def splitStrands(text, strand="+"): - # positive_strands = [] - # negative_strands = [] - if strand == "+": - if re.search(("(\[1\])"), text): - return text - elif strand == "-": - if re.search(("(\[-1\])"), text): - return text - # return positive_strands, negative_strands - - -def parse_a_range(pair, start, end): - """ - Takes an input data tuple from a fasta tuple pair and keeps only those within the input sequence range - ---> data : fasta tuple data - ---> start : start range to keep - ---> end : end range to keep (will need to + 1) - """ - matches = [] - for each_pair in pair: - - s = re.search(("[\d]+\.\."), each_pair[0]).group(0) # Start of the sequence - s = int(s.split("..")[0]) - e = re.search(("\.\.[\d]+"), each_pair[0]).group(0) - e = int(e.split("..")[1]) - if start - 1 <= s and e <= end + 1: - matches.append(each_pair) - else: - continue - # else: - # continue - # if matches != []: - return matches - # else: - # print('no candidates within selected range') - - -def grabLocs(text): - """ - Grabs the locations of the spanin based on NT location (seen from ORF). Grabs the ORF name, as per named from the ORF class/module - from cpt.py - """ - start = re.search(("[\d]+\.\."), text).group(0) # Start of the sequence ; looks for [numbers].. - end = re.search(("\.\.[\d]+"), text).group(0) # End of the sequence ; Looks for ..[numbers] - orf = re.search(("(ORF)[\d]+"), text).group(0) # Looks for ORF and the numbers that are after it - if re.search(("(\[1\])"), text): # stores strand - strand = "+" - elif re.search(("(\[-1\])"), text): # stores strand - strand = "-" - start = int(start.split("..")[0]) - end = int(end.split("..")[1]) - vals = [start, end, orf, strand] - - return vals - - -def spaninProximity(isp, osp, max_dist=30): - """ - _NOTE THIS FUNCTION COULD BE MODIFIED TO RETURN SEQUENCES_ - Compares the locations of i-spanins and o-spanins. max_dist is the distance in NT measurement from i-spanin END site - to o-spanin START. The user will be inputting AA distance, so a conversion will be necessary (<user_input> * 3) - I modified this on 07.30.2020 to bypass the pick + or - strand. To - INPUT: list of OSP and ISP candidates - OUTPUT: Return (improved) candidates for overlapping, embedded, and separate list - """ - - embedded = {} - overlap = {} - separate = {} - for iseq in isp: - embedded[iseq[2]] = [] - overlap[iseq[2]] = [] - separate[iseq[2]] = [] - for oseq in osp: - if iseq[3] == "+": - if oseq[3] == "+": - if iseq[0] < oseq[0] < iseq[1] and oseq[1] < iseq[1]: - ### EMBEDDED ### - combo = [ - iseq[0], - iseq[1], - oseq[2], - oseq[0], - oseq[1], - iseq[3], - ] # ordering a return for dic - embedded[iseq[2]] += [combo] - elif iseq[0] < oseq[0] <= iseq[1] and oseq[1] > iseq[1]: - ### OVERLAP / SEPARATE ### - if (iseq[1] - oseq[0]) < 6: - combo = [iseq[0], iseq[1], oseq[2], oseq[0], oseq[1],iseq[3]] - separate[iseq[2]] += [combo] - else: - combo = [iseq[0], iseq[1], oseq[2], oseq[0], oseq[1],iseq[3]] - overlap[iseq[2]] += [combo] - elif iseq[1] <= oseq[0] <= iseq[1] + max_dist: - combo = [iseq[0], iseq[1], oseq[2], oseq[0], oseq[1],iseq[3]] - separate[iseq[2]] += [combo] - else: - continue - if iseq[3] == "-": - if oseq[3] == "-": - if iseq[0] <= oseq[1] <= iseq[1] and oseq[0] > iseq[0]: - ### EMBEDDED ### - combo = [ - iseq[0], - iseq[1], - oseq[2], - oseq[0], - oseq[1], - iseq[3], - ] # ordering a return for dict - embedded[iseq[2]] += [combo] - elif iseq[0] <= oseq[1] <= iseq[1] and oseq[0] < iseq[0]: - if (oseq[1] - iseq[0]) < 6: - combo = [iseq[0], iseq[1], oseq[2], oseq[0], oseq[1],iseq[3]] - separate[iseq[2]] += [combo] - else: - combo = [iseq[0], iseq[1], oseq[2], oseq[0], oseq[1],iseq[3]] - overlap[iseq[2]] += [combo] - elif iseq[0] - 10 < oseq[1] < iseq[0]: - combo = [iseq[0], iseq[1], oseq[2], oseq[0], oseq[1],iseq[3]] - separate[iseq[2]] += [combo] - else: - continue - - embedded = {k: embedded[k] for k in embedded if embedded[k]} - overlap = {k: overlap[k] for k in overlap if overlap[k]} - separate = {k: separate[k] for k in separate if separate[k]} - - return embedded, overlap, separate - - -def check_for_usp(): - " pass " - -############################################### TEST RANGE ######################################################################### -#################################################################################################################################### -if __name__ == "__main__": - - #### TMD TEST - test_desc = ["one", "two", "three", "four", "five"] - test_seq = [ - "XXXXXXXXXXXXXXXFMCFMCFMCFMCFMCXXXXXXXXXXXXXXXXXXXXXXXXXX", - "XXXXXXXXAAKKKKKKKKKKKKKKKXXXXXXXXXXXXX", - "XXXXXXX", - "XXXXXXXXXXXKXXXXXXXXXX", - "XXXXXXXXXXAKXXXXXXXXXXAKXXXXXXXX", - ] - # for l in - # combo = zip(test_desc,test_seq) - pairs = zip(test_desc, test_seq) - tmd = [] - for each_pair in pairs: - # print(each_pair) - try: - tmd += find_tmd(pair=each_pair) - except (IndexError, TypeError): - continue - # try:s = each_pair[1] - # tmd += find_tmd(seq=s, tmsize=15) - # print('\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n') - # print(tmd) - # print('\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n') - - #### tuple-fasta TEST - # fasta_file = 'out_isp.fa' - # ret = tuple_fasta(fasta_file) - # print('=============') - # for i in ret: - # print(i[1]) - - #### LipoBox TEST - test_desc = ["one", "two", "three", "four", "five", "six", "seven"] - test_seq = [ - "XXXXXXXXXTGGCXXXXXXXXXXXXXXXX", - "XXXXXXXXAAKKKKKKKKKKKKKKKXXXXXXXXXXXXX", - "XXXXXXX", - "AGGCXXXXXXXXXXXXXXXXXXXXTT", - "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXTGGC", - "XXXXXXXXXXXXXXXXXXXXXXXXXXTGGC", - "MSTLRELRLRRALKEQSMRYLLSIKKTLPRWKGALIGLFLICVATISGCASESKLPEPPMVSVDSSLMVEPNLTTEMLNVFSQ*", - ] - pairs = zip(test_desc, test_seq) - lipo = [] - for each_pair in pairs: - #print(each_pair) - # try: - try: - lipo += find_lipobox(pair=each_pair, regex=2) # , minimum=8) - except TypeError: # catches if something doesnt have the min/max requirements (something is too small) - continue - # except: - # continue - # print('\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n') - #############################3 - # g = prep_a_gff3(fa='putative_isp.fa', spanin_type='isp') - # print(g) - # write_gff3(data=g)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/generate-putative-isp.py Mon Jun 05 02:51:35 2023 +0000 @@ -0,0 +1,314 @@ +#!/usr/bin/env python + +##### findSpanin.pl --> findSpanin.py +######### Incooperated from the findSpanin.pl script, but better and more snakey. + +import argparse +from cpt import OrfFinder +from Bio import SeqIO +from Bio import Seq +import re +from spaninFuncs import * +import os + +# if __name__ == '__main__': +# pass +############################################################################### + +if __name__ == "__main__": + + # Common parameters for both ISP / OSP portion of script + + parser = argparse.ArgumentParser( + description="Get putative protein candidates for spanins" + ) + parser.add_argument( + "fasta_file", type=argparse.FileType("r"), help="Fasta file" + ) # the "input" argument + + parser.add_argument( + "-f", + "--format", + dest="seq_format", + default="fasta", + help="Sequence format (e.g. fasta, fastq, sff)", + ) # optional formats for input, currently just going to do ntFASTA + + parser.add_argument( + "--strand", + dest="strand", + choices=("both", "forward", "reverse"), + default="both", + help="select strand", + ) # Selection of +, -, or both strands + + parser.add_argument( + "--table", dest="table", default=11, help="NCBI Translation table", type=int + ) # Uses "default" NCBI codon table. This should always (afaik) be what we want... + + parser.add_argument( + "-t", + "--ftype", + dest="ftype", + choices=("CDS", "ORF"), + default="ORF", + help="Find ORF or CDSs", + ) # "functional type(?)" --> Finds ORF or CDS, for this we want just the ORF + + parser.add_argument( + "-e", + "--ends", + dest="ends", + choices=("open", "closed"), + default="closed", + help="Open or closed. Closed ensures start/stop codons are present", + ) # includes the start and stop codon + + parser.add_argument( + "-m", + "--mode", + dest="mode", + choices=("all", "top", "one"), + default="all", # I think we want this to JUST be all...nearly always + help="Output all ORFs/CDSs from sequence, all ORFs/CDSs with max length, or first with maximum length", + ) + + parser.add_argument( + "--switch", + dest="switch", + default="all", + help="switch between ALL putative osps, or a range. If not all, insert a range of two integers separated by a colon (:). Eg: 1234:4321", + ) + # isp parameters + parser.add_argument( + "--isp_min_len", + dest="isp_min_len", + default=60, + help="Minimum ORF length, measured in codons", + type=int, + ) + parser.add_argument( + "--isp_on", + dest="out_isp_nuc", + type=argparse.FileType("w"), + default="_out_isp.fna", + help="Output nucleotide sequences, FASTA", + ) + parser.add_argument( + "--isp_op", + dest="out_isp_prot", + type=argparse.FileType("w"), + default="_out_isp.fa", + help="Output protein sequences, FASTA", + ) + parser.add_argument( + "--isp_ob", + dest="out_isp_bed", + type=argparse.FileType("w"), + default="_out_isp.bed", + help="Output BED file", + ) + parser.add_argument( + "--isp_og", + dest="out_isp_gff3", + type=argparse.FileType("w"), + default="_out_isp.gff3", + help="Output GFF3 file", + ) + parser.add_argument( + "--isp_min_dist", + dest="isp_min_dist", + default=10, + help="Minimal distance to first AA of TMD, measured in AA", + type=int, + ) + parser.add_argument( + "--isp_max_dist", + dest="isp_max_dist", + default=30, + help="Maximum distance to first AA of TMD, measured in AA", + type=int, + ) + parser.add_argument( + "--putative_isp", + dest="putative_isp_fa", + type=argparse.FileType("w"), + default="_putative_isp.fa", + help="Output of putative FASTA file", + ) + parser.add_argument( + "--min_tmd_size", + dest="min_tmd_size", + default=10, + help="Minimal size of the TMD domain", + type=int, + ) + parser.add_argument( + "--max_tmd_size", + dest="max_tmd_size", + default=20, + help="Maximum size of the TMD domain", + type=int, + ) + parser.add_argument( + "--summary_isp_txt", + dest="summary_isp_txt", + type=argparse.FileType("w"), + default="_summary_isp.txt", + help="Summary statistics on putative i-spanins", + ) + parser.add_argument( + "--putative_isp_gff", + dest="putative_isp_gff", + type=argparse.FileType("w"), + default="_putative_isp.gff3", + help="gff3 output for putative i-spanins", + ) + + parser.add_argument( + "--max_isp", + dest="max_isp", + default=230, + help="Maximum size of the ISP", + type=int, + ) + + parser.add_argument("--isp_mode", action="store_true", default=True) + + parser.add_argument( + "--peri_min", + type=int, + default=18, + help="amount of residues after TMD is found min", + ) + + parser.add_argument( + "--peri_max", + type=int, + default=206, + help="amount of residues after TMD is found max", + ) + # parser.add_argument('-v', action='version', version='0.3.0') # Is this manually updated? + args = parser.parse_args() + the_args = vars(parser.parse_args()) + + ### isp output, naive ORF finding: + isps = OrfFinder(args.table, args.ftype, args.ends, args.isp_min_len, args.strand) + isps.locate( + args.fasta_file, + args.out_isp_nuc, + args.out_isp_prot, + args.out_isp_bed, + args.out_isp_gff3, + ) + """ + >T7_EIS MLEFLRKLIPWVLVGMLFGLGWHLGSDSMDAKWKQEVHNEYVKRVEAAKSTQRAIGAVSAKYQEDLAALEGSTDRIISDLRSDNKRLRVRVKTTGISDGQCGFEPDGRAELDDRDAKRILAVTQKGDAWIRALQDTIRELQRK + >lambda_EIS MSRVTAIISALVICIIVCLSWAVNHYRDNAITYKAQRDKNARELKLANAAITDMQMRQRDVAALDAKYTKELADAKAENDALRDDVAAGRRRLHIKAVCQSVREATTASGVDNAASPRLADTAERDYFTLRERLITMQKQLEGTQKYINEQCR + """ + args.fasta_file.close() + args.fasta_file = open(args.fasta_file.name, "r") + args.out_isp_prot.close() + args.out_isp_prot = open(args.out_isp_prot.name, "r") + + pairs = tuple_fasta(fasta_file=args.out_isp_prot) + + # print(pairs) + + have_tmd = [] # empty candidates list to be passed through the user input criteria + + for ( + each_pair + ) in ( + pairs + ): # grab transmembrane domains from spaninFuncts (queries for lysin snorkels # and a range of hydrophobic regions that could be TMDs) + if len(each_pair[1]) <= args.max_isp: + try: + have_tmd += find_tmd( + pair=each_pair, + minimum=args.isp_min_dist, + maximum=args.isp_max_dist, + TMDmin=args.min_tmd_size, + TMDmax=args.max_tmd_size, + isp_mode=args.isp_mode, + peri_min=args.peri_min, + peri_max=args.peri_max, + ) + except TypeError: + continue + + if args.switch == "all": + pass + else: + # for each_pair in have_lipo: + range_of = args.switch + range_of = re.search(("[\d]+:[\d]+"), range_of).group(0) + start = int(range_of.split(":")[0]) + end = int(range_of.split(":")[1]) + have_tmd = parse_a_range(pair=have_tmd, start=start, end=end) + + total_isp = len(have_tmd) + + # ORF = [] # mightttttttttttt use eventually + length = [] # grabbing length of the sequences + candidate_dict = {k: v for k, v in have_tmd} + with args.putative_isp_fa as f: + for desc, s in candidate_dict.items(): # description / sequence + f.write(">" + str(desc)) + f.write("\n" + lineWrapper(str(s).replace("*", "")) + "\n") + length.append(len(s)) + # ORF.append(desc) + if not length: + raise Exception("Parameters yielded no candidates.") + bot_size = min(length) + top_size = max(length) + avg = (sum(length)) / total_isp + n = len(length) + if n == 0: + raise Exception("no median for empty data") + if n % 2 == 1: + med = length[n // 2] + else: + i = n // 2 + med = (length[i - 1] + length[i]) / 2 + + #### Extra statistics + args.out_isp_prot.close() + all_orfs = open(args.out_isp_prot.name, "r") + all_isps = open(args.putative_isp_fa.name, "r") + # record = SeqIO.read(all_orfs, "fasta") + # print(len(record)) + n = 0 + for line in all_orfs: + if line.startswith(">"): + n += 1 + all_orfs_counts = n + + c = 0 + for line in all_isps: + if line.startswith(">"): + c += 1 + all_isps_counts = c + + # print(f"{n} -> {c}") + # count = 0 + # for feature in record.features: + # count += 1 + # print(count) + + with args.summary_isp_txt as f: + f.write("total potential o-spanins: " + str(total_isp) + "\n") + f.write("average length (AA): " + str(avg) + "\n") + f.write("median length (AA): " + str(med) + "\n") + f.write("maximum orf in size (AA): " + str(top_size) + "\n") + f.write("minimum orf in size (AA): " + str(bot_size) + "\n") + f.write("ratio of isps found from naive orfs: " + str(c) + "/" + str(n)) + + # Output the putative list in gff3 format + args.putative_isp_fa = open(args.putative_isp_fa.name, "r") + gff_data = prep_a_gff3( + fa=args.putative_isp_fa, spanin_type="isp", org=args.fasta_file + ) + write_gff3(data=gff_data, output=args.putative_isp_gff) + + """https://docs.python.org/3.4/library/subprocess.html""" + """https://github.tamu.edu/CPT/Galaxy-Tools/blob/f0bf4a4b8e5124d4f3082d21b738dfaa8e1a3cf6/tools/phage/transmembrane.py"""
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/generate-putative-isp.xml Mon Jun 05 02:51:35 2023 +0000 @@ -0,0 +1,81 @@ +<tool id="edu.tamu.cpt2.spanin.generate-putative-isp" name="ISP candidates" version="1.0"> + <description>constructs a putative list of potential i-spanin from an input genomic FASTA</description> + <macros> + <import>macros.xml</import> + <import>cpt-macros.xml</import> + </macros> + <expand macro="requirements"> + </expand> + <command detect_errors="aggressive"><![CDATA[ +python '$__tool_directory__/generate-putative-isp.py' +'$fasta_file' +--strand '$strand' +--switch '$switch' +--isp_on '$isp_on' +--isp_op '$isp_op' +--isp_ob '$isp_ob' +--isp_og '$isp_og' +--isp_min_len '$isp_min_len' +--isp_min_dist '$isp_min_dist' +--isp_max_dist '$isp_max_dist' +--min_tmd_size '$min_tmd_size' +--max_tmd_size '$max_tmd_size' +--putative_isp '$putative_isp' +--summary_isp_txt '$summary_isp' +--putative_isp_gff '$putative_isp_gff' +--isp_max '$isp_max' +--peri_min '$peri_min' +--peri_max '$peri_max' + +]]></command> + <inputs> + <param type="select" label="Strand Choice" name="strand"> + <option value="both">both</option> + <option value="forward">+</option> + <option value="reverse">-</option> + </param> + <param label="Single Genome FASTA" name="fasta_file" type="data" format="fasta"/> + <param label="i-spanin minimal length" name="isp_min_len" type="integer" value="60"/> + <param label="i-spanin maximum length" name="isp_max" type="integer" value="230"/> + <param label="Range Selection; default is all; for a specific range to check for a spanin input integers separated by a colon (eg. 1000:2000)" type="text" name="switch" value="all"/> + <param label="TMD minimal distance from start codon" name="isp_min_dist" type="integer" value="10"/> + <param label="TMD maximum distance from start codon" name="isp_max_dist" type="integer" value="35" help="Searches for a TMD between TMDmin and TMDmax ie [TMDmin,TMDmax]"/> + <param label="TMD minimal size" name="min_tmd_size" type="integer" value="10"/> + <param label="TMD maximum size" name="max_tmd_size" type="integer" value="25"/> + <param label="Periplasmic minimal residue amount" name="peri_min" type="integer" value="16"/> + <param label="Periplasmic maximum residue amount" name="peri_max" type="integer" value="206"/> + </inputs> + <outputs> + <data format="fasta" name="isp_on" label="NucSequences.fa" hidden="true"/> + <data format="fasta" name="isp_op" label="ProtSequences.fa" hidden="true"/> + <data format="bed" name="isp_ob" label="BED_Output.bed" hidden="true"/> + <data format="gff3" name="isp_og" label="GFF_Output.gff" hidden="true"/> + <data format="fasta" name="putative_isp" label="putative_isp.fa"/> + <data format="txt" name="summary_isp" label="summary_isp.txt"/> + <data format="gff3" name="putative_isp_gff" label="putative_isp.gff3"/> + </outputs> + <help><![CDATA[ + +**What it does** +Searches a genome for candidate i-spanins (ISPs), a phage protein involved in outer membrane disruption during Gram-negative bacterial host cell lysis. + +**METHODOLOGY** + +Locates ALL potential start sequences, based on TTG / ATG / GTG (M / L / V). This list is pared down to those within the user-set min/max lengths. That filtered list generates a set of files with the ORFs in FASTA (nt and aa), BED, and GFF3 file formats. + +With the protein FASTA, the tool next reads in each potential sequence and determines if it has a putative transmembrane domain (TMD) with the following criteria: + + 1. Presence of snorkeling Lysine residues surrounded by hydrophobic residues described for TMD below, within the range the user specifies. + 2. A putative transmembrane domain, or TMD, defined as a repeated hydrophobic region within the sequence ([FIWLVMYCATGSP]), of length and position within the range the user inputs. + 3. Length of expected periplasmic region. User defines minimum and maximum thresholds for required number of residues after TMD. + +**INPUT** --> Genomic FASTA +*NOTE: This tool only takes a SINGLE genomic fasta. It does not work with multiFASTAs.* + +**OUTPUT** --> putative_isp.fa (FASTA) file, putative_isp.gff3, and basic summary statistics as summary_isp.txt. + +Protein sequences which passed the above filters are returned as the candidate ISPs. + +]]></help> + <expand macro="citations-crr"/> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Mon Jun 05 02:51:35 2023 +0000 @@ -0,0 +1,74 @@ +<macros> + <xml name="requirements"> + <requirements> + <requirement type="package">progressivemauve</requirement> + <!--<requirement type="package" version="2.7">python</requirement>--> + <requirement type="package" version="0.6.4">bcbiogff</requirement> + <yield/> + </requirements> + </xml> + <token name="@WRAPPER_VERSION@">2.4.0</token> + <xml name="citation/progressive_mauve"> + <citation type="doi">10.1371/journal.pone.0011147</citation> + </xml> + <xml name="citation/gepard"> + <citation type="doi">10.1093/bioinformatics/btm039</citation> + </xml> + <token name="@XMFA_INPUT@"> + '$xmfa' + </token> + <xml name="xmfa_input" token_formats="xmfa"> + <param type="data" format="@FORMATS@" name="xmfa" label="XMFA MSA"/> + </xml> + <token name="@XMFA_FA_INPUT@"> + '$sequences' + </token> + <xml name="xmfa_fa_input"> + <param type="data" format="fasta" name="sequences" label="Sequences in alignment" help="These sequences should be the SAME DATASET that was used in the progressiveMauve run. Failing that, they should be provided in the same order as in original progressiveMauve run"/> + </xml> + <xml name="genome_selector"> + <conditional name="reference_genome"> + <param name="reference_genome_source" type="select" label="Reference Genome"> + <option value="history" selected="True">From History</option> + <option value="cached">Locally Cached</option> + </param> + <when value="cached"> + <param name="fasta_indexes" type="select" label="Source FASTA Sequence"> + <options from_data_table="all_fasta"/> + </param> + </when> + <when value="history"> + <param name="genome_fasta" type="data" format="fasta" label="Source FASTA Sequence"/> + </when> + </conditional> + </xml> + <xml name="gff3_input"> + <param label="GFF3 Annotations" name="gff3_data" type="data" format="gff3"/> + </xml> + <xml name="input/gff3+fasta"> + <expand macro="gff3_input"/> + <expand macro="genome_selector"/> + </xml> + <token name="@INPUT_GFF@"> + '$gff3_data' + </token> + <token name="@INPUT_FASTA@"> + #if str($reference_genome.reference_genome_source) == 'cached': + '${reference_genome.fasta_indexes.fields.path}' + #else if str($reference_genome.reference_genome_source) == 'history': + genomeref.fa + #end if + </token> + <token name="@GENOME_SELECTOR_PRE@"> + #if $reference_genome.reference_genome_source == 'history': + ln -s '$reference_genome.genome_fasta' genomeref.fa; + #end if + </token> + <token name="@GENOME_SELECTOR@"> + #if str($reference_genome.reference_genome_source) == 'cached': + '${reference_genome.fasta_indexes.fields.path}' + #else if str($reference_genome.reference_genome_source) == 'history': + genomeref.fa + #end if + </token> +</macros>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/spaninFuncs.py Mon Jun 05 02:51:35 2023 +0000 @@ -0,0 +1,530 @@ +""" +PREMISE +### Functions/Classes that are used in both generate-putative-osp.py and generate-putative-isp.py +###### Main premise here is to make the above scripts a little more DRY, as well as easily readable for execution. +###### Documentation will ATTEMPT to be thourough here +""" + +import re +from Bio import SeqIO +from Bio import Seq +from collections import OrderedDict + +# Not written in OOP for a LITTLE bit of trying to keep the complication down in case adjustments are needed by someone else. +# Much of the manipulation is string based; so it should be straightforward as well as moderately quick +################## GLobal Variables +Lys = "K" + + +def check_back_end_snorkels(seq, tmsize): + """ + Searches through the backend of a potential TMD snorkel. This is the 2nd part of a TMD snorkel lysine match. + --> seq : should be the sequence fed from the "search_region" portion of the sequence + --> tmsize : size of the potential TMD being investigated + """ + found = [] + if seq[tmsize - 4] == Lys and re.search(("[FIWLVMYCATGS]"), seq[tmsize - 5]): + found = "match" + return found + elif seq[tmsize - 3] == Lys and re.search(("[FIWLVMYCATGS]"), seq[tmsize - 4]): + found = "match" + return found + elif seq[tmsize - 2] == Lys and re.search(("[FIWLVMYCATGS]"), seq[tmsize - 3]): + found = "match" + return found + elif seq[tmsize - 1] == Lys and re.search(("[FIWLVMYCATGS]"), seq[tmsize - 2]): + found = "match" + return found + else: + found = "NOTmatch" + return found + + +def prep_a_gff3(fa, spanin_type, org): + """ + Function parses an input detailed 'fa' file and outputs a 'gff3' file + ---> fa = input .fa file + ---> output = output a returned list of data, easily portable to a gff3 next + ---> spanin_type = 'isp' or 'osp' + """ + with org as f: + header = f.readline() + orgacc = header.split(" ") + orgacc = orgacc[0].split(">")[1].strip() + fa_zip = tuple_fasta(fa) + data = [] + for a_pair in fa_zip: + # print(a_pair) + if re.search(("(\[1\])"), a_pair[0]): + strand = "+" + elif re.search(("(\[-1\])"), a_pair[0]): + strand = "-" # column 7 + start = re.search(("[\d]+\.\."), a_pair[0]).group(0).split("..")[0] # column 4 + end = re.search(("\.\.[\d]+"), a_pair[0]).group(0).split("..")[1] # column 5 + orfid = re.search(("(ORF)[\d]+"), a_pair[0]).group(0) # column 1 + if spanin_type == "isp": + methodtype = "CDS" # column 3 + spanin = "isp" + elif spanin_type == "osp": + methodtype = "CDS" # column 3 + spanin = "osp" + elif spanin_type == "usp": + methodtype = "CDS" + spanin = "usp" + else: + raise "need to input spanin type" + source = "cpt.py|putative-*.py" # column 2 + score = "." # column 6 + phase = "." # column 8 + attributes = ( + "ID=" + orgacc + "|" + orfid + ";ALIAS=" + spanin + ";SEQ=" + a_pair[1] + ) # column 9 + sequence = [ + [orgacc, source, methodtype, start, end, score, strand, phase, attributes] + ] + data += sequence + return data + + +def write_gff3(data, output="results.gff3"): + """ + Parses results from prep_a_gff3 into a gff3 file + ---> input : list from prep_a_gff3 + ---> output : gff3 file + """ + data = data + filename = output + with filename as f: + f.write("#gff-version 3\n") + for value in data: + f.write( + "{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format( + value[0], + value[1], + value[2], + value[3], + value[4], + value[5], + value[6], + value[7], + value[8], + ) + ) + f.close() + + +def find_tmd( + pair, + minimum=10, + maximum=30, + TMDmin=10, + TMDmax=20, + isp_mode=False, + peri_min=18, + peri_max=206, +): + """ + Function that searches for lysine snorkels and then for a spanning hydrophobic region that indicates a potential TMD + ---> pair : Input of tuple with description and AA sequence (str) + ---> minimum : How close from the initial start codon a TMD can be within + ---> maximum : How far from the initial start codon a TMD can be within + ---> TMDmin : The minimum size that a transmembrane can be (default = 10) + ---> TMDmax : The maximum size tha ta transmembrane can be (default = 20) + """ + # hydrophobicAAs = ['P', 'F', 'I', 'W', 'L', 'V', 'M', 'Y', 'C', 'A', 'T', 'G', 'S'] + tmd = [] + s = str(pair[1]) # sequence being analyzed + # print(s) # for trouble shooting + if maximum > len(s): + maximum = len(s) + search_region = s[minimum - 1 : maximum + 1] + # print(f"this is the search region: {search_region}") + # print(search_region) # for trouble shooting + + for tmsize in range(TMDmin, TMDmax + 1, 1): + # print(f"this is the current tmsize we're trying: {tmsize}") + # print('==============='+str(tmsize)+'================') # print for troubleshooting + pattern = ( + "[PFIWLVMYCATGS]{" + str(tmsize) + "}" + ) # searches for these hydrophobic residues tmsize total times + # print(pattern) + # print(f"sending to regex: {search_region}") + if re.search( + ("[K]"), search_region[1:8] + ): # grabbing one below with search region, so I want to grab one ahead here when I query. + store_search = re.search( + ("[K]"), search_region[1:8] + ) # storing regex object + where_we_are = store_search.start() # finding where we got the hit + if re.search( + ("[PFIWLVMYCATGS]"), search_region[where_we_are + 1] + ) and re.search( + ("[PFIWLVMYCATGS]"), search_region[where_we_are - 1] + ): # hydrophobic neighbor + # try: + g = re.search( + ("[PFIWLVMYCATGS]"), search_region[where_we_are + 1] + ).group() + backend = check_back_end_snorkels(search_region, tmsize) + if backend == "match": + if isp_mode: + g = re.search((pattern), search_region).group() + end_of_tmd = re.search((g), s).end() + 1 + amt_peri = len(s) - end_of_tmd + if peri_min <= amt_peri <= peri_max: + pair_desc = pair[0] + ", peri_count~=" + str(amt_peri) + new_pair = (pair_desc, pair[1]) + tmd.append(new_pair) + else: + tmd.append(pair) + else: + continue + # else: + # print("I'm continuing out of snorkel loop") + # print(f"{search_region}") + # continue + if re.search((pattern), search_region): + # print(f"found match: {}") + # print("I AM HEREEEEEEEEEEEEEEEEEEEEEEE") + # try: + if isp_mode: + g = re.search((pattern), search_region).group() + end_of_tmd = re.search((g), s).end() + 1 + amt_peri = len(s) - end_of_tmd + if peri_min <= amt_peri <= peri_max: + pair_desc = pair[0] + ", peri_count~=" + str(amt_peri) + new_pair = (pair_desc, pair[1]) + tmd.append(new_pair) + else: + tmd.append(pair) + else: + continue + + return tmd + + +def find_lipobox( + pair, minimum=10, maximum=50, min_after=30, max_after=185, regex=1, osp_mode=False +): + """ + Function that takes an input tuple, and will return pairs of sequences to their description that have a lipoobox + ---> minimum - min distance from start codon to first AA of lipobox + ---> maximum - max distance from start codon to first AA of lipobox + ---> regex - option 1 (default) => more strict regular expression ; option 2 => looser selection, imported from LipoRy + + """ + if regex == 1: + pattern = "[ILMFTV][^REKD][GAS]C" # regex for Lipobox from findSpanin.pl + elif regex == 2: + pattern = "[ACGSILMFTV][^REKD][GAS]C" # regex for Lipobox from LipoRy + + candidates = [] + s = str(pair[1]) + # print(s) # trouble shooting + search_region = s[ + minimum - 1 : maximum + 5 + ] # properly slice the input... add 4 to catch if it hangs off at max input + # print(search_region) # trouble shooting + patterns = ["[ILMFTV][^REKD][GAS]C", "AW[AGS]C"] + for pattern in patterns: + # print(pattern) # trouble shooting + if re.search((pattern), search_region): # lipobox must be WITHIN the range... + # searches the sequence with the input RegEx AND omits if + g = re.search( + (pattern), search_region + ).group() # find the exact group match + amt_peri = len(s) - re.search((g), s).end() + 1 + if min_after <= amt_peri <= max_after: # find the lipobox end region + if osp_mode: + pair_desc = pair[0] + ", peri_count~=" + str(amt_peri) + new_pair = (pair_desc, pair[1]) + candidates.append(new_pair) + else: + candidates.append(pair) + + return candidates + + +def tuple_fasta(fasta_file): + """ + #### INPUT: Fasta File + #### OUTPUT: zipped (zip) : pairwise relationship of description to sequence + #### + """ + fasta = SeqIO.parse(fasta_file, "fasta") + descriptions = [] + sequences = [] + for r in fasta: # iterates and stores each description and sequence + description = r.description + sequence = str(r.seq) + if ( + sequence[0] != "I" + ): # the translation table currently has I as a potential start codon ==> this will remove all ORFs that start with I + descriptions.append(description) + sequences.append(sequence) + else: + continue + + return zip(descriptions, sequences) + + +def lineWrapper(text, charactersize=60): + + if len(text) <= charactersize: + return text + else: + return ( + text[:charactersize] + + "\n" + + lineWrapper(text[charactersize:], charactersize) + ) + + +def getDescriptions(fasta): + """ + Takes an output FASTA file, and parses retrieves the description headers. These headers contain information needed + for finding locations of a potential i-spanin and o-spanin proximity to one another. + """ + desc = [] + with fasta as f: + for line in f: + if line.startswith(">"): + desc.append(line) + return desc + + +def splitStrands(text, strand="+"): + # positive_strands = [] + # negative_strands = [] + if strand == "+": + if re.search(("(\[1\])"), text): + return text + elif strand == "-": + if re.search(("(\[-1\])"), text): + return text + # return positive_strands, negative_strands + + +def parse_a_range(pair, start, end): + """ + Takes an input data tuple from a fasta tuple pair and keeps only those within the input sequence range + ---> data : fasta tuple data + ---> start : start range to keep + ---> end : end range to keep (will need to + 1) + """ + matches = [] + for each_pair in pair: + + s = re.search(("[\d]+\.\."), each_pair[0]).group(0) # Start of the sequence + s = int(s.split("..")[0]) + e = re.search(("\.\.[\d]+"), each_pair[0]).group(0) + e = int(e.split("..")[1]) + if start - 1 <= s and e <= end + 1: + matches.append(each_pair) + else: + continue + # else: + # continue + # if matches != []: + return matches + # else: + # print('no candidates within selected range') + + +def grabLocs(text): + """ + Grabs the locations of the spanin based on NT location (seen from ORF). Grabs the ORF name, as per named from the ORF class/module + from cpt.py + """ + start = re.search(("[\d]+\.\."), text).group( + 0 + ) # Start of the sequence ; looks for [numbers].. + end = re.search(("\.\.[\d]+"), text).group( + 0 + ) # End of the sequence ; Looks for ..[numbers] + orf = re.search(("(ORF)[\d]+"), text).group( + 0 + ) # Looks for ORF and the numbers that are after it + if re.search(("(\[1\])"), text): # stores strand + strand = "+" + elif re.search(("(\[-1\])"), text): # stores strand + strand = "-" + start = int(start.split("..")[0]) + end = int(end.split("..")[1]) + vals = [start, end, orf, strand] + + return vals + + +def spaninProximity(isp, osp, max_dist=30): + """ + _NOTE THIS FUNCTION COULD BE MODIFIED TO RETURN SEQUENCES_ + Compares the locations of i-spanins and o-spanins. max_dist is the distance in NT measurement from i-spanin END site + to o-spanin START. The user will be inputting AA distance, so a conversion will be necessary (<user_input> * 3) + I modified this on 07.30.2020 to bypass the pick + or - strand. To + INPUT: list of OSP and ISP candidates + OUTPUT: Return (improved) candidates for overlapping, embedded, and separate list + """ + + embedded = {} + overlap = {} + separate = {} + for iseq in isp: + embedded[iseq[2]] = [] + overlap[iseq[2]] = [] + separate[iseq[2]] = [] + for oseq in osp: + if iseq[3] == "+": + if oseq[3] == "+": + if iseq[0] < oseq[0] < iseq[1] and oseq[1] < iseq[1]: + ### EMBEDDED ### + combo = [ + iseq[0], + iseq[1], + oseq[2], + oseq[0], + oseq[1], + iseq[3], + ] # ordering a return for dic + embedded[iseq[2]] += [combo] + elif iseq[0] < oseq[0] <= iseq[1] and oseq[1] > iseq[1]: + ### OVERLAP / SEPARATE ### + if (iseq[1] - oseq[0]) < 6: + combo = [ + iseq[0], + iseq[1], + oseq[2], + oseq[0], + oseq[1], + iseq[3], + ] + separate[iseq[2]] += [combo] + else: + combo = [ + iseq[0], + iseq[1], + oseq[2], + oseq[0], + oseq[1], + iseq[3], + ] + overlap[iseq[2]] += [combo] + elif iseq[1] <= oseq[0] <= iseq[1] + max_dist: + combo = [iseq[0], iseq[1], oseq[2], oseq[0], oseq[1], iseq[3]] + separate[iseq[2]] += [combo] + else: + continue + if iseq[3] == "-": + if oseq[3] == "-": + if iseq[0] <= oseq[1] <= iseq[1] and oseq[0] > iseq[0]: + ### EMBEDDED ### + combo = [ + iseq[0], + iseq[1], + oseq[2], + oseq[0], + oseq[1], + iseq[3], + ] # ordering a return for dict + embedded[iseq[2]] += [combo] + elif iseq[0] <= oseq[1] <= iseq[1] and oseq[0] < iseq[0]: + if (oseq[1] - iseq[0]) < 6: + combo = [ + iseq[0], + iseq[1], + oseq[2], + oseq[0], + oseq[1], + iseq[3], + ] + separate[iseq[2]] += [combo] + else: + combo = [ + iseq[0], + iseq[1], + oseq[2], + oseq[0], + oseq[1], + iseq[3], + ] + overlap[iseq[2]] += [combo] + elif iseq[0] - 10 < oseq[1] < iseq[0]: + combo = [iseq[0], iseq[1], oseq[2], oseq[0], oseq[1], iseq[3]] + separate[iseq[2]] += [combo] + else: + continue + + embedded = {k: embedded[k] for k in embedded if embedded[k]} + overlap = {k: overlap[k] for k in overlap if overlap[k]} + separate = {k: separate[k] for k in separate if separate[k]} + + return embedded, overlap, separate + + +def check_for_usp(): + "pass" + + +############################################### TEST RANGE ######################################################################### +#################################################################################################################################### +if __name__ == "__main__": + + #### TMD TEST + test_desc = ["one", "two", "three", "four", "five"] + test_seq = [ + "XXXXXXXXXXXXXXXFMCFMCFMCFMCFMCXXXXXXXXXXXXXXXXXXXXXXXXXX", + "XXXXXXXXAAKKKKKKKKKKKKKKKXXXXXXXXXXXXX", + "XXXXXXX", + "XXXXXXXXXXXKXXXXXXXXXX", + "XXXXXXXXXXAKXXXXXXXXXXAKXXXXXXXX", + ] + # for l in + # combo = zip(test_desc,test_seq) + pairs = zip(test_desc, test_seq) + tmd = [] + for each_pair in pairs: + # print(each_pair) + try: + tmd += find_tmd(pair=each_pair) + except (IndexError, TypeError): + continue + # try:s = each_pair[1] + # tmd += find_tmd(seq=s, tmsize=15) + # print('\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n') + # print(tmd) + # print('\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n') + + #### tuple-fasta TEST + # fasta_file = 'out_isp.fa' + # ret = tuple_fasta(fasta_file) + # print('=============') + # for i in ret: + # print(i[1]) + + #### LipoBox TEST + test_desc = ["one", "two", "three", "four", "five", "six", "seven"] + test_seq = [ + "XXXXXXXXXTGGCXXXXXXXXXXXXXXXX", + "XXXXXXXXAAKKKKKKKKKKKKKKKXXXXXXXXXXXXX", + "XXXXXXX", + "AGGCXXXXXXXXXXXXXXXXXXXXTT", + "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXTGGC", + "XXXXXXXXXXXXXXXXXXXXXXXXXXTGGC", + "MSTLRELRLRRALKEQSMRYLLSIKKTLPRWKGALIGLFLICVATISGCASESKLPEPPMVSVDSSLMVEPNLTTEMLNVFSQ*", + ] + pairs = zip(test_desc, test_seq) + lipo = [] + for each_pair in pairs: + # print(each_pair) + # try: + try: + lipo += find_lipobox(pair=each_pair, regex=2) # , minimum=8) + except TypeError: # catches if something doesnt have the min/max requirements (something is too small) + continue + # except: + # continue + # print('\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n') + #############################3 + # g = prep_a_gff3(fa='putative_isp.fa', spanin_type='isp') + # print(g) + # write_gff3(data=g)