Mercurial > repos > cpt > cpt_find_spanins
changeset 2:1a7fef71aee3 draft
Uploaded
| author | cpt |
|---|---|
| date | Fri, 17 Jun 2022 12:40:59 +0000 |
| parents | a02deaec6462 |
| children | fd70980a516b |
| files | cpt_find_spanins/cpt-macros.xml cpt_find_spanins/cpt.py cpt_find_spanins/findSpanin.py cpt_find_spanins/findSpanin.xml cpt_find_spanins/macros.xml cpt_find_spanins/spaninFuncs.py |
| diffstat | 6 files changed, 1509 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cpt_find_spanins/cpt-macros.xml Fri Jun 17 12:40:59 2022 +0000 @@ -0,0 +1,115 @@ +<?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@unpublished{galaxyTools, + author = {E. Mijalis, H. Rasche}, + title = {CPT Galaxy Tools}, + year = {2013-2017}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } +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 + @unpublished{galaxyTools, + author = {E. Mijalis, H. Rasche}, + title = {CPT Galaxy Tools}, + year = {2013-2017}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } +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 + @unpublished{galaxyTools, + author = {C. Ross}, + title = {CPT Galaxy Tools}, + year = {2020-}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + 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 + @unpublished{galaxyTools, + author = {E. Mijalis, H. Rasche}, + title = {CPT Galaxy Tools}, + year = {2013-2017}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } +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 + @unpublished{galaxyTools, + author = {A. Criscione}, + title = {CPT Galaxy Tools}, + year = {2019-2021}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + 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>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cpt_find_spanins/cpt.py Fri Jun 17 12:40:59 2022 +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)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cpt_find_spanins/findSpanin.py Fri Jun 17 12:40:59 2022 +0000 @@ -0,0 +1,477 @@ +##### findSpanin.pl --> findSpanin.py +######### Much of this code is very "blocked", in the sense that one thing happens...then a function happens on the return...then another function...etc...etc... + +import argparse +import os +import re # new +import itertools # new +from collections import Counter, OrderedDict +from spaninFuncs import getDescriptions, grabLocs, spaninProximity, splitStrands, tuple_fasta, lineWrapper + +### Requirement Inputs +#### INPUT : putative_isp.fa & putative_osp.fa (in that order) +#### PARAMETERS : + +############################################################################### +def write_output(candidates): + """ output file function...maybe not needed """ + pass + +def reconfigure_dict(spanins): + """ + re organizes dictionary to be more friendly for checks + """ + + new_spanin_dict = {} + + for each_spanin_type, data_dict in spanins.items(): + #print(f"{each_spanin_type} == {data_dict}") + new_spanin_dict[each_spanin_type] = {} + new_spanin_dict[each_spanin_type]['positive'] = {} + new_spanin_dict[each_spanin_type]['negative'] = {} + new_spanin_dict[each_spanin_type]['positive']['coords'] = [] + new_spanin_dict[each_spanin_type]['negative']['coords'] = [] + for outter_orf, inner_data in data_dict.items(): + list_of_hits = [] + for data_content in inner_data: + #print(data_content) + data_content.insert(0, outter_orf) + #print(f"new data_content -> {data_content}") + #print(data_content) + #list_of_hits += [data_content] + #new_spanin_dict[each_spanin_type] += [data_content] + if data_content[6] == "+": + #print(f"{each_spanin_type} @ POSITIVE") + new_spanin_dict[each_spanin_type]['positive']['coords'] += [data_content] + elif data_content[6] == "-": + #print(f"{each_spanin_type} @ NEGATIVE") + new_spanin_dict[each_spanin_type]['negative']['coords'] += [data_content] + #print(new_spanin_dict[each_spanin_type]) + #print(reorganized) + #print(f"{outter_orf} => {inner_data}") + #print(new_spanin_dict) + + #print('\n') + #for k, v in new_spanin_dict.items(): + #print(k) + #print(v) + return new_spanin_dict + + + +def check_for_uniques(spanins): + """ + Checks for unique spanins based on spanin_type. + If the positive strand end site is _the same_ for a i-spanin, we would group that as "1". + i.e. if ORF1, ORF2, and ORF3 all ended with location 4231, they would not be unique. + """ + pair_dict = {} + pair_dict = { + 'pairs' : { + 'location_amount' : [], + 'pair_number' : {}, + } + } + for each_spanin_type, spanin_data in spanins.items(): + #print(f"{each_spanin_type} ===> {spanin_data}") + # early declarations for cases of no results + pos_check = [] # end checks + pos_uniques = [] + neg_check = [] # start checks + neg_uniques = [] + unique_ends = [] + pos_amt_unique = 0 + neg_amt_unique = 0 + amt_positive = 0 + amt_negative = 0 + spanin_data['uniques'] = 0 + spanin_data['amount'] = 0 + #spanin_data['positive']['amt_positive'] = 0 + #spanin_data['positive']['pos_amt_unique'] = 0 + #spanin_data['positive']['isp_match'] = [] + #spanin_data['negative']['amt_negative'] = 0 + #spanin_data['negative']['neg_amt_unique'] = 0 + #spanin_data['negative']['isp_match'] = [] + #print(spanin_data) + if spanin_data['positive']['coords']: + # do something... + #print('in other function') + #print(spanin_data['positive']['coords']) + for each_hit in spanin_data['positive']['coords']: + pos_check.append(each_hit[2]) + pair_dict['pairs']['location_amount'].append(each_hit[2]) + pos_uniques = list(set([end_site for end_site in pos_check if pos_check.count(end_site) >= 1])) + #print(pos_check) + #print(pos_uniques) + amt_positive = len(spanin_data['positive']['coords']) + pos_amt_unique = len(pos_uniques) + if amt_positive: + spanin_data['positive']['amt_positive'] = amt_positive + spanin_data['positive']['pos_amt_unique'] = pos_amt_unique + #pair_dict['pairs']['locations'].extend(pos_uniques) + else: + spanin_data['positive']['amt_positive'] = 0 + spanin_data['positive']['pos_amt_unique'] = 0 + if spanin_data['negative']['coords']: + + # do something else... + #print('in other function') + #print(spanin_data['negative']['coords']) + for each_hit in spanin_data['negative']['coords']: + neg_check.append(each_hit[1]) + pair_dict['pairs']['location_amount'].append(each_hit[1]) + neg_uniques = list(set([start_site for start_site in neg_check if neg_check.count(start_site) >= 1])) + #print(neg_uniques) + amt_negative = len(spanin_data['negative']['coords']) + neg_amt_unique = len(neg_uniques) + if amt_negative: + spanin_data['negative']['amt_negative'] = amt_negative + spanin_data['negative']['neg_amt_unique'] = neg_amt_unique + #pair_dict['pairs']['locations'].extend(neg_uniques) + else: + spanin_data['negative']['amt_negative'] = 0 + spanin_data['negative']['neg_amt_unique'] = 0 + spanin_data['uniques'] += (spanin_data['positive']['pos_amt_unique'] + spanin_data['negative']['neg_amt_unique']) + spanin_data['amount'] += (spanin_data['positive']['amt_positive'] + spanin_data['negative']['amt_negative']) + #print(spanin_data['uniques']) + list(set(pair_dict['pairs']['location_amount'])) + pair_dict['pairs']['location_amount'] = dict(Counter(pair_dict['pairs']['location_amount'])) + for data in pair_dict.values(): + #print(data['locations']) + #print(type(data['locations'])) + v = 0 + for loc, count in data['location_amount'].items(): + #data['pair_number'] = {loc + v += 1 + data['pair_number'][loc] = v + #print(dict(Counter(pair_dict['pairs']['locations']))) + #print(pair_dict) + spanins['total_amount'] = spanins['EMBEDDED']['amount'] + spanins['SEPARATED']['amount'] + spanins['OVERLAPPED']['amount'] + spanins['total_unique'] = spanins['EMBEDDED']['uniques'] + spanins['SEPARATED']['uniques'] + spanins['OVERLAPPED']['uniques'] + #spanins['total_unique'] = len(pair_dict['pairs']['pair_number']) + return spanins, pair_dict + +if __name__ == "__main__": + + # Common parameters for both ISP / OSP portion of script + + parser = argparse.ArgumentParser( + description="Trim the putative protein candidates and find potential i-spanin / o-spanin pairs" + ) + + parser.add_argument( + "putative_isp_fasta_file", + type=argparse.FileType("r"), + help='Putative i-spanin FASTA file, output of "generate-putative-isp"', + ) # the "input" argument + + parser.add_argument( + "putative_osp_fasta_file", + type=argparse.FileType("r"), + help='Putative o-spanin FASTA file, output of "generate-putative-osp"', + ) + + parser.add_argument( + "--max_isp_osp_distance", + dest="max_isp_osp_distance", + default=10, + type=int, + help="max distance from end of i-spanin to start of o-spanin, measured in AAs", + ) + + parser.add_argument( + "--embedded_txt", + dest="embedded_txt", + type=argparse.FileType("w"), + default="_findSpanin_embedded_results.txt", + help="Results of potential embedded spanins", + ) + parser.add_argument( + "--overlap_txt", + dest="overlap_txt", + type=argparse.FileType("w"), + default="_findSpanin_overlap_results.txt", + help="Results of potential overlapping spanins", + ) + parser.add_argument( + "--separate_txt", + dest="separate_txt", + type=argparse.FileType("w"), + default="_findSpanin_separated_results.txt", + help="Results of potential separated spanins", + ) + + parser.add_argument( + "--summary_txt", + dest="summary_txt", + type=argparse.FileType("w"), + default="_findSpanin_summary.txt", + help="Results of potential spanin pairs", + ) + parser.add_argument( + "-v", action="version", version="0.3.0" + ) # Is this manually updated? + args = parser.parse_args() + + + #### RE-WRITE + SPANIN_TYPES = {} + SPANIN_TYPES['EMBEDDED'] = {} + SPANIN_TYPES['OVERLAPPED'] = {} + SPANIN_TYPES['SEPARATED'] = {} + #SPANIN_TYPES = { + # 'EMBEDDED' : {}, + # 'OVERLAPPED' : {}, + # 'SEPARATED' : {}, + #} + + isp = getDescriptions(args.putative_isp_fasta_file) + args.putative_isp_fasta_file = open(args.putative_isp_fasta_file.name, "r") + isp_full = tuple_fasta(args.putative_isp_fasta_file) + + osp = getDescriptions(args.putative_osp_fasta_file) + args.putative_osp_fasta_file = open(args.putative_osp_fasta_file.name, "r") + osp_full = tuple_fasta(args.putative_osp_fasta_file) + + #### location data + location_data = { + 'isp' : [], + 'osp' : [] + } + spanins = [isp, osp] + for idx, each_spanin_type in enumerate(spanins): + for description in each_spanin_type: + locations = grabLocs(description) + if idx == 0: # i-spanin + location_data['isp'].append(locations) + elif idx == 1: # o-spanin + location_data['osp'].append(locations) + + #### Check for types of spanins + embedded, overlap, separate = spaninProximity( + isp=location_data['isp'], + osp=location_data['osp'], + max_dist=args.max_isp_osp_distance * 3 + ) + + SPANIN_TYPES['EMBEDDED'] = embedded + SPANIN_TYPES['OVERLAPPED'] = overlap + SPANIN_TYPES['SEPARATED'] = separate + + #for spanin_type, spanin in SPANIN_TYPES.items(): + # s = 0 + # for sequence in spanin.values(): + # s += len(sequence) + # SPANIN_TYPES[spanin_type]['amount'] = s + # SPANIN_TYPES[spanin_type]['unique'] = len(spanin.keys()) + + #check_for_unique_spanins(SPANIN_TYPES) + spanins = reconfigure_dict(SPANIN_TYPES) + spanins, pair_dict = check_for_uniques(spanins) + #print(pair_dict) + with args.summary_txt as f: + for each_spanin_type, spanin_data in spanins.items(): + try: + if each_spanin_type not in ["total_amount","total_unique"]: + #print(each_spanin_type) + #print(each_spanin_type) + f.write("=~~~~~= "+str(each_spanin_type) +" Spanin Candidate Statistics =~~~~~=\n") + f.writelines("Total Candidate Pairs = "+str(spanin_data['amount'])+"\n") + f.writelines("Total Unique Pairs = "+str(spanin_data['uniques'])+"\n") + if each_spanin_type == "EMBEDDED": + for k, v in SPANIN_TYPES['EMBEDDED'].items(): + #print(k) + f.writelines(""+str(k)+" ==> Amount of corresponding candidate o-spanins(s): "+str(len(v))+"\n") + if each_spanin_type == "SEPARATED": + for k, v in SPANIN_TYPES['SEPARATED'].items(): + f.writelines(""+str(k)+ " ==> Amount of corresponding candidate o-spanins(s): "+str(len(v))+"\n") + if each_spanin_type == "OVERLAPPED": + for k, v in SPANIN_TYPES['OVERLAPPED'].items(): + f.writelines(""+str(k)+" ==> Amount of corresponding candidate o-spanins(s): "+str(len(v))+"\n") + except TypeError: + continue + f.write("\n=~~~~~= Tally from ALL spanin types =~~~~~=\n") + f.writelines("Total Candidates = "+str(spanins['total_amount'])+"\n") + f.writelines("Total Unique Candidate Pairs = "+str(spanins['total_unique'])+"\n") + + args.putative_isp_fasta_file = open(args.putative_isp_fasta_file.name, "r") + isp_full = tuple_fasta(args.putative_isp_fasta_file) + + args.putative_osp_fasta_file = open(args.putative_osp_fasta_file.name, "r") + osp_full = tuple_fasta(args.putative_osp_fasta_file) + + #print(isp_full) + isp_seqs = [] + osp_seqs = [] + for isp_tupe in isp_full: + #print(isp_tupe) + for pisp, posp in embedded.items(): + #print(f"ISP = searching for {pisp} in {isp_tupe[0]}") + if re.search(("("+str(pisp)+")\D"), isp_tupe[0]): + #print(isp_tupe[0]) + #print(peri_count) + peri_count = str.split(isp_tupe[0],"~=")[1] + isp_seqs.append((pisp,isp_tupe[1],peri_count)) + #print(isp_seqs) + for osp_tupe in osp_full: + for pisp, posp in embedded.items(): + for data in posp: + #print(f"OSP = searching for {data[3]} in {osp_tupe[0]}, coming from this object: {data}") + if re.search(("("+str(data[3])+")\D"), osp_tupe[0]): + peri_count = str.split(osp_tupe[0],"~=")[1] + osp_seqs.append((data[3],osp_tupe[1],peri_count)) + + with args.embedded_txt as f: + f.write("================ embedded spanin candidates =================\n") + f.write("isp\tisp_start\tisp_end\tosp\tosp_start\tosp_end\tstrand\tpair_number\n") + if embedded != {}: + #print(embedded) + for pisp, posp in embedded.items(): + #print(f"{pisp} - {posp}") + f.write(pisp + "\n") + for each_posp in posp: + #print(posp) + f.write( + "\t{}\t{}\t{}\t{}\t{}\t{}\t".format( + each_posp[1], + each_posp[2], + each_posp[3], + each_posp[4], + each_posp[5], + each_posp[6], + ) + ) + if each_posp[6] == "+": + if each_posp[2] in pair_dict['pairs']['pair_number'].keys(): + f.write(""+str(pair_dict['pairs']['pair_number'][each_posp[2]])+"\n") + elif each_posp[6] == "-": + if each_posp[1] in pair_dict['pairs']['pair_number'].keys(): + f.write(""+str(pair_dict['pairs']['pair_number'][each_posp[1]])+"\n") + else: + f.write("nothing found") + + with open(args.embedded_txt.name, "a") as f: + f.write("\n================= embedded candidate sequences ================\n") + f.write("======================= isp ==========================\n\n") + for isp_data in isp_seqs: + #print(isp_data) + f.write(">isp_orf::{}-peri_count~={}\n{}\n".format(isp_data[0],isp_data[2],lineWrapper(isp_data[1]))) + f.write("\n======================= osp ========================\n\n") + for osp_data in osp_seqs: + f.write(">osp_orf::{}-peri_count~={}\n{}\n".format(osp_data[0],osp_data[2],lineWrapper(osp_data[1]))) + + args.putative_isp_fasta_file = open(args.putative_isp_fasta_file.name, "r") + isp_full = tuple_fasta(args.putative_isp_fasta_file) + + args.putative_osp_fasta_file = open(args.putative_osp_fasta_file.name, "r") + osp_full = tuple_fasta(args.putative_osp_fasta_file) + + isp_seqs = [] + osp_seqs = [] + for isp_tupe in isp_full: + peri_count = str.split(isp_tupe[0],"~=")[1] + for pisp, posp in overlap.items(): + if re.search(("("+str(pisp)+")\D"), isp_tupe[0]): + peri_count = str.split(isp_tupe[0],"~=")[1] + isp_seqs.append((pisp,isp_tupe[1],peri_count)) + + for osp_tupe in osp_full: + for pisp, posp in overlap.items(): + for data in posp: + if re.search(("("+str(data[3])+")\D"), osp_tupe[0]): + peri_count = str.split(osp_tupe[0],"~=")[1] + osp_seqs.append((data[3],osp_tupe[1],peri_count)) + + + + with args.overlap_txt as f: + f.write("================ overlap spanin candidates =================\n") + f.write("isp\tisp_start\tisp_end\tosp\tosp_start\tosp_end\tstrand\tpair_number\n") + if overlap != {}: + for pisp, posp in overlap.items(): + f.write(pisp + "\n") + for each_posp in posp: + f.write( + "\t{}\t{}\t{}\t{}\t{}\t{}\t".format( + each_posp[1], + each_posp[2], + each_posp[3], + each_posp[4], + each_posp[5], + each_posp[6], + ) + ) + if each_posp[6] == "+": + if each_posp[2] in pair_dict['pairs']['pair_number'].keys(): + #print('ovl ; +') + f.write(""+str(pair_dict['pairs']['pair_number'][each_posp[2]])+"\n") + elif each_posp[6] == "-": + if each_posp[1] in pair_dict['pairs']['pair_number'].keys(): + f.write(""+str(pair_dict['pairs']['pair_number'][each_posp[1]])+"\n") + else: + f.write("nothing found") + + with open(args.overlap_txt.name, "a") as f: + #print(isp_seqs) + f.write("\n================= overlap candidate sequences ================\n") + f.write("======================= isp ==========================\n\n") + for isp_data in isp_seqs: + f.write(">isp_orf::{}-pericount~={}\n{}\n".format(isp_data[0],isp_data[2],lineWrapper(isp_data[1]))) + f.write("\n======================= osp ========================\n\n") + for osp_data in osp_seqs: + f.write(">osp_orf::{}-pericount~={}\n{}\n".format(osp_data[0],osp_data[2],lineWrapper(osp_data[1]))) + + args.putative_isp_fasta_file = open(args.putative_isp_fasta_file.name, "r") + isp_full = tuple_fasta(args.putative_isp_fasta_file) + args.putative_osp_fasta_file = open(args.putative_osp_fasta_file.name, "r") + osp_full = tuple_fasta(args.putative_osp_fasta_file) + + isp_seqs = [] + osp_seqs = [] + for isp_tupe in isp_full: + for pisp, posp in separate.items(): + if re.search(("("+str(pisp)+")\D"), isp_tupe[0]): + peri_count = str.split(isp_tupe[0],"~=")[1] + isp_seqs.append((pisp,isp_tupe[1],peri_count)) + #print(isp_seqs) + for osp_tupe in osp_full: + for pisp, posp in separate.items(): + for data in posp: + if re.search(("("+str(data[3])+")\D"), osp_tupe[0]): + peri_count = str.split(osp_tupe[0],"~=")[1] + osp_seqs.append((data[3],osp_tupe[1],peri_count)) + + with args.separate_txt as f: + f.write("================ separated spanin candidates =================\n") + f.write("isp\tisp_start\tisp_end\tosp\tosp_start\tosp_end\tstrand\tpair_number\n") + if separate != {}: + for pisp, posp in separate.items(): + f.write(pisp + "\n") + for each_posp in posp: + f.write( + "\t{}\t{}\t{}\t{}\t{}\t{}\t".format( + each_posp[1], + each_posp[2], + each_posp[3], + each_posp[4], + each_posp[5], + each_posp[6], + ) + ) + if each_posp[6] == "+": + if each_posp[2] in pair_dict['pairs']['pair_number'].keys(): + f.write(""+str(pair_dict['pairs']['pair_number'][each_posp[2]])+"\n") + elif each_posp[6] == "-": + if each_posp[1] in pair_dict['pairs']['pair_number'].keys(): + f.write(""+str(pair_dict['pairs']['pair_number'][each_posp[1]])+"\n") + else: + f.write("nothing found") + + with open(args.separate_txt.name, "a") as f: + f.write("\n================= separated candidate sequences ================\n") + f.write("======================= isp ==========================\n\n") + for isp_data in isp_seqs: + f.write(">isp_orf::{}-pericount~={}\n{}\n".format(isp_data[0],isp_data[2],lineWrapper(isp_data[1]))) + f.write("\n======================= osp ========================\n\n") + for osp_data in osp_seqs: + f.write(">osp_orf::{}-pericount~={}\n{}\n".format(osp_data[0],osp_data[2],lineWrapper(osp_data[1])))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cpt_find_spanins/findSpanin.xml Fri Jun 17 12:40:59 2022 +0000 @@ -0,0 +1,44 @@ +<?xml version="1.1"?> +<tool id="edu.tamu.cpt2.spanin.findSpanin" name="Find Spanin" version="1.0"> + <description>With the outputs from the ISP and OSP candidate tools, cull the list down to candidate pairs</description> + <macros> + <import>macros.xml</import> + <import>cpt-macros.xml</import> + </macros> + <expand macro="requirements"> + </expand> + <command detect_errors="aggressive"><![CDATA[ +python $__tool_directory__/findSpanin.py +$putative_isp_fasta_file +$putative_osp_fasta_file +--max_isp_osp_distance $max_isp_osp_distance +--embedded_txt $embedded_txt +--overlap_txt $overlap_txt +--separate_txt $separate_txt +--summary_txt $summary_txt +]]></command> + <inputs> + <param label="(putative) i-spanin FASTA file" name="putative_isp_fasta_file" type="data" format="fasta"/> + <param label="(putative) o-spanin FASTA file" name="putative_osp_fasta_file" type="data" format="fasta"/> + <param label="max distance from end of i-spanin to beginning of o-spanin (measured in AAs)" name="max_isp_osp_distance" type="integer" value="10" /> + </inputs> + <outputs> + <data format="txt" name="summary_txt" label="FindSpanin_summary.txt"/> + <data format="tabular" name="embedded_txt" label="embedded_results.txt"/> + <data format="tabular" name="overlap_txt" label="overlap_results.txt"/> + <data format="tabular" name="separate_txt" label="separate_results.txt"/> + </outputs> + <help><![CDATA[ +**What it does** +Compares the protein FASTA files with candidate i-spanins and o-spanins from a genome and matches them into candidate pairs based on position in a strand-aware fashion. + +**INPUT** --> Putative i-spanin and o-spanin protein multiFASTAs (generated from the ISP/OSP Candidate Tools). + +**METHODOLOGY** +Does a pairwise comparison between candidate i-spanins and o-spanins based on their genomic location, and classifies them into the known bimolecular spanin genetic architectures. Classes are: embedded, overlapping, separated, or NOT a potential pair. + +**OUTPUT** --> File with candidate pairs for each bimolecular spanin class and a basic summary statistics file. + +]]></help> + <expand macro="citations-2020" /> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cpt_find_spanins/macros.xml Fri Jun 17 12:40:59 2022 +0000 @@ -0,0 +1,63 @@ +<?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>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cpt_find_spanins/spaninFuncs.py Fri Jun 17 12:40:59 2022 +0000 @@ -0,0 +1,469 @@ +""" +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)