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Commit 2fe7ca8b authored by Jorge Navarro Muñoz's avatar Jorge Navarro Muñoz
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Flex. CDS overlap, minor improvmnts (glocal mode, compute resources) 

- Added another criteria to start glocal expansion: the seed slice
contains a core gene
- Don't include bgcs with unwanted classes very eary on (so don't
calculate domains, align them, keep their distances...)
- If CDSs overlap, allow for an overlap of up to 10% of the shortest CDS
(was causing trouble with some true positive overlapping CDSs)
parent 068ec751
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bigscape.py
View file @ 2fe7ca8b
......@@ -249,7 +249,6 @@ def process_gbk_files(gbk, clusterName, dirpath, current_dir, min_bgc_size, bgc_
bgc_locus_tags.append(fasta_header)
locus_sequences[fasta_header] = prot_seq
locus_coordinates[fasta_header] = (gene_start, gene_end, len(prot_seq))
# TODO: if len(biosynthetic_genes) == 0, traverse record again
......@@ -274,6 +273,18 @@ def process_gbk_files(gbk, clusterName, dirpath, current_dir, min_bgc_size, bgc_
else:
product = "-".join(product_set) # likely a hybrid
# Don't keep this bgc if its type not in valid classes specified by user
# This will avoid redundant tasks like domain detection
subproduct = set()
for p in product.split("-"):
subproduct.add(sort_bgc(p).lower())
if "nrps" in subproduct and ("pksi" in subproduct or "pksother" in subproduct):
subproduct.add("pks-nrp_hybrids")
if len(valid_classes & subproduct) == 0:
if verbose:
print(" Skipping {} (type: {})".format(clusterName, product))
return False
# assuming that the definition field is the same in all records
# product: antiSMASH predicted class of metabolite
......@@ -311,6 +322,10 @@ def process_gbk_files(gbk, clusterName, dirpath, current_dir, min_bgc_size, bgc_
# CDS C. If len(A) > len(B) > len(C) and we first compare A vs B
# and delete A, then B vs C and delete B: would that be a better
# solution than removing B? Could this actually happen?
# TODO What if the overlapping CDS is in the reverse strand?
# maybe it should be kept as it is
# TODO what are the characterized differences in prokarytote
# vs eukaryote CDS overlap?
del_list = set()
for a, b in combinations(bgc_locus_tags, 2):
a_start, a_end, a_len = locus_coordinates[a]
......@@ -319,12 +334,32 @@ def process_gbk_files(gbk, clusterName, dirpath, current_dir, min_bgc_size, bgc_
if b_end <= a_start or b_start >= a_end:
pass
else:
if a_len > b_len:
del_list.add(b)
# calculate overlap
if a_start > b_start:
ov_start = a_start
else:
ov_start = b_start
if a_end < b_end:
ov_end = a_end
else:
del_list.add(a)
ov_end = b_end
overlap_length = ov_end - ov_start
# allow the overlap to be as large as 10% of the
# shortest CDS. Overlap length is in nucleotides
# here, whereas a_len, b_len are protein
# sequence lengths
if overlap_length/3 > 0.1*min(a_len,b_len):
if a_len > b_len:
del_list.add(b)
else:
del_list.add(a)
for locus in del_list:
if verbose:
print(" Removing {} because it overlaps with other ORF".format(locus))
bgc_locus_tags.remove(locus)
with open(outputfile,'w') as fastaHandle:
......@@ -820,7 +855,12 @@ def cluster_distance_lcs(A, B, A_domlist, B_domlist, dcg_A, dcg_b, core_pos_A, c
# Expansion is relatively costly. We ask for a minimum of 3 genes
# for the core overlap before proceeding with expansion.
if sliceLengthA >= 3:
biosynthetic_hit_A = False
for biosynthetic_position in core_pos_A:
if biosynthetic_position >= sliceStartA and biosynthetic_position <= (sliceStartA+sliceLengthA):
biosynthetic_hit_A = True
break
if sliceLengthA >= 3 or biosynthetic_hit_A:
# LEFT SIDE
# Find which bgc has the least genes to the left. If both have the same
# number, find the one that drives the expansion with highest possible score
......@@ -2260,7 +2300,6 @@ if __name__=="__main__":
get_gbk_files(options.inputdir, output_folder, bgc_fasta_folder, int(options.min_bgc_size), options.exclude_gbk_str, bgc_info)
# clusters and sampleDict contain the necessary structure for all-vs-all and sample analysis
clusters = list(genbankDict.keys())
......@@ -2509,57 +2548,50 @@ if __name__=="__main__":
# is activated. We have to open the pfd files to get the gene labels for
# each domain
# We now always have to have this data so the alignments are produced
pfd_dict_domains = defaultdict(int)
orf_keys = {}
for outputbase in baseNames:
DomainCountGene[outputbase] = array('B')
corebiosynthetic_position[outputbase] = array('B')
BGCGeneOrientation[outputbase] = array('b')
pfdFile = os.path.join(pfd_folder, outputbase + ".pfd")
filtered_matrix = [[part.strip() for part in line.split('\t')] for line in open(pfdFile)]
domain_counter = 0
gene_number = 0
gene_label = filtered_matrix[0][-1] # initialize with first label
has_corebio = False
for row in filtered_matrix:
if row[-1] != gene_label:
# we changed to a new gene. Check whether previous has a
# core biosynthetic / anchor domain hit
if has_corebio:
corebiosynthetic_position[outputbase].append(gene_number)
has_corebio = False
if gene_label[-1] == "+":
BGCGeneOrientation[outputbase].append(1)
else:
BGCGeneOrientation[outputbase].append(-1)
gene_label = row[-1] # update current label
gene_number += 1 # advance gene number
# record number of domains in previous gene
DomainCountGene[outputbase].append(domain_counter)
domain_counter = 1 # reset domain counter
#pfd_dict_domains contains the number of domains annotated in the
# pfd file for each orf tag
with open(pfdFile,"r") as pfdf:
for line in pfdf:
pfd_dict_domains[line.strip().split("\t")[-1]] += 1
# extract the orf number from the tag and use it to traverse the BGC
for orf in pfd_dict_domains.keys():
orf_num = int(orf.split(":")[0].split("_ORF")[1])
orf_keys[orf_num] = orf
orf_num = 0
for orf_key in sorted(orf_keys.keys()):
orf = orf_keys[orf_key]
if orf[-1] == "+":
BGCGeneOrientation[outputbase].append(1)
else:
domain_counter += 1 # increase domain counter
BGCGeneOrientation[outputbase].append(-1)
# TODO: if len(corebiosynthetic_position[outputbase]) == 0
# do something with the list of pfam ids. Specifically, mark
# (in this case TODO or always?) as biosynthetic genes, the ones that contain
# domains from a special list. This list of special domains
# comes from predicted domains within the CDSs marked as 'sec_met'
# by antismash
if row[-1] in bgc_info[outputbase].biosynthetic_genes:
has_corebio = True
# There is no transition when we finish, so analyze last gene
if gene_label[-1] == "+":
BGCGeneOrientation[outputbase].append(1)
else:
BGCGeneOrientation[outputbase].append(-1)
DomainCountGene[outputbase].append(domain_counter)
if has_corebio:
corebiosynthetic_position[outputbase].append(gene_number)
DomainCountGene[outputbase].append(pfd_dict_domains[orf])
if orf in bgc_info[outputbase].biosynthetic_genes:
corebiosynthetic_position[outputbase].append(orf_num)
orf_num += 1
pfd_dict_domains.clear()
orf_keys.clear()
## TODO: if len(corebiosynthetic_position[outputbase]) == 0
## do something with the list of pfam ids. Specifically, mark
## (in this case TODO or always?) as biosynthetic genes, the ones that contain
## domains from a special list. This list of special domains
## comes from predicted domains within the CDSs marked as 'sec_met'
## by antismash
# Get the ordered list of domains
print(" Reading the ordered list of domains from the pfs files")
for outputbase in baseNames:
......@@ -2769,6 +2801,8 @@ if __name__=="__main__":
if predicted_class.lower() in valid_classes:
mix_set.append(clusterIdx)
print("\n {} ({} BGCs)".format(bgc_class, str(len(mix_set))))
# create output directory
create_directory(os.path.join(network_files_folder, "mix"), " Mix", False)
......
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