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sqanti3_qc.py
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sqanti3_qc.py
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#!/usr/bin/env python3
# SQANTI: Structural and Quality Annotation of Novel Transcript Isoforms
# Authors: Lorena de la Fuente, Hector del Risco, Cecile Pereira and Manuel Tardaguila
# Modified by Liz (etseng@pacb.com) as SQANTI2/3 versions
# Modified by Fran (francisco.pardo.palacios@gmail.com) currently as SQANTI3 version (05/15/2020)
__author__ = "etseng@pacb.com"
__version__ = '5.2' # Python 3.7
import pdb
import os, re, sys, subprocess, timeit, glob, copy
import shutil
import distutils.spawn
import itertools
import bisect
import argparse
import math
import csv
import numpy as np
from statistics import mean
from collections import defaultdict, Counter, namedtuple
from collections.abc import Iterable
from csv import DictWriter, DictReader
from multiprocessing import Process
utilitiesPath = os.path.join(os.path.dirname(os.path.realpath(__file__)), "utilities")
sys.path.insert(0, utilitiesPath)
import pandas as pd
from rt_switching import rts
from indels_annot import calc_indels_from_sam
from short_reads import *
from cupcake.sequence.err_correct_w_genome import err_correct
from cupcake.sequence.sam_to_gff3 import convert_sam_to_gff3
from cupcake.sequence.STAR import STARJunctionReader
from cupcake.sequence.BED import LazyBEDPointReader
import cupcake.sequence.coordinate_mapper as cordmap
from cupcake.tofu.compare_junctions import compare_junctions
from cupcake.tofu.filter_away_subset import read_count_file
from cupcake.io.BioReaders import GMAPSAMReader
from cupcake.io.GFF import collapseGFFReader, write_collapseGFF_format
try:
from Bio.Seq import Seq
from Bio import SeqIO
from Bio.SeqRecord import SeqRecord
except ImportError:
print("Unable to import Biopython! Please make sure Biopython is installed.", file=sys.stderr)
sys.exit(-1)
try:
from bx.intervals import Interval, IntervalTree
except ImportError:
print("Unable to import bx-python! Please make sure bx-python is installed.", file=sys.stderr)
sys.exit(-1)
try:
from BCBio import GFF as BCBio_GFF
except ImportError:
print("Unable to import BCBio! Please make sure bcbiogff is installed.", file=sys.stderr)
sys.exit(-1)
GMAP_CMD = "gmap --cross-species -n 1 --max-intronlength-middle=2000000 --max-intronlength-ends=2000000 -L 3000000 -f samse -t {cpus} -D {dir} -d {name} -z {sense} {i} > {o}"
#MINIMAP2_CMD = "minimap2 -ax splice --secondary=no -C5 -O6,24 -B4 -u{sense} -t {cpus} {g} {i} > {o}"
MINIMAP2_CMD = "minimap2 -ax splice --secondary=no -C5 -u{sense} -t {cpus} {g} {i} > {o}"
DESALT_CMD = "deSALT aln {dir} {i} -t {cpus} -x ccs -o {o}"
ULTRA_CMD = "uLTRA pipeline {g} {a} {i} {o_dir} --t {cpus} --prefix {prefix} --isoseq"
GMSP_PROG = os.path.join(utilitiesPath, "gmst", "gmst.pl")
GMST_CMD = "perl " + GMSP_PROG + " -faa --strand direct --fnn --output {o} {i}"
GTF2GENEPRED_PROG = os.path.join(utilitiesPath,"gtfToGenePred")
GFFREAD_PROG = "gffread"
if distutils.spawn.find_executable(GTF2GENEPRED_PROG) is None:
print("Cannot find executable {0}. Abort!".format(GTF2GENEPRED_PROG), file=sys.stderr)
sys.exit(-1)
if distutils.spawn.find_executable(GFFREAD_PROG) is None:
print("Cannot find executable {0}. Abort!".format(GFFREAD_PROG), file=sys.stderr)
sys.exit(-1)
seqid_rex1 = re.compile('PB\.(\d+)\.(\d+)$')
seqid_rex2 = re.compile('PB\.(\d+)\.(\d+)\|\S+')
seqid_fusion = re.compile("PBfusion\.(\d+)\.(\d+)\S*")
FIELDS_JUNC = ['isoform', 'chrom', 'strand', 'junction_number', 'genomic_start_coord',
'genomic_end_coord', 'transcript_coord', 'junction_category',
'start_site_category', 'end_site_category', 'diff_to_Ref_start_site',
'diff_to_Ref_end_site', 'bite_junction', 'splice_site', 'canonical',
'RTS_junction', 'indel_near_junct',
'phyloP_start', 'phyloP_end', 'sample_with_cov', "total_coverage_unique", "total_coverage_multi"] #+coverage_header
FIELDS_CLASS = ['isoform', 'chrom', 'strand', 'length', 'exons', 'structural_category',
'associated_gene', 'associated_transcript', 'ref_length', 'ref_exons',
'diff_to_TSS', 'diff_to_TTS', 'diff_to_gene_TSS', 'diff_to_gene_TTS',
'subcategory', 'RTS_stage', 'all_canonical',
'min_sample_cov', 'min_cov', 'min_cov_pos', 'sd_cov', 'FL', 'n_indels',
'n_indels_junc', 'bite', 'iso_exp', 'gene_exp', 'ratio_exp',
'FSM_class', 'coding', 'ORF_length', 'CDS_length', 'CDS_start',
'CDS_end', 'CDS_genomic_start', 'CDS_genomic_end', 'predicted_NMD',
'perc_A_downstream_TTS', 'seq_A_downstream_TTS',
'dist_to_CAGE_peak', 'within_CAGE_peak',
'dist_to_polyA_site', 'within_polyA_site',
'polyA_motif', 'polyA_dist', 'polyA_motif_found', 'ORF_seq', 'ratio_TSS']
RSCRIPTPATH = distutils.spawn.find_executable('Rscript')
RSCRIPT_REPORT = '/report_qc/SQANTI3_report.R'
if os.system(RSCRIPTPATH + " --version")!=0:
print("Rscript executable not found! Abort!", file=sys.stderr)
sys.exit(-1)
def get_split_dir(args):
split_prefix=os.path.join(os.path.abspath(args.dir), args.output)
split_directory = split_prefix+'_splits/'
return split_directory
class genePredReader(object):
def __init__(self, filename):
self.f = open(filename)
def __iter__(self):
return self
def __next__(self):
line = self.f.readline().strip()
if len(line) == 0:
raise StopIteration
return genePredRecord.from_line(line)
class genePredRecord(object):
def __init__(self, id, chrom, strand, txStart, txEnd, cdsStart, cdsEnd, exonCount, exonStarts, exonEnds, gene=None):
self.id = id
self.chrom = chrom
self.strand = strand
self.txStart = txStart # 1-based start
self.txEnd = txEnd # 1-based end
self.cdsStart = cdsStart # 1-based start
self.cdsEnd = cdsEnd # 1-based end
self.exonCount = exonCount
self.exonStarts = exonStarts # 0-based starts
self.exonEnds = exonEnds # 1-based ends
self.gene = gene
self.length = 0
self.exons = []
for s,e in zip(exonStarts, exonEnds):
self.length += e-s
self.exons.append(Interval(s, e))
# junctions are stored (1-based last base of prev exon, 1-based first base of next exon)
self.junctions = [(self.exonEnds[i],self.exonStarts[i+1]) for i in range(self.exonCount-1)]
@property
def segments(self):
return self.exons
@classmethod
def from_line(cls, line):
raw = line.strip().split('\t')
return cls(id=raw[0],
chrom=raw[1],
strand=raw[2],
txStart=int(raw[3]),
txEnd=int(raw[4]),
cdsStart=int(raw[5]),
cdsEnd=int(raw[6]),
exonCount=int(raw[7]),
exonStarts=[int(x) for x in raw[8][:-1].split(',')], #exonStarts string has extra , at end
exonEnds=[int(x) for x in raw[9][:-1].split(',')], #exonEnds string has extra , at end
gene=raw[11] if len(raw)>=12 else None,
)
def get_splice_site(self, genome_dict, i):
"""
Return the donor-acceptor site (ex: GTAG) for the i-th junction
:param i: 0-based junction index
:param genome_dict: dict of chrom --> SeqRecord
:return: splice site pattern, ex: "GTAG", "GCAG" etc
"""
assert 0 <= i < self.exonCount-1
d = self.exonEnds[i]
a = self.exonStarts[i+1]
seq_d = genome_dict[self.chrom].seq[d:d+2]
seq_a = genome_dict[self.chrom].seq[a-2:a]
if self.strand == '+':
return (str(seq_d)+str(seq_a)).upper()
else:
return (str(seq_a.reverse_complement())+str(seq_d.reverse_complement())).upper()
class myQueryTranscripts:
def __init__(self, id, tss_diff, tts_diff, num_exons, length, str_class, subtype=None,
genes=None, transcripts=None, chrom=None, strand=None, bite ="NA",
RT_switching ="????", canonical="NA", min_cov ="NA",
min_cov_pos ="NA", min_samp_cov="NA", sd ="NA", FL ="NA", FL_dict={},
nIndels ="NA", nIndelsJunc ="NA", proteinID=None,
ORFlen="NA", CDS_start="NA", CDS_end="NA",
CDS_genomic_start="NA", CDS_genomic_end="NA",
ORFseq="NA",
is_NMD="NA",
isoExp ="NA", geneExp ="NA", coding ="non_coding",
refLen ="NA", refExons ="NA",
refStart = "NA", refEnd = "NA",
q_splicesite_hit = 0,
q_exon_overlap = 0,
FSM_class = None, percAdownTTS = None, seqAdownTTS=None,
dist_CAGE='NA', within_CAGE='NA',
dist_polyA_site='NA', within_polyA_site='NA',
polyA_motif='NA', polyA_dist='NA',
polyA_motif_found='NA', ratio_TSS='NA'):
self.id = id
self.tss_diff = tss_diff # distance to TSS of best matching ref
self.tts_diff = tts_diff # distance to TTS of best matching ref
self.tss_gene_diff = 'NA' # min distance to TSS of all genes matching the ref
self.tts_gene_diff = 'NA' # min distance to TTS of all genes matching the ref
self.genes = genes if genes is not None else []
self.AS_genes = set() # ref genes that are hit on the opposite strand
self.transcripts = transcripts if transcripts is not None else []
self.num_exons = num_exons
self.length = length
self.str_class = str_class # structural classification of the isoform
self.chrom = chrom
self.strand = strand
self.subtype = subtype
self.RT_switching= RT_switching
self.canonical = canonical
self.min_samp_cov = min_samp_cov
self.min_cov = min_cov
self.min_cov_pos = min_cov_pos
self.sd = sd
self.proteinID = proteinID
self.ORFlen = ORFlen
self.ORFseq = ORFseq
self.CDS_start = CDS_start
self.CDS_end = CDS_end
self.coding = coding
self.CDS_genomic_start = CDS_genomic_start # 1-based genomic coordinate of CDS start - strand aware
self.CDS_genomic_end = CDS_genomic_end # 1-based genomic coordinate of CDS end - strand aware
self.is_NMD = is_NMD # (TRUE,FALSE) for NMD if is coding, otherwise "NA"
self.FL = FL # count for a single sample
self.FL_dict = FL_dict # dict of sample -> FL count
self.nIndels = nIndels
self.nIndelsJunc = nIndelsJunc
self.isoExp = isoExp
self.geneExp = geneExp
self.refLen = refLen
self.refExons = refExons
self.refStart = refStart
self.refEnd = refEnd
self.q_splicesite_hit = q_splicesite_hit
self.q_exon_overlap = q_exon_overlap
self.FSM_class = FSM_class
self.bite = bite
self.percAdownTTS = percAdownTTS
self.seqAdownTTS = seqAdownTTS
self.dist_CAGE = dist_CAGE
self.within_CAGE = within_CAGE
self.within_polyA_site = within_polyA_site
self.dist_polyA_site = dist_polyA_site # distance to the closest polyA site (--polyA_peak, BEF file)
self.polyA_motif = polyA_motif
self.polyA_dist = polyA_dist # distance to the closest polyA motif (--polyA_motif_list, 6mer motif list)
self.polyA_motif_found = polyA_motif_found # boolean output for polyA motif
self.ratio_TSS = ratio_TSS
def get_total_diff(self):
return abs(self.tss_diff)+abs(self.tts_diff)
def modify(self, ref_transcript, ref_gene, tss_diff, tts_diff, refLen, refExons):
self.transcripts = [ref_transcript]
self.genes = [ref_gene]
self.tss_diff = tss_diff
self.tts_diff = tts_diff
self.refLen = refLen
self.refExons = refExons
def geneName(self):
geneName = "_".join(set(self.genes))
return geneName
def ratioExp(self):
if self.geneExp == 0 or self.geneExp == "NA":
return "NA"
else:
ratio = float(self.isoExp)/float(self.geneExp)
return(ratio)
def CDSlen(self):
if self.coding == "coding":
return(str(int(self.CDS_end) - int(self.CDS_start) + 1))
else:
return("NA")
def __str__(self):
return "%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" % (self.chrom, self.strand,
str(self.length), str(self.num_exons),
str(self.str_class), "_".join(set(self.genes)),
self.id, str(self.refLen), str(self.refExons),
str(self.tss_diff), str(self.tts_diff),
self.subtype, self.RT_switching,
self.canonical, str(self.min_samp_cov),
str(self.min_cov), str(self.min_cov_pos),
str(self.sd), str(self.FL), str(self.nIndels),
str(self.nIndelsJunc), self.bite, str(self.isoExp),
str(self.geneExp), str(self.ratioExp()),
self.FSM_class, self.coding, str(self.ORFlen),
str(self.CDSlen()), str(self.CDS_start), str(self.CDS_end),
str(self.CDS_genomic_start), str(self.CDS_genomic_end), str(self.is_NMD),
str(self.percAdownTTS),
str(self.seqAdownTTS),
str(self.dist_CAGE),
str(self.within_CAGE),
str(self.dist_polyA_site),
str(self.within_polyA_site),
str(self.polyA_motif),
str(self.polyA_dist),str(self.polyA_motif_found), str(self.ratio_TSS))
def as_dict(self):
d = {'isoform': self.id,
'chrom': self.chrom,
'strand': self.strand,
'length': self.length,
'exons': self.num_exons,
'structural_category': self.str_class,
'associated_gene': "_".join(set(self.genes)),
'associated_transcript': "_".join(set(self.transcripts)),
'ref_length': self.refLen,
'ref_exons': self.refExons,
'diff_to_TSS': self.tss_diff,
'diff_to_TTS': self.tts_diff,
'diff_to_gene_TSS': self.tss_gene_diff,
'diff_to_gene_TTS': self.tts_gene_diff,
'subcategory': self.subtype,
'RTS_stage': self.RT_switching,
'all_canonical': self.canonical,
'min_sample_cov': self.min_samp_cov,
'min_cov': self.min_cov,
'min_cov_pos': self.min_cov_pos,
'sd_cov': self.sd,
'FL': self.FL,
'n_indels': self.nIndels,
'n_indels_junc': self.nIndelsJunc,
'bite': self.bite,
'iso_exp': self.isoExp,
'gene_exp': self.geneExp,
'ratio_exp': self.ratioExp(),
'FSM_class': self.FSM_class,
'coding': self.coding,
'ORF_length': self.ORFlen,
'ORF_seq': self.ORFseq,
'CDS_length': self.CDSlen(),
'CDS_start': self.CDS_start,
'CDS_end': self.CDS_end,
'CDS_genomic_start': self.CDS_genomic_start,
'CDS_genomic_end': self.CDS_genomic_end,
'predicted_NMD': self.is_NMD,
'perc_A_downstream_TTS': self.percAdownTTS,
'seq_A_downstream_TTS': self.seqAdownTTS,
'dist_to_CAGE_peak': self.dist_CAGE,
'within_CAGE_peak': self.within_CAGE,
'dist_to_polyA_site': self.dist_polyA_site,
'within_polyA_site': self.within_polyA_site,
'polyA_motif': self.polyA_motif,
'polyA_dist': self.polyA_dist,
'polyA_motif_found':self.polyA_motif_found,
'ratio_TSS' : self.ratio_TSS
}
for sample,count in self.FL_dict.items():
d["FL."+sample] = count
return d
class myQueryProteins:
def __init__(self, cds_start, cds_end, orf_length, orf_seq=None, proteinID="NA"):
self.orf_length = orf_length
self.cds_start = cds_start # 1-based start on transcript
self.cds_end = cds_end # 1-based end on transcript (stop codon), ORF is seq[cds_start-1:cds_end].translate()
self.cds_genomic_start = None # 1-based genomic start of ORF, if - strand, is greater than end
self.cds_genomic_end = None # 1-based genomic end of ORF
self.orf_seq = orf_seq
self.proteinID = proteinID
def write_collapsed_GFF_with_CDS(isoforms_info, input_gff, output_gff):
"""
Augment a collapsed GFF with CDS information
*NEW* Also, change the "gene_id" field to use the classification result
:param isoforms_info: dict of id -> QueryTranscript
:param input_gff: input GFF filename
:param output_gff: output GFF filename
"""
with open(output_gff, 'w') as f:
reader = collapseGFFReader(input_gff)
for r in reader:
r.geneid = isoforms_info[r.seqid].geneName() # set the gene name
s = isoforms_info[r.seqid].CDS_genomic_start # could be 'NA'
e = isoforms_info[r.seqid].CDS_genomic_end # could be 'NA'
r.cds_exons = []
if s!='NA' and e!='NA': # has ORF prediction for this isoform
if r.strand == '+':
assert s < e
s = s - 1 # make it 0-based
else:
assert e < s
s, e = e, s
s = s - 1 # make it 0-based
for i,exon in enumerate(r.ref_exons):
if exon.end > s: break
r.cds_exons = [Interval(s, min(e,exon.end))]
for exon in r.ref_exons[i+1:]:
if exon.start > e: break
r.cds_exons.append(Interval(exon.start, min(e, exon.end)))
write_collapseGFF_format(f, r)
def get_corr_filenames(args, dir=None):
d = dir if dir is not None else args.dir
corrPathPrefix = os.path.join(d, args.output)
corrGTF = corrPathPrefix +"_corrected.gtf"
corrSAM = corrPathPrefix +"_corrected.sam"
corrFASTA = corrPathPrefix +"_corrected.fasta"
corrORF = corrPathPrefix +"_corrected.faa"
return corrGTF, corrSAM, corrFASTA, corrORF
def get_isoform_hits_name(args, dir=None):
d = dir if dir is not None else args.dir
corrPathPrefix = os.path.join(d, args.output)
isoform_hits_name = corrPathPrefix +"_isoform_hits.txt"
return isoform_hits_name
def get_class_junc_filenames(args, dir=None):
d = dir if dir is not None else args.dir
outputPathPrefix = os.path.join(d, args.output)
outputClassPath = outputPathPrefix + "_classification.txt"
outputJuncPath = outputPathPrefix + "_junctions.txt"
return outputClassPath, outputJuncPath
def get_omitted_name(args, dir=None):
d = dir if dir is not None else args.dir
corrPathPrefix = os.path.join(d, args.output)
omitted_name = corrPathPrefix +"_omitted_due_to_min_ref_len.txt"
return omitted_name
def correctionPlusORFpred(args, genome_dict):
"""
Use the reference genome to correct the sequences (unless a pre-corrected GTF is given)
"""
global corrORF
global corrGTF
global corrSAM
global corrFASTA
corrGTF, corrSAM, corrFASTA, corrORF = get_corr_filenames(args)
p = os.path.splitext(os.path.basename(corrSAM))[0]
n_cpu = max(1, args.cpus // args.chunks)
# Step 1. IF GFF or GTF is provided, make it into a genome-based fasta
# IF sequence is provided, align as SAM then correct with genome
if os.path.exists(corrFASTA):
print("Error corrected FASTA {0} already exists. Using it...".format(corrFASTA), file=sys.stderr)
else:
if args.fasta:
if os.path.exists(corrSAM):
print("Aligned SAM {0} already exists. Using it...".format(corrSAM), file=sys.stderr)
else:
if args.aligner_choice == "gmap":
print("****Aligning reads with GMAP...", file=sys.stdout)
cmd = GMAP_CMD.format(cpus=n_cpu,
dir=os.path.dirname(args.gmap_index),
name=os.path.basename(args.gmap_index),
sense=args.sense,
i=args.isoforms,
o=corrSAM)
elif args.aligner_choice == "minimap2":
print("****Aligning reads with Minimap2...", file=sys.stdout)
cmd = MINIMAP2_CMD.format(cpus=n_cpu,
sense=args.sense,
g=args.genome,
i=args.isoforms,
o=corrSAM)
elif args.aligner_choice == "deSALT":
print("****Aligning reads with deSALT...", file=sys.stdout)
cmd = DESALT_CMD.format(cpus=n_cpu,
dir=args.gmap_index,
i=args.isoforms,
o=corrSAM)
elif args.aligner_choice == "uLTRA":
print("****Aligning reads with uLTRA...", file=sys.stdout)
cmd = ULTRA_CMD.format(cpus=n_cpu,
prefix= "../" + p,
g=args.genome,
a=args.annotation,
i=args.isoforms,
o_dir=args.dir + "/uLTRA_out/")
if subprocess.check_call(cmd, shell=True)!=0:
print("ERROR running alignment cmd: {0}".format(cmd), file=sys.stderr)
sys.exit(-1)
# error correct the genome (input: corrSAM, output: corrFASTA)
err_correct(args.genome, corrSAM, corrFASTA, genome_dict=genome_dict)
# convert SAM to GFF --> GTF
convert_sam_to_gff3(corrSAM, corrGTF+'.tmp', source=os.path.basename(args.genome).split('.')[0]) # convert SAM to GFF3
cmd = "{p} {o}.tmp -T -o {o}".format(o=corrGTF, p=GFFREAD_PROG)
if subprocess.check_call(cmd, shell=True)!=0:
print("ERROR running cmd: {0}".format(cmd), file=sys.stderr)
sys.exit(-1)
else:
print("Skipping aligning of sequences because GTF file was provided.", file=sys.stdout)
ind = 0
with open(args.isoforms, 'r') as isoforms_gtf:
for line in isoforms_gtf:
if line[0] != "#" and len(line.split("\t"))!=9:
sys.stderr.write("\nERROR: input isoforms file with not GTF format.\n")
sys.exit()
elif len(line.split("\t"))==9:
ind += 1
if ind == 0:
print("WARNING: GTF has {0} no annotation lines.".format(args.isoforms), file=sys.stderr)
# GFF to GTF (in case the user provides gff instead of gtf)
corrGTF_tpm = corrGTF+".tmp"
try:
subprocess.call([GFFREAD_PROG, args.isoforms , '-T', '-o', corrGTF_tpm])
except (RuntimeError, TypeError, NameError):
sys.stderr.write('ERROR: File %s without GTF/GFF format.\n' % args.isoforms)
raise SystemExit(1)
# check if gtf chromosomes inside genome file
with open(corrGTF, 'w') as corrGTF_out:
with open(corrGTF_tpm, 'r') as isoforms_gtf:
for line in isoforms_gtf:
if line[0] != "#":
chrom = line.split("\t")[0]
type = line.split("\t")[2]
if chrom not in list(genome_dict.keys()):
sys.stderr.write("\nERROR: gtf \"%s\" chromosome not found in genome reference file.\n" % (chrom))
sys.exit()
elif type in ('transcript', 'exon'):
corrGTF_out.write(line)
os.remove(corrGTF_tpm)
if not os.path.exists(corrSAM):
sys.stdout.write("\nIndels will be not calculated since you ran SQANTI3 without alignment step (SQANTI3 with gtf format as transcriptome input).\n")
# GTF to FASTA
subprocess.call([GFFREAD_PROG, corrGTF, '-g', args.genome, '-w', corrFASTA])
# ORF generation
print("**** Predicting ORF sequences...", file=sys.stdout)
gmst_dir = os.path.join(os.path.abspath(args.dir), "GMST")
gmst_pre = os.path.join(gmst_dir, "GMST_tmp")
if not os.path.exists(gmst_dir):
os.makedirs(gmst_dir)
# sequence ID example: PB.2.1 gene_4|GeneMark.hmm|264_aa|+|888|1682
gmst_rex = re.compile('(\S+\t\S+\|GeneMark.hmm)\|(\d+)_aa\|(\S)\|(\d+)\|(\d+)')
orfDict = {} # GMST seq id --> myQueryProteins object
if args.skipORF:
print("WARNING: Skipping ORF prediction because user requested it. All isoforms will be non-coding!", file=sys.stderr)
elif os.path.exists(corrORF):
print("ORF file {0} already exists. Using it....".format(corrORF), file=sys.stderr)
for r in SeqIO.parse(open(corrORF), 'fasta'):
# now process ORFs into myQueryProtein objects
m = gmst_rex.match(r.description)
if m is None:
print("Expected GMST output IDs to be of format '<pbid> gene_4|GeneMark.hmm|<orf>_aa|<strand>|<cds_start>|<cds_end>' but instead saw: {0}! Abort!".format(r.description), file=sys.stderr)
sys.exit(-1)
orf_length = int(m.group(2))
cds_start = int(m.group(4))
cds_end = int(m.group(5))
orfDict[r.id] = myQueryProteins(cds_start, cds_end, orf_length, str(r.seq), proteinID=r.id)
else:
cur_dir = os.path.abspath(os.getcwd())
os.chdir(args.dir)
if args.orf_input is not None:
print("Running ORF prediction of input on {0}...".format(args.orf_input))
cmd = GMST_CMD.format(i=os.path.realpath(args.orf_input), o=gmst_pre)
else:
cmd = GMST_CMD.format(i=corrFASTA, o=gmst_pre)
if subprocess.check_call(cmd, shell=True, cwd=gmst_dir)!=0:
print("ERROR running GMST cmd: {0}".format(cmd), file=sys.stderr)
sys.exit(-1)
os.chdir(cur_dir)
# Modifying ORF sequences by removing sequence before ATG
with open(corrORF, "w") as f:
for r in SeqIO.parse(open(gmst_pre+'.faa'), 'fasta'):
m = gmst_rex.match(r.description)
if m is None:
print("Expected GMST output IDs to be of format '<pbid> gene_4|GeneMark.hmm|<orf>_aa|<strand>|<cds_start>|<cds_end>' but instead saw: {0}! Abort!".format(r.description), file=sys.stderr)
sys.exit(-1)
id_pre = m.group(1)
orf_length = int(m.group(2))
orf_strand = m.group(3)
cds_start = int(m.group(4))
cds_end = int(m.group(5))
pos = r.seq.find('M')
if pos!=-1:
# must modify both the sequence ID and the sequence
orf_length -= pos
cds_start += pos*3
newid = "{0}|{1}_aa|{2}|{3}|{4}".format(id_pre, orf_length, orf_strand, cds_start, cds_end)
newseq = str(r.seq)[pos:]
orfDict[r.id] = myQueryProteins(cds_start, cds_end, orf_length, newseq, proteinID=newid)
f.write(">{0}\n{1}\n".format(newid, newseq))
else:
new_rec = r
orfDict[r.id] = myQueryProteins(cds_start, cds_end, orf_length, str(r.seq), proteinID=r.id)
f.write(">{0}\n{1}\n".format(new_rec.description, new_rec.seq))
if len(orfDict) == 0:
print("WARNING: All input isoforms were predicted as non-coding", file=sys.stderr)
return(orfDict)
def reference_parser(args, genome_chroms):
"""
Read the reference GTF file
:param args:
:param genome_chroms: list of chromosome names from the genome fasta, used for sanity checking
:return: (refs_1exon_by_chr, refs_exons_by_chr, junctions_by_chr, junctions_by_gene)
"""
global referenceFiles
referenceFiles = os.path.join(args.dir, "refAnnotation_"+args.output+".genePred")
print("**** Parsing Reference Transcriptome....", file=sys.stdout)
if os.path.exists(referenceFiles):
print("{0} already exists. Using it.".format(referenceFiles), file=sys.stdout)
else:
## gtf to genePred
if not (args.genename or args.isoAnnotLite):
subprocess.call([GTF2GENEPRED_PROG, args.annotation, referenceFiles, '-genePredExt', '-allErrors', '-ignoreGroupsWithoutExons'])
else:
subprocess.call([GTF2GENEPRED_PROG, args.annotation, referenceFiles, '-genePredExt', '-allErrors', '-ignoreGroupsWithoutExons', '-geneNameAsName2'])
## parse reference annotation
# 1. ignore all miRNAs (< 200 bp)
# 2. separately store single exon and multi-exon references
refs_1exon_by_chr = defaultdict(lambda: IntervalTree()) #
refs_exons_by_chr = defaultdict(lambda: IntervalTree())
# store donors as the exon end (1-based) and acceptor as the exon start (0-based)
# will convert the sets to sorted list later
junctions_by_chr = defaultdict(lambda: {'donors': set(), 'acceptors': set(), 'da_pairs': set()})
# dict of gene name --> set of junctions (don't need to record chromosome)
junctions_by_gene = defaultdict(lambda: set())
# dict of gene name --> list of known begins and ends (begin always < end, regardless of strand)
known_5_3_by_gene = defaultdict(lambda: {'begin':set(), 'end': set()})
for r in genePredReader(referenceFiles):
if r.exonCount == 1:
refs_1exon_by_chr[r.chrom].insert(r.txStart, r.txEnd, r)
known_5_3_by_gene[r.gene]['begin'].add(r.txStart)
known_5_3_by_gene[r.gene]['end'].add(r.txEnd)
else:
refs_exons_by_chr[r.chrom].insert(r.txStart, r.txEnd, r)
# only store junctions for multi-exon transcripts
for d, a in r.junctions:
junctions_by_chr[r.chrom]['donors'].add(d)
junctions_by_chr[r.chrom]['acceptors'].add(a)
junctions_by_chr[r.chrom]['da_pairs'].add((d,a))
junctions_by_gene[r.gene].add((d,a))
known_5_3_by_gene[r.gene]['begin'].add(r.txStart)
known_5_3_by_gene[r.gene]['end'].add(r.txEnd)
# check that all genes' chromosomes are in the genome file
ref_chroms = set(refs_1exon_by_chr.keys()).union(list(refs_exons_by_chr.keys()))
diff = ref_chroms.difference(genome_chroms)
if len(diff) > 0:
print("WARNING: ref annotation contains chromosomes not in genome: {0}\n".format(",".join(diff)), file=sys.stderr)
# convert the content of junctions_by_chr to sorted list
for k in junctions_by_chr:
junctions_by_chr[k]['donors'] = list(junctions_by_chr[k]['donors'])
junctions_by_chr[k]['donors'].sort()
junctions_by_chr[k]['acceptors'] = list(junctions_by_chr[k]['acceptors'])
junctions_by_chr[k]['acceptors'].sort()
junctions_by_chr[k]['da_pairs'] = list(junctions_by_chr[k]['da_pairs'])
junctions_by_chr[k]['da_pairs'].sort()
return dict(refs_1exon_by_chr), dict(refs_exons_by_chr), dict(junctions_by_chr), dict(junctions_by_gene), dict(known_5_3_by_gene)
def isoforms_parser(args):
"""
Parse input isoforms (GTF) to dict (chr --> sorted list)
"""
global queryFile
queryFile = os.path.splitext(corrGTF)[0] +".genePred"
print("**** Parsing Isoforms....", file=sys.stderr)
# gtf to genePred
cmd = GTF2GENEPRED_PROG + " {0} {1} -genePredExt -allErrors -ignoreGroupsWithoutExons".format(\
corrGTF, queryFile)
if subprocess.check_call(cmd, shell=True)!=0:
print("ERROR running cmd: {0}".format(cmd), file=sys.stderr)
sys.exit(-1)
isoforms_list = defaultdict(lambda: []) # chr --> list to be sorted later
for r in genePredReader(queryFile):
isoforms_list[r.chrom].append(r)
for k in isoforms_list:
isoforms_list[k].sort(key=lambda r: r.txStart)
return isoforms_list
def STARcov_parser(coverageFiles): # just valid with unstrand-specific RNA-seq protocols.
"""
:param coverageFiles: comma-separated list of STAR junction output files or a directory containing junction files
:return: list of samples, dict of (chrom,strand) --> (0-based start, 1-based end) --> {dict of sample -> (uniq,multi) reads supporting this junction}
"""
if os.path.isdir(coverageFiles):
cov_files = glob.glob(coverageFiles + "/*SJ.out.tab")
elif coverageFiles.count(',') > 0:
cov_files = coverageFiles.split(",")
else:
cov_files = glob.glob(coverageFiles)
print("Input pattern: {0}.\nThe following files found and to be read as junctions:\n{1}".format(\
coverageFiles, "\n".join(cov_files) ), file=sys.stderr)
cov_by_chrom_strand = defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: (0,0))))
undefined_strand_count = 0
all_read = 0
samples = []
for file in cov_files:
prefix = os.path.basename(file[:file.rfind('.')]) # use this as sample name
samples.append(prefix)
for r in STARJunctionReader(file):
if r.strand == 'NA':
# undefined strand, so we put them in BOTH strands otherwise we'll lose all non-canonical junctions from STAR
cov_by_chrom_strand[(r.chrom, '+')][(r.start, r.end)][prefix] = (r.unique_count, r.multi_count)
cov_by_chrom_strand[(r.chrom, '-')][(r.start, r.end)][prefix] = (r.unique_count, r.multi_count)
undefined_strand_count += 1
else:
cov_by_chrom_strand[(r.chrom, r.strand)][(r.start, r.end)][prefix] = (r.unique_count, r.multi_count)
all_read += 1
print("{0} junctions read. {1} junctions added to both strands because no strand information from STAR.".format(all_read, undefined_strand_count), file=sys.stderr)
return samples, cov_by_chrom_strand
EXP_KALLISTO_HEADERS = ['target_id', 'length', 'eff_length', 'est_counts', 'tpm']
EXP_RSEM_HEADERS = ['transcript_id', 'length', 'effective_length', 'expected_count', 'TPM']
def mergeDict(dict1, dict2):
""" Merge dictionaries to collect info from several files"""
dict3 = {**dict1, **dict2}
for key, value in dict3.items():
if key in dict1 and key in dict2:
dict3[key] = [value , dict1[key]]
return dict3
def flatten(lis):
for item in lis:
if isinstance(item, Iterable) and not isinstance(item, str):
for x in flatten(item):
yield x
else:
yield item
def expression_parser(expressionFile):
"""
Currently accepts expression format: Kallisto or RSEM
:param expressionFile: Kallisto or RSEM
:return: dict of PBID --> TPM
Include the possibility of providing an expression matrix --> first column must be "ID"
"""
if os.path.isdir(expressionFile)==True:
exp_paths = [os.path.join(expressionFile,fn) for fn in next(os.walk(expressionFile))[2]]
else:
exp_paths = expressionFile.split(",")
exp_all = {}
ismatrix = False
for exp_file in exp_paths:
reader = DictReader(open(exp_file), delimiter='\t')
if all(k in reader.fieldnames for k in EXP_KALLISTO_HEADERS):
print("Detected Kallisto expression format. Using 'target_id' and 'tpm' field.", file=sys.stderr)
name_id, name_tpm = 'target_id', 'tpm'
elif all(k in reader.fieldnames for k in EXP_RSEM_HEADERS):
print("Detected RSEM expression format. Using 'transcript_id' and 'TPM' field.", file=sys.stderr)
name_id, name_tpm = 'transcript_id', 'TPM'
elif reader.fieldnames[0]=="ID":
print("Detected expression matrix format")
ismatrix = True
name_id = 'ID'
else:
print("Expected Kallisto or RSEM file format from {0}. Abort!".format(expressionFile), file=sys.stderr)
exp_sample = {}
if ismatrix:
for r in reader:
exp_sample[r[name_id]] = np.average(list(map(float,list(r.values())[1:])))
else:
for r in reader:
exp_sample[r[name_id]] = float(r[name_tpm])
exp_all = mergeDict(exp_all, exp_sample)
exp_dict = {}
if len(exp_paths)>1:
for k in exp_all:
exp_all[k] = list(flatten(exp_all[k]))
exp_dict[k] = mean(exp_all[k])
return exp_dict
else:
exp_dict=exp_all
return exp_dict
def transcriptsKnownSpliceSites(isoform_hits_name, refs_1exon_by_chr, refs_exons_by_chr, start_ends_by_gene, trec, genome_dict, nPolyA):
"""
:param refs_1exon_by_chr: dict of single exon references (chr -> IntervalTree)
:param refs_exons_by_chr: dict of multi exon references (chr -> IntervalTree)
:param trec: id record (genePredRecord) to be compared against reference
:param genome_dict: dict of genome (chrom --> SeqRecord)
:param nPolyA: window size to look for polyA
:return: myQueryTranscripts object that indicates the best reference hit
"""
def calc_overlap(s1, e1, s2, e2):
if s1=='NA' or s2=='NA': return 0
if s1 > s2:
s1, e1, s2, e2 = s2, e2, s1, e1
return max(0, min(e1,e2)-max(s1,s2))
def gene_overlap(ref1, ref2):
if ref1==ref2: return True # same gene, diff isoforms
# return True if the two reference genes overlap
s1, e1 = min(start_ends_by_gene[ref1]['begin']), max(start_ends_by_gene[ref1]['end'])
s2, e2 = min(start_ends_by_gene[ref2]['begin']), max(start_ends_by_gene[ref2]['end'])
if s1 <= s2:
return e1 <= s2
else:
return e2 <= s1
def calc_splicesite_agreement(query_exons, ref_exons):
q_sites = {}
for e in query_exons:
q_sites[e.start] = 0
q_sites[e.end] = 0
for e in ref_exons:
if e.start in q_sites: q_sites[e.start] = 1
if e.end in q_sites: q_sites[e.end] = 1
return sum(q_sites.values())
def calc_exon_overlap(query_exons, ref_exons):
q_bases = {}
for e in query_exons:
for b in range(e.start, e.end): q_bases[b] = 0
for e in ref_exons:
for b in range(e.start, e.end):
if b in q_bases: q_bases[b] = 1
return sum(q_bases.values())
def get_diff_tss_tts(trec, ref):
if trec.strand == '+':
diff_tss = trec.txStart - ref.txStart
diff_tts = ref.txEnd - trec.txEnd
else:
diff_tts = trec.txStart - ref.txStart
diff_tss = ref.txEnd - trec.txEnd
return diff_tss, diff_tts
def get_gene_diff_tss_tts(isoform_hit):
# now that we know the reference (isoform) it hits
# add the nearest start/end site for that gene (all isoforms of the gene)
nearest_start_diff, nearest_end_diff = float('inf'), float('inf')
for ref_gene in isoform_hit.genes:
for x in start_ends_by_gene[ref_gene]['begin']:
d = trec.txStart - x
if abs(d) < abs(nearest_start_diff):
nearest_start_diff = d
for x in start_ends_by_gene[ref_gene]['end']:
d = trec.txEnd - x
if abs(d) < abs(nearest_end_diff):
nearest_end_diff = d
if trec.strand == '+':
isoform_hit.tss_gene_diff = nearest_start_diff if nearest_start_diff!=float('inf') else 'NA'
isoform_hit.tts_gene_diff = nearest_end_diff if nearest_end_diff!=float('inf') else 'NA'
else:
isoform_hit.tss_gene_diff = -nearest_end_diff if nearest_start_diff!=float('inf') else 'NA'
isoform_hit.tts_gene_diff = -nearest_start_diff if nearest_end_diff!=float('inf') else 'NA'
def categorize_incomplete_matches(trec, ref):
"""
intron_retention --- at least one trec exon covers at least two adjacent ref exons
complete --- all junctions agree and is not IR
5prime_fragment --- all junctions agree but trec has less 5' exons. The isoform is a 5' fragment of the reference transcript
3prime_fragment --- all junctions agree but trec has less 3' exons. The isoform is a 3' fragment of the reference transcript
internal_fragment --- all junctions agree but trec has less 5' and 3' exons
"""
# check intron retention
ref_exon_tree = IntervalTree()
for i,e in enumerate(ref.exons): ref_exon_tree.insert(e.start, e.end, i)
for e in trec.exons:
if len(ref_exon_tree.find(e.start, e.end)) > 1: # multiple ref exons covered
return "intron_retention"
agree_front = trec.junctions[0]==ref.junctions[0]
agree_end = trec.junctions[-1]==ref.junctions[-1]
if agree_front:
if agree_end:
return "complete"
else: # front agrees, end does not
return ("5prime_fragment" if trec.strand=='+' else '3prime_fragment')
else:
if agree_end: # front does not agree, end agrees
return ("3prime_fragment" if trec.strand=='+' else '5prime_fragment')
else:
return "internal_fragment"
# Transcript information for a single query id and comparison with reference.
# Intra-priming: calculate percentage of "A"s right after the end
if trec.strand == "+":
pos_TTS = trec.exonEnds[-1]
seq_downTTS = str(genome_dict[trec.chrom].seq[pos_TTS:pos_TTS+nPolyA]).upper()
else: # id on - strand
pos_TTS = trec.exonStarts[0]
seq_downTTS = str(genome_dict[trec.chrom].seq[pos_TTS-nPolyA:pos_TTS].reverse_complement()).upper()
percA = float(seq_downTTS.count('A'))/nPolyA*100
isoform_hit = myQueryTranscripts(id=trec.id, tts_diff="NA", tss_diff="NA",\
num_exons=trec.exonCount,
length=trec.length,
str_class="", \
chrom=trec.chrom,
strand=trec.strand, \
subtype="no_subcategory",\
percAdownTTS=str(percA),\
seqAdownTTS=seq_downTTS)
##***************************************##
########### SPLICED TRANSCRIPTS ###########
##***************************************##
cat_ranking = {'full-splice_match': 5, 'incomplete-splice_match': 4, 'anyKnownJunction': 3, 'anyKnownSpliceSite': 2,
'geneOverlap': 1, '': 0}
#if trec.id.startswith('PB.1961.2'):
# pdb.set_trace()
if trec.exonCount >= 2:
hits_by_gene = defaultdict(lambda: []) # gene --> list of hits
best_by_gene = {} # gene --> best isoform_hit
if trec.chrom in refs_exons_by_chr:
for ref in refs_exons_by_chr[trec.chrom].find(trec.txStart, trec.txEnd):
hits_by_gene[ref.gene].append(ref)
if trec.chrom in refs_1exon_by_chr:
for ref in refs_1exon_by_chr[trec.chrom].find(trec.txStart, trec.txEnd):
hits_by_gene[ref.gene].append(ref)
if len(hits_by_gene) == 0: return isoform_hit
for ref_gene in sorted(hits_by_gene):
isoform_hit = myQueryTranscripts(id=trec.id, tts_diff="NA", tss_diff="NA", \
num_exons=trec.exonCount,
length=trec.length,
str_class="", \
chrom=trec.chrom,
strand=trec.strand, \
subtype="no_subcategory", \
percAdownTTS=str(percA), \
seqAdownTTS=seq_downTTS)
for ref in hits_by_gene[ref_gene]:
if trec.strand != ref.strand:
# opposite strand, just record it in AS_genes
isoform_hit.AS_genes.add(ref.gene)
continue
#if trec.id.startswith('PB.102.9'):
# pdb.set_trace()
if ref.exonCount == 1: # mono-exonic reference, handle specially here
if calc_exon_overlap(trec.exons, ref.exons) > 0 and cat_ranking[isoform_hit.str_class] < cat_ranking["geneOverlap"]:
isoform_hit = myQueryTranscripts(trec.id, "NA", "NA", trec.exonCount, trec.length,
"geneOverlap",