def fasta_seq_length(fa_name):
"""
general information about contigs lengths in a FASTA file
"""
from operator import itemgetter
from gfftools import helper
seq_info = dict()
fah = helper.open_file(fa_name)
for rec in SeqIO.parse(fah, "fasta"):
seq_info[rec.id] = len(rec.seq)
print rec.id, len(rec.seq)
fah.close()
print
print 'Number of FASTA entries: ', len(seq_info)
for long_one in sorted(seq_info.items(), key=itemgetter(1), reverse=True):
print 'Long contig length (bp): ', long_one[0], long_one[1]
break
for short_one in sorted(seq_info.items(), key=itemgetter(1)):
print 'Short contig length (bp): ', short_one[0], short_one[1]
break
flength = 0
for ele in sorted(seq_info.items(), key=itemgetter(1)):
flength += ele[1]
print 'Average length of FASTA contig (bp): ', (flength / len(seq_info))
print
def translate_trsk_genes(gtf_file, fas_file, out_seq_fname):
"""
translate the trsk genes to protein sequence
@args gtf_file: genome annotation file
@type gtf_file: str
@args fas_file: genome sequence file
@type fas_file: str
@args out_seq_fname: output file in fasta format
@type out_seq_fname: str
"""
if filecmp.cmp(gtf_file, fas_file):
exit("Do the two files are exactly same? Please check that!")
## reading the TSkim file to get the features
sys.stdout.write('reading genome features from %s\n' % gtf_file)
anno_db = GFFParser.Parse(gtf_file)
total_genes = len(anno_db)
## genome sequence file reading
sys.stdout.write('reading genome sequence from %s\n' % fas_file)
seqlab.chrom_name_consistency(fas_file, anno_db)
cds_idx = [] # deleting the empty cds lines
for idp, feat in enumerate(anno_db):
if not feat['cds_exons'][0].any(): # TSkim annotation expects only single transcript from a region
cds_idx.append(idp)
anno_db = np.delete(anno_db, cds_idx)
genes_with_cds = len(anno_db)
fasFH = helper.open_file(fas_file)
out_seq_fh = open(out_seq_fname, "w")
for rec in SeqIO.parse(fasFH, "fasta"):
for idx, feature in enumerate(anno_db):
if rec.id == feature['chr']:
## iterate over cds_exons
cds_seq = ''
for ex in feature['cds_exons'][0]:## single transcript by TSkim
cds_seq += rec.seq[ex[0]-1:ex[1]]
if feature['strand'] == '-':
cds_seq = cds_seq.reverse_complement()
##
#sys.stdout.write(str(cds_seq.translate()) + "\n")
## fasta output
if cds_seq:
prt_seq = SeqRecord(cds_seq.translate(), id=feature['name'], description='protein sequence')
out_seq_fh.write(prt_seq.format("fasta"))
# FIXME need an efficient way to translate multiple gene
# iterate over chromosome
fasFH.close()
out_seq_fh.close()
sys.stdout.write('total genes fetched: %d\n' % total_genes)
sys.stdout.write('total genes translated: %d\n' % genes_with_cds)
sys.stdout.write('protein sequence stored at %s\n' % out_seq_fname)
def fasta_seq_length(fa_name):
"""
general information about contigs lengths in a FASTA file
"""
from operator import itemgetter
from gfftools import helper
seq_info = dict()
fah = helper.open_file(fa_name)
for rec in SeqIO.parse(fah, "fasta"):
seq_info[rec.id] = len(rec.seq)
print rec.id, len(rec.seq)
fah.close()
print
print 'Number of FASTA entries: ', len(seq_info)
for long_one in sorted(seq_info.items(), key=itemgetter(1), reverse=True):
print 'Long contig length (bp): ', long_one[0], long_one[1]
break
for short_one in sorted(seq_info.items(), key=itemgetter(1)):
print 'Short contig length (bp): ', short_one[0], short_one[1]
break
flength = 0
for ele in sorted(seq_info.items(), key=itemgetter(1)):
flength += ele[1]
print 'Average length of FASTA contig (bp): ', (flength/len(seq_info))
print
def check_splice_site_consensus(fas_file, splice_region):
"""
splice site consensus check
"""
sys.stdout.write( "splice site sequence consensus check started...\n")
get_gene_models = defaultdict()
splice_site_con = 0
fas_fh = helper.open_file(fas_file)
for fas_rec in SeqIO.parse(fas_fh, "fasta"):
if fas_rec.id in splice_region:
for details in splice_region[fas_rec.id]:
for genes, regions in details.items():
acc_cons_cnt = 0
don_cons_cnt = 0
if len(regions) == 1:## single exon long transcripts no checks
get_gene_models[(fas_rec.id, genes[0], genes[1], genes[2])] = 1
continue
for region in regions:
if genes[-1] == '+':
#if not numpy.isnan(region[0]):## acceptor splice site
if region[0]:## acceptor splice site
acc_seq = fas_rec.seq[int(region[0])-3:int(region[0])-1]
if str(acc_seq).upper() == "AG":
acc_cons_cnt += 1
if region[1]:
don_seq = fas_rec.seq[int(region[1]):int(region[1])+2]
if str(don_seq).upper() == "GT":
don_cons_cnt +=1
elif genes[-1] == '-':
if region[0]: ## donor splice site
don_seq = fas_rec.seq[int(region[0])-3:int(region[0])-1]
don_seq = don_seq.reverse_complement()
if str(don_seq).upper() == "GT":
don_cons_cnt +=1
if region[1]:
acc_seq = fas_rec.seq[int(region[1]):int(region[1])+2]
acc_seq = acc_seq.reverse_complement()
if str(acc_seq).upper() == "AG":
acc_cons_cnt += 1
## check for half of the consensus sites
if acc_cons_cnt > (len(regions)/2) and don_cons_cnt > (len(regions)/2):
get_gene_models[(fas_rec.id, genes[0], genes[1], genes[2])] = 1
else:
splice_site_con +=1
fas_fh.close()
sys.stdout.write( "...considering %d best transcripts\n" % len(get_gene_models))
sys.stdout.write( "discarding transcripts...\n")
sys.stdout.write( "\t%d splice-site consensus sequence missing\n" % splice_site_con)
return get_gene_models
def read_genome_file(fas_file):
"""
read genome file in fasta and return the list of chromosomes/contigs
@args fas_file: genome sequence in fasta file
@type fas_file: str
"""
# get the filehandler from input file
try:
fh = helper.open_file(fas_file)
except Exception, errmsg:
stop_err('error in reading file '+ errmsg)
def read_genome_file(fas_file):
"""
read genome file in fasta and return the list of chromosomes/contigs
@args fas_file: genome sequence in fasta file
@type fas_file: str
returns a list with contig_names and length
"""
# get the filehandler from input file
try:
fh = helper.open_file(fas_file)
except Exception, errmsg:
stop_err('error in reading file ' + errmsg)
def clean_genome_file(chr_names, fas_file, fas_out):
"""
make a stable genome file with valid contigs
@args chr_names: different contig names with a valid genome sequence
@type chr_names: dict
@args fas_file: genome sequence in fasta file
@type fas_file: str
@args fas_out: new genome sequence file in fasta format
@type fas_out: str
"""
# get the filehandler from input file
try:
fh = helper.open_file(fas_file)
except Exception, errmsg:
stop_err('error in reading file ' + errmsg)
def clean_anno_file(chr_names, gtf_file, gtf_out):
"""
make stable annotation file with valid contig name
@args chr_names: different contig names with a valid genome sequence
@type chr_names: dict
@args gtf_file: genome sequence in fasta file
@type gtf_file: str
@args gtf_out: new genome sequence file in fasta format
@type gtf_out: str
"""
# get the filehandler from input file
try:
fh = helper.open_file(gtf_file)
except Exception, errmsg:
stop_err('error %s in reading file %s' % (errmsg, gtf_file))
def genome_file_rec_extract(chr_pattn, fas_file, fas_out):
"""
get all contings based on a matiching string in the record identifier
@args chr_pattn: pattern to be searched in contig names
@type chr_pattn: str
@args fas_file: genome sequence in fasta file
@type fas_file: str
@args fas_out: new genome sequence file in fasta format
@type fas_out: str
"""
# get the filehandler from input file
try:
fh = helper.open_file(fas_file)
except Exception, errmsg:
stop_err('error in reading file '+ errmsg)
def clean_anno_file(chr_names, gtf_file, gtf_out):
"""
make stable annotation file with valid contig name
@args chr_names: different contig names with a valid genome sequence
@type chr_names: dict
@args gtf_file: genome annotation in gtf/gff form
@type gtf_file: str
@args gtf_out: new genome annotation in gtf/gff form
@type gtf_out: str
"""
# get the filehandler from input file
try:
fh = helper.open_file(gtf_file)
except Exception, errmsg:
stop_err('error %s in reading file %s' % (errmsg, gtf_file))
def clean_genome_file(chr_names, fas_file, fas_out):
"""
make a stable genome file with valid contigs
@args chr_names: different contig names with a valid genome sequence
@type chr_names: dict
@args fas_file: genome sequence in fasta file
@type fas_file: str
@args fas_out: new genome sequence file in fasta format
@type fas_out: str
"""
# get the filehandler from input file
try:
fh = helper.open_file(fas_file)
except Exception, errmsg:
stop_err('error in reading file '+ errmsg)
def genome_file_rec_extract(chr_pattn, fas_file, fas_out):
"""
get all contings based on a matiching string in the record identifier
@args chr_pattn: pattern to be searched in contig names
@type chr_pattn: str
@args fas_file: genome sequence in fasta file
@type fas_file: str
@args fas_out: new genome sequence file in fasta format
@type fas_out: str
"""
# get the filehandler from input file
try:
fh = helper.open_file(fas_file)
except Exception, errmsg:
stop_err('error in reading file ' + errmsg)
def __main__():
try:
fname = sys.argv[1]
fa_out = sys.argv[2]
except:
print __doc__
sys.exit(-1)
# get the valid chromosome identifier from user as STDIN
chrom = dict()
for chr in sys.stdin:
chr = chr.strip()
chrom[chr] = 1
# get the filehandler from input file
try:
fh = helper.open_file(fname)
except Exception, errmsg:
stop_err('error in reading file '+ errmsg)
def true_ss_seq_fetch(fnam, Label, boundary):
"""
true splice signals
"""
foh = helper.open_file(fnam)
don_cnt_pl = don_cnt_mi = acc_cnt_pl = acc_cnt_mi = 0
don_in_pl = don_in_mi = acc_in_pl = acc_in_mi = 0
for rec in SeqIO.parse(foh, "fasta"):
if rec.id in Label:
for Lfeat in Label[rec.id]:
for fid, loc in Lfeat.items():
acc_ind = don_ind = 0
if loc[-1] == '+':
acc_mot_seq = rec.seq[(int(loc[0])-boundary)-2:(int(loc[0])+boundary)-2]
if not acc_mot_seq:
acc_ind = 1
if str(acc_mot_seq[boundary-1:boundary+1]).upper() != 'AG':
acc_ind = 1
if acc_ind:
acc_in_pl += 1
else:
acc_cnt_pl += 1
don_mot_seq = rec.seq[(int(loc[1])-boundary)+1:(int(loc[1])+boundary)+1]
if not don_mot_seq:
don_ind = 1
if str(don_mot_seq[boundary-1:boundary+1]).upper() != 'GT':
don_ind = 1
if don_ind:
don_in_pl += 1
else:
don_cnt_pl += 1
elif loc[-1] == '-':
don_mot_seq = rec.seq[(int(loc[0])-boundary)-2:(int(loc[0])+boundary)-2]
if not don_mot_seq:
don_ind = 1
if str(don_mot_seq[boundary-1:boundary+1]).upper() != 'AC':
don_ind = 1
if don_ind:
don_in_mi += 1
else:
don_cnt_mi += 1
acc_mot_seq = rec.seq[(int(loc[1])-boundary)+1:(int(loc[1])+boundary)+1]
if not acc_mot_seq:
acc_ind = 1
if str(acc_mot_seq[boundary-1:boundary+1]).upper() != 'CT':
acc_ind = 1
if acc_ind:
acc_in_mi += 1
else:
acc_cnt_mi += 1
print
print '%d and %d Consensus DONOR sites on positive and negative strand' % (don_cnt_pl, don_cnt_mi)
print '%d and %d Non-Consensus DONOR sites on positive and negative strand' % (don_in_pl, don_in_mi)
print
print '%d and %d Consensus ACCEPTOR sites on positive and negative strand' % (acc_cnt_pl, acc_cnt_mi)
print '%d and %d Non-Consensus ACCEPTOR sites on positive and negative strand' % (acc_in_pl, acc_in_mi)
print
foh.close()
return don_cnt_pl+don_cnt_mi, acc_cnt_pl+acc_cnt_mi, don_in_pl+don_in_mi, acc_in_pl+acc_in_mi
def experiment_db(config_file, opt_action):
"""
function to collect details of each organism
FIXME descriptions
@args config_file: yaml file contain the information for the experiment
@type config_file: str
"""
## parsing the config file
config_map = yaml.safe_load(open(config_file, "rU"))
data_path = config_map["genome_data_path"]["dir"]
exp_path = config_map["experiment_data_path"]["dir"]
org_fasta_file = dict(
A_carolinensis="%s/A_carolinensis/ensembl_release_79/ensembl_release_79.fas" % data_path,
M_mulatta="%s/M_mulatta/ensembl_release_79/ensembl_release_79.fas.bz2" % data_path,
O_cuniculus="%s/O_cuniculus/ensembl_release_79/ensembl_release_79.fas" % data_path,
M_gallopavo="%s/M_gallopavo/ensembl_release_79/ensembl_release_79.fas.bz2" % data_path,
B_anthracis="%s/B_anthracis/ensembl_release-21/Bacillus_anthracis_str_a0193.GCA_000181915.1.21.dna.toplevel.fa"
% data_path,
C_familiaris="%s/C_familiaris/ensembl_release_79/ensembl_release_79.fas.bz2" % data_path,
D_melanogaster="%s/D_melanogaster/ensembl_release_79/ensembl_release_79.fas" % data_path,
E_caballus="%s/E_caballus/ensembl_release_79/ensembl_release_79.fas.bz2" % data_path,
M_domestica="%s/M_domestica/ensembl_release-69/Monodelphis_domestica.BROADO5.69.dna.toplevel.fa" % data_path,
O_sativa="%s/O_sativa/phytozome_v9.0/Osativa_204.fa" % data_path,
A_gambiae="%s/A_gambiae/anoGam1_ucsc/anoGam1_rm.fasta" % data_path,
B_rapa="%s/B_rapa/phytozome_v9.0/Brapa_197_stable.fa" % data_path,
C_japonica="%s/C_japonica/STARgenome/Caenorhabditis_japonica.C_japonica-7.0.1.22.dna_sm.stable.fa" % data_path,
G_gallus="%s/G_gallus/ensembl_release_79/ensembl_release_79.fas" % data_path,
M_musculus="%s/M_musculus/ensembl_release_79/ensembl_release_79.fas.bz2" % data_path,
V_vinifera="%s/V_vinifera/phytozome_v9.0/phytozome_v9.0.fas.bz2" % data_path,
A_mellifera="%s/A_mellifera/apiMel3_ucsc/apiMel3_sm.fasta" % data_path,
B_taurus="%s/B_taurus/ensembl_release_79/ensembl_release_79.fas.bz2" % data_path,
C_rubella="%s/C_rubella/phytozome_v9.0/Crubella_183.fa.gz" % data_path,
D_rerio="%s/D_rerio/ensembl_release_79/ensembl_release_79.fas" % data_path,
G_max="%s/G_max/phytozome_v9.0/Gmax_189_filter.fa" % data_path,
M_truncatula="%s/M_truncatula/STARgenome/Mtruncatula_198.fa" % data_path,
P_pacificus="%s/P_pacificus/STARgenome/Pristionchus_pacificus.P_pacificus-5.0.22.dna_sm.stable.fa" % data_path,
S_scrofa="%s/S_scrofa/ensembl_release_79/ensembl_release_79.fas.bz2" % data_path,
X_tropicalis="%s/X_tropicalis/JGIv4-1/JGIv4-1.fa" % data_path,
C_sativus="%s/C_sativus/phytozome_v9.0/Csativus_122_filtered.fa" % data_path,
D_simulans="%s/D_simulans/ensembl_release_22/ensembl_release_22.fas.bz2" % data_path,
H_sapiens="%s/H_sapiens/hg19_bowtie2/hg19.fa" % data_path,
O_anatinus="%s/O_anatinus/ensembl_release-69/Ornithorhynchus_anatinus.OANA5.69-filtered_dna.fa" % data_path,
N_vitripennis="%s/N_vitripennis/ensembl_release_22/N_vitripennis_dna_sm.fa" % data_path,
P_troglodytes="%s/P_troglodytes/ensembl_release_79/ensembl_release_79.fas.bz2" % data_path,
S_tuberosum="%s/S_tuberosum/phytozome_v9.0/Stuberosum_206.fa" % data_path,
Z_mays="%s/Z_mays/phytozome_v9.0/Zmays_181.fa" % data_path,
A_thaliana="%s/A_thaliana/arabidopsis_tair10/sequences/TAIR9_chr_all.fas" % data_path,
O_aries="%s/O_aries/ensembl_release_79/ensembl_release_79.fas" % data_path,
C_jacchus="%s/C_jacchus/ensembl_release_79/ensembl_release_79.fas" % data_path,
C_elegans="%s/C_elegans/ensembl_release_79/ensembl_release_79.fas" % data_path,
O_latipes="%s/O_latipes/ensembl_release_79/ensembl_release_79.fas" % data_path,
R_norvegicus="%s/R_norvegicus/ensembl_release_79/ensembl_release_79.fas" % data_path,
C_briggsae="%s/C_briggsae/ensembl_release_22/ensembl_release_22.fas.bz2" % data_path,
T_nigroviridis="%s/T_nigroviridis/ensembl_release_79/ensembl_release_79.fas" % data_path,
)
org_gtf_file = dict(
A_carolinensis="%s/A_carolinensis/ensembl_release_79/ensembl_release_79.gtf" % data_path,
M_mulatta="%s/M_mulatta/ensembl_release_79/ensembl_release_79.gtf.bz2" % data_path,
O_cuniculus="%s/O_cuniculus/ensembl_release_79/ensembl_release_79.gtf" % data_path,
M_gallopavo="%s/M_gallopavo/ensembl_release_79/ensembl_release_79.gtf.bz2" % data_path,
B_anthracis="%s/B_anthracis/ensembl_release-21/Bacillus_anthracis" % data_path,
C_familiaris="%s/C_familiaris/ensembl_release_79/ensembl_release_79.gtf.bz2" % data_path,
D_melanogaster="%s/D_melanogaster/ensembl_release_79/ensembl_release_79.gtf" % data_path,
E_caballus="%s/E_caballus/ensembl_release_79/ensembl_release_79.gtf.bz2" % data_path,
M_domestica="%s/M_domestica/ensembl_release-69/Monodelphis_domestica.BROADO5.69.gtf" % data_path,
O_sativa="%s/O_sativa/phytozome_v9.0/Osativa_204_gene.gff3" % data_path,
A_gambiae="%s/A_gambiae/anoGam1_ucsc/anoGam1_ucsc.gtf" % data_path,
B_rapa="%s/B_rapa/phytozome_v9.0/Brapa_197_gene.gff3" % data_path,
C_japonica="%s/C_japonica/ensembl_release-22/Caenorhabditis_japonica.C_japonica-7.0.1.22.gff3" % data_path,
G_gallus="%s/G_gallus/ensembl_release_79/ensembl_release_79.gtf" % data_path,
M_musculus="%s/M_musculus/ensembl_release_79/ensembl_release_79.gtf.bz2" % data_path,
V_vinifera="%s/V_vinifera/phytozome_v9.0/phytozome_v9.0.gff.bz2" % data_path,
A_mellifera="%s/A_mellifera/apiMel3_ucsc/apiMel2_ucsc.gtf" % data_path,
B_taurus="%s/B_taurus/ensembl_release_79/ensembl_release_79.gtf.bz2" % data_path,
C_jacchus="%s/C_jacchus/ensembl_release_79/ensembl_release_79.gtf" % data_path,
C_rubella="%s/C_rubella/phytozome_v9.0/Crubella_183.gff3" % data_path,
D_rerio="%s/D_rerio/ensembl_release_79/ensembl_release_79.gtf" % data_path,
G_max="%s/G_max/phytozome_v9.0/Gmax_189_gene.gff3" % data_path,
N_vitripennis="%s/N_vitripennis/ensembl_release_22/N_vitripennis.gtf" % data_path,
O_aries="%s/O_aries/ensembl_release_79/ensembl_release_79.gtf" % data_path,
M_truncatula="%s/M_truncatula/" % data_path,
P_pacificus="%s/P_pacificus/ensembl_release-22/Pristionchus_pacificus.P_pacificus-5.0.22.gtf" % data_path,
S_scrofa="%s/S_scrofa/ensembl_release_79/ensembl_release_79.gtf.bz2" % data_path,
X_tropicalis="%s/X_tropicalis/JGIv4-1/JGIv4-1.gff" % data_path,
C_sativus="%s/C_sativus/phytozome_v9.0/Csativus_122_gene.gff3" % data_path,
D_simulans="%s/D_simulans/ensembl_release_22/ensembl_release_22.gff.bz2" % data_path,
H_sapiens="%s/H_sapiens/ensembl_release_79/ensembl_release_79.gtf.bz2" % data_path,
O_anatinus="%s/O_anatinus/ensembl_release-69/" % data_path,
P_troglodytes="%s/P_troglodytes/ensembl_release_79/ensembl_release_79.gtf.bz2" % data_path,
S_tuberosum="%s/S_tuberosum/phytozome_v9.0/Stuberosum_206_gene.gff3" % data_path,
Z_mays="%s/Z_mays/phytozome_v9.0/Zmays_181_gene.gff3" % data_path,
A_thaliana="%s/A_thaliana/arabidopsis_tair10/annotations/TAIR10_GFF3_genes.gff" % data_path,
C_elegans="%s/C_elegans/ensembl_release_79/ensembl_release_79.gtf" % data_path,
D_discoideum="%s/D_discoideum/" % data_path,
D_yakuba="%s/D_yakuba/ensembl_release-22/Drosophila_yakuba.dyak_r1.3_FB2008_07.22.gff3" % data_path,
O_latipes="%s/O_latipes/ensembl_release_79/ensembl_release_79.gtf" % data_path,
R_norvegicus="%s/R_norvegicus/ensembl_release_79/ensembl_release_79.gtf" % data_path,
C_briggsae="%s/C_briggsae/ensembl_release_22/ensembl_release_22.gff.bz2" % data_path,
C_brenneri="%s/C_brenneri/ensembl_release-22/Caenorhabditis_brenneri.C_brenneri-6.0.1b.22.gff3" % data_path,
C_remanei="%s/C_remanei/ensembl_release-22/Caenorhabditis_remanei.C_remanei-15.0.1.22.gff3" % data_path,
D_pseudoobscura="%s/D_pseudoobscura/ensembl_release-22/Drosophila_pseudoobscura.HGSC2.22.gff3" % data_path,
T_pseudonana="%s/T_pseudonana/Thaps3/Thaps3_chromosomes_geneModels_FilteredModels2.gff" % data_path,
T_nigroviridis="%s/T_nigroviridis/ensembl_release_79/ensembl_release_79.gtf" % data_path,
)
## TODO algorithms details
## experiment details
org_db = defaultdict()
for ent in config_map["experiment"]:
species_name = ent["organism_name"]
sra_run_id = ent["sra_run_id"]
genome_build_version = ent["genome_build_version"]
db_server = ent["release_db"]
## mapping to short names arabidopsis_thaliana --> A_thaliana
genus, species = species_name.strip().split("_")
short_name = "%s_%s" % (genus[0].upper(), species)
org_db[short_name] = dict(short_name=short_name)
org_db[short_name]["long_name"] = species_name
org_db[short_name]["sra_run_id"] = sra_run_id
org_db[short_name]["genome_release_db"] = genome_build_version
## the broad path to the experiment
org_db[short_name]["genome_dir"] = data_path
org_db[short_name]["experiment_dir"] = exp_path
build_release = genome_build_version.split("_")
org_db[short_name]["release_db"] = db_server ## ensembl_metazoa, phytozome
org_db[short_name]["release_nb"] = build_release[-1] ## build number
sra_files = [] ## sequencing reads files
if os.path.isdir("%s/%s/source_data" % (exp_path, short_name)):
for sra_file in os.listdir("%s/%s/source_data" % (exp_path, short_name)):
file_prefx, ext = os.path.splitext(sra_file)
if ext == ".sra": ## skipping the original .sra binary file
continue
if re.search(sra_run_id, sra_file):
sra_files.append(sra_file)
else:
print "warning: missing sequencing read trace file %s/%s/source_data" % (exp_path, short_name)
org_db[short_name]["fastq_path"] = "%s/%s/source_data" % (exp_path, short_name)
org_db[short_name]["fastq"] = sra_files
## read mapping, read assembly and label generation working folders
for sub_dir in ["read_mapping", "signal_labels", "trans_pred"]:
work_path = "%s/%s/%s" % (exp_path, short_name, sub_dir)
if not os.path.isdir(work_path):
try:
os.makedirs(work_path)
except OSError:
print "error: cannot create the directory %s." % work_path
sys.exit(0)
org_db[short_name]["read_map_dir"] = "%s/%s/read_mapping" % (exp_path, short_name)
org_db[short_name]["read_assembly_dir"] = "%s/%s/trans_pred" % (exp_path, short_name)
org_db[short_name]["labels_dir"] = "%s/%s/signal_labels" % (exp_path, short_name)
## calculate the sequence read length
readlength = 0
if opt_action in ["2", "3"]: ## perform this action only for selected options
if sra_files:
fqfile = os.path.join(org_db[short_name]["fastq_path"], sra_files[0])
print "using sequencing read file %s to determine readLength" % fqfile
fh = helper.open_file(fqfile)
for rec in SeqIO.parse(fh, "fastq"):
readlength = len(rec.seq)
break
fh.close()
org_db[short_name]["read_length"] = readlength
## check for the genome sequence file
if short_name in org_fasta_file:
if os.path.isfile(org_fasta_file[short_name]):
org_db[short_name]["fasta"] = org_fasta_file[short_name]
else:
org_db[short_name]["fasta"] = None
else:
print "warning: missing genome sequence file for %s under %s/%s/%s" % (
short_name,
data_path,
short_name,
genome_build_version,
)
org_db[short_name]["fasta"] = None
if not os.path.isdir("%s/%s/%s/STARgenome" % (data_path, short_name, genome_build_version)):
try:
os.makedirs("%s/%s/%s/STARgenome" % (data_path, short_name, genome_build_version))
except OSError:
print "error: cannot create the directory %s/%s/%s" % (data_path, short_name, genome_build_version)
sys.exit(0)
org_db[short_name]["genome_index_dir"] = "%s/%s/%s/STARgenome/" % (data_path, short_name, genome_build_version)
## check the genome annotation
if short_name in org_gtf_file:
if os.path.isfile(org_gtf_file[short_name]):
org_db[short_name]["gtf"] = org_gtf_file[short_name]
if opt_action in ["2", "3", "4", "c"]: ## perform this action only for selected options
## get the gtf feature lengths
from fetch_remote_data import prepare_data as pd
feat_len_db = pd.make_anno_db(org_gtf_file[short_name])
org_db[short_name]["max_intron_len"] = feat_len_db["max_intron"]
org_db[short_name]["max_exon_len"] = feat_len_db["max_exon"]
else:
print "error: the provided gtf file %s is not available to read. Please check!" % org_gtf_file[
short_name
]
sys.exit(-1)
else:
print "warning: missing annotation file for %s under %s/%s/%s" % (
short_name,
data_path,
short_name,
genome_build_version,
)
org_db[short_name]["gtf"] = None
org_db[short_name]["max_intron_len"] = None
org_db[short_name]["max_exon_len"] = None
print "fetched details for %s" % short_name
return org_db
print '\t'.join(rec)
def fasta_reader(fname):
"""
reading a FASTA file
"""
regions_removed = collections.defaultdict(list)
for rec in SeqIO.parse(fname, "fasta"):
#rec.id = 'chr'+rec.id
Nindex = [item for item in range(len(rec.seq)) if rec.seq[item]=="N"] ##index of the desired nucleotide
for xn, xp in itertools.groupby(enumerate(Nindex), lambda (i,x):i-x): ##
cod_range = map(itemgetter(1), xp)
regions_removed[rec.id].append((cod_range[0], cod_range[-1]))
return regions_removed
try:
ffa=sys.argv[1]
fbz=sys.argv[2]
except:
print __doc__
sys.exit(-1)
dis_cod = fasta_reader(ffa)
bzh = helper.open_file(fbz)
pred_score(bzh, dis_cod)
def __main__():
try:
fastq_path = sys.argv[1]
except:
print __doc__
sys.exit(-1)
fastq_1_file = None
fastq_2_file = None
# TODO expecting the left and right reads in the base folder with .fastq ending. Make it is common general form
## get the files from base path
for filename in os.listdir(fastq_path):
if re.search(r'_1.fastq', filename):
fastq_1_file = filename
if re.search(r'_2.fastq', filename):
fastq_2_file = filename
print fastq_1_file, fastq_2_file
print
## count the number of reads and calculate the sub sample
fqh = helper.open_file('%s/%s' % (fastq_path, fastq_1_file))
read_cnt = 0
for rec in SeqIO.parse(fqh, 'fastq'):
read_cnt += 1
fqh.close()
print '%d Number of reads in FASTQ' % read_cnt
print
## what percentage sub-sample
percentage = 1
sub_count = int(round((percentage * read_cnt) / 100.0))
assert sub_count <= read_cnt, ' %d (sub-sample count) should be less than total read count %d' % (
sub_count, read_cnt)
print "%d Sub sample count" % sub_count
print
try:
accept_prob = (1.0 * sub_count) / read_cnt
except:
accept_prob = 1
print accept_prob
sub_fastq_1_file = "%d_percentage_%s.bz2" % (percentage, fastq_1_file)
sub_fastq_2_file = "%d_percentage_%s.bz2" % (percentage, fastq_2_file)
## writing out sub sample files
try:
subFile_1 = bz2.BZ2File("%s/%s" % (fastq_path, sub_fastq_1_file), 'wb')
subFile_2 = bz2.BZ2File("%s/%s" % (fastq_path, sub_fastq_2_file), 'wb')
except Exception as error:
sys.exit(error)
total_cnt = 0
sample_cnt = 0
left_reads = dict()
fqh = helper.open_file('%s/%s' % (fastq_path, fastq_1_file))
for rec in SeqIO.parse(fqh, 'fastq'):
rnb = random.random()
total_cnt += 1
if rnb <= accept_prob:
## @UNC15-SN850_63:4:1101:1103:2151/1 @UNC15-SN850_63:4:1101:1103:2151/2
read_id = rec.id.split('/')
if len(read_id) > 1:
left_reads[read_id[0]] = 0
else:
## @UNC11-SN627:294:C236MACXX:5:1101:1430:2218 1:N:0:GGNTAC @UNC11-SN627:294:C236MACXX:5:1101:1430:2218 2:N:0:GGNTAC
left_reads[rec.id] = 0
sample_cnt += 1
subFile_1.write(rec.format("fastq"))
if sub_count == sample_cnt:
break
fqh.close()
subFile_1.close()
fqh = helper.open_file('%s/%s' % (fastq_path, fastq_2_file))
for rec in SeqIO.parse(fqh, 'fastq'):
read_id = rec.id.split('/')
if len(read_id) > 1:
## @UNC15-SN850_63:4:1101:1103:2151/1
if read_id[0] in left_reads:
subFile_2.write(rec.format("fastq"))
else:
## @UNC11-SN627:294:C236MACXX:5:1101:1430:2218 1:N:0:GGNTAC
if rec.id in left_reads:
subFile_2.write(rec.format("fastq"))
fqh.close()
subFile_2.close()
print "%s/%s" % (fastq_path, sub_fastq_1_file)
print "%s/%s" % (fastq_path, sub_fastq_2_file)
print
print '%d Number of reads scanned' % total_cnt
print '%d Number of reads in' % sample_cnt
print
def run_star_alignment(org_db, read_type='PE', max_mates_gap_length=100000, num_cpus=1):
"""
wrapper for running STAR program
@args org_db: a python dictionary with all details about a single organism
@type org_db: defaultdict
@args read_type: library type - paired-end or single-end (default: PE)
@type read_type: str
@args max_mates_gap_length: maximum insert size from the sample (default: 10000)
@type max_mates_gap_length: int
@args num_cpus: number of threads to use for the run (default: 1)
@type num_cpus: int
"""
try:
subprocess.call(["STAR"], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
except:
exit("Please make sure that the `STAR` binary is in your $PATH")
from gfftools import helper, GFFParser
genome_dir = org_db['genome_index_dir']## genome indices and annotation file
gtf_db = org_db['gtf']
if gtf_db != None:
## check for the annotation file type gff or gtf
gff_hand = helper.open_file(gtf_db)
for rec in gff_hand:
rec = rec.strip('\n\r')
# skip empty line fasta identifier and commented line
if not rec or rec[0] in ['#', '>']:
continue
# skip the genome sequence
if not re.search('\t', rec):
continue
parts = rec.split('\t')
assert len(parts) >= 8, rec
ftype, tags = GFFParser.attribute_tags(parts[-1])
break
gff_hand.close()
## library type
if read_type == 'PE':
read_file = "%s/%s %s/%s" % (org_db['fastq_path'], org_db['fastq'][0], org_db['fastq_path'], org_db['fastq'][1])
else:
read_file = "%s/%s" % (org_db['fastq_path'], org_db['fastq'][0])
## getting the command to uncompress the read file
zip_type = {".gz" : "gzip -c", ".bz2" : "bzip2 -d -c"}
file_prefx, ext = os.path.splitext(org_db['fastq'][0])
out_prefix = '%s/%s_' % (org_db['read_map_dir'], org_db['short_name'])
## genomic feature information
max_lenth_intron = org_db['max_intron_len']
## according to the file type
if gtf_db == None:
make_star_run = "STAR \
--genomeDir %s \
--readFilesIn %s \
--readFilesCommand %s \
--outFileNamePrefix %s \
--runThreadN %d \
--outFilterMultimapScoreRange 2 \
--outFilterMultimapNmax 30 \
--outFilterMismatchNmax 4 \
--sjdbScore 1 \
--sjdbOverhang 5 \
--outSAMstrandField intronMotif \
--outFilterIntronMotifs RemoveNoncanonical \
--outSAMtype BAM Unsorted \
--genomeLoad LoadAndRemove" % (genome_dir, read_file,
zip_type[ext], out_prefix, num_cpus)
elif ftype:
make_star_run = "STAR \
--genomeDir %s \
--readFilesIn %s \
--readFilesCommand %s \
--outFileNamePrefix %s \
--runThreadN %d \
--outFilterMultimapScoreRange 2 \
--outFilterMultimapNmax 30 \
--outFilterMismatchNmax 4 \
--alignIntronMax %d \
--sjdbGTFfile %s \
--sjdbGTFtagExonParentTranscript Parent \
--sjdbScore 1 \
--sjdbOverhang 5 \
--outSAMstrandField intronMotif \
--outFilterIntronMotifs RemoveNoncanonical \
--outSAMtype BAM Unsorted \
--genomeLoad LoadAndRemove" % (genome_dir, read_file,
zip_type[ext], out_prefix, num_cpus, max_lenth_intron, gtf_db)
else:
make_star_run = "STAR \
--genomeDir %s \
--readFilesIn %s \
--readFilesCommand %s \
--outFileNamePrefix %s \
--runThreadN %d \
--outFilterMultimapScoreRange 2 \
--outFilterMultimapNmax 30 \
--outFilterMismatchNmax 4 \
--alignIntronMax %d \
--sjdbGTFfile %s \
--sjdbGTFfeatureExon exon \
--sjdbScore 1 \
--sjdbOverhang 5 \
--outSAMstrandField intronMotif \
--outFilterIntronMotifs RemoveNoncanonical \
--outSAMtype BAM Unsorted \
--genomeLoad LoadAndRemove" % (genome_dir, read_file,
zip_type[ext], out_prefix, num_cpus, max_lenth_intron, gtf_db)
sys.stdout.write('\trunning STAR program as: %s \n' % make_star_run)
try:
process = subprocess.Popen(make_star_run, shell=True)
returncode = process.wait()
if returncode !=0:
raise Exception, "Exit status return code = %i" % returncode
sys.stdout.write("STAR run completed. result file stored at %sAligned.out.bam\n" % out_prefix)
except Exception, e:
sys.exit("Error running STAR.\n%s" % str( e ))
def translate_trsk_genes(gtf_file, fas_file, out_seq_fname):
"""
translate the trsk genes to protein sequence
@args gtf_file: genome annotation file
@type gtf_file: str
@args fas_file: genome sequence file
@type fas_file: str
@args out_seq_fname: output file in fasta format
@type out_seq_fname: str
"""
if filecmp.cmp(gtf_file, fas_file):
exit("Do the two files are exactly same? Please check that!")
## reading the TSkim file to get the features
sys.stdout.write('reading genome features from %s\n' % gtf_file)
anno_db = GFFParser.Parse(gtf_file)
total_genes = len(anno_db)
## genome sequence file reading
sys.stdout.write('reading genome sequence from %s\n' % fas_file)
seqlab.chrom_name_consistency(fas_file, anno_db)
cds_idx = [] # deleting the empty cds lines
for idp, feat in enumerate(anno_db):
if not feat['cds_exons'][0].any(
): # TSkim annotation expects only single transcript from a region
cds_idx.append(idp)
anno_db = np.delete(anno_db, cds_idx)
genes_with_cds = len(anno_db)
fasFH = helper.open_file(fas_file)
out_seq_fh = open(out_seq_fname, "w")
for rec in SeqIO.parse(fasFH, "fasta"):
for idx, feature in enumerate(anno_db):
if rec.id == feature['chr']:
## iterate over cds_exons
cds_seq = ''
for ex in feature['cds_exons'][
0]: ## single transcript by TSkim
cds_seq += rec.seq[ex[0] - 1:ex[1]]
if feature['strand'] == '-':
cds_seq = cds_seq.reverse_complement()
##
#sys.stdout.write(str(cds_seq.translate()) + "\n")
## fasta output
if cds_seq:
prt_seq = SeqRecord(cds_seq.translate(),
id=feature['name'],
description='protein sequence')
out_seq_fh.write(prt_seq.format("fasta"))
# FIXME need an efficient way to translate multiple gene
# iterate over chromosome
fasFH.close()
out_seq_fh.close()
sys.stdout.write('total genes fetched: %d\n' % total_genes)
sys.stdout.write('total genes translated: %d\n' % genes_with_cds)
sys.stdout.write('protein sequence stored at %s\n' % out_seq_fname)
def create_star_genome_index(fasta_file,
out_dir,
genome_anno=None,
num_workers=1,
onematelength=100):
"""
Creating STAR genome index with or without using genome annotation
@args fasta_file: reference genome sequence file .fasta format
@type fasta_file: str
@args out_dir: genome index binary file storage place
@type out_dir: str
@args genome_anno: genome annotation file (optional)
@type genome_anno: str
@args num_workers: number of threads to run (default value = 1)
@type num_workers: int
@args onematelength: One Mate Length (default value=100)
@type onematelength: int
"""
try:
subprocess.call(["STAR"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
except:
exit("Please make sure that the `STAR` binary is in your $PATH")
file_prefx, ext = os.path.splitext(fasta_file)
if ext in [".bz2", ".gz", ".lzma"
]: ## checking for the compressed form of the file extension
exit(
"error: STAR - Generating genome indexes - recommended to use the uncompressed FASTA file %s."
% fasta_file)
if not genome_anno:
cli_cmd = 'STAR \
--runMode genomeGenerate \
--genomeDir %s \
--genomeFastaFiles %s \
--runThreadN %d' % (out_dir, fasta_file, num_workers)
else:
file_prefx, ext = os.path.splitext(genome_anno)
if ext in [".bz2", ".gz", ".lzma"]:
exit(
"error: STAR - Generating genome indexes - recommended to use the uncompressed GTF/GFF file %s."
% genome_anno)
## check for the file type
gff_hand = helper.open_file(genome_anno)
for rec in gff_hand:
rec = rec.strip('\n\r')
# skip empty line fasta identifier and commented line
if not rec or rec[0] in ['#', '>']:
continue
# skip the genome sequence
if not re.search('\t', rec):
continue
parts = rec.split('\t')
assert len(parts) >= 8, rec
ftype, tags = GFFParser.attribute_tags(parts[-1])
break
gff_hand.close()
## according to the file type
if ftype:
cli_cmd = 'STAR \
--runMode genomeGenerate \
--genomeDir %s \
--genomeFastaFiles %s \
--runThreadN %d \
--sjdbGTFfile %s \
--sjdbGTFtagExonParentTranscript Parent \
--sjdbOverhang %d' % (out_dir, fasta_file, num_workers,
genome_anno, onematelength)
else:
cli_cmd = 'STAR \
--runMode genomeGenerate \
--genomeDir %s \
--genomeFastaFiles %s \
--runThreadN %d \
--sjdbGTFfile %s \
--sjdbGTFfeatureExon exon \
--sjdbOverhang %d' % (out_dir, fasta_file, num_workers,
genome_anno, onematelength)
## create downloadpath if doesnot exists
if not os.path.exists(out_dir):
try:
os.makedirs(out_dir)
except OSError:
exit("error: cannot create the directory %s." % out_dir)
else: ## if present any other old index files clean up the folder
for the_file in os.listdir(out_dir):
file_path = os.path.join(out_dir, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception, e:
print(e)
def run_star_alignment(org_db, read_type='PE', max_mates_gap_length=100000, num_cpus=1):
"""
wrapper for running STAR program
@args org_db: a python dictionary with all details about a single organism
@type org_db: defaultdict
@args read_type: library type - paired-end or single-end (default: PE)
@type read_type: str
@args max_mates_gap_length: maximum insert size from the sample (default: 10000)
@type max_mates_gap_length: int
@args num_cpus: number of threads to use for the run (default: 1)
@type num_cpus: int
"""
try:
subprocess.call(["STAR"], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
except:
exit("Please make sure that the `STAR` binary is in your $PATH")
from gfftools import helper, GFFParser
genome_dir = org_db['genome_index_dir']## genome indices and annotation file
gtf_db = org_db['gtf']
if gtf_db != None:
## check for the annotation file type gff or gtf
gff_hand = helper.open_file(gtf_db)
for rec in gff_hand:
rec = rec.strip('\n\r')
# skip empty line fasta identifier and commented line
if not rec or rec[0] in ['#', '>']:
continue
# skip the genome sequence
if not re.search('\t', rec):
continue
parts = rec.split('\t')
assert len(parts) >= 8, rec
ftype, tags = GFFParser.attribute_tags(parts[-1])
break
gff_hand.close()
## library type
if read_type == 'PE':
read_file = "%s/%s %s/%s" % (org_db['fastq_path'], org_db['fastq'][0], org_db['fastq_path'], org_db['fastq'][1])
else:
read_file = "%s/%s" % (org_db['fastq_path'], org_db['fastq'][0])
## getting the command to uncompress the read file
zip_type = {".gz" : "gzip -c", ".bz2" : "bzip2 -d -c"}
file_prefx, ext = os.path.splitext(org_db['fastq'][0])
out_prefix = '%s/%s_' % (org_db['read_map_dir'], org_db['short_name'])
## genomic feature information
max_lenth_intron = org_db['max_intron_len']
##sjdbOverhang
mate_len = org_db['mate_length']
## according to the file type
if gtf_db == None:
make_star_run = "STAR \
--genomeDir %s \
--readFilesIn %s \
--readFilesCommand %s \
--outFileNamePrefix %s \
--runThreadN %d \
--outFilterMultimapScoreRange 2 \
--outFilterMultimapNmax 30 \
--outFilterMismatchNmax 3 \
--sjdbScore 1 \
--sjdbOverhang %d \
--outSAMstrandField intronMotif \
--outFilterIntronMotifs RemoveNoncanonical \
--outSAMtype BAM Unsorted \
--genomeLoad LoadAndRemove" % (genome_dir, read_file,
zip_type[ext], out_prefix, num_cpus, mate_len)
elif ftype:
make_star_run = "STAR \
--genomeDir %s \
--readFilesIn %s \
--readFilesCommand %s \
--outFileNamePrefix %s \
--runThreadN %d \
--outFilterMultimapScoreRange 2 \
--outFilterMultimapNmax 30 \
--outFilterMismatchNmax 3 \
--alignIntronMax %d \
--sjdbGTFfile %s \
--sjdbGTFtagExonParentTranscript Parent \
--sjdbScore 1 \
--sjdbOverhang %d \
--outSAMstrandField intronMotif \
--outFilterIntronMotifs RemoveNoncanonical \
--outSAMtype BAM Unsorted \
--genomeLoad LoadAndRemove" % (genome_dir, read_file,
zip_type[ext], out_prefix, num_cpus, max_lenth_intron, gtf_db, mate_len)
else:
make_star_run = "STAR \
--genomeDir %s \
--readFilesIn %s \
--readFilesCommand %s \
--outFileNamePrefix %s \
--runThreadN %d \
--outFilterMultimapScoreRange 2 \
--outFilterMultimapNmax 30 \
--outFilterMismatchNmax 3 \
--alignIntronMax %d \
--sjdbGTFfile %s \
--sjdbGTFfeatureExon exon \
--sjdbScore 1 \
--sjdbOverhang %d \
--outSAMstrandField intronMotif \
--outFilterIntronMotifs RemoveNoncanonical \
--outSAMtype BAM Unsorted \
--genomeLoad LoadAndRemove" % (genome_dir, read_file,
zip_type[ext], out_prefix, num_cpus, max_lenth_intron, gtf_db, mate_len)
sys.stdout.write('\trunning STAR program as: %s \n' % make_star_run)
try:
process = subprocess.Popen(make_star_run, shell=True)
returncode = process.wait()
if returncode !=0:
raise Exception, "Exit status return code = %i" % returncode
sys.stdout.write("STAR run completed. result file stored at %sAligned.out.bam\n" % out_prefix)
except Exception, e:
sys.exit("Error running STAR.\n%s" % str( e ))
def create_star_genome_index(fasta_file, out_dir, genome_anno=None, num_workers=1, onematelength=100):
"""
Creating STAR genome index with or without using genome annotation
TODO check whether the fasta and gtf files are uncompressed star works with uncompressed files in this step.
@args fasta_file: reference genome sequence file .fasta format
@type fasta_file: str
@args out_dir: genome index binary file storage place
@type out_dir: str
@args genome_anno: genome annotation file (optional)
@type genome_anno: str
@args num_workers: number of threads to run (default value = 1)
@type num_workers: int
@args onematelength: One Mate Length (default value=100)
@type num_workers: int
"""
try:
subprocess.call(["STAR"], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
except:
exit("Please make sure that the `STAR` binary is in your $PATH")
if not genome_anno:
cli_cmd = 'STAR \
--runMode genomeGenerate \
--genomeDir %s \
--genomeFastaFiles %s \
--runThreadN %d' % (out_dir, fasta_file, num_workers)
else:
## check for the file type
gff_hand = helper.open_file(genome_anno)
for rec in gff_hand:
rec = rec.strip('\n\r')
# skip empty line fasta identifier and commented line
if not rec or rec[0] in ['#', '>']:
continue
# skip the genome sequence
if not re.search('\t', rec):
continue
parts = rec.split('\t')
assert len(parts) >= 8, rec
ftype, tags = GFFParser.attribute_tags(parts[-1])
break
gff_hand.close()
## according to the file type
if ftype:
cli_cmd = 'STAR \
--runMode genomeGenerate \
--genomeDir %s \
--genomeFastaFiles %s \
--runThreadN %d \
--sjdbGTFfile %s \
--sjdbGTFtagExonParentTranscript Parent \
--sjdbOverhang %d' % (out_dir, fasta_file, num_workers, genome_anno, onematelength)
else:
cli_cmd = 'STAR \
--runMode genomeGenerate \
--genomeDir %s \
--genomeFastaFiles %s \
--runThreadN %d \
--sjdbGTFfile %s \
--sjdbGTFfeatureExon exon \
--sjdbOverhang %d' % (out_dir, fasta_file, num_workers, genome_anno, onematelength)
## create downloadpath if doesnot exists
if not os.path.exists(out_dir):
try:
os.makedirs(out_dir)
except OSError:
print "error: cannot create the directory %s." % out_dir
sys.exit(0)
else:## if present any other old index files clean up the folder
for the_file in os.listdir(out_dir):
file_path = os.path.join(out_dir, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception, e:
print e
def experiment_db(config_file, opt_action):
"""
function to collect details of each organism
FIXME descriptions
@args config_file: yaml file contain the information for the experiment
@type config_file: str
"""
## parsing the config file
config_map = yaml.safe_load(open(config_file, "rU"))
data_path = config_map['genome_data_path']['dir']
exp_path = config_map['experiment_data_path']['dir']
org_fasta_file = dict(
A_carolinensis = '%s/A_carolinensis/ensembl_release_79/ensembl_release_79.fas' % data_path,
M_mulatta = "%s/M_mulatta/ensembl_release_79/ensembl_release_79.fas" % data_path,
O_cuniculus = "%s/O_cuniculus/ensembl_release_79/ensembl_release_79.fas" % data_path,
M_gallopavo = "%s/M_gallopavo/ensembl_release_79/ensembl_release_79.fas.bz2" % data_path,
B_anthracis = '%s/B_anthracis/' % data_path,
C_familiaris = '%s/C_familiaris/ensembl_release_79/ensembl_release_79.fas' % data_path,
D_melanogaster = '%s/D_melanogaster/ensembl_release_79/ensembl_release_79.fas' % data_path,
E_caballus = '%s/E_caballus/ensembl_release_79/ensembl_release_79.fas' % data_path,
M_domestica = '%s/M_domestica/ensembl_release_79/ensembl_release_79.fas' % data_path,
O_sativa = '%s/O_sativa/phytozome_v9.0/Osativa_204.fa' % data_path,
A_gambiae = '%s/A_gambiae/ensembl_release_28/ensembl_release_28.fas' % data_path,
B_rapa = '%s/B_rapa/phytozome_v9.0/Brapa_197_stable.fa' % data_path,
G_gallus = '%s/G_gallus/ensembl_release_79/ensembl_release_79.fas' % data_path,
M_musculus = '%s/M_musculus/ensembl_release_79/ensembl_release_79.fas' % data_path,
V_vinifera = '%s/V_vinifera/phytozome_v9.0/phytozome_v9.0.fas.bz2' % data_path,
A_mellifera = '%s/A_mellifera/ensembl_release_28/ensembl_release_28.fas' % data_path,
B_taurus = '%s/B_taurus/ensembl_release_79/ensembl_release_79.fas' % data_path,
C_rubella = '%s/C_rubella/phytozome_v9.0/Crubella_183.fa.gz' % data_path,
D_rerio = '%s/D_rerio/ensembl_release_79/ensembl_release_79.fas' % data_path,
G_max = '%s/G_max/phytozome_v9.0/Gmax_189_filter.fa' % data_path,
M_truncatula = '%s/M_truncatula/STARgenome/Mtruncatula_198.fa' % data_path,
P_pacificus = '%s/P_pacificus/' % data_path,
S_scrofa = '%s/S_scrofa/ensembl_release_79/ensembl_release_79.fas' % data_path,
X_tropicalis = '%s/X_tropicalis/JGIv4-1/JGIv4-1.fa' % data_path,
C_sativus = '%s/C_sativus/phytozome_v9.0/Csativus_122_filtered.fa' % data_path,
D_simulans = '%s/D_simulans/ensembl_release_28/ensembl_release_28.fas' % data_path,
H_sapiens = '%s/H_sapiens/ensembl_release_79/STARgenome/hg19_chrOnly.fa' % data_path,
N_vitripennis = '%s/N_vitripennis/ensembl_release_22/N_vitripennis_dna_sm.fa' % data_path,
P_troglodytes = '%s/P_troglodytes/ensembl_release_79/ensembl_release_79.fas' % data_path,
S_tuberosum = '%s/S_tuberosum/phytozome_v9.0/Stuberosum_206.fa' % data_path,
Z_mays = '%s/Z_mays/phytozome_v9.0/Zmays_181.fa' % data_path,
A_thaliana = '%s/A_thaliana/arabidopsis_tair10/sequences/TAIR9_chr_all.fas' % data_path,
O_aries = '%s/O_aries/ensembl_release_79/ensembl_release_79.fas' % data_path,
C_jacchus = '%s/C_jacchus/ensembl_release_79/ensembl_release_79.fas' % data_path,
C_elegans = '%s/C_elegans/ensembl_release-69/Caenorhabditis_elegans.WBcel215.69.dna.toplevel.fa' % data_path,
O_latipes = '%s/O_latipes/ensembl_release_79/ensembl_release_79.fas' % data_path,
R_norvegicus = '%s/R_norvegicus/ensembl_release_79/ensembl_release_79.fas' % data_path,
G_gorilla = '%s/G_gorilla/ensembl_release_79/ensembl_release_79.fas' % data_path,
P_paniscus = '%s/P_paniscus/eva_mpg_de/eva_mpg_de.fas' % data_path,
C_porcellus = '%s/C_porcellus/ensembl_release_79/ensembl_release_79.fas' % data_path,
O_anatinus = '%s/O_anatinus/ensembl_release_79/ensembl_release_79.fas' % data_path,
A_platyrhynchos = '%s/A_platyrhynchos/ensembl_release_79/ensembl_release_79.fas' % data_path,
O_niloticus = '%s/O_niloticus/ensembl_release_79/ensembl_release_79.fas' % data_path,
L_chalumnae = '%s/L_chalumnae/ensembl_release_79/ensembl_release_79.fas' % data_path,
H_glaber = '%s/H_glaber/naked_mole_rat_db/naked_mole_rat_db.fas' % data_path,
M_eugenii = '%s/M_eugenii/ensembl_release_79/ensembl_release_79.fas' % data_path,
C_briggsae = '%s/C_briggsae/ensembl_release_28/ensembl_release_28.fas' % data_path,
C_japonica = '%s/C_japonica/ensembl_release_28/ensembl_release_28.fas' % data_path,
C_remanei = '%s/C_remanei/ensembl_release_28/ensembl_release_28.fas' % data_path,
P_marinus = '%s/P_marinus/ensembl_release_79/ensembl_release_79.fas' % data_path,
C_brenneri = '%s/C_brenneri/ensembl_release_28/ensembl_release_28.fas' % data_path,
C_intestinalis = '%s/C_intestinalis/ensembl_release_79/ensembl_release_79.fas' % data_path,
S_cerevisiae = '%s/S_cerevisiae/ensembl_release_79/ensembl_release_79.fas' % data_path,
S_pombe = '%s/S_pombe/ensembl_release_28/ensembl_release_28.fas' % data_path,
A_aegypti = '%s/A_aegypti/ensembl_release_28/ensembl_release_28.fas' % data_path,
C_hircus = '%s/C_hircus/ncbi_genome/ncbi_genome.fas' % data_path,
F_catus = '%s/F_catus/ensembl_release_79/ensembl_release_79.fas' % data_path,
T_nigroviridis = '%s/T_nigroviridis/ensembl_release_79/ensembl_release_79.fas' % data_path
)
#C_elegans = '%s/C_elegans/ensembl_release_79/ensembl_release_79.fas' % data_path,
org_gtf_file = dict(
A_carolinensis = '%s/A_carolinensis/ensembl_release_79/ensembl_release_79.gtf' % data_path,
M_mulatta = "%s/M_mulatta/ensembl_release_79/ensembl_release_79.gtf" % data_path,
O_cuniculus = "%s/O_cuniculus/ensembl_release_79/ensembl_release_79.gtf" % data_path,
M_gallopavo = "%s/M_gallopavo/ensembl_release_79/ensembl_release_79.gtf.bz2" % data_path,
B_anthracis = '%s/B_anthracis/ensembl_release-21/Bacillus_anthracis' % data_path,
C_familiaris = '%s/C_familiaris/ensembl_release_79/ensembl_release_79.gtf' % data_path,
D_melanogaster = '%s/D_melanogaster/ensembl_release_79/ensembl_release_79.gtf' % data_path,
E_caballus = '%s/E_caballus/ensembl_release_79/ensembl_release_79.gtf' % data_path,
M_domestica = '%s/M_domestica/ensembl_release_79/ensembl_release_79.gtf' % data_path,
O_sativa = '%s/O_sativa/phytozome_v9.0/Osativa_204_gene.gff3' % data_path,
A_gambiae = '%s/A_gambiae/ensembl_release_28/ensembl_release_28.gtf' % data_path,
B_rapa = '%s/B_rapa/phytozome_v9.0/Brapa_197_gene.gff3' % data_path,
G_gallus = '%s/G_gallus/ensembl_release_79/ensembl_release_79.gtf' % data_path,
M_musculus = '%s/M_musculus/ensembl_release_79/ensembl_release_79.gtf' % data_path,
V_vinifera = '%s/V_vinifera/phytozome_v9.0/phytozome_v9.0.gff.bz2' % data_path,
A_mellifera = '%s/A_mellifera/ensembl_release_28/ensembl_release_28.gtf' % data_path,
B_taurus = '%s/B_taurus/ensembl_release_79/ensembl_release_79.gtf' % data_path,
C_jacchus = '%s/C_jacchus/ensembl_release_79/ensembl_release_79.gtf' % data_path,
C_rubella = '%s/C_rubella/phytozome_v9.0/Crubella_183.gff3' % data_path,
D_rerio = '%s/D_rerio/ensembl_release_79/ensembl_release_79.gtf' % data_path,
G_max = '%s/G_max/phytozome_v9.0/Gmax_189_gene.gff3' % data_path,
N_vitripennis = '%s/N_vitripennis/ensembl_release_22/N_vitripennis.gtf' % data_path,
O_aries = '%s/O_aries/ensembl_release_79/ensembl_release_79.gtf' % data_path,
M_truncatula = '%s/M_truncatula/' % data_path,
P_pacificus = '%s/P_pacificus/ensembl_release-22/.gtf' % data_path,
S_scrofa = '%s/S_scrofa/ensembl_release_79/ensembl_release_79.gtf' % data_path,
X_tropicalis = '%s/X_tropicalis/JGIv4-1/JGIv4-1.gff' % data_path,
C_sativus = '%s/C_sativus/phytozome_v9.0/Csativus_122_gene.gff3' % data_path,
D_simulans = '%s/D_simulans/ensembl_release_28/ensembl_release_28.gtf' % data_path,
H_sapiens = '%s/H_sapiens/ensembl_release_79/ensembl_release_79.gtf' % data_path,
P_troglodytes = '%s/P_troglodytes/ensembl_release_79/ensembl_release_79.gtf' % data_path,
S_tuberosum = '%s/S_tuberosum/phytozome_v9.0/Stuberosum_206_gene.gff3' % data_path,
Z_mays = '%s/Z_mays/phytozome_v9.0/Zmays_181_gene.gff3' % data_path,
A_thaliana = '%s/A_thaliana/arabidopsis_tair10/annotations/TAIR10_GFF3_genes.gff' % data_path,
C_elegans = '%s/C_elegans/ensembl_release_79/ensembl_release_79.gtf' % data_path,
D_discoideum = '%s/D_discoideum/' % data_path,
D_yakuba = '%s/D_yakuba/ensembl_release-22/.gff3' % data_path,
O_latipes = '%s/O_latipes/ensembl_release_79/ensembl_release_79.gtf' % data_path,
R_norvegicus = '%s/R_norvegicus/ensembl_release_79/ensembl_release_79.gtf' % data_path,
D_pseudoobscura = '%s/D_pseudoobscura/ensembl_release-22/.gff3' % data_path,
T_pseudonana = '%s/T_pseudonana/Thaps3/.gff' % data_path,
G_gorilla = '%s/G_gorilla/ensembl_release_79/ensembl_release_79.gtf' % data_path,
C_porcellus = '%s/C_porcellus/ensembl_release_79/ensembl_release_79.gtf' % data_path,
O_anatinus = '%s/O_anatinus/ensembl_release_79/ensembl_release_79.gtf' % data_path,
A_platyrhynchos = '%s/A_platyrhynchos/ensembl_release_79/ensembl_release_79.gtf' % data_path,
O_niloticus = '%s/O_niloticus/ensembl_release_79/ensembl_release_79.gtf' % data_path,
L_chalumnae = '%s/L_chalumnae/ensembl_release_79/ensembl_release_79.gtf' % data_path,
C_briggsae = '%s/C_briggsae/ensembl_release_28/ensembl_release_28.gtf' % data_path,
M_eugenii = '%s/M_eugenii/ensembl_release_79/ensembl_release_79.gtf' % data_path,
C_remanei = '%s/C_remanei/ensembl_release_28/ensembl_release_28.gtf' % data_path,
C_brenneri = '%s/C_brenneri/ensembl_release_28/ensembl_release_28.gtf' % data_path,
C_intestinalis = '%s/C_intestinalis/ensembl_release_79/ensembl_release_79.gtf' % data_path,
S_pombe = '%s/S_pombe/ensembl_release_28/ensembl_release_28.gtf' % data_path,
P_marinus = '%s/P_marinus/ensembl_release_79/ensembl_release_79.gtf' % data_path,
S_cerevisiae = '%s/S_cerevisiae/ensembl_release_79/ensembl_release_79.gtf' % data_path,
C_japonica = '%s/C_japonica/ensembl_release_28/ensembl_release_28.gtf' % data_path,
F_catus = '%s/F_catus/ensembl_release_79/ensembl_release_79.gtf' % data_path,
A_aegypti = '%s/A_aegypti/ensembl_release_28/ensembl_release_28.gtf' % data_path,
C_hircus = '%s/C_hircus/ncbi_genome/ncbi_genome.gff' % data_path,
T_nigroviridis = '%s/T_nigroviridis/ensembl_release_79/ensembl_release_79.gtf' % data_path
)
## TODO algorithms details
#A_aegypti = '%s/A_aegypti/ensembl_release_28/ensembl_release_28.gtf' % data_path,
#F_catus = '%s/F_catus/ensembl_release_79/ensembl_release_79.gtf' % data_path,
#C_hircus = '%s/C_hircus/ncbi_genome/ncbi_genome.gff' % data_path,
#H_glaber = '%s/H_glaber/naked_mole_rat_db/naked_mole_rat_db.gtf' % data_path,
## experiment details
org_db = defaultdict()
for ent in config_map['experiment']:
species_name = ent['organism_name']
sra_run_id = ent['sra_run_id']
genome_build_version = ent['genome_build_version']
db_server = ent['release_db']
## mapping to short names arabidopsis_thaliana --> A_thaliana
genus, species = species_name.strip().split("_")
short_name = "%s_%s" % (genus[0].upper(), species)
org_db[short_name] = dict(short_name = short_name)
org_db[short_name]['long_name'] = species_name
org_db[short_name]['sra_run_id'] = sra_run_id
org_db[short_name]['genome_release_db'] = genome_build_version
## the broad path to the experiment
org_db[short_name]['genome_dir'] = data_path
org_db[short_name]['experiment_dir'] = exp_path
build_release = genome_build_version.split("_")
org_db[short_name]['release_db'] = db_server ## ensembl_metazoa, phytozome
org_db[short_name]['release_nb'] = build_release[-1] ## build number
sra_files = [] ## sequencing reads files
if os.path.isdir("%s/%s/source_data" % (exp_path, short_name)):
for sra_file in os.listdir("%s/%s/source_data" % (exp_path, short_name)):
file_prefx, ext = os.path.splitext(sra_file)
if ext == ".sra": ## skipping the original .sra binary file
continue
if re.search(sra_run_id, sra_file):
sra_files.append(sra_file)
else:
print "warning: missing sequencing read trace file %s/%s/source_data" % (exp_path, short_name)
org_db[short_name]['fastq_path'] = "%s/%s/source_data" % (exp_path, short_name)
org_db[short_name]['fastq'] = sra_files
## read mapping, read assembly and label generation working folders
for sub_dir in ['read_mapping', 'signal_labels', 'trans_pred', 'source_data']:
work_path = "%s/%s/%s" % (exp_path, short_name, sub_dir)
if not os.path.isdir(work_path):
try:
os.makedirs(work_path)
except OSError:
exit("error: cannot create the directory %s." % work_path)
org_db[short_name]['read_map_dir'] = "%s/%s/read_mapping" % (exp_path, short_name)
org_db[short_name]['read_assembly_dir'] = "%s/%s/trans_pred" % (exp_path, short_name)
org_db[short_name]['labels_dir'] = "%s/%s/signal_labels" % (exp_path, short_name)
## calculate the sequence read length
readlength = 0
if opt_action in ["2", "3"]: ## perform this action only for selected options
if sra_files:
fqfile = os.path.join(org_db[short_name]['fastq_path'], sra_files[0])
print 'using sequencing read file %s to determine readLength' % fqfile
fh = helper.open_file(fqfile)
for rec in SeqIO.parse(fh, "fastq"):
readlength = len(rec.seq)
break
fh.close()
org_db[short_name]['read_length'] = readlength
## check for the genome sequence file
if short_name in org_fasta_file:
if os.path.isfile(org_fasta_file[short_name]):
org_db[short_name]['fasta'] = org_fasta_file[short_name]
else:
org_db[short_name]['fasta'] = None
else:
print "warning: missing genome sequence file for %s under %s/%s/%s" % (short_name, data_path, short_name, genome_build_version)
org_db[short_name]['fasta'] = None
if not os.path.isdir("%s/%s/%s/STARgenome" % (data_path, short_name, genome_build_version)):
try:
os.makedirs("%s/%s/%s/STARgenome" % (data_path, short_name, genome_build_version))
except OSError:
exit("error: cannot create the directory %s/%s/%s" % (data_path, short_name, genome_build_version))
org_db[short_name]['genome_index_dir'] = "%s/%s/%s/STARgenome/" % (data_path, short_name, genome_build_version)
## check the genome annotation
if short_name in org_gtf_file:
if os.path.isfile(org_gtf_file[short_name]):
org_db[short_name]['gtf'] = org_gtf_file[short_name]
if opt_action in ["2", "3", "4", "c"]: ## perform this action only for selected options
## get the gtf feature lengths
from fetch_remote_data import prepare_data as pd
feat_len_db = pd.make_anno_db(org_gtf_file[short_name])
org_db[short_name]['max_intron_len'] = feat_len_db['max_intron']
org_db[short_name]['max_exon_len'] = feat_len_db['max_exon']
else:
exit("error: the provided gtf file %s is not available to read. Please check!" % org_gtf_file[short_name])
else:
print("warning: missing annotation file for %s under %s/%s/%s" % (short_name, data_path, short_name, genome_build_version))
org_db[short_name]['gtf'] = None
org_db[short_name]['max_intron_len'] = None
org_db[short_name]['max_exon_len'] = None
print("fetched details for %s" % short_name)
return org_db
def __main__():
try:
fastq_path = sys.argv[1]
except:
print __doc__
sys.exit(-1)
fastq_1_file = None
fastq_2_file = None
# TODO expecting the left and right reads in the base folder with .fastq ending. Make it is common general form
## get the files from base path
for filename in os.listdir(fastq_path):
if re.search(r'_1.fastq', filename):
fastq_1_file = filename
if re.search(r'_2.fastq', filename):
fastq_2_file = filename
print fastq_1_file, fastq_2_file
print
## count the number of reads and calculate the sub sample
fqh = helper.open_file('%s/%s' % (fastq_path, fastq_1_file))
read_cnt = 0
for rec in SeqIO.parse(fqh, 'fastq'):
read_cnt +=1
fqh.close()
print '%d Number of reads in FASTQ' % read_cnt
print
## what percentage sub-sample
percentage = 1
sub_count = int(round((percentage*read_cnt)/100.0))
assert sub_count <= read_cnt, ' %d (sub-sample count) should be less than total read count %d' % (sub_count, read_cnt)
print "%d Sub sample count" % sub_count
print
try:
accept_prob = (1.0*sub_count)/read_cnt
except:
accept_prob = 1
print accept_prob
sub_fastq_1_file = "%d_percentage_%s.bz2" % (percentage, fastq_1_file)
sub_fastq_2_file = "%d_percentage_%s.bz2" % (percentage, fastq_2_file)
## writing out sub sample files
try:
subFile_1 = bz2.BZ2File("%s/%s" % (fastq_path, sub_fastq_1_file), 'wb')
subFile_2 = bz2.BZ2File("%s/%s" % (fastq_path, sub_fastq_2_file), 'wb')
except Exception as error:
sys.exit(error)
total_cnt = 0
sample_cnt = 0
left_reads = dict()
fqh = helper.open_file('%s/%s' % (fastq_path, fastq_1_file))
for rec in SeqIO.parse(fqh, 'fastq'):
rnb = random.random()
total_cnt += 1
if rnb <= accept_prob:
## @UNC15-SN850_63:4:1101:1103:2151/1 @UNC15-SN850_63:4:1101:1103:2151/2
read_id = rec.id.split('/')
if len(read_id) > 1:
left_reads[read_id[0]] = 0
else:
## @UNC11-SN627:294:C236MACXX:5:1101:1430:2218 1:N:0:GGNTAC @UNC11-SN627:294:C236MACXX:5:1101:1430:2218 2:N:0:GGNTAC
left_reads[rec.id] = 0
sample_cnt += 1
subFile_1.write(rec.format("fastq"))
if sub_count == sample_cnt:
break
fqh.close()
subFile_1.close()
fqh = helper.open_file('%s/%s' % (fastq_path, fastq_2_file))
for rec in SeqIO.parse(fqh, 'fastq'):
read_id = rec.id.split('/')
if len(read_id) > 1:
## @UNC15-SN850_63:4:1101:1103:2151/1
if read_id[0] in left_reads:
subFile_2.write(rec.format("fastq"))
else:
## @UNC11-SN627:294:C236MACXX:5:1101:1430:2218 1:N:0:GGNTAC
if rec.id in left_reads:
subFile_2.write(rec.format("fastq"))
fqh.close()
subFile_2.close()
print "%s/%s" % (fastq_path, sub_fastq_1_file)
print "%s/%s" % (fastq_path, sub_fastq_2_file)
print
print '%d Number of reads scanned' % total_cnt
print '%d Number of reads in' % sample_cnt
print
def check_splice_site_consensus(fas_file, splice_region):
"""
splice site consensus check
"""
sys.stdout.write("splice site sequence consensus check started...\n")
get_gene_models = defaultdict()
splice_site_con = 0
fas_fh = helper.open_file(fas_file)
for fas_rec in SeqIO.parse(fas_fh, "fasta"):
if fas_rec.id in splice_region:
for details in splice_region[fas_rec.id]:
for genes, regions in details.items():
acc_cons_cnt = 0
don_cons_cnt = 0
if len(regions
) == 1: ## single exon long transcripts no checks
get_gene_models[(fas_rec.id, genes[0], genes[1],
genes[2])] = 1
continue
for region in regions:
if genes[-1] == '+':
#if not numpy.isnan(region[0]):## acceptor splice site
if region[0]: ## acceptor splice site
acc_seq = fas_rec.seq[int(region[0]) -
3:int(region[0]) - 1]
if str(acc_seq).upper() == "AG":
acc_cons_cnt += 1
if region[1]:
don_seq = fas_rec.seq[int(region[1]
):int(region[1]) + 2]
if str(don_seq).upper() == "GT":
don_cons_cnt += 1
elif genes[-1] == '-':
if region[0]: ## donor splice site
don_seq = fas_rec.seq[int(region[0]) -
3:int(region[0]) - 1]
don_seq = don_seq.reverse_complement()
if str(don_seq).upper() == "GT":
don_cons_cnt += 1
if region[1]:
acc_seq = fas_rec.seq[int(region[1]
):int(region[1]) + 2]
acc_seq = acc_seq.reverse_complement()
if str(acc_seq).upper() == "AG":
acc_cons_cnt += 1
## check for half of the consensus sites
if acc_cons_cnt > (len(regions) / 2) and don_cons_cnt > (
len(regions) / 2):
get_gene_models[(fas_rec.id, genes[0], genes[1],
genes[2])] = 1
else:
splice_site_con += 1
fas_fh.close()
sys.stdout.write("...considering %d best transcripts\n" %
len(get_gene_models))
sys.stdout.write("discarding transcripts...\n")
sys.stdout.write("\t%d splice-site consensus sequence missing\n" %
splice_site_con)
return get_gene_models
