def standard_test_procedure(self, cline):
"""Standard testing procedure used by all tests."""
# Overwrite existing files.
cline.force = True
# Mark output files for later cleanup.
self.add_file_to_clean(cline.outfile)
if cline.guidetree_out:
self.add_file_to_clean(cline.guidetree_out)
input_records = SeqIO.to_dict(SeqIO.parse(cline.infile, "fasta"))
self.assertEqual(str(eval(repr(cline))), str(cline))
output, error = cline()
self.assertTrue(not output or output.strip().startswith("CLUSTAL"))
# Test if ClustalOmega executed successfully.
self.assertTrue(error.strip() == "" or
error.startswith("WARNING: Sequence type is DNA.") or
error.startswith("WARNING: DNA alignment is still experimental."))
# Check the output...
align = AlignIO.read(cline.outfile, "clustal")
output_records = SeqIO.to_dict(SeqIO.parse(cline.outfile, "clustal"))
self.assertEqual(len(set(input_records.keys())), len(set(output_records.keys())))
for record in align:
self.assertEqual(str(record.seq), str(output_records[record.id].seq))
# TODO - Try and parse this with Bio.Nexus?
if cline.guidetree_out:
self.assertTrue(os.path.isfile(cline.guidetree_out))
def calculate_global_identity_blastp_fasta(args):
# import proteins as fasta
protein_dict = SeqIO.to_dict(SeqIO.parse(args.protein_fp, "fasta"))
proteindb_dict = SeqIO.to_dict(SeqIO.parse(args.protdb_fp, "fasta"))
final = open(args.output_fp + "/final_protein_identity.txt", 'w')
for result in open(args.output_fp + "/blastp_results.txt", 'r'):
id_number1 = result.split()[0]
print(id_number1)
id_number2 = result.split()[1]
print(id_number2)
SeqIO.write(protein_dict[id_number1], args.output_fp + "/temp_fasta1.fa", "fasta")
SeqIO.write(proteindb_dict[id_number2], args.output_fp + "/temp_fasta2.fa", "fasta")
needle = "needle -outfile=" + args.output_fp + "/temp_identity.txt -asequence " + args.output_fp + "/temp_fasta1.fa -bsequence " + args.output_fp + "/temp_fasta2.fa -gapope\
n=10 -gapextend=0.5"
h.run_command(needle)
identity = "NA"
for line in open(args.output_fp + "/temp_identity.txt", 'r'):
if line.startswith("# Identity:"):
identity = line.split("(")[1].rstrip("\n").rstrip(")").rstrip("%")
final.write(id_number1 + "\t" + retrieve_annotations(id_number1, args.anno_tab) + "\t"+ id_number2 + "\t" + identity + "\n")
h.run_command("rm " + args.output_fp + "/temp_*")
def readFASTA_SeqIO(x):
"""
Is sequence file? Load from file if so. File should be FASTA format
Use SeqIO
"""
o = []
if type(x) is list:
for idx, i in enumerate(x):
if os.path.isfile(i):
with open(i, "r") as f:
d = SeqIO.to_dict(SeqIO.parse(f, "fasta"))
o.append(d)
else:
o.append(x)
elif os.path.isfile(str(x)):
with open(x, "r") as f:
o = SeqIO.to_dict(SeqIO.parse(f, "fasta"))
elif isinstance(x,dict):
#proper offset
if isinstance([i for i in x.values()][0], SeqIO.SeqRecord):
o = x
else:
raise TypeError("input must be type filename or SeqIO.SeqRecord)")
elif isinstance(x, SeqIO.SeqRecord):
o = x
else:
raise TypeError("input must be type filename or SeqIO.SeqRecord)")
return o
def genebank_fix_n_read(gb_fname, key_func_type="gi"):
"""locations formatted as '1^593' cause BioPython error while reading genbanks ...
We are addressing that by fixing genbank source on the fly ..."""
print "Reading %s with genebank records from the NCBI fetch ..." % gb_fname
# choose the key-function based on the 'key_func_type' argument:
if key_func_type == "gi":
key_function = lambda rec: rec.annotations["gi"]
if key_func_type == "id":
key_function = lambda rec: rec.id
print "Using %s as a key." % key_func_type
#
with __warnings.catch_warnings():
# e.g. BiopythonParserWarning: Dropping bond qualifier in feature location
#
__warnings.simplefilter("ignore", __BiopythonParserWarning)
#
gb_recs_iter = __SeqIO.parse(gb_fname, "gb")
try:
gbrecs = __SeqIO.to_dict(gb_recs_iter, key_function=key_function)
except ValueError, er_msg:
print "Catched ValueError: %s" % str(er_msg)
# #
# Invalid between location '1^593'
# Try to fix that thing, by replacing '^' with '..'
file_string_io = __fix_genbank(er_msg, gb_fname)
gb_recs_iter = __SeqIO.parse(file_string_io, "gb")
gbrecs = __SeqIO.to_dict(gb_recs_iter, key_function=key_function)
def read_seqs(self, sequence_file):
"""
read sequences from uniprot files (.dat or .fasta) or from lists or dicts of BioPython SeqRecords
and make them available for fast search. Appending also with this function.
:param sequence_file: uniprot files (.dat or .fasta)
:return:
"""
recs = sequence_file
if not isinstance(sequence_file, dict) and not isinstance(sequence_file, list):
try:
with open(sequence_file, 'rb') as f:
if sequence_file.endswith('.fa') or sequence_file.endswith('.fasta'):
recs = SeqIO.to_dict(SeqIO.parse(f, "fasta"))
else: # assume it is a dat file
recs = SeqIO.to_dict(SeqIO.parse(open(sequence_file), 'swiss'))
except:
warnings.warn("Could not read file", UserWarning)
return
if isinstance(sequence_file, list):
recs = SeqIO.to_dict(sequence_file)
if recs:
self.collection.update(recs)
self.searchstring = '#'.join([str(x.seq) for x in self.collection.values()]).decode('ascii')
self.accs = self.collection.keys()
self.idx = list()
self.idx.append(0)
for i, v in enumerate(self.collection.values()):
self.idx.append(1 + self.idx[-1] + len(self.collection.values()[i].seq))
return
def aa2nt_aln(aa_aln,nt_fasta,outfile): # store the nucleotide sequences in a dictionary nt_file = open(nt_fasta,"r") nt_dict = SeqIO.to_dict(SeqIO.parse(nt_file, "fasta")) nt_file.close() # store the nucleotide sequences in a dictionary aa_file = open(aa_aln,"r") aa_dict = SeqIO.to_dict(SeqIO.parse(aa_file, "fasta")) aa_file.close() # read through an aa sequence one site at a time # if the site is not a gap insert the corresponding codon into a new nt sequence # if it is a gap, insert three gap characters seq_name=1 for seq in aa_dict: new_seq="" counter=0 for character in aa_dict[seq]: if character != '-': if character != '*': new_seq = new_seq+nt_dict[seq].seq[counter:counter+3] counter = counter+3 else: new_seq = new_seq+"---" counter = counter+3 else: new_seq = new_seq+"---" print >>outfile, ">seq"+str(seq_name) print >>outfile, new_seq seq_name=seq_name+1 outfile.close()
def create_fastas(componentDict, plasmidFile):
"""Create new nucleotide and amino acid fasta files that are separate
for each component from the assembly with ESX genes"""
with open(plasmidFile) as infile:
for line in infile:
line = line.strip().split()
strain = line[0]
aa = 'newAnnotations/{0}_plasmid/{0}_plasmid.faa'.format(strain)
nuc = 'newAnnotations/{0}_plasmid/{0}_plasmid.ffn'.format(strain)
gff = 'newAnnotations/{0}_plasmid/{0}_plasmid.gff'.format(strain)
aa_dict = SeqIO.to_dict(SeqIO.parse(aa, "fasta"))
nuc_dict = SeqIO.to_dict(SeqIO.parse(nuc, "fasta"))
for c in componentDict[strain]:
new_aa = []
new_nuc = []
with open(gff, 'r') as gff_file:
for line in gff_file:
if line[0] != '#':
if line[0] == '>':
break
line = line.strip().split()
component = line[0][-2]
gene = line[8].split(';')[0][3:]
if component == c:
try:
new_aa.append(aa_dict[gene])
new_nuc.append(nuc_dict[gene])
except KeyError:
print("{0} does not exist".format(gene))
SeqIO.write(new_aa, "{0}_{1}.faa".format(strain, c), "fasta")
SeqIO.write(new_nuc, "{0}_{1}.ffn".format(strain, c), "fasta")
def test_seqfile_source ( self ) :
"""
Test BioSeqs.from_seqfile() and BioSeqs.write() methods.
"""
infile = 'Fasta/f001.fasta'
self.assertTrue(os.path.isfile(infile))
seq_db = BioSeqs.from_seqfile(infile, 'fasta')
outfile = 'tmp_test.gb'
outrepfile = 'tmp_test.rep'
self.files_to_clean.add(outfile)
self.files_to_clean.add(outrepfile)
seq_db.write(outfile)
self.assertTrue(os.path.isfile(outfile))
# Check the content of both sequence files
indict = SeqIO.to_dict(SeqIO.parse(infile, 'fasta'))
outdict = SeqIO.to_dict(SeqIO.parse(outfile, 'gb'))
self.assertEqual(len(indict), len(outdict))
for key, value in viewitems(indict) :
self.assertEqual(str(value.seq), str(outdict[key].seq))
# Check the content of the report file
with open(outrepfile, 'r') as repfile :
for line in repfile.readlines() :
self.assertTrue(('Num. sequences: 50' in line) or
('History:' in line) or
(bool(re.match(r"""\d\d\d\d/\d\d/\d\d\ \d\d:\d\d:\d\d[ ]+
local[ ]+.*Tests/Fasta/f001\.fasta
[ ]+fasta""", line, re.VERBOSE))))
def runAlignments(myFileProt, outDir):
# assumes that nucleotide and amino acid files have the smae name scheme except for extension
# faa versus fna
baseName = os.path.splitext(os.path.basename(myFileProt))[0]
myFileDna = os.path.join(os.path.dirname(myFileProt), baseName+".fna")
protAliOutFile = os.path.join(outDir, baseName+".aln")
pal2nalOutFile = os.path.join(outDir, baseName+".pal2nal")
sequences = SeqIO.to_dict(SeqIO.parse(myFileProt, 'fasta'))
if len(sequences) == 1:
# nothing to align, just write to files and continute to next file
SeqIO.write(sequences.values(), open( protAliOutFile, 'w'), 'clustal')
sequences = SeqIO.to_dict(SeqIO.parse(myFileDna, 'fasta'))
SeqIO.write(sequences.values(), open(pal2nalOutFile, 'w'), 'clustal')
print "Single seq file, exiting"
return
clustalWCmd = ['/Users/mahdi/programs/clustalw-2.1-macosx/clustalw2', '-ALIGN',
'-INFILE=%s' % myFileProt, '-TYPE=PROTEIN', '-OUTFILE=%s' % protAliOutFile, '-OUTORDER=INPUT']
# mafftCmd = ['mafft-linsi', '--amino', '--clustalout', myFileProt , '>', protAliOutFile]
pal2nalcmd = ['perl', '/Users/mahdi/programs/pal2nal.v14/pal2nal.pl', protAliOutFile, myFileDna, '-codontable', '9', ]
with open(os.devnull, 'w') as fnull, open(pal2nalOutFile, 'w') as out:
subprocess.call(clustalWCmd, stderr=fnull, stdout=fnull)
subprocess.call(pal2nalcmd, stdout=out)
print "completed file %s" % baseName
def concat_seqs(trimmedDir, outputFile):
"""
Pull out all the trimmed sequences associated with one genome and concatenate into one sequence.
Then put all the sequences together in one file.
"""
alignedFiles = glob.glob(trimmedDir+'/*.afa')
#Use the first one to get the list of strains
strainNames = [x.split('|')[0] for x in SeqIO.to_dict(SeqIO.parse(open(alignedFiles[0], 'r'), 'fasta')).keys()]
#Create a dictionary of empty strings to contain the concatenated sequences
concatSeqs = {k:'' for k in strainNames}
for fileName in alignedFiles:
with open(fileName,'r') as FID:
seqs = SeqIO.to_dict(SeqIO.parse(FID, 'fasta'))
for strainName, record in seqs.items():
concatSeqs[strainName.split('|')[0]] += record.seq.tostring()
with open(outputFile, 'w') as FID:
for strain in strainNames:
FID.write('>{0}\n'.format(strain))
FID.write(concatSeqs[strain])
FID.write('\n')
def test_join ( self ) :
"""
Test BioSeqs.join() method.
"""
infile1 = 'Fasta/f001.fasta'
infile2 = 'Phylip/f003.phylip'
self.assertTrue(os.path.isfile(infile1))
self.assertTrue(os.path.isfile(infile2))
seq_db = BioSeqs.from_seqfile(infile1, 'fasta')
extra_db = BioSeqs.from_seqfile(infile2, 'phylip')
seq_db.join(extra_db)
# Check the sequence data
indict1 = SeqIO.to_dict(SeqIO.parse(infile1, 'fasta'))
indict2 = SeqIO.to_dict(SeqIO.parse(infile2, 'phylip'))
self.assertEqual(len(indict1) + len(indict2), len(seq_db))
for key, value in viewitems(indict1) :
self.assertEqual(str(value.seq), str(seq_db.data[key].seq))
for key, value in viewitems(indict2) :
self.assertEqual(str(value.seq), str(seq_db.data[key].seq))
# Check the report information
self.assertIn('local', seq_db._report[0][1])
self.assertIn('Tests/Fasta/f001.fasta', seq_db._report[0][2])
self.assertIn('fasta', seq_db._report[0][3])
self.assertIn('local', seq_db._report[1][1])
self.assertIn('Tests/Phylip/f003.phylip', seq_db._report[1][2])
self.assertIn('phylip', seq_db._report[1][3])
def standard_test_procedure(self, cline):
"""Standard testing procedure used by all tests."""
self.assertTrue(str(eval(repr(cline))) == str(cline))
input_records = SeqIO.to_dict(SeqIO.parse(cline.infile, "fasta"),
lambda rec : rec.id.replace(":", "_"))
#Determine name of tree file
if cline.newtree:
tree_file = cline.newtree
else:
#Clustalw will name it based on the input file
tree_file = os.path.splitext(cline.infile)[0] + ".dnd"
# Mark generated files for later removal
self.add_file_to_clean(cline.outfile)
self.add_file_to_clean(tree_file)
output, error = cline()
self.assertTrue(output.strip().startswith("CLUSTAL"))
self.assertTrue(error.strip() == "")
#Check the output...
align = AlignIO.read(cline.outfile, "clustal")
#The length of the alignment will depend on the version of clustalw
#(clustalw 2.1 and clustalw 1.83 are certainly different).
output_records = SeqIO.to_dict(SeqIO.parse(cline.outfile,"clustal"))
self.assertTrue(set(input_records.keys()) == set(output_records.keys()))
for record in align:
self.assertTrue(str(record.seq) == str(output_records[record.id].seq))
self.assertTrue(str(record.seq).replace("-", "") ==
str(input_records[record.id].seq))
#Check the DND file was created.
#TODO - Try and parse this with Bio.Nexus?
self.assertTrue(os.path.isfile(tree_file))
def main():
d = raw_input("DNA filename: ")
p = raw_input("matching Protein filename: ")
a_seq = SeqIO.to_dict(SeqIO.parse(p,'fasta'))
d_seq = SeqIO.to_dict(SeqIO.parse(d,'fasta'))
t = "\t".join(["Seqid","Cohort","NT_len","AA_len","NT_N","NT_R","NT_Y","NT_W","NT_S","NT_M","NT_K","NT_H","NT_B","NT_V","NT_D","AA_X","AA_B","AA_Z"])+"\n"
o = open(d+".freq","w")
o.write(t)
for sid in a_seq.keys():
a = sid.split('|')
NT_len = str(len(d_seq[sid].seq))
AA_len = str(len(a_seq[sid].seq))
NT_N = str(d_seq[sid].seq.count('N'))
NT_R = str(d_seq[sid].seq.count('R'))
NT_Y = str(d_seq[sid].seq.count('Y'))
NT_W = str(d_seq[sid].seq.count('W'))
NT_S = str(d_seq[sid].seq.count('S'))
NT_M = str(d_seq[sid].seq.count('M'))
NT_K = str(d_seq[sid].seq.count('K'))
NT_H = str(d_seq[sid].seq.count('H'))
NT_B = str(d_seq[sid].seq.count('B'))
NT_V = str(d_seq[sid].seq.count('V'))
NT_D = str(d_seq[sid].seq.count('D'))
AA_X = str(a_seq[sid].seq.count('X'))
AA_B = str(a_seq[sid].seq.count('B'))
AA_Z = str(a_seq[sid].seq.count('Z'))
o.write("\t".join([a[0],a[1],NT_len,AA_len,NT_N,NT_R,NT_Y,NT_W,NT_S,NT_M,NT_K,NT_H,NT_B,NT_V,NT_D,AA_X,AA_B,AA_Z])+"\n")
o.close()
def standard_test ( self, informat, outformat, params ) :
"""
Standard testing procedure used by all tests.
Arguments :
informat ( string )
Input file format.
outformat ( string )
Output file format.
params ( string )
Arguments passed to the alignment tool.
"""
infile = '{}/f001.{}'.format(informat.capitalize(), informat)
outfile = 'tmp_test.aln'
self.add_file_to_clean(outfile)
# Check the input
self.assertTrue(os.path.isfile(infile))
self.assertEqual(len(list(SeqIO.parse(infile, informat))), 50)
# Generate the alignment
Align.get_alignment(muscle_exe, infile, informat, args=params,
outfile=outfile, outfile_format=outformat)
# Check the output
self.assertTrue(os.path.isfile(outfile))
out_align = SeqIO.to_dict(SeqIO.parse(outfile, outformat))
prevfile = '{}/f001.muscle_{}.aln'.format(outformat.capitalize(),
params)
self.assertTrue(os.path.isfile(prevfile))
prev_align = SeqIO.to_dict(SeqIO.parse(prevfile, outformat))
self.assertEqual(len(viewkeys(out_align)), len(viewkeys(prev_align)))
for key, value in viewitems(out_align) :
self.assertEqual(str(value.seq), str(prev_align[key].seq))
def init_pair(pair):
"""Return the genomes SeqIO'ed as a dict from the bases and aa."""
genomes = {"base": [],
"aa": []}
for specie in pair:
aa_genome = os.path.join(c.BASE_PATH, c.OUTPUT, specie)
base_genome = os.path.join(c.BASE_PATH, c.GENOMES, specie)
if not os.path.isfile(base_genome):
raise IOError("File {0} doesn't exist".format(specie))
# Translate both genomes only if they doesn't exist
if not os.path.isfile(aa_genome):
translate_fasta(base_genome,
output_path=os.path.dirname(aa_genome))
# If we are here without error, append each genome to genomes
genomes["base"].append(
SeqIO.to_dict(SeqIO.parse(base_genome, "fasta")))
genomes["aa"].append(
SeqIO.to_dict(SeqIO.parse(aa_genome, "fasta")))
genomes["base"] = clean_dict(genomes["base"])
genomes["aa"] = clean_dict(genomes["aa"])
return genomes
def main(infile, gff, outfile, ftype='CDS', use_phase=False, translate=False):
ref_seq = SeqIO.to_dict(SeqIO.parse(infile, format="fasta"))
# Parse GFF annotations.
genome_with_features = GFF.parse(
gff,
base_dict=ref_seq
)
""" bcbio-gff codes exons, mRNA etc as subfeatures which is now
depreciated in biopython, this code fixes that issue. """
new_genome_with_features = list()
for scaffold in genome_with_features:
new_features = list()
for feature in scaffold.features:
gene_features = subfeatures(feature)
new_features.extend(gene_features)
scaffold.features = new_features
new_genome_with_features.append(scaffold)
""" Genome with features doesn't have scaffolds without any gff
features. Here I update the existing records in genome with the
new ones containing features. """
ref_seq.update(SeqIO.to_dict(new_genome_with_features))
sequences = list()
for scaffold, sequence in ref_seq.items():
for feature in sequence.features:
if feature.type != ftype:
continue
start = feature.location.start
end = feature.location.end
try:
phase = int(feature.qualifiers['phase'][0])
except KeyError:
phase = 0
strand = feature.location.strand
if use_phase:
fseq = feature.extract(sequence)[phase:]
else:
fseq = feature.extract(sequence)
fseq.id = feature.id
fseq.name = feature.id
strand = '-' if strand == -1 else '+'
fseq.description = "{}:{}-{}[{}]".format(
scaffold,
start,
end,
strand,
)
if translate:
tseq = fseq.seq.translate()
fseq.seq = tseq
sequences.append(fseq)
SeqIO.write(sequences, outfile, 'fasta')
return
def main():
parser = OptionParser()
parser.add_option("-i", "--input", action="store", dest="input", help="input file to make phylotree")
parser.add_option("-g", "--germline", action="store", dest="germline", help="germline fasta")
parser.add_option("-o", "--output" , action="store", dest="output", help="the file where you want all your data")
(options,args) = parser.parse_args()
if len(sys.argv) < 2:
dowhat()
parser.print_help()
exit()
open(options.output, 'w').write("Your Sequence Results:\n\n")
copy(options.input, "workable.fasta")
copy(options.germline, "germ.fasta")
list_of_database_files = SeqIO.to_dict(SeqIO.parse("workable.fasta", "fasta"))
while list_of_database_files:
list_of_database_files = SeqIO.to_dict(SeqIO.parse("workable.fasta", "fasta"))
populate_database("workable.fasta")
print "***DatabasePopulated***"
newsequence_search = open("germ.fasta" , "r")
cline = NcbiblastpCommandline(matrix="PAM30", evalue="20", word_size="2", query="germ.fasta", cmd='blastp', db="temporary_database", out="blastout")
newsequence_search.close
print "****Cline = *** --->", cline
call_blast(cline)
print "***Call_blast_successful***"
result_handle = open('blastout')
print "***result handle successful***"
blast_parser = NCBIStandalone.BlastParser()
print "***blast_parser****"
blast_record = blast_parser.parse(result_handle)
print "***blast_record***"
newsequence_search = open("germ.fasta", 'w')
newsequence_search.write(">" + str(blast_record.alignments[0].title[2:]) + "\n" + str(blast_record.alignments[0].hsps[0].sbjct))
current_object = blast_record.alignments[0].title[2:]
print current_object
newfile = open(options.output, 'a')
newfile.write(str(blast_record.alignments[0].hsps[0].query[:]) + "----> Query\n")
newfile.write(str(blast_record.alignments[0].hsps[0].match[:]) + "----> Score of: " + str(blast_record.alignments[0].hsps[0].score) + "\n")
newfile.write(str(blast_record.alignments[0].hsps[0].sbjct[:]) + "----> Template\n\n")
list_of_database_files.pop(current_object)
SeqIO.write(list_of_database_files.values(), "workable.fasta", "fasta")
def fasta_dict(nast_file, inf_file): '''Make dictionaries of sequence objects. I'll assume they are untouched by jalview but sanity code should be here, before loading the files as dicts. ''' nast_d= SeqIO.to_dict(SeqIO.parse(open(nast_file, 'rU'), 'fasta')) inf_d= SeqIO.to_dict(SeqIO.parse(open(inf_file, 'rU'), 'fasta')) return nast_d, inf_d
def read_seqs(self, sequence_file):
"""
read sequences from Ensemble protein files (.fasta) or from lists or dicts of BioPython SeqRecords
and make them available for fast search. Appending also with this function.
:param sequence_file: Ensemble files (.dat or .fasta)
:return:
"""
recs = sequence_file
if not isinstance(sequence_file, dict) and not isinstance(sequence_file, list):
try:
with open(sequence_file, 'rb') as f:
if sequence_file.endswith('.fa') or sequence_file.endswith('.fasta'):
recs = SeqIO.to_dict(SeqIO.parse(f, "fasta"))
else: # assume it is a dat file
recs = SeqIO.to_dict(SeqIO.parse(open(sequence_file), 'swiss'))
except:
warnings.warn("Could not read file", UserWarning)
return
if isinstance(sequence_file, list):
recs = SeqIO.to_dict(sequence_file)
if recs:
self.collection.update(recs)
self.searchstring = '#'.join([str(x.seq) for x in self.collection.values()]).decode('ascii')
self.accs = self.collection.keys()
self.idx = list()
self.idx.append(0)
for i, v in enumerate(self.collection.values()):
self.idx.append(1 + self.idx[-1] + len(self.collection.values()[i].seq))
for i in recs.items():
ensg = None
enst = None
ensp = i[0]
ks = i[1].description.split(' ')
for j in ks:
if j.startswith('transcript:'):
enst = j.strip('transcript:')
elif j.startswith('gene:'):
ensg = j.strip('gene:')
if ensg and enst and ensp:
if ensg not in self.ensg2enst:
self.ensg2enst[ensg] = list()
self.ensg2ensp[ensg] = list()
self.ensg2enst[ensg].append(enst)
self.ensg2ensp[ensg].append(ensp)
self.enst2ensg[enst] = ensg
self.enst2ensp[enst] = ensp
self.ensp2ensg[ensp] = ensg
self.ensp2enst[ensp] = enst
else:
warnings.warn("Unparsable filecontents", UserWarning)
return
def readNexFile(fileName):
seq = {}
if fileName != '':
# read data set file, requires biopython
handle = open(fileName, 'r')
if fileName.endswith('nex') or fileName.endswith('nexus'):
seq = SeqIO.to_dict(SeqIO.parse(handle, 'nexus'))
elif fileName.endswith('phy') or fileName.endswith('phylip'):
seq = SeqIO.to_dict(SeqIO.parse(handle, 'phylip'))
elif fileName.endswith('fasta'):
seq = SeqIO.to_dict(SeqIO.parse(handle, 'fasta'))
handle.close()
return seq
def t_gff3_to_gff3(self):
"""Read in and write out GFF3 without any loss of information.
"""
recs = SeqIO.to_dict(GFF.parse(self._test_gff_file))
out_handle = StringIO.StringIO()
GFF.write(recs.values(), out_handle)
wrote_handle = StringIO.StringIO(out_handle.getvalue())
recs_two = SeqIO.to_dict(GFF.parse(wrote_handle))
orig_rec = recs.values()[0]
re_rec = recs.values()[0]
assert len(orig_rec.features) == len(re_rec.features)
for i, orig_f in enumerate(orig_rec.features):
assert str(orig_f) == str(re_rec.features[i])
def createNucleotideCluster(config):
'''Grab Nucleotide-Sequences of ORFs and turn into unaligned clusters'''
logging.info("Creating nucleotide clusters")
print "Creating nucleotide-clusters"
sequences = {}
snp_sequences = {}
for organism_config in config["INPUT"]:
organism = config["INPUT"][organism_config]
handle = open(config["OUTPUT"]["folder"]+"orfs/"+organism["prefix"]+"-nucleotide-orfs.fasta","r")
sequences[organism["prefix"]] = SeqIO.to_dict(SeqIO.parse(handle, "fasta"))
handle.close()
if config["SNP"]["call_snps"] == "True":
handle = open(config["OUTPUT"]["folder"]+"orfs/"+organism["prefix"]+"-nucleotide-orfs.fasta","r")
snp_sequences[organism["prefix"]] = SeqIO.to_dict(SeqIO.parse(handle,"fasta"))
handle.close()
cluster_handle = open(config["OUTPUT"]["folder"]+"cluster/paralog-free-clusters.csv","r")
clusters = {}
for line in cluster_handle:
seq_names = {}
line_array = line.strip().split("\t")
cluster_name = line_array[0][:line_array[0].find("(")]
name_array = line_array[1].split(" ")
for name in name_array:
prefix = name[:name.find("|")]
cluster_id = name[name.find("|")+1:name.find("(")]
seq_names[prefix] = cluster_id
clusters[cluster_name] = seq_names
if os.path.isdir(config["OUTPUT"]["folder"] + "cluster/nucleotide_clusters/"):
request = "rm " + config["OUTPUT"]["folder"] + "cluster/nucleotide_clusters/*"
subprocess.call(request, shell=True)
else:
os.makedirs(config["OUTPUT"]["folder"]+"cluster/nucleotide_clusters/")
for name,cids in clusters.items():
out_file = open(config["OUTPUT"]["folder"] + "cluster/nucleotide_clusters/" + name + ".fasta","w")
if config["SNP"]["call_snps"] == "True":
snp_out_file = open(config["OUTPUT"]["folder"] + "cluster/nucleotide_clusters/" + name + "-snps.fasta","w")
for prefix,seqid in cids.items():
out_file.write(">"+prefix+"\n")
out_file.write(str(sequences[prefix][seqid].seq)+"\n")
if config["SNP"]["call_snps"] == "True":
snp_out_file.write(">"+prefix+"\n")
snp_out_file.write(str(sequences[prefix][seqid].seq)+"\n")
out_file.close()
if config["SNP"]["call_snps"] == "True":
snp_out_file.close()
logging.info("Created nucleotide-clusters")
print "Created nucleotide-clusters"
def mdrMapping(self, mdrDir = '/w/simulation_fr_the_beginning/reAssemble/everybodyelse/out/pAss11'):
'''
Some error from the alignment process
'''
file = "{}/{}.fna".format(mdrDir, self.koi)
contigs = SeqIO.to_dict(SeqIO.parse(self.query, "fasta"))
mdrs = SeqIO.to_dict(SeqIO.parse(file, "fasta"))
#this is already in the description
mdrInContig = []
alignment = {}
for contigID in mdrs:
contig = str(contigs[contigID].seq).upper()
mdr = str(mdrs[contigID].seq).upper()
revcom = str(mdrs[contigID].seq.reverse_complement()).upper()
#damn sian, i'm not getting paid
norm = re.search("\(ntRev\)", mdrs[contigID].description)
if norm != None:
seq = mdr
sign = '+'
logging.debug("norm {}".format(contigID))
else:
seq = revcom
sign = '-'
logging.debug("revcom {}".format(contigID))
if seq in contig:
start = contig.find(seq)
nGapsS = 0
nGapsQ = 0
else:
logging.info("Complete alignment fail : {}".format(contigID))
alignments = [a for a in pw.align.globalms(contig, seq, 5, -4, -10, -1)]
alignment[contigID] = alignments[0]
start = abs(len(re.sub("^-+", "", alignments[0][1])) - len(alignments[0][1]))
#Tracer('Linux')()
nGapsS = re.sub("-+$", "", re.sub("^-+", "", alignments[0][0])).count("-")
nGapsQ = re.sub("-+$", "", re.sub("^-+", "", alignments[0][1])).count("-")
df = pd.DataFrame({
'contig': contigID,
'start': start,
'end' : start + len(seq),
'length': len(seq),
'strand': sign,
'sGaps': nGapsS,
'qGaps': nGapsQ,
}, index=[1])
mdrInContig.append(df)
df = pd.concat(mdrInContig, ignore_index=True)
df['ko'] = self.koi
return {'df':df, 'alignments': alignment}
def t_fasta_directive(self):
"""Parse FASTA sequence information contained in a GFF3 file.
"""
recs = SeqIO.to_dict(GFF.parse(self._gff_file))
assert len(recs) == 1
test_rec = recs['chr17']
assert str(test_rec.seq) == "GATTACAGATTACA"
def not_t_full_celegans(self):
"""Test the full C elegans chromosome and GFF files.
This is used to test GFF on large files and is not run as a standard
test. You will need to download the files and adjust the paths
to run this.
"""
# read the sequence information
seq_file = os.path.join(self._full_dir, "c_elegans.WS199.dna.fa")
gff_file = os.path.join(self._full_dir, "c_elegans.WS199.gff3")
seq_handle = open(seq_file)
seq_dict = SeqIO.to_dict(SeqIO.parse(seq_handle, "fasta"))
seq_handle.close()
#with open(gff_file) as gff_handle:
# possible_limits = feature_adder.available_limits(gff_handle)
# pprint.pprint(possible_limits)
rnai_types = [('Orfeome', 'PCR_product'),
('GenePair_STS', 'PCR_product'),
('Promoterome', 'PCR_product')]
gene_types = [('Non_coding_transcript', 'gene'),
('Coding_transcript', 'gene'),
('Coding_transcript', 'mRNA'),
('Coding_transcript', 'CDS')]
limit_info = dict(gff_source_type = rnai_types + gene_types)
for rec in GFF.parse(gff_file, seq_dict, limit_info=limit_info):
pass
def gather_est2genome_seqs(refseq_obj, est2genome_handle, log_line, velvet_file):
seq_dir = log_line.split("\t")[1]
tmp_refseq = seq_dir.split("/")[3].replace(".","%2E")#hardcoded in this position
gff_file = refseq_obj.id + ".velvet_contigs.maker.output/" + seq_dir + "/" + tmp_refseq + ".gff"
gff_handle = open(gff_file,'r')
for gff_line in gff_handle:
if(re.search("est2gneome",gff_line) and \
re.search("\texpressed_sequence_match\t",gff_line)):
curr_start = int(gff_line.split("\t")[3])
curr_stop = int(gff_line.split("\t")[4])
curr_strand = gff_line.split("\t")[6]
tmp_handle = open(velvet_file,'r')
tmp_fasta = SeqIO.to_dict(SeqIO.parse(tmp_handle,"fasta"))
tmp_handle.close()
if seq_dir.split("/")[3] in tmp_fasta:
curr_record = tmp_fasta[seq_dir.split("/")[3]]
else:
continue
new_seq = curr_record.seq[curr_start - 1:curr_stop]
if(curr_strand == "-"):
new_seq = curr_record.seq[curr_start - 1:curr_stop].reverse_complement()
new_record = SeqRecord(new_seq,id=seqname,name=seqname,description="")
SeqIO.write(est2genome_handle,"fasta")
def _get_seq_dict(self):
"""Internal reusable function to get the sequence dictionary.
"""
seq_handle = open(self._test_seq_file)
seq_dict = SeqIO.to_dict(SeqIO.parse(seq_handle, "fasta"))
seq_handle.close()
return seq_dict
def main():
if len (sys.argv) != 4 :
print "Please provide file, the file format, and the desired file format "
sys.exit (1)
else:
f = sys.argv[1]
fout = "".join(f.split('.')[:-1])
formatin = sys.argv[2]
formatout = sys.argv[3]
if formatout == 'nexus':
AlignIO.convert(f,formatin,fout+'.'+formatout,formatout,alphabet= IUPAC.ambiguous_dna)
if formatout == 'mega':
handle = open(f, "rU")
record_dict = SeqIO.to_dict(SeqIO.parse(handle, "phylip-relaxed"))
handle.close()
outfile = open(fout+'.'+formatout,'w')
outfile.write('#mega'+"\n")
outfile.write('!Title Mytitle;'+"\n")
outfile.write('!Format DataType=DNA indel=-;'+"\n\n")
for n in record_dict:
outfile.write('#'+n+"\n")
newseq=wrap(str(record_dict[n].seq),60)
for s in newseq:
outfile.write(s+"\n")
outfile.close()
else:
AlignIO.convert(f,formatin,fout+'.'+formatout,formatout)
def t_ensembl_nested_features(self):
"""Test nesting of features with GFF2 files using transcript_id.
"""
rec_dict = SeqIO.to_dict(GFF.parse(self._ensembl_file))
assert len(rec_dict["I"].features) == 2
t_feature = rec_dict["I"].features[0]
assert len(t_feature.sub_features) == 32
def check_reference_alignment(reference_alignment, pdb_files_dir):
'''Try to read alignment (with Bio.SeqIO); Exit if reading fails;
Print warining message (but do not exit) if a structure is not in the
alignment'''
print('\nChecking reference alignment: %s' % reference_alignment)
try:
ref_alignment = SeqIO.parse(reference_alignment, 'fasta')
print('Valid alignment present: %s' % reference_alignment)
except:
print('Alignment not present or not in required format (fasta).')
print('Please check the file "%s".' % reference_alignment)
sys.exit(1)
seq_names_alignment = SeqIO.to_dict(ref_alignment).keys()
structures = os.listdir(pdb_files_dir)
not_in_alignment = [x for x in structures if x not in seq_names_alignment]
if len(not_in_alignment) > 0:
print('WARNING: The sequence of the following %s PDB-structures is no '
'present in the reference alignment and will be excluded from '
'analysis:\n%s' % (len(not_in_alignment), not_in_alignment))
print('If the dataset is large enought without these structures, this '
'is not a problem.')
print('Note: The sequences in the alignment must have the same names '
'as their corresponding structures in the directory "%s".'
% pdb_files_dir)
else:
print('All structures are present in the reference alignment.')
def methyl(args):
global global_ref_c
global nr_of_reads
#meth_dict = {'Methylation_List','Family','UUID','position'}
#CG_dict = {'Chr','Start','Stop'}
print "Loading the bam file"
samfile = pysam.AlignmentFile(args.bam, "rb")
ref = SeqIO.to_dict(SeqIO.parse(open(args.r), 'fasta'))
faChrCheck = "chr" in SeqIO.parse(open(args.r), 'fasta').next().id
header = ["chr", "start", "stop", "UUID", "methylated CpGs", "position"]
CpG = pd.DataFrame(columns=header)
if ":" in args.region and "-" in args.region:
chrom = args.region.split(":")[0]
start = int(args.region.split(":")[1].split("-")[0])
end = int(args.region.split("-")[1])
if not faChrCheck:
chrom = args.region.split(":")[0][3:]
else:
#TODO: add system.stderr message
print "Either no region given or the region is not given in the needed way chr:start-stop. If it was not your intention, please refer to the argument --region"
print "We will infer that your reference only contains one element, i.e. an L1 element you want to align agains"
## infering chrom and start stop. There should be only one element in the input fasta
chrom = ref.keys()[0]
start = 0
end = len(ref.get(ref.keys()[0]))
#print "chrom: %s" %chrom
#print "!"*20
#print "sequence: %s " %ref.get(chrom)[start:end].seq
#print "!"*20
## fill the dataframe with all CpGs from the reference to then check the methylation status of each read for each CpG
referenceCpGs = initializeCpG(CpG,
ref.get(chrom)[start:end].seq.upper(), chrom,
start, end)
## checking if bam file is indexed by samtools
if not samfile.check_index():
print "Input bam file needs to be indexed!"
print "Please index the bam file using the following command: samtools index " + args.bam
exit(1)
global_ref_c = len(referenceCpGs)
if args.region == "":
for read in samfile.fetch():
#print read
nr_of_reads += 1
CpG = updateCpG(read, CpG, faChrCheck, referenceCpGs, start,
args.strict_cpg)
else:
for read in samfile.fetch(
args.region.split(":")[0],
int(args.region.split(":")[1].split("-")[0]),
int(args.region.split("-")[1])):
#print read
nr_of_reads += 1
CpG = updateCpG(read, CpG, faChrCheck, referenceCpGs, start,
args.strict_cpg)
CpG = equalizeCpG(CpG)
samfile.close()
return CpG.dropna(thresh=4)
def _load_helitrons(helitrons_file):
return SeqIO.to_dict(SeqIO.parse(helitrons_file, "fasta"))
if len(sys.argv) != 6:
print(
'please enter 5 command line arguments to run this script, example to run script i.e. python3 peptides2sequenceStringMatching.py dir/2/peptides/file dir/to/allProteinsSeqs/ dir/to/output/directory n_threads suffix for the files to search into'
)
else:
peptides_seq_f = sys.argv[1]
prot_seqs_dir = sys.argv[2]
out_dir = sys.argv[3]
n_threads = int(sys.argv[4])
suffix = sys.argv[5]
if not os.path.isdir(out_dir):
os.mkdir(out_dir)
print('reading all peptide sequences into memory')
peptide_seqs = SeqIO.to_dict(SeqIO.parse(peptides_seq_f, 'fasta'))
print('reading all proteins into memory')
all_prot_files = [
file for file in os.listdir(prot_seqs_dir) if file.endswith(suffix)
]
peptide_seq_list = [(peptide_id, str(peptide_seqs[peptide_id].seq))
for peptide_id in peptide_seqs]
new2oldPeptide_dic = dict()
new_peptide_list = list()
print('converting peptides I 2 L')
for peptide in peptide_seq_list:
new_peptide = peptide[1].replace('I', 'L')
new_tuple = (peptide[0], new_peptide)
def read_fasta(self):
"""
Reads a fasta file using SeqIO
"""
self.dict_genome = SeqIO.to_dict(SeqIO.parse(self.file, "fasta"))
def get_fasta(genome_file):
genome_dict = SeqIO.to_dict(SeqIO.parse(genome_file, "fasta"))
contig2length = {}
for j in genome_dict:
contig2length[j] = len(genome_dict[j])
return genome_dict, contig2length
import os
import sys
import subprocess
import re
import shlex
from Bio import SeqIO
from collections import defaultdict
proteinortho = open(sys.argv[1], "r")
proteins = open(sys.argv[2], "r")
ortho_out = open(sys.argv[1] + ".cluster", "w")
# get protein lengths
ldict = {}
prot_dict = SeqIO.to_dict(SeqIO.parse(proteins, "fasta"))
for i in prot_dict:
record = prot_dict[i]
ldict[record.id] = len(record.seq)
# parse cluster IDs
tally = 0
cluster_list = []
longest_dict = defaultdict(float)
final_dict = {}
rep_list = []
for i in proteinortho.readlines():
if i.startswith("#"):
name = re.sub(".names.faa", "", i)
name2 = re.sub("# ", "", name)
ortho_out.write("Cluster\tRepresentative\tRep_Length\t" + name2)
else:
".", "%2E") #hardcoded in this position
gff_file = refseq.id + ".velvet_contigs.maker.output/" + seq_dir + "/" + tmp_refseq + ".gff"
gff_handle = open(gff_file, 'r')
for gff_line in gff_handle:
if(re.search("est2genome",gff_line) and \
re.search("\texpressed_sequence_match\t",gff_line)):
curr_start = int(gff_line.split("\t")[3])
curr_stop = int(gff_line.split("\t")[4])
curr_strand = gff_line.split("\t")[6]
should_be_length = curr_stop - curr_start
print gff_line
print should_be_length
tmp_handle = open(filesPerSeqID[refseq.id], 'r')
tmp_fasta = SeqIO.to_dict(SeqIO.parse(tmp_handle, "fasta"))
tmp_handle.close()
if seq_dir.split("/")[3] in tmp_fasta:
curr_record = tmp_fasta[seq_dir.split("/")[3]]
else:
continue
new_seq = curr_record.seq[curr_start - 1:curr_stop]
if (curr_strand == "-"):
new_seq = curr_record.seq[
curr_start - 1:curr_stop].reverse_complement()
print len(new_seq)
new_record = SeqRecord(new_seq,
id=seqname,
name=seqname,
description="")
def generate_genbank(
input_fna, input_gff3, input_faa, output_prefix, organism_name,
data_file_division, taxonomy):
'''Generate GenBank format'''
# Output file name
outfile = '{}.gb'.format(output_prefix)
# First, import input_fna in dictionary
d_fna = SeqIO.to_dict(SeqIO.parse(input_fna, 'fasta'))
d_faa = SeqIO.to_dict(SeqIO.parse(input_faa, 'fasta'))
d_fna_sorted = sorted(
d_fna.items(),
key=lambda x: int(re.findall(r'\d+', x[0])[0]))
# Make dictionary for CDS
d_cds = defaultdict(list)
d_exon = defaultdict(list)
for record in parse_gff3(input_gff3):
if record.type == 'exon':
exon_parent = record.attributes['Parent']
d_exon[exon_parent].append(record)
elif record.type == 'CDS':
cds_parent = record.attributes['Parent']
d_cds[cds_parent].append(record)
my_seq_records = []
for scaffold, seq in d_fna_sorted:
my_seq = Seq(str(seq.seq))
my_seq_record = SeqRecord(my_seq)
my_seq_record.description = '{} {}'.format(organism_name, scaffold)
date = datetime.today().strftime('%d-%^b-%Y')
my_seq_record.annotations['date'] = date
my_seq_record.annotations['organism'] = organism_name
my_seq_record.data_file_division = data_file_division
my_seq_record.annotations['keywords'] = [
'Whole genome sequencing project']
my_seq_record.annotations['taxonomy'] = taxonomy.split('; ')
my_seq_record.annotations['source'] = organism_name
for record in parse_gff3(input_gff3):
if scaffold != record.seqid:
continue
my_feature_type = record.type
if my_feature_type == ('exon', 'CDS'):
continue
# GFFRecord(seqid='contig1', source='AUGUSTUS', type='gene',
# start=16942, end=19008, score=0.22, strand='+', phase=None,
# attributes={'Source': 'braker_Y1:g3308.t1', 'ID': 'Triga_00001'})
my_start = record.start
my_end = record.end
my_strand = 1 if record.strand == '+' else -1
# Set qualifies for gene
if my_feature_type == 'gene':
gene_start = my_start
gene_end = my_end
gene_feature_location = FeatureLocation(
gene_start, gene_end, strand=my_strand)
gene_qualifiers = {}
gene_locus_tag = record.attributes['ID']
gene_qualifiers['locus_tag'] = gene_locus_tag
gene_feature = SeqFeature(
gene_feature_location, type=my_feature_type,
qualifiers=gene_qualifiers)
# Append my feature to seq_record
my_seq_record.features.append(gene_feature)
elif my_feature_type == 'mRNA':
sorted_exon_records = sorted(
d_exon[record.attributes['ID']], key=lambda x: x.start)
sorted_cds_records = sorted(
d_cds[record.attributes['ID']], key=lambda x: x.start)
# Feature locations
# mRNA location is needed to be modified
fl_mrna_list = []
for exon_record in sorted_exon_records:
fl_element = FeatureLocation(
exon_record.start, exon_record.end, strand=my_strand)
fl_mrna_list.append(fl_element)
if len(fl_mrna_list) == 1:
mrna_feature_location = fl_mrna_list[0]
else:
mrna_feature_location = CompoundLocation(fl_mrna_list)
fl_cds_list = []
for cds_record in sorted_cds_records:
fl_element = FeatureLocation(
cds_record.start, cds_record.end, strand=my_strand)
fl_cds_list.append(fl_element)
# If fl_cds_list is more than 1 use CompoundLocation
if len(fl_cds_list) == 1:
cds_feature_location = fl_cds_list[0]
else:
cds_feature_location = CompoundLocation(fl_cds_list)
# Qualifier
mrna_qualifiers = {}
cds_qualifiers = {}
mrna_locus_tag = record.attributes['ID']
mrna_qualifiers['locus_tag'] = mrna_locus_tag
if record.score:
mrna_qualifiers['note'] = 'prediction score=%s' % (
record.score)
cds_qualifiers['locus_tag'] = mrna_locus_tag
# Get phase
if my_strand == 1:
phase = int(sorted_cds_records[0].phase) + 1
elif my_strand == -1:
phase = int(sorted_cds_records[-1].phase) + 1
cds_qualifiers['codon_start'] = phase
cds_qualifiers['translation'] = str(d_faa[mrna_locus_tag].seq)
mrna_feature = SeqFeature(
mrna_feature_location, type='mRNA',
qualifiers=mrna_qualifiers)
cds_feature = SeqFeature(
cds_feature_location, type='CDS', qualifiers=cds_qualifiers)
# Append my feature to seq_record
my_seq_record.features.append(mrna_feature)
my_seq_record.features.append(cds_feature)
my_seq_records.append(my_seq_record)
SeqIO.write(my_seq_records, outfile, 'genbank')
def writeallAA(gff, genomefasta):
#Make gff database
print 'Indexing gff...'
gff_fn = gff
db_fn = os.path.abspath(gff_fn) + '.db'
if os.path.isfile(db_fn) == False:
gffutils.create_db(gff_fn, db_fn, merge_strategy = 'merge', verbose = True)
db = gffutils.FeatureDB(db_fn)
print 'Done indexing!'
print 'Indexing genome sequence...'
if os.path.basename(genomefasta).endswith('.gz'):
seq_dict = SeqIO.to_dict(SeqIO.parse(gzip.open(genomefasta), 'fasta'))
else:
seq_dict = SeqIO.to_dict(SeqIO.parse(genomefasta, 'fasta'))
print 'Done indexing!'
aaseqs = {} #{ensg_enst : aaseq}
genes = db.features_of_type('gene')
genecounter = 0
for gene in genes:
genecounter +=1
if genecounter % 5000 == 0:
print 'Gene {0}...'.format(genecounter)
#Only protein-coding genes
if 'protein_coding' not in gene.attributes['gene_type']:
continue
for transcript in db.children(gene, featuretype = 'transcript'):
#Only protein-coding transcripts
if 'protein_coding' not in transcript.attributes['transcript_type']:
continue
#Stitch together CDS pieces
cdsseq = ''
if transcript.strand == '+':
for cds in db.children(transcript, featuretype = 'CDS', order_by = 'start'):
try:
seq = seq_dict[transcript.chrom].seq[cds.start - 1 : cds.end]
except KeyError: #This chromosome isn't in the fasta
print 'Chromosome {0} is not in the genome sequence fasta.'.format(transcript.chrom)
continue
cdsseq += seq
elif transcript.strand == '-':
for cds in db.children(transcript, featuretype = 'CDS', order_by = 'start', reverse = True):
try:
seq = seq_dict[transcript.chrom].seq[cds.start - 1 : cds.end].reverse_complement()
except KeyError: #This chromosome isn't in the fasta
print 'Chromosome {0} is not in the genome sequence fasta.'.format(transcript.chrom)
continue
cdsseq += seq
if cdsseq:
aaseq = str(cdsseq.translate())
aaseqs[gene.id + '_' + transcript.id] = aaseq
fcounter = 1
txcounter = 0
totaltxcounter = 0
longtxcounter = 0
outfh = open('AAseqs_{0}.fa'.format(fcounter), 'w')
for tx in aaseqs:
txcounter +=1
totaltxcounter +=1
if txcounter == 10000:
txcounter = 1
fcounter += 1
outfh.close()
outfh = open('AAseqs_{0}.fa'.format(fcounter), 'w')
if len(aaseqs[tx]) < 8000:
outfh.write('>' + tx + '\n' + aaseqs[tx] + '\n')
else:
longtxcounter +=1
outfh.close()
print totaltxcounter, longtxcounter
def outparalog_separation(fasta, cfg, **kargs):
"""
Main function for STEP4:
interp.
kmer (int) The number of consecutive bases to be examine
for computing the Kmer score.
min_sequences_overlap (int) The minimum overlap between two sequences
required for computing pairwise distances with
the EMBOSS dismat software.
outgroups (list(str)) List of the outgroups used to separate putative
outparalogs.
max_alleles (int) Maximum number of putative alleles allowed
within a cluster during the cluster fusion step.
The final number of putative alleles in the
alignemnt can exceed this value in latter steps.
max_jaccard_value (float) Value used during the component fusion step.
Pairs of components that exceed this value of
the Jaccard Index (JI) for their taxa are not
considered for fusion. Lower value means higher
stringency.
min_association_ratio (float) Parameter that together with the Jaccard Index
value controles the stringency of the fusion of
components. A higher ratio increases the
stringency.
max_distance (float) Max distance between sequences allowed during
the assignment of unclustered sequences to their
components.
min_taxa_in_alignment (int) Number of taxa to serve as threshold between
what is deemed to be a 'complete' alignment and
an 'imcomplete' one.
large_component_ratio (float) Value of the Size ratio of the largest component
that is used to distinguish small components:
a small component has
size < (size largest) * large_component_ratio.
Large components are used during the clustering
phase with JI.
"""
in_suffix = cfg["input_suffix"]
out_suffix = cfg["output_suffix"]
out_folder = cfg["output_folder"]
kmers = cfg["kmers"]
min_overlap = cfg["min_sequences_overlap"]
outgroups = cfg["outgroups"]
max_alleles = cfg["max_alleles"]
max_jaccard = cfg["max_jaccard_value"]
association_ratio = cfg["min_association_ratio"]
max_dist = cfg["max_distance"]
min_taxa = cfg["min_taxa_in_alignment"]
large_comp_ratio = cfg["large_component_ratio"]
basename = fasta.split(in_suffix)[0]
error_template = "An exception of type {0} occurred when trying to \
{2}. Arguments:\n{1!r}"
DEBUG = True
try:
assert (0 <= cfg["min_association_ratio"] <= 1
and 0 <= cfg["max_jaccard_value"] <= 1
and 0 <= cfg["large_component_ratio"] <= 1)
except:
s = "Wrong parameters: 'min_association_ratio', 'max_jaccard_value' \
and large_component_ratio must be in [0, 1]"
raise ValueError(s)
sys.exit(s)
try:
cfg["dna_model"] in [0, 1, 2, 3, 4, 5]
except:
s = "Wrong parameters: 'dna_model' must be in [0, 1, 2, 3, 4, 5]"
raise ValueError(s)
sys.exit(s)
try:
records = SeqIO.to_dict(SeqIO.parse(fasta, "fasta"))
except:
exception = "[Error]: Cannot open alignment {}\n\
Verify that the file is in fasta format".format(fasta)
return exception
n_seq = len(records)
try:
assert n_seq > 3
except:
s_i = "STEP4 Done with fasta: {}".format(fasta)
s_d = "No transformation performed on fasta {0}\n\
which only contains {1} sequences".format(fasta, n_seq)
return (s_i, s_d)
length = len(list(records.values())[0])
try:
assert min_overlap < length
except:
exception = "[Error]: for fasta {} 'min_sequences_overlap'\
exceeds the length of the alignment".format(fasta)
return (exception, None)
if DEBUG:
print("working on fasta {}".format(fasta))
# 1- Remove empty sequences.
if DEBUG:
print("1- Remove empty sequences.")
for record in records.keys():
if set(list(str(records[record].seq))) == set(["-"]):
records.pop(record, None)
# 2- Return the original aln if no outgroup
if (set(outgroups).intersection(
set([x.split("|")[0] for x in records.keys()])) == set()):
SeqIO.write(
[x[1] for x in sorted(records.items(), key=lambda x: x[0])],
basename + out_suffix + "_no_outgroup.fasta",
"fasta",
)
shutil.move(basename + out_suffix + "_no_outgroup.fasta", out_folder)
s_i = "Done with fasta {}".format(fasta)
s_d = "No outgroup is present for fasta {}".format(fasta)
return (s_i, s_d)
# Calculate distance matrix used to assign sequences to components
if DEBUG:
print("Calculate distance matrix")
calculate_distance_matrix(fasta, cfg)
taxa = {records[rec].name: str(records[rec].seq) for rec in records.keys()}
# For each sequence we recode 0 for '-', 1 otherwise,
# into a dictionary {name_number: profile}
taxa_profiles = sequence_profiler(taxa)
if DEBUG:
print("Test different kmer in the kmers list")
# 3- Test different kmer in the kmers list in order to obtain several components.
#
retained_comp = None
for kmer in kmers:
idxs_length = len(list(records.values())[0].seq) - kmer
if idxs_length <= 20:
s_i = "Done with fasta {}".format(fasta)
s_d = "In fasta {0} with kmer {1}, \n\
the alignment is too short to perform STEP4".format(fasta, kmer)
return (s_i, s_d)
comp_1, aln_dict, accepted_outgroup = obtain_components(
fasta, taxa, idxs_length, kmer, outgroups, min_overlap,
taxa_profiles)
if DEBUG:
if comp_1:
print("# components: {0} with Kmer {1}".format(
len(comp_1), kmer))
if comp_1 is None:
continue
elif comp_1 is not None and len(comp_1) == 1:
if validate_component(comp_1[0], taxa_profiles, min_overlap,
max_alleles):
retained_comp = comp_1
break
else:
retained_comp = comp_1
else:
retained_comp = comp_1
break
# 4- If no components are obtained, return the original aln.
#
if retained_comp is None or len(retained_comp) == 0:
SeqIO.write(
[x[1] for x in sorted(records.items(), key=lambda x: x[0])],
basename + out_suffix + "_no_splitting.fasta",
"fasta",
)
try:
shutil.move(basename + out_suffix + "_no_splitting.fasta",
out_folder)
except Exception as ex:
s = "move fasta: {}".format(fasta)
message = error_template.format(type(ex).__name__, ex.args, s)
print(message)
s_i = "Done with fasta {}".format(fasta)
s_d = "No components were found,\n\
using kmer {}: no inparalog removal was attempted".format(kmer)
return (s_i, s_d)
# 4. If a single component is found, use it to assign the sequence
# no included based on a distance method.
if DEBUG:
print("Cluster with distances")
# Common parameter for all calls of dist_assignment()
dist_assignmet_para = [
basename,
out_folder,
max_dist,
min_taxa,
outgroups,
min_overlap,
out_suffix,
large_comp_ratio,
]
if len(retained_comp) == 1:
final_comp = dist_assignment(retained_comp, records, taxa_profiles,
*dist_assignmet_para)
if DEBUG:
print("writing a single component")
write_fasta(final_comp, records, basename, out_folder, min_taxa,
out_suffix)
s_i = "Done with fasta {}".format(fasta)
s_d = "# retained components: {}".format(len(final_comp))
return (s_i, s_d)
# 5. If there are some components, compute the Jaccard index
# between pair of components:
else:
jaccard = []
for pair in itertools.combinations(retained_comp, 2):
jaccard.append(calculate_Jaccard_index(pair))
jaccard_filtered = [x for x in jaccard if x[1] <= max_jaccard] ###
#
# 6. case 1: Jaccard index can be calculated,
# but is set too high therefore clustering all components.
# No additional component clustering is performed and seq
# are assigned based on a distance method.
if len(jaccard_filtered) == 0:
if DEBUG:
print("Cluster without Jaccard")
final_comp = dist_assignment(comp_1, records, taxa_profiles,
*dist_assignmet_para)
if DEBUG:
print("writing a several component")
write_fasta(final_comp, records, basename, out_folder, min_taxa,
out_suffix)
s_i = "Done with fasta {}".format(fasta)
s_d = "# retained components: {}".format(len(comp_1))
return (s_i, s_d)
#
# 6. case 2: Jaccard index can be calculated and is below the set
# maximum. Associations between components are checked to see
# if they can be justified given the data.
# Remaining individual sequences are clustered by distance.
elif len(jaccard_filtered) > 0:
# At each position we calculate the components.
if DEBUG:
print("Cluster with Jaccard")
JI_gp = {}
for pair in jaccard_filtered:
gp = validate_associations(pair, list(aln_dict.values()))
JI_gp[(tuple(pair[0][0]), tuple(pair[0][1]))] = {
"JI": pair[1],
"gp": gp,
}
edges = [(
key[0],
key[1],
{
"dist":
JI_gp[key]["gp"][0] /
(JI_gp[key]["gp"][0] + JI_gp[key]["gp"][1])
},
) if (JI_gp[key]["gp"][1] + JI_gp[key]["gp"][0]) != 0 else
(key[0], key[1], {
"dist": 0.0
}) for key in JI_gp.keys()]
filtered_edges = [
e for e in edges if e[2]["dist"] > association_ratio
]
# Separate components clustered with Jaccard index
# from unclustered components.
single_edges = []
for e in filtered_edges:
single_edges.append(e[0])
single_edges.append(e[1])
# Components that are linked by an edge
L_single_edges = list(set(single_edges))
# Components that lack an edge to any other component
# are separated from the rest
unclustered_comp = []
for e in edges:
if e[0] not in L_single_edges:
unclustered_comp.append(e[0])
if e[1] not in L_single_edges:
unclustered_comp.append(e[1])
L_unclustered_comp = list(set(unclustered_comp))
# Compatible components are clustered and the remaining
# components are assigned using a distance method.
if filtered_edges:
# Assembly of components that are associated with JI
G_2 = nx.Graph()
G_2.add_edges_from(filtered_edges)
filtered_comp = [
list(set(itertools.chain(*c.nodes())))
for c in nx.connected_component_subgraphs(G_2)
]
comp_2 = filtered_comp + [list(x) for x in L_unclustered_comp]
# add the seq from components that have not been clustered
final_comp = dist_assignment(comp_2, records, taxa_profiles,
*dist_assignmet_para)
write_fasta(final_comp, records, basename, out_folder,
min_taxa, out_suffix)
s_i = "Done with fasta {}".format(fasta)
s_d = "# retained components: {}".format(len(final_comp))
if DEBUG:
print("Components are compatible, " + s_d)
return (s_i, s_d)
# Components are incompatible.
# Return all components as separated alignments.
# Add unclustered seq using a distance method.
else:
final_comp = dist_assignment(comp_1, records, taxa_profiles,
*dist_assignmet_para)
write_fasta(final_comp, records, basename, out_folder,
min_taxa, out_suffix)
s_i = "Done with fasta {}".format(fasta)
s_d = "# retained components: {}".format(len(final_comp))
if DEBUG:
print("Components are incompatible, " + s_d)
return (s_i, s_d)
parser = argparse.ArgumentParser(
description="Output reads that didn't classifiy to a set of refseqs")
parser.add_argument("RefSeq",
type=str,
help="File of Sequences that we care about")
parser.add_argument("taxonomer_out",
type=str,
help="Taxonomer classifier output")
parser.add_argument("fasta",
type=str,
help="fasta file of reads that we want to filter")
args = parser.parse_args()
#make a hash of the the reference sequences
handle = open(args.RefSeq, 'r')
refseq_dict = SeqIO.to_dict(SeqIO.parse(handle, "fasta"))
handle.close()
#Make a hash of the taxonomer output
#If a thing didn't classifiy to one of the reference sequences, count it as unclassified
classified_hash = {}
handle = open(args.taxonomer_out, 'r')
for line in handle:
parts = line.split("\t")
if (parts[0] == "U"):
classified_hash[parts[1]] = 1
else:
if (not parts[3] in refseq_dict):
classified_hash[parts[1]] = 1
handle.close()
def cDNAcoordstoseq(cDNAcoords, genomefasta, outputfasta):
cDNAseqs = {} #{ENSTRANS : seq}
print 'Indexing genome sequence...'
seq_dict = SeqIO.to_dict(SeqIO.parse(gzip.open(genomefasta), 'fasta'))
print 'Done indexing!'
chrmswithoutseq = [
] #Chromsome names that are in allCDScoords but that don't have a fasta entry in genomefasta
for transcript in cDNAcoords:
cDNAseq = ''
#Ensembl vs UCSC transcript names
#Ensembl
if 'NM_' not in transcript and 'NR_' not in transcript:
tname = transcript.split('_')[0].replace(
'transcript:', ''
) #in cDNAcoords txnames are like 'transcript:ENSMUST00000038375_chr2_+'
chrm = transcript.split('_')[1]
strand = transcript.split('_')[2]
#UCSC
elif 'NM_' in transcript or 'NR_' in transcript:
if ':' in transcript:
tname = transcript.split('_chr')[0].split(':')[
1] #transcript:NM_175684_chr18_-
else:
tname = transcript.split('_chr')[0]
chrm = transcript.split('_')[-2]
strand = transcript.split('_')[-1]
#Is this chromosome in genomefasta?
if chrm not in seq_dict:
if chrm not in chrmswithoutseq:
print 'WARNING: No entry for chromosome {0} in genomefasta.'.format(
chrm)
print transcript
chrmswithoutseq.append(chrm)
continue
for exon in cDNAcoords[transcript]:
start = exon[0]
end = exon[1]
if strand == '+':
exonseq = seq_dict[chrm].seq[start - 1:end].upper()
cDNAseq += exonseq
elif strand == '-':
exonseq = seq_dict[chrm].seq[start - 1:end].reverse_complement(
).upper()
newseq = exonseq + cDNAseq
cDNAseq = newseq
cDNAseqs[tname] = cDNAseq
print 'Found sequence data for {0} of {1} transcripts.'.format(
len(cDNAseqs), len(cDNAcoords))
with open(outputfasta, 'w') as f:
for tname in cDNAseqs:
seq = str(cDNAseqs[tname])
#If transcript IDs have a dot (like ENSMUST0000046543.2)
if '.' in tname:
tname = tname.split('.')[0]
f.write('>' + tname + '\n' + seq + '\n')
return cDNAseqs
sys.exit()
#open the BLAST files
try:
blast1F = open(args.blast1, 'r')
except IOError:
print("\n BLAST 1 not found in folder.")
sys.exit()
try:
blast2F = open(args.blast2, 'r')
except IOError:
print("\n BLAST 2 not found in folder.")
sys.exit()
#read in the protein file of isolate 1 and for each protein, get the length and name (SeqIO auto cuts it at first space) and save to a dictionary
prots1all = SeqIO.to_dict(SeqIO.parse(args.protein1, "fasta"))
prots1 = {}
for prot in prots1all:
prots1[prot] = len(prots1all[prot].seq)
#repeat for the protein file of isolate 2
prots2all = SeqIO.to_dict(SeqIO.parse(args.protein2, "fasta"))
prots2 = {}
for prot in prots2all:
prots2[prot] = len(prots2all[prot].seq)
#get the totals
T1 = len(prots1)
T2 = len(prots2)
#go through the first BLAST file and for each protein that has a match meeting the minimum requirements, add 1 to the counter and the protein name to the list (to avoid counting the same protein twice)
C1 = 0.0
print(str(err))
sys.exit(2)
for o, a in opts:
if o in ('-m', '--mode'):
mode = a
if o in ('-p', '--path'):
path = a
if o in ('-w', '--window'):
window = a
mode2serial = {
'transcript_train': '0',
'transcript_test': '2',
'cdna_train': '1',
'cdna_test': '3'
}
human_seq = SeqIO.to_dict(
SeqIO.parse(path + "human_" + mode + '.txt', "fasta"))
mouse_seq = SeqIO.to_dict(
SeqIO.parse(path + "mouse_" + mode + '.txt', "fasta"))
human_site = open(path + "human_pku" + mode2serial[mode], "r")
mouse_site = open(path + "mouse_pku" + mode2serial[mode], "r")
pos_sample = open(path + window + '/' + mode + "/p_samples", "w")
neg_sample = open(path + window + '/' + mode + "/n_samples", "w")
generate_sample_seq(human_seq, mouse_seq, human_site, mouse_site, window,
pos_sample, neg_sample)
if __name__ == '__main__':
main(sys.argv)
# also: write to 'exons_only.fasta/partition' if no intron file exists
import sys,os
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
if len(sys.argv) < 4:
print("Usage: python combine_alignments.py exon.fasta intron.fasta[or any value if no intron] geneName")
sys.exit(1)
exon_fn = sys.argv[1]
intron_fn = sys.argv[2]
geneName = sys.argv[3]
exon_dict = SeqIO.to_dict(SeqIO.parse(exon_fn,'fasta'))
exonLength = len(next(exon_dict.itervalues()))
if os.path.isfile(intron_fn):
with open("{}.combined.fasta".format(geneName),'w') as outfile:
for seq in SeqIO.parse(intron_fn,'fasta'):
intronLength = len(seq)
sampleID = seq.id.split("-")[0]
newseq = exon_dict[sampleID].seq + seq.seq
outfile.write(">{}\n{}\n".format(sampleID,newseq))
partition = """DNA, codon1 = 1-{}\\3
DNA, codon2 = 2-{}\\3
DNA, codon3 = 3-{}\\3
DNA, intron = {}-{}
""".format(exonLength, exonLength, exonLength, exonLength+1,exonLength+intronLength)
with open("{}.combined.partition".format(geneName),'w') as partitionfile:
#!/usr/bin/python3
from Bio import SeqIO
sequences_handle = SeqIO.parse('strong_hits.fasta', 'fasta')
seq_dict = SeqIO.to_dict(sequences_handle)
remove_list = open('delete_claudins', 'r')
for next_line in remove_list:
next_line = next_line.strip()
del seq_dict[next_line]
output_file = open('strong_hits_trimmed.fasta', 'w')
for i in seq_dict:
SeqIO.write(seq_dict[i], output_file, 'fasta')
remove_list.close()
output_file.close()
annot = text[10] for name in names: if name in annot: dict_annot[name].append(id[:-1]) lista_id.append(id[:-1]) lista_id = list(set(lista_id)) lista_id.sort() for name in dict_annot: li = dict_annot[name] li = list(set(li)) li.sort() dict_annot[name] = li print "Loading sequences" handle = open(file[:-5], "rU") sequen = SeqIO.to_dict(SeqIO.parse(handle, "fasta")) handle.close() print "Getting sequences..." for name in dict_annot: for el in dict_annot[name]: fas_1 = ">%s\n%s\n" % (el+"/1", str(sequen[el+"1"].seq)) fas_2 = ">%s\n%s\n" % (el+"/2", str(sequen[el+"2"].seq)) dict_name_1[name].write(fas_1) dict_name_2[name].write(fas_2) for el in lista_id: fas_1 = ">%s\n%s\n" % (el+"/1", str(sequen[el+"1"].seq)) fas_2 = ">%s\n%s\n" % (el+"/2", str(sequen[el+"2"].seq)) out_in1.write(fas_1) out_in2.write(fas_2) del sequen[el+"1"]
for souche in dicoFastaSample.keys():
#print(souche)
if BR32ID in dicoFastaSample[souche].keys():
#print(souche+"\t"+basename+"\t"+MGG+"\t"+dicoFastaSample[souche][BR32ID].id)
if first == 0:
count += 1
try:
outputFile.close()
except:
pass
outputFile = open(pathDirectoryOut.pathDirectory +
basename, "w") # Open output file
# Ouverture des sequences fasta et chargement dans dictionnaire et ecriture d'un fichier out qui contiendra les sequences renommer
record_dict = SeqIO.to_dict(
SeqIO.parse(open(filename, "rU"), "fasta"))
for ID, record in record_dict.items():
SeqIO.write(record, outputFile, "fasta")
first = 1
record = dicoFastaSample[souche][BR32ID]
newRecord = SeqRecord(Seq(str(record.seq)),
id=souche,
name=souche,
description=record.id)
SeqIO.write(newRecord, outputFile, "fasta")
try:
outputFile.close()
except:
pass
print(count)
"--sam_filename",
required=True,
help="Aligned SAM filename.")
parser.add_argument("-g",
"--genome_filename",
required=True,
help="Genome fasta.")
parser.add_argument("-o",
"--output_prefix",
required=True,
help="Output prefix.")
parser.add_argument("--gff",
default=None,
help="Annotation GFF (optional).")
args = parser.parse_args()
# read genome
print >> sys.stderr, "Reading genome {0}...".format(args.genome_filename)
genome_d = SeqIO.to_dict(SeqIO.parse(open(args.genome_filename), 'fasta'))
# read gff
if args.gff is not None:
print >> sys.stderr, "Reading annotation {0}...".format(args.gff)
junction_info = read_annotation_for_junction_info(args.gff)
else:
junction_info = None
evaluate_alignment_sam(args.input, args.sam_filename, genome_d,
args.output_prefix, junction_info)
def test_order_to_dict(self):
"""Check to_dict preserves order in indexed file."""
d = SeqIO.to_dict(SeqIO.parse(self.f, "fasta"))
self.assertEqual(self.ids, list(d))
def infer_vcf(args):
stats = Counter()
model = models.load_model(args.weights_hd5,
custom_objects=models.get_all_custom_objects(
args.labels))
vcf_reader = pysam.VariantFile(args.negative_vcf, 'r')
vcf_writer = pysam.VariantFile(args.output_vcf,
'w',
header=vcf_reader.header)
print('got vcfs.')
reference = SeqIO.to_dict(SeqIO.parse(args.reference_fasta, "fasta"))
print('Loaded reference FASTA:', args.reference_fasta)
samfile = pysam.AlignmentFile(args.bam_file, "rb")
print('got sam.')
if args.chrom:
intervals = {args.chrom: [int(args.start_pos), int(args.end_pos)]}
elif args.bed_file:
intervals = td.bed_file_to_dict(args.bed_file)
else:
raise ValueError(
'What do you want to iterate over? Use arguments --bed_file or --chrom --start_pos --end_pos'
)
tensor_batch = np.zeros((args.batch_size, ) +
defines.tensor_shape_from_args(args))
gpos_batch = []
print(len(intervals), 'intervals to iterate over, contigs:',
intervals.keys())
start_time = time.time()
for k in intervals:
contig = reference[k]
args.chrom = k
for start, stop in zip(intervals[k][0], intervals[k][1]):
cur_pos = start
for cur_pos in range(start, stop, args.window_size):
record = contig[cur_pos:cur_pos + args.window_size]
t = td.make_calling_tensor(args, samfile, record, cur_pos,
stats)
if not t is None:
tensor_batch[stats['cur_tensor']] = t
gpos_batch.append((k, cur_pos, record))
stats['cur_tensor'] += 1
if stats['cur_tensor'] == args.batch_size:
predictions = model.predict(
tensor_batch) # predictions is a numpy arra
predictions_to_variants(args, predictions, gpos_batch,
tensor_batch, vcf_writer, contig)
tensor_batch = np.zeros(
(args.batch_size, ) +
defines.tensor_shape_from_args(args))
stats['cur_tensor'] = 0
stats['batches_processed'] += 1
gpos_batch = []
if stats['batches_processed'] % 100 == 0:
elapsed = time.time() - start_time
t_per_minute = stats[
'batches_processed'] * args.batch_size / (elapsed /
60)
print('At genomic position:', k, cur_pos,
'Tensors per minute:', t_per_minute,
'Batches processed:', stats['batches_processed'])
for s in stats.keys():
print(s, 'has:', stats[s])
for s in stats.keys():
print(s, 'has:', stats[s])
global name_neg_file
with open(output_dir + name_neg_file + "_" + str(p) + "_neg.bed",
"w") as OUT:
# map(lambda x: OUT.write(x+"\n"),positions_neg_region)
for feature in positions_neg_region:
OUT.write(feature + "\n")
OUT.close()
################################################ MAIN ################################################
random.seed(a=seed_rand) # seeding randomness
bin_size_list = []
print("\n" + "=" * 50 + "\n")
try:
print("loading genome file")
Fasta_tair = SeqIO.to_dict(SeqIO.parse(name_fasta_file, "fasta"))
print("importing information file")
info_regions = pybedtools.BedTool(name_bed_file)
(positions_pos_regions_filename, Nmin,
Nmax) = treat_positive_region(name_pos_file, name_fasta_file,
name_bed_file)
list_pos = np.genfromtxt(positions_pos_regions_filename,
dtype=[('chrom', 'S20'), ('start', int),
('stop', int), ('type', 'S300'),
('GC', float), ('length', int)])
# list_=np.unique([v for i,v in enumerate(list_pos['type'])]).tolist()
# for elt in list_:
# print(elt,len([i for i,v in enumerate(list_pos['type']) if elt == v]))
print("cutting genome into pieces - please wait")
genome_regions_library = {}
(genome_regions_library,
#/usr/bin/python
from Bio import SeqIO
import sys
arg1 = sys.argv[1]
arg2 = sys.argv[2]
arg3 = sys.argv[3]
filename = arg1
reads_dict = SeqIO.to_dict(SeqIO.parse(arg2, "fasta"))
with open(arg3, "w") as output_file:
n = 0
for record in SeqIO.parse(filename, "fasta"):
if record.id in reads_dict:
#n=n+1
SeqIO.write(record, output_file, "fasta")
else:
#SeqIO.write(record, output_file, "fasta")
n = n + 1
print(n)
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from Bio import SeqIO
import sys
mapfolder = ''
#%%
filenumber = sys.argv[1]
fqread1 = SeqIO.to_dict(
SeqIO.parse(mapfolder + 'Split1-' + str(filenumber), 'fastq'))
fqread2 = SeqIO.to_dict(
SeqIO.parse(mapfolder + 'Split2-' + str(filenumber), 'fastq'))
sublib = pd.read_pickle('../../code/design/LIBRARY/threelibrary210.pkl')
sublib['barcode'] = sublib.varseq.apply(lambda x: x[18:30])
readsmap = pd.Series([''], index=sublib.index)
liftfor = 'TGCGAGTTAGGGGACGGT'
#upstream='ACTAGTTTACGACGGGTT'
for read in fqread1.keys():
if (fqread1[read].seq.find(liftfor) > -1):
testbc = fqread1[read].seq[fqread1[read].seq.find(liftfor) +
18:fqread1[read].seq.find(liftfor) + 30]
readsmap.loc[sublib[sublib.barcode == testbc].index] = readsmap.loc[
sublib[sublib.barcode == testbc].index] + ' ' + [
str(fqread2[read].seq)
import matplotlib.ticker as tck
import mpl_toolkits.axisartist as axisartist
from matplotlib.patches import Rectangle
def check_location_intersection(location1, location2):
if location1.start in location2 or location1.end in location2:
return True
if location2.start in location1 or location2.end in location1:
return True
return False
annotation_file = "/home/mahajrod/Reference_genomes/drosophila_melanogaster/r6.03/gtf/sbr.gtf"
with open(annotation_file, "r") as ann_fd:
annotations_dict = SeqIO.to_dict(GFF.parse(ann_fd))
CAGE_dir = "/media/mahajrod/d9e6e5ee-1bf7-4dba-934e-3f898d9611c8/CAGE/GSE66284/"
annotation_file = "/home/mahajrod/Reference_genomes/drosophila_melanogaster/r6.03/gtf/sbr_locus.gtf"
description_file = CAGE_dir + "good_samples_description.t"
length_file = "length_sam_file.len"
histo_file = "nxf_region_filtered_sorted_filtered.histo"
with open(annotation_file, "r") as ann_fd:
annotations_dict = SeqIO.to_dict(GFF.parse(ann_fd))
samples_description_dict = {"sample": ["SRR488285",
#"SRR488282",
#"SRR488271",
"SRR488272"],
"description": ["Adult Mated Male 4 days Post-eclosion Testes",
def proteinToCodonAlignment(proteinAlignment, extraDnaSeqs=None):
protSeqDict = {}
for seqRecord in proteinAlignment:
protSeqDict[seqRecord.id] = seqRecord
dnaFasta = patric_api.getSequenceOfFeatures(protSeqDict.keys(), 'dna')
#if Debug:
# LOG.write("dnaFasta sample: %s\n"%dnaFasta[:100])
dnaSeqDict = SeqIO.to_dict(
SeqIO.parse(StringIO(dnaFasta),
"fasta",
alphabet=IUPAC.IUPACAmbiguousDNA()))
for seqId in protSeqDict:
if extraDnaSeqs and seqId in extraDnaSeqs:
dnaSeqDict[seqId] = extraDnaSeqs[seqId]
if Debug:
LOG.write("appending extra DNA seq %s\n" % seqId)
if set(dnaSeqDict.keys()) != set(protSeqDict.keys()):
raise Exception(
"Protein and DNA sets differ:\nProteins: %s\nDNA: %s\n" %
(", ".join(sorted(protSeqDict)), ", ".join(sorted(dnaSeqDict))))
for seqId in dnaSeqDict:
if not len(dnaSeqDict[seqId].seq):
#del(dnaSeqDict[seqId])
LOG.write("warning: seqId %s length of dna was zero\n" % seqId)
dnaSeqRecords = []
for proteinSeq in proteinAlignment:
dnaSeqRecords.append(dnaSeqDict[proteinSeq.id])
if Debug:
LOG.write("dna seqs has %d seqs\n" % (len(dnaSeqRecords)))
#LOG.write("DNA seq ids: %s\n"%(", ".join(sorted(dnaSeqDict))))
#LOG.write("pro seq ids: %s\n"%(", ".join(sorted(protSeqDict))))
#LOG.write("first two aligned DNA seqs:\n")
#SeqIO.write(dnaSeqRecords[:2], LOG, "fasta")
#LOG.flush()
"""
# now check length of protein vs dna sequences, extend dna if needed to make match in numbers of codons
for i, protRec in enumerate(proteinAlignment):
protSeq = str(protRec.seq)
protSeq.replace('-','')
protLen = len(protSeq)
if len(dnaSeqs[i].seq) < protLen*3:
shortfall = (protLen*3) - len(dnaSeqs[i].seq)
if Debug:
LOG.write("DNA seq for %s is too short for protein, shortfall = %d\n"%(protRec.id, shortfall))
# extend on both ends to be safe
dnaSeqs[i].seq = "N"*shortfall + dnaSeqs[i].seq + "N"*shortfall
"""
returnValue = None
#with warnings.catch_warnings():
#warnings.simplefilter('ignore', BiopythonWarning)
#try:
#ambiguous_nucleotide_values = {'K': 'GT', 'M': 'AC', 'N': 'ACGT', 'S': 'CG', 'R': 'AG', 'W': 'AT', 'Y': 'CT'}
#ambiguous_protein_values = {'X': 'ACDEFGHIKLMNOPQRSTVWY', 'J': 'IL', 'B': 'DN', 'Z': 'EQ'}
#ambiguous_codon_table = CodonTable.AmbiguousCodonTable(CodonTable.ambiguous_dna_by_name["Standard"], IUPAC.IUPACAmbiguousDNA(), ambiguous_nucleotide_values, IUPAC.protein, ambiguous_protein_values)
#returnValue = codonalign.build(pro_align=proteinAlignment, nucl_seqs=dnaSeqRecords, codon_table=ambiguous_codon_table, max_score=1000)
returnValue = codonalign.build(pro_align=proteinAlignment,
nucl_seqs=dnaSeqRecords,
max_score=1000)
for dnaSeq in returnValue:
proteinRecord = protSeqDict[dnaSeq.id]
if proteinRecord.annotations:
dnaSeq.annotations = proteinRecord.annotations.copy()
#except Exception as e:
# LOG.write("problem in codonalign, skipping\n%s\n"%str(e))
# raise(e)
return returnValue
aln_out = group + '.aln'
run_mafft_commandline(args, seq_outf, aln_out)
remove(seq_outf)
# check gap percentage
gap_perc = gaps_in_alignment(aln_out)
if gap_perc < args.gap:
used_group.append(aln_out)
else:
print('{0} was discarded due to higher gaps {1:5.2f}% (>= {2:5.2f}%)'.format(aln_out, gap_perc, args.gap))
remove(aln_out)
if (args.con and (len(used_group) > 1)) or args.orthogrp:
# concatenate all single gene alignments
conseq_file = 'concatenated_seqs_%d.aln' % len(used_group)
print('Concatenating %d alignments into one file (%s)' % (len(used_group), conseq_file))
seqs = SeqIO.to_dict(SeqIO.parse(used_group[0], 'fasta'))
remove(used_group[0])
for seq_file in used_group[1:]:
other_seqs = SeqIO.to_dict(SeqIO.parse(seq_file, 'fasta'))
for seq_id in seqs:
seqs[seq_id].seq += other_seqs[seq_id].seq
remove(seq_file)
SeqIO.write(seqs.values(), conseq_file, 'fasta')
used_group = [conseq_file,]
#### D. using the alignment(s) to make tree(s)
for idx, group in enumerate(used_group):
out_file = group.split('.')[0] + '.tree'
idx += 1
print('Making a tree to %s (%d/%d)' % (out_file, idx, len(used_group)))
run_fasttree_commandline(args, group, out_file)
parser.add_argument("-i",
"--input",
action="store",
dest="input",
help="Genbank file with annotations")
parser.add_argument(
"--fast_parsing",
action="store_true",
dest="fast_parsing",
help="Fast parsing mode - high memory consumption. Default: false")
parser.add_argument("-o",
"--output_prefix",
action="store",
dest="output_prefix",
default="output",
help="Prefix of output files")
args = parser.parse_args()
record_dict = SeqIO.to_dict(SeqIO.parse(
args.input, format="genbank")) if args.fast_parsing else SeqIO.index_db(
"temp_index.idx", [args.input], format="genbank")
SequenceRoutines.get_protein_marking_by_exons_from_genbank(
record_dict,
args.output_prefix,
protein_id_field_in_cds_feature="protein_id")
#os.remove("temp_index.idx")
def get_DR_basePair_freq(matF, FastaFile, threshold, factorTranscription,
Interdistance_maxValue, matRev):
# This line allows to retrieve all the sequences from the fasta file
sequences = SeqIO.to_dict(SeqIO.parse(FastaFile, "fasta"))
print " There are %s sequence(s) to analyze" % (len(sequences))
allScoresPos = []
allScoresNeg = []
sens = ""
# We will store in these lists all the occurences of each kind of interdistances between motifs found in all sequences.
DRmotif = []
DR = [0] * (Interdistance_maxValue + 1)
ER = [0] * (Interdistance_maxValue + 1)
IR = [0] * (Interdistance_maxValue + 1)
index = 0
#print("dep : ",dep)
# We look at all the fasta sequences:
for s in sequences:
# We will store in this list all the best scores (see the threshold after) found for subsequences of one sequence
#if type(threshold) is list:
good_score_positions = []
bestScore = 0
positionOfTheBestScore = 0
# This line allows to retrieve the DNA sequence
seq = sequences[s].seq
id = sequences[s].id
# We look at each sub-sequences of the whole sequence. Each sub-sequence has the same length that the matrix length.
for c in range(len(seq) - (lenMotif - 1)):
strandPos = seq[c:c + lenMotif].upper()
#print("strandPos : ",strandPos)
test = 0
for nu in strandPos:
if nu not in ["A", "C", "G", "T"]:
test = 1
if test == 1:
score = "NA"
else:
n = 0
#These lines allows to calculate a score for one sub-sequence
scoreStrandPos = 0
scoreStrandNeg = 0
while n < lenMotif:
if strandPos[n] == 'A':
scoreStrandPos = scoreStrandPos + matF[n * 4]
scoreStrandNeg = scoreStrandNeg + matRev[n * 4]
elif strandPos[n] == 'C':
scoreStrandPos = scoreStrandPos + matF[n * 4 + 1]
scoreStrandNeg = scoreStrandNeg + matRev[n * 4 + 1]
elif strandPos[n] == 'G':
scoreStrandPos = scoreStrandPos + matF[n * 4 + 2]
scoreStrandNeg = scoreStrandNeg + matRev[n * 4 + 2]
elif strandPos[n] == 'T':
scoreStrandPos = scoreStrandPos + matF[n * 4 + 3]
scoreStrandNeg = scoreStrandNeg + matRev[n * 4 + 3]
n += 1
#These lines allows to retrieve the position and the strand where there is a predicted binding site.
#You can change the threshold.
if scoreStrandPos > threshold:
#good_score_positions.append([c+1,">",scoreStrandPos,str(strandPos[0:1]),str(strandPos[1:2]),str(strandPos[2:3]),str(strandPos[3:4]),str(strandPos[4:5]),str(strandPos[5:6]),str(strandPos[6:7]),str(strandPos[7:8]),str(strandPos[8:9]),str(strandPos[9:10]),index])
good_score_positions.append([
c + 1, ">", scoreStrandPos,
str(strandPos[3:10]), index
])
#good_score_positions.append([c+1,">",scoreStrandPos,str(strandPos[0:10])+str(seq[c+lenMotif:c+lenMotif+1+lenMotif]),index])
if scoreStrandNeg > threshold:
good_score_positions.append([
c + 1, "<", scoreStrandPos,
str(strandPos[0:7]), index
])
# Once we have stored all the positions, we calculate all the interdistances:
for first in range(0, len(good_score_positions) - 1):
firstSubSeq = good_score_positions[first]
for second in range(first + 1, len(good_score_positions)):
secondSubSeq = good_score_positions[second]
if factorTranscription == "ARF2":
if firstSubSeq[1] == ">" and secondSubSeq[1] == ">":
d = (int(secondSubSeq[0]) + 2) - (int(firstSubSeq[0]) +
lenMotif - 2)
if d == 8:
DRmotif.append(firstSubSeq)
if factorTranscription == "ARF5":
if firstSubSeq[1] == ">" and secondSubSeq[1] == ">":
d = (int(secondSubSeq[0]) + 3) - (int(firstSubSeq[0]) +
lenMotif - 1)
#if d == 5 :
#DRmotif.append(firstSubSeq)
if firstSubSeq[1] == "<" and secondSubSeq[1] == ">":
d = (int(secondSubSeq[0]) + 3) - (int(firstSubSeq[0]) +
lenMotif - 3)
if d == 0:
DRmotif.append(
str(firstSubSeq[3]) + str(secondSubSeq[3]))
index = index + 1
#print("DRmotif : ",DRmotif)
#DRmotif_without_doublon = [list(i) for i in set(map(tuple, DRmotif))]
DRbp1 = []
DRbp2 = []
DRbp3 = []
DRbp4 = []
DRbp5 = []
DRbp6 = []
DRbp7 = []
DRbp8 = []
DRbp9 = []
DRbp10 = []
#for i in DRmotif_without_doublon :
#DRbp1.append(i[3])
#DRbp2.append(i[4])
#DRbp3.append(i[5])
#DRbp4.append(i[6])
#DRbp5.append(i[7])
#DRbp6.append(i[8])
#DRbp7.append(i[9])
#DRbp8.append(i[10])
#DRbp9.append(i[11])
#DRbp10.append(i[12])
return (DRbp1, DRbp2, DRbp3, DRbp4, DRbp5, DRbp6, DRbp7, DRbp8, DRbp9,
DRbp10, DRmotif)
stdout=subprocess.PIPE)
temp2.write(p.communicate()[0])
temp.close()
temp2.flush()
temp2.seek(0)
listToMerge = []
lastLine = []
fusion = False
losgehts = False
if not strandSpecific:
sys.stderr.write(
'read fasta records into memory (for strand unspecific protocols only). \n'
)
handle = open(refGenome, "rU")
record_dict = SeqIO.to_dict(SeqIO.parse(handle, "fasta"))
handle.close()
sys.stderr.write('process the sorted split reads. \n')
for line in temp2.readlines():
columns = line.strip().split('\t')
if (losgehts):
if (columns[0] == lastLine[0]):
listToMerge.append(columns)
else:
listofmappings = []
listofmappings = getmapping(listToMerge)
for i in range(0, len(listofmappings)):
transcripts += 1
if (len(listofmappings[i]) > 2):
transcriptsMore += 1
fusion = isFusion(listofmappings[i])
