def get_longest(contig_file, outfile, min_length):
#returns a dictionary of header sequence pairs of the longest contig in
#each locus
locus_dict = defaultdict(list)
contig_dict = {}
for h, s in FastaReader(contig_file):
if len(h.split("_")) > 1:
locus_dict[h.split("_")[1]].append((h, s))
else:
contig_dict[h] = s
longest_contigs = []
for locus in locus_dict:
max_len = 0
for h, s in locus_dict[locus]:
if len(s) > max_len:
max_len = len(s)
curr = (h, s)
longest_contigs.append(curr)
with open(outfile, 'w') as outfas:
for contig in longest_contigs:
if len(contig[1]) >= min_length:
outfas.write(">" + contig[0] + "\n")
outfas.write(contig[1] + "\n")
for contig in contig_dict:
if len(contig_dict[contig]) >= min_length:
outfas.write(">" + contig + "\n")
outfas.write(contig_dict[contig] + "\n")
return longest_contigs
def split_genome(fastafile, outputprefix, n_splits, n_reps):
seqs = []
total_length = 0
for h, s in FastaReader(fastafile):
seqs.append((h, s))
if len(seqs) > 1:
raise ValueError(
'Multiple contigs in fasta file which isnt currently supported!')
h = ""
s = seqs[0][1]
print "Genome length:", len(s)
for r in range(n_reps):
outputfile = outputprefix + "_rep_" + str(r) + ".fasta"
# generate random split locations
cuts = np.random.choice(len(s), n_splits)
cuts = sorted(np.append(cuts, len(s)))
print "cuts:", cuts
prev = 0
with open(outputfile, 'w') as outfile:
for i, cut in enumerate(cuts):
outfile.write(">split_" + str(i) + "\n")
outfile.write(s[prev:cut] + "\n")
prev = cut
print "Final contig end position:", len(s)
return
def remove_shorter_sequences(fasta_file, per_within_max, len_cutoff
, outputfile):
locus_dict = {}
#Load in fasta file
for h,s in FastaReader(fasta_file):
locus_name = h.split("_")[1]
if locus_name not in locus_dict:
locus_dict[locus_name]=Locus(h)
locus_dict[locus_name].transcripts.append(Transcript(h, s))
if locus_dict[locus_name].max_trans_length < len(s):
locus_dict[locus_name].max_trans_length = len(s)
#now output with cleaning
with open(outputfile,'w') as outfile:
for locus_name in locus_dict:
for t in locus_dict[locus_name].transcripts:
if ((len(t.sequence)/float(locus_dict[locus_name].max_trans_length))
< (1-per_within_max)): #too far away from the longest
continue
if len(t.sequence) < len_cutoff:
continue
outfile.write(">"+t.name+"\n")
outfile.write(t.sequence+"\n")
def get_rask_var(raskFasta, contig_file, fileName, outdir, verbose):
#run blast against the rask VAR genes
blast_out = run_blast(raskFasta, contig_file, outdir, verbose)
rask_hits = set()
with open(blast_out, 'r') as blastfile:
for line in blastfile:
rask_hits.add(line.split()[0])
rask = outdir + fileName + "_rask.fa"
non_rask = outdir + fileName + "_nonrask.fa"
count_rask = 0
count_non_rask = 0
with open(rask, 'w') as raskout:
with open(non_rask, 'w') as nonraskout:
for h, s in FastaReader(contig_file):
if h in rask_hits:
raskout.write(">" + h + "\n")
raskout.write(s + "\n")
count_rask += 1
else:
nonraskout.write(">" + h + "\n")
nonraskout.write(s + "\n")
count_non_rask += 1
if verbose:
print count_rask, " annotated to rask DB..."
print count_non_rask, " remaining"
return rask, non_rask
def merge_files(easy, hmmMatch, outputdir):
out_file = (outputdir + os.path.splitext(os.path.basename(easy))[0] +
"_finalTranslated.fa")
with open(out_file, 'w') as outfile:
for h, s in FastaReader(easy):
outfile.write(">" + h + "\n")
outfile.write(s + "\n")
for h, s in FastaReader(hmmMatch):
outfile.write(">" + h + "\n")
outfile.write(s + "\n")
print "Success!"
return
def translate_6_frame(in_fasta, out_fasta):
with open(out_fasta, 'w') as outfile:
for h,s in FastaReader(in_fasta):
for frame, seq in sixFrameTranslation(s).items():
outfile.write(">"+h+" _frame_"+str(frame)+"\n")
outfile.write(seq+"\n")
return out_fasta
def filter_Locus_1(outputdir, folder_prefix, verbose=False):
final_transcripts = []
locus_dict = defaultdict(list)
for transcript_file in glob.glob(outputdir + folder_prefix +
'*/transcripts.fa'):
#now need to extract all transcripts that are the only member of
#their Locus
name = transcript_file.strip('/transcripts.fa')[-2:]
print name
print transcript_file
for h, s in FastaReader(transcript_file):
locus = h.split("_")[1] + "_" + name
locus_dict[locus].append((h + "_K" + name, s))
#Now we want to write out all Locus' of length 1 to the outfile
out_fileA = outputdir + "filter_locus1_transcripts_keep61.fa"
with open(out_fileA, 'w') as outfile:
for locus in locus_dict:
if (locus.split("_")[-1] == '61') or (len(locus_dict[locus]) == 1):
for t in locus_dict[locus]:
outfile.write(">" + t[0] + "\n")
outfile.write(t[1] + "\n")
# out_fileB = outputdir + "filter_locus1_transcripts.fa"
# with open(out_fileB, 'w') as outfile:
# for locus in locus_dict:
# if len(locus_dict[locus]) == 1:
# for t in locus_dict[locus]:
# outfile.write(">" + t[0] + "\n")
# outfile.write(t[1] + "\n")
return out_fileA
def filter_with_HMMER(orfFile, hmmfile, hmmThresh, outputdir):
outname = os.path.splitext(os.path.basename(orfFile))[0]
search_file = searchhmmer(orfFile, hmmfile, 9.97, outputdir, outname, True)
output_file = (outputdir + os.path.splitext(os.path.basename(orfFile))[0] +
"_matchedHMMER.fa")
# target name accession tlen query name accession qlen E-value score bias # of c-Evalue i-Evalue score bias from to from to from to acc description of target
head_hits = defaultdict(set)
with open(search_file, 'r') as searchfile:
for line in searchfile:
if line[0] == '#':
continue
line = line.strip().split()
sequence = line[0]
hmmHit = line[3]
head_hits[sequence].add(hmmHit)
num_orfs = 0
num_keep = 0
with open(output_file, 'w') as outfile:
for h, s in FastaReader(orfFile):
num_orfs += 1
if len(head_hits[h]) >= hmmThresh:
outfile.write(">" + h + "\n")
outfile.write(s + "\n")
num_keep += 1
print str(num_keep) + " orfs kept out of " + str(num_orfs)
return output_file
def filterWithHMMER(inputfile, prefix, cpu, verbose):
hmmer_cmd = (HMMERSEARCH + " -o /dev/null" + " --domT 80" +
" --domtblout " + "hmmerDBLalphaSearch.txt" + " --cpu " +
str(cpu) + " " + DBLA_HMM + " " + inputfile)
if verbose:
print "running... ", hmmer_cmd
check_call(hmmer_cmd, shell=True)
#Now run through and get DBLalpha seqs
keep = set()
with open("hmmerDBLalphaSearch.txt", 'rU') as infile:
for line in infile:
if line[0] == "#": continue
keep.add(line.split()[0])
with open(prefix + "_DBLa_cleaned.fasta", 'w') as dblfile:
with open(prefix + "_NOT_dblalpha.fasta", 'w') as contamfile:
for h, s in FastaReader(inputfile):
if h in keep:
dblfile.write(">" + h + "\n" + s + "\n")
else:
contamfile.write(">" + h + "\n" + s + "\n")
def trim_contigs(length, contig_file, outdir):
out_file = outdir + "trim_transcripts.fa"
with open(out_file, 'w') as outfile:
for h, s in FastaReader(contig_file):
if len(s) < length:
continue
outfile.write(">" + h + "\n")
outfile.write(s + "\n")
return out_file
def reNameContigs(contig_file, fileName, outputdir):
renamed = outputdir + fileName + "renamed.fa"
with open(renamed, 'w') as outfile:
for h, s in FastaReader(contig_file):
h = h.split()[0]
if len(h.split("_")) > 4: #oases transcript
h = h.split("_")
h = "_".join([h[0], h[1], h[3]])
outfile.write(">" + h + "\n")
outfile.write(s + "\n")
return renamed
def convert(gfffile, fastafile, outputfile):
with open(outputfile, 'w') as outfile:
outfile.write("##gff-version 3\n")
for h, s in FastaReader(fastafile):
h = h.split()[0]
outfile.write(" ".join(["##sequence-region", h, "1",
str(len(s))]) + "\n")
with open(gfffile, 'rU') as infile:
for line in infile:
if line[0] != "#":
outfile.write(line)
outfile.write("##FASTA\n")
for h, s in FastaReader(fastafile):
h = h.split()[0]
outfile.write(">" + h + "\n" + s + "\n")
return
def get_contaminants(fasta_ref_files, contig_file, fileName, percent_overlap,
outdir, verbose):
#first get list of contigs
contigs = {}
for h, s in FastaReader(contig_file):
contigs[h] = s
if verbose:
print("Number of contigs before contaminant filtering: ",
len(contigs.keys()))
#now run blast against the reference files which we want not to be
#present in the data i.e. human
blast_files = []
for reference in fasta_ref_files:
blast_files.append(run_blast(reference, contig_file, outdir, verbose))
#now iterate through blast results file removing contigs that have to
#high a proportion of hits
bad_contigs = set()
for blast_file in blast_files:
blast_name = os.path.splitext(os.path.basename(blast_file))[0]
with open(blast_file, 'r') as bfile:
for line in bfile:
tokens = line.strip().split()
name = tokens[0]
overlap = int(tokens[3]) / float(len(contigs[name]))
if overlap > percent_overlap:
#we don't want this contig
print "removing", name, "overlapped", blast_name
bad_contigs.add(name)
#now write out a fasta file of contaminant sequences
contaminant_file = outdir + fileName + "contaminants.fa"
with open(contaminant_file, 'w') as outfile:
for contig in bad_contigs:
outfile.write(">" + contig + "\n")
outfile.write(contigs[contig] + "\n")
#now write contigs without contaminants to a file
non_contaminant_file = outdir + fileName + "Non_contaminants.fa"
with open(non_contaminant_file, 'w') as outfile:
for contig in contigs:
if contig not in bad_contigs:
outfile.write(">" + contig + "\n")
outfile.write(contigs[contig] + "\n")
if verbose:
print("Number of contigs after filtering: ",
len(contigs.keys()) - len(bad_contigs))
return non_contaminant_file, contaminant_file
def combineReadFiles(outputfile, verbose):
if verbose:
print "combining sample files..."
#combine read files appending sample name to read headers
with open(outputfile, 'w') as outfile:
for f in glob.glob("*_lowSupportFiltered.fasta"):
for h, s in FastaReader(f):
outfile.write(">" + h + "sample=" +
f.split("_demultiplex")[0] + "\n")
outfile.write(s + "\n")
return
def main_new(fastafile,bkp):
distance_name=["ab","ac","bc"];
seq_name=[]
for h,s in FastaReader(fastafile):
seq_name.append(h)
aln = AlignIO.read(open(fastafile), 'fasta')
calculator = DistanceCalculator('blosum62')
segment_1 = calculator.get_distance(aln[:, :bkp])
segment_2 = calculator.get_distance(aln[:, bkp:])
distance=[segment_1[seq_name[1]][0],segment_1[seq_name[2]][0],segment_1[seq_name[2]][1],segment_2[seq_name[1]][0],segment_2[seq_name[2]][0],segment_2[seq_name[2]][1]];
#distance=[segment_1[seq_name[1]][0],segment_1[seq_name[2]][0],segment_1[seq_name[2]][1],segment_2[seq_name[1]][0],segment_2[seq_name[2]][0],segment_2[seq_name[2]][1]];
compare_distance=[abs(distance[0]-distance[3]),abs(distance[1]-distance[4]),abs(distance[2]-distance[5])]##in order of ab,ac,bc
temp2 = distance_name[compare_distance.index(min(compare_distance))]
string = "abc";string = string.replace(temp2[0],"");string = string.replace(temp2[1],"")
rec=seq_name["abc".index(string)]
return rec
def filter_length(contig_file, length_filter, fileName, outdir, verbose):
length_file = outdir + fileName + "lenFilt.fa"
short_count = 0
with open(length_file, 'w') as outfile:
for h, s in FastaReader(contig_file):
if len(s) < length_filter:
short_count += 1
else:
outfile.write(">" + h + "\n")
outfile.write(s + "\n")
if verbose:
print short_count, " contigs removed as too short..."
return length_file
def annotate_w_ntDB(contig_file, fileName, outdir, verbose):
blastOut = outdir + fileName + "nonRaskBlast.txt"
#first run a special blast using the nt database
blast_cmd = (
"blastn " + "-evalue 10 " +
"""-outfmt "6 qseqid sseqid stitle length pident qstart qend sstart send evalue" """
# + "-num_alignments " + str(num_hits) + " "
+ "-num_threads 10 -max_target_seqs 3 " + "-db " + BLAST_NT_DB + " " +
"-query " + contig_file + " " + "-out " + blastOut)
if verbose:
print blast_cmd
check_call(blast_cmd, shell=True)
#now retrieve annotation information
contigs = defaultdict(str)
contigs_perID = defaultdict(float)
with open(blastOut, 'r') as blastfile:
for line in blastfile:
line = line.strip().split("\t")
contigs[line[0]] = (contigs[line[0]] + " [" + line[2] +
"_alignLen_" + line[3] + "_perID_" + line[4] +
"] ")
contigs_perID[line[0]] = max(contigs_perID[line[0]],
float(line[4]))
#now re-write the fasta file with the annotations in the headers
annotated = outdir + fileName + "nonRask_annotated.fa"
unknown_blastOut = outdir + fileName + "ForManualInspection.fa"
with open(annotated, 'w') as outfileKnown:
with open(unknown_blastOut, 'w') as outfileUnknoen:
for h, s in FastaReader(contig_file):
if h in contigs:
if contigs_perID[h] > 97:
outfileKnown.write(">" + h + " " + contigs[h] + "\n")
outfileKnown.write(s + "\n")
continue
if h in contigs:
outfileUnknoen.write(">" + h + " " + contigs[h] + "\n")
else:
outfileUnknoen.write(">" + h + " none\n")
outfileUnknoen.write(s + "\n")
return annotated
def pull_out_long_ORFs(bad_file, len_cutoff, outputdir):
out_file = (outputdir + os.path.splitext(os.path.basename(bad_file))[0] +
"_TranslongORFS.fa")
num_seqs = 0
num_orfs = 0
with open(out_file, 'w') as outfile:
for h, s in FastaReader(bad_file):
num_seqs += 1
translation = sixFrameTranslation(s)
orfs = get_long_ORFS(translation, len_cutoff)
for o in orfs:
num_orfs += 1
outfile.write(">" + h + o[0] + "\n")
outfile.write(o[1] + "\n")
print str(num_seqs) + " sequences translated into " + str(
num_orfs) + " ORFs"
return out_file
def filter_ref_with_blast(fasta_ref_files, contig_file, percent_overlap,
outfile, outdir):
#first get list of contigs
contigs = {}
for h, s in FastaReader(contig_file):
contigs[h] = s
print "Number of contigs before filtering: ", len(contigs.keys())
#now run blast against the reference files which we want not to be
#present in the data i.e. human
blast_files = []
for reference in fasta_ref_files:
blast_files.append(run_blast(reference, contig_file, outdir, True))
#now iterate through blast results file removing contigs that have to
#high a proportion of hits
bad_contigs = set()
for blast_file in blast_files:
blast_name = os.path.splitext(os.path.basename(blast_file))[0]
with open(blast_file, 'r') as bfile:
for line in bfile:
tokens = line.strip().split()
name = tokens[0]
overlap = int(tokens[3]) / float(len(contigs[name]))
if overlap > percent_overlap:
#we don't want this contig
print "removing", name, "overlapped", blast_name
bad_contigs.add(name)
for name in bad_contigs:
del contigs[name]
#now write resulting contigs to a file
with open(outfile, 'w') as outfas:
for contig in contigs:
outfas.write(">" + contig + "\n")
outfas.write(contigs[contig] + "\n")
print "Number of contigs after filtering: ", len(contigs.keys())
def split_easy_from_hard(inputfile, outputdir):
seqCount = 0
badSeqs = 0
bad_lengths = []
output_file = (outputdir +
os.path.splitext(os.path.basename(inputfile))[0] +
"_translatedL2stops.fa")
with open(output_file + "_BadSeqs", 'w') as badfile:
with open(output_file, 'w') as outfile:
for h, s in FastaReader(inputfile):
stops = 9999
translation = sixFrameTranslation(s)
for frame in translation:
st = translation[frame].count('*')
if st < stops:
best = frame
stops = st
if stops <= 2:
outfile.write(">" + h + " frame_" + str(best) + "\n")
outfile.write(translation[best] + "\n")
else:
badSeqs += 1
bad_lengths.append(len(s))
badfile.write(">" + h + "\n")
badfile.write(s + "\n")
seqCount += 1
print(
str((100.0 * badSeqs) / seqCount) + "percent or " + str(badSeqs) +
" out of " + str(seqCount) + " were not translated.")
return output_file, output_file + "_BadSeqs"
is_fasta = True
else:
is_fasta = False
if uproc:
uprocOut = run_uproc(outdir+prefix+"_uprocList.csv", read1, read2)
hmmerIn = process_uproc_results(uprocOut, outdir+prefix+"_UprocReads.fa"
, read1, read2)
# hmmerIn = outdir+prefix+"_UprocReads.fa"
else:
hmmerIn = outdir+prefix+"_NoFilterList.fa"
with open(hmmerIn, 'w') as outfile:
if not is_fasta:
if read2:
for h,s in FastaReader(read2):
outfile.write(">"+h+"\n"+s+"\n")
for h,s in FastaReader(read1):
outfile.write(">"+h+"\n"+s+"\n")
else:
if read2:
for h,s,q in FastqReader(read2):
outfile.write(">"+h+"\n"+s+"\n")
for h,s,q in FastqReader(read1):
outfile.write(">"+h+"\n"+s+"\n")
uproc_reads_6frame = translate_6_frame(hmmerIn
, outdir+prefix+"_Uproc_6frame.fa")
hmm_out = allocate_w_hmmer(uproc_reads_6frame
, outdir+prefix+"_nhmmOut.txt", evalue)
input=dir+"complement_chunks"
output=dir+"results/result_all.csv"
headers=["chunk","target","db1","db2","rec","sv"]
with open(output, 'a+') as outfile:
writer = csv.writer(outfile)
writer.writerow(headers)
for fasta_file in glob.glob(input+"/*.fasta"):
bkp=int(fasta_file.split(".fasta")[0].split("_")[-1])
chunk=fasta_file.split("/chunk")[1].split("_")[0]
initial= main_new(fasta_file,bkp)
#print initial
mapping_dict={};seq_name=[]
pvalue=0;permutation=100;
for h,s in FastaReader(fasta_file):
mapping_dict[h]=s;seq_name.append(h);full_alignment_length=len(s)
for m in range(permutation):
index_1 = np.random.choice(bkp, bkp, replace=True);shuffled_sequences_1=[]
for n in [mapping_dict[seq_name[0]][0:bkp],mapping_dict[seq_name[1]][0:bkp],mapping_dict[seq_name[2]][0:bkp]]:
temp_list_1="";
for j in index_1:
temp_list_1 = temp_list_1+ n[j];
shuffled_sequences_1.append(temp_list_1)
s2_length= full_alignment_length-bkp
index_2 = np.random.choice(s2_length, s2_length, replace=True);shuffled_sequences_2=[]
for n in [mapping_dict[seq_name[0]][bkp:],mapping_dict[seq_name[1]][bkp:],mapping_dict[seq_name[2]][bkp:]]:
temp_list_2="";
for j in index_2:
temp_list_2 = temp_list_2+ n[j];
shuffled_sequences_2.append(temp_list_2)
import sys, os
from third_party_runners import run_blast
from mungo.fasta import FastaReader
contig_file = sys.argv[1]
blastdb_file = sys.argv[2]
outdir = sys.argv[3]
name = os.path.splitext(os.path.basename(contig_file))[0]
blast_out = run_blast(blastdb_file, contig_file, outdir, True)
rask_hits = set()
with open(blast_out, 'r') as blastfile:
for line in blastfile:
rask_hits.add(line.split()[0])
with open(outdir + name + "_rask.fa", 'w') as raskout:
with open(outdir + name + "_nonrask.fa", 'w') as nonraskout:
for h, s in FastaReader(contig_file):
if h in rask_hits:
raskout.write(">" + h + "\n")
raskout.write(s + "\n")
else:
nonraskout.write(">" + h + "\n")
nonraskout.write(s + "\n")
else:
with open(
dir + "temp/chunk_" + str(i) +
"/original_chunk_db.txt", "rU") as db_infile:
for n, db_line in enumerate(db_infile.readlines()):
line1 = db_line.strip().split()
h1 = line1[0]
s1 = line1[1]
if n == k:
outfile.write(">" + h + "\n" +
s[bkp[k - 1]:bkp[k]] + "\n" + ">" +
h1 + "\n" + s1 + "\n")
### Step3: add the part for mafft alignment. use replace not split.
mapping_dict_seq_identifier = {}
for h, s in FastaReader(dir + "data/simulated_seqs_recombined.fasta"):
mapping_dict_seq_identifier[h] = s
for i in range(Chunk_count):
db_line_index = range(startline[i] + 2, endline[i] + 1)
line_index = [startline[i]] + range(startline[i] + 2, endline[i] + 1)
for k in range(mosaic_output_dbcount[i] - 1):
with open(input, 'rU') as infile:
for j, line in enumerate(infile.readlines()):
if j == db_line_index[k]:
line1 = line.strip().split()
h1 = line1[0]
s1 = line1[1]
if len(s1) >= 10:
original_seq = mapping_dict_seq_identifier[h1]
s = line1[1].replace("-", "")
with open(
def countReads(fastafile):
count = 0
for h, s in FastaReader(fastafile):
count += 1
return count
from third_party_runners import run_blast
from mungo.fasta import FastaReader
OVERLAP = 0.7
IDENTITY = 95
sequences = sys.argv[1]
outdir = sys.argv[2]
#first run all-vs-all blast
bfile = run_blast(sequences, sequences, outdir, True)
#now get the lengths of all the contigs
contig_len = {}
for h,s in FastaReader(sequences):
contig_len[h]=len(s)
#now iterate through search finding redundant contigs
redundant_contigs = set()
with open(bfile, 'r') as blastsearch:
for line in blastsearch:
tokens = line.strip().split()
if tokens[0]==tokens[1]: #matching itself
continue
if float(tokens[2]) >= IDENTITY:
# print "identity ",tokens[2]
if ((float(tokens[3])/contig_len[tokens[0]] >= OVERLAP) or
(float(tokens[3])/contig_len[tokens[1]] >= OVERLAP)):
# print "overlap", max(float(tokens[3])/contig_len[tokens[0]],float(tokens[3])/contig_len[tokens[1]])
#we want to remove the shorted contig
def assemble_paired_reads_soapDeNovoTrans(fasta_single,
fasta_paired,
outputdir,
ins_length=False,
verbose=False):
#first move in temp directory so the output is kept nicely
curr_dir = os.getcwd()
os.chdir(outputdir)
scriptPath = getScriptPath()
if fasta_paired == None:
config_str = ("""#maximal read length
max_rd_len=250
[LIB]
#maximal read length in this lib
rd_len_cutof=250
#average insert size
avg_ins=0
#if sequence needs to be reversed
reverse_seq=0
#in which part(s) the reads are used
asm_flags=3
#minimum aligned length to contigs for a reliable read location (at least 32 for short insert size)
map_len=32
#fasta file for single reads
q=""" + fasta_single)
elif (fasta_single == None) or (os.stat(fasta_single).st_size
== 0): #check if single assembly
script = "python " + scriptPath + "/third-party/khmer/scripts/split-paired-reads.py"
#first split files so PEAR can use them
split_cmd = (script + " " + fasta_paired)
if verbose:
print split_cmd
check_call(split_cmd, shell=True)
config_str = ("""#maximal read length
max_rd_len=250
[LIB]
#maximal read length in this lib
rd_len_cutof=250
#average insert size
avg_ins=0
#if sequence needs to be reversed
reverse_seq=0
#in which part(s) the reads are used
asm_flags=3
#minimum aligned length to contigs for a reliable read location (at least 32 for short insert size)
map_len=32
#fasta file for single reads
q1=""" + fasta_paired + ".1\n" + "q2=" + fasta_paired + ".2")
else:
script = "python " + scriptPath + "/third-party/khmer/scripts/split-paired-reads.py"
#first split files so PEAR can use them
split_cmd = (script + " " + fasta_paired)
if verbose:
print split_cmd
check_call(split_cmd, shell=True)
config_str = ("""#maximal read length
max_rd_len=250
[LIB]
#maximal read length in this lib
rd_len_cutof=250
#average insert size
avg_ins=0
#if sequence needs to be reversed
reverse_seq=0
#in which part(s) the reads are used
asm_flags=3
#minimum aligned length to contigs for a reliable read location (at least 32 for short insert size)
map_len=32
#fasta file for single reads
q1=""" + fasta_paired + ".1\n" + "q2=" + fasta_paired + ".2\n" + "q=" +
fasta_single)
scriptPath = getScriptPath()
script = (
scriptPath +
"/third-party/SOAPdenovo-Trans-bin-v1.03/SOAPdenovo-Trans-127mer")
for K in ["21", "31", "41", "51", "61"]:
config_file = outputdir + "soapConfigK" + K
outputGraph = outputdir + "soapGraphK" + K
soap_cmd = (
script + " all" + " -s " + config_file + " -o " + outputGraph +
" -K " + K +
" -L 50" #not actually important as we only use the contigs
)
with open(config_file, 'w') as outfile:
outfile.write(config_str)
if verbose:
print soap_cmd
check_call(soap_cmd, shell=True)
#now to combine the contig files
outfile = outputdir + "combined_soapDeNovo.fa"
#now rename transcripts to include which Kmer run they came from and combine
# into one file
with open(outfile, 'w') as output:
for transcript_file in glob.glob("*.contig"):
fname = os.path.splitext(os.path.basename(transcript_file))[0]
for h, s in FastaReader(transcript_file):
h = h.strip().split()
output.write(
(">" + h[0] + "_" + fname + " " + " ".join(h[1:]) + "\n"))
output.write(s + "\n")
#return to previous directory
os.chdir(curr_dir)
return outfile
def calculateSummaryStatistics(pairedMIDs, outputfile, total_reads,
total_reads_before_contaminant_filtering,
total_reads_after_contaminant_filtering,
blast_isolate_counts, chimeric_filt,
cleaned_read_file, verbose):
##Calculate flexbar summary statisics
flexBarStats = readFlexBarLog("flexbarRun1.log")
logfiles = glob.glob("*flexbarRun2.log")
for f in logfiles:
tempLog = readFlexBarLog(f)
flexBarStats["processed_reads"] += tempLog["processed_reads"]
flexBarStats["skip_uncalled_bases"] += tempLog["skip_uncalled_bases"]
flexBarStats["skip_unassigned_reads"] += tempLog[
"skip_unassigned_reads"]
flexBarStats["skip_short_reads"] += tempLog["skip_short_reads"]
flexBarStats["skip_single_reads"] += tempLog["skip_single_reads"]
flexBarStats["discarded_reads"] += tempLog["discarded_reads"]
##Calculate pear merging statistics
#Now merge files based on MID combinations
pearStats = {}
for mid in pairedMIDs:
filenames = glob.glob("*B_" + mid[0] + "_*B_" + mid[1] +
"_*_pearOut.log")
if mid[0] != mid[1]:
#add other direction
filenames += glob.glob("*B_" + mid[1] + "_*B_" + mid[0] +
"_*_pearOut.log")
sample = pairedMIDs[mid]
if len(filenames) > 0:
pearStats[sample] = readPearLog(filenames[0])
else:
pearStats[sample] = {}
pearStats[sample]["total"] = 0
pearStats[sample]["assembled"] = 0
pearStats[sample]["discarded"] = 0
pearStats[sample]["not_assembled"] = 0
print "Did not find", sample, mid
for f in filenames[1:]:
tempPear = readPearLog(f)
pearStats[sample]["total"] += tempPear["total"]
pearStats[sample]["assembled"] += tempPear["assembled"]
pearStats[sample]["discarded"] += tempPear["discarded"]
pearStats[sample]["not_assembled"] += tempPear["not_assembled"]
##Calculate support filtering statistics
#Count number of reads before support filtering
initalCounts = {}
for f in glob.glob("*_demultiplexTrimMerged.fasta"):
sample = f.split("_demultiplexTrimMerged")[0]
initalCounts[sample] = countReads(f)
#Count number of chimeric reads
if chimeric_filt:
chimericCounts = Counter()
for f in glob.glob("*_ncdenovo.fasta"):
sample = f.split("_demultiplexTrimMerged")[0]
chimericCounts[sample] = countReads(f)
else:
for sample in initalCounts:
chimericCounts[sample] = 0
#Count number of centroids
centroidCounts = Counter()
for f in glob.glob("*_demultiplexTrimMerged_centroids.fasta"):
sample = f.split("_demultiplexTrimMerged")[0]
centroidCounts[sample] = countReads(f)
#Count number of centroids with sufficient support
supportCentroids = Counter()
for f in glob.glob("*_demultiplexTrimMerged_lowSupportFiltered.fasta"):
sample = f.split("_demultiplexTrimMerged")[0]
supportCentroids[sample] = countReads(f)
#Count number of reads remaining after contaminant filtering
contamFilteredCounts = Counter()
for h, s in FastaReader(cleaned_read_file):
sample = h.strip().split("sample=")[1]
contamFilteredCounts[sample] += 1
##write to outputfile
with open(outputfile, 'w') as outfile:
outfile.write("# Total paired end reads: " + str(total_reads) + "\n")
outfile.write("# Total reads prior to contaminant filtering: " +
str(total_reads_before_contaminant_filtering) + "\n")
outfile.write("# Total reads after contaminant filtering: " +
str(total_reads_after_contaminant_filtering) + "\n")
outfile.write("####### FlexBar Summary statistics #######\n")
outfile.write("# Total reads processed: " +
str(flexBarStats["processed_reads"]) + "\n")
outfile.write("# Reads skipped due to uncalled bases: " +
str(flexBarStats["skip_uncalled_bases"]) + "\n")
outfile.write("# Unassigned reads: " +
str(flexBarStats["skip_unassigned_reads"]) + "\n")
outfile.write("# Short reads skipped: " +
str(flexBarStats["skip_short_reads"]) + "\n")
outfile.write("# Single reads skipped: " +
str(flexBarStats["skip_single_reads"]) + "\n")
outfile.write("# Total reads discarded: " +
str(flexBarStats["discarded_reads"]) + "\n")
p = 100.0 * (1 - flexBarStats["discarded_reads"] /
float(flexBarStats["processed_reads"]))
outfile.write("# Proportion of reads kept: " + str(p) + "\n")
outfile.write("####### Sample specific summary statistics #######\n")
#now write out sample specific statistics
outfile.write(",".join([
"Sample", "PreMerge", "Merged", "Discarded", "Not Assembled",
"Filtered", "Chimeric", "Centroids", "Centroids with support",
"After contaminant filtering", "3D7", "DD2", "HB3"
]) + "\n")
for sample in pearStats:
outfile.write(",".join([
sample,
str(pearStats[sample]["total"]),
str(pearStats[sample]["assembled"]),
str(pearStats[sample]["discarded"]),
str(pearStats[sample]["not_assembled"]),
str(pearStats[sample]["total"] - initalCounts[sample]),
str(chimericCounts[sample]),
str(centroidCounts[sample]),
str(supportCentroids[sample]),
str(contamFilteredCounts[sample]),
str(blast_isolate_counts[sample]["3D7"]),
str(blast_isolate_counts[sample]["DD2"]),
str(blast_isolate_counts[sample]["HB3"])
]) + "\n")
for sample in pearStats:
if pearStats[sample]["total"] == 0:
outfile.write("WARNING: Sample " + sample +
" resulted in 0 reads!\n")
return
#######################################################################
from mungo.fasta import FastaReader
import sys, os
import random
input_par1 = int(sys.argv[1])
input_par2 = int(sys.argv[2])
input_par3 = int(sys.argv[3])
input_par4 = int(sys.argv[4])
input_fasta = sys.argv[5]
seqs = {}
count = 0
seq_length = 0
temp_length = []
for h, s in FastaReader(input_fasta):
seqs[h] = s
count += 1
temp_length.append(len(s))
seq_length = min(
temp_length
) ## length should always be the same without considering indel events.
if input_par1 == 0: ## eg:set 25 jump once seqs and 25 is nonrecombinants, then input_par4==50
need_seqs_num = 2 * input_par2 + input_par4 - input_par2
seq_index = random.sample(range(need_seqs_num), k=2 * input_par2)
with open(input_fasta[:-20] + "simulated_seqs_recombined.fasta",
'w') as outfile:
for count in range(input_par2):
left_source = seq_index[2 * count]
#!/usr/bin/env python
"""
calcATContent.py
Author: Tony Papenfuss
Date: Wed Jul 8 10:18:32 EST 2009
"""
import os, sys
from mungo.fasta import FastaReader
iFilename = sys.argv[1]
nAT = 0
total = 0
for h,s in FastaReader(iFilename):
nAT += s.count("A") + s.count("T")
total += len(s)
print "AT content %0.2f" % (100.0*nAT/total)