def convert_axt(in_file, out_file):
Outfile = open(out_file, 'w')
dicHD2seq = kang.Fasta2dic(in_file)
print(out_file, file=Outfile)
for strHD in dicHD2seq:
print(dicHD2seq[strHD], file=Outfile)
Outfile.close()
#!/usr/bin/python
from __future__ import print_function
import subprocess
import numpy as np
import pandas as pd
import sys
from tqdm import tqdm
sys.path.append('/mnt/c/ubuntu.download/pipelines/')
import pysam
import kang
import math
file_bam = sys.argv[1] #'intron3000.merge.sorted.bam'
file_fa = sys.argv[2] #'Creinhardtii_281_v5.0.fa'
file_pk = sys.argv[3]
dicHD2seq = kang.Fasta2dic(file_fa)
def get_block(array, depth_cut=0):
lim_len_block = 100 # size of read fragment
#depth_cut = 0 # ... 10. ... .. .. ..
block_list = []
#print(len(np.shape(array)))
if len(np.shape(array)) == 1:
rows = 1
block = []
for n, j in enumerate(array):
if j > depth_cut:
block.append(n)
else:
if len(block) > lim_len_block:
import os
import time
from slackclient import SlackClient
import kang
import pandas as pd
import primer3
dicFA = kang.Fasta2dic('../References/Athaliana/Araport11_genes.201606.cdna.fasta')
dicFA_func = kang.Fasta2dic_all('../References/Athaliana/Araport11_genes.201606.cdna.fasta')
genelist = dicFA.keys()
keys = [x.split('|')[0].replace('>','').strip() for x in dicFA_func.keys()]
values = [x.split('|')[1].strip() for x in dicFA_func.keys()]
dicG2F = dict(zip(keys,values))
# starterbot's ID as an environment variable
BOT_ID = os.environ.get("BOT_ID")
# constants
AT_BOT = "<@" + BOT_ID + ">"
COMMAND_list = ["seq","func","primer"]
# instantiate Slack & Twilio clients
slack_client = SlackClient(os.environ.get('SLACK_BOT_TOKEN'))
def get_opt_cloningprimer_pair(seq):
oligo_calc = primer3.thermoanalysis.ThermoAnalysis(mv_conc=20, dv_conc=1.5, dntp_conc=0.8, dna_conc=50, max_nn_length=60)
# Change if you have personal condition for PCR
## mv_conc : The millimolar (mM) concentration of monovalent salt cations (usually KCl) in the PCR.
import numpy as np
import matplotlib.cm as cm
import matplotlib.pyplot as plt
try:
file_tmap = sys.argv[
1] #'./predicted/cuffcmp.my_csv.csv.addgene.gff3.sort.gff3.tmap'
except IndexError:
print(''' args : tmap, refcds, pep, cds ''')
exit()
file_ref_cds = sys.argv[
2] #'/ref/analysis/References/Creinhardtii/annotation/Creinhardtii_281_v5.5.cds.fa'
file_pep = sys.argv[3] #'../gff2cds/pep.fa'
file_cds = sys.argv[4] #'../gff2cds/cds.fa'
dicrefcds = kang.Fasta2dic(file_ref_cds)
dicpep = kang.Fasta2dic(file_pep)
diccds = kang.Fasta2dic(file_cds)
df_tmap = pd.read_csv(file_tmap, sep='\t', comment='#')
mask = (df_tmap['class_code'] == '=')
TID_ws = set(df_tmap['ref_id'][mask]) # ws : well supported
GID_ws = set(df_tmap['ref_gene_id'][mask])
mask = (df_tmap['class_code'] == 'c')
TID_cs = set(df_tmap['ref_id'][mask]) # cs : partial supported
GID_cs = set(df_tmap['ref_gene_id'][mask])
mask = (df_tmap['class_code'] == 'j')
TID_js = set(df_tmap['ref_id'][mask]) # js : isoform supported
#!/usr/bin/python3
import glob, kang, numpy, subprocess
file_list = glob.glob('*.prealn.fa')
for file_in in file_list:
dic = kang.Fasta2dic(file_in)
len_list = []
for key in dic:
len_list.append(len(dic[key]))
fAvr = numpy.average(len_list)
fStd = numpy.std(len_list)
if fStd / fAvr > 0.1:
continue
#print(numpy.average(len_list),numpy.std(len_list))
subprocess.call('cp %s %s.ok.fa' % (file_in, file_in), shell=True)
genelist = []
with open(file_gff3 + '.merge.gff3', 'a') as f:
for ix in set(df_tmap_sub_ix.index):
genename = '.'.join(ix.split('.')[0:2])
if genename not in set(genelist):
edf = df_gff_genename_ix.loc[genename]
edf = edf[edf[2] == 'gene']
edf.to_csv(f, header=None, index=None, sep='\t')
genelist.append(genename)
else:
pass
edf = df_gff_transcript_ix.loc[ix]
edf.to_csv(f, header=None, index=None, sep='\t')
# new gene protein seq ret
file_fa = file_pep_new
dicfa = kang.Fasta2dic(file_fa)
df_new = df_tmap_sub
df_new['seq'] = df_new['cuff_id'].apply(lambda x: dicfa[x])
with open(file_pep_new + '.new_gene.fa', 'w') as f:
for ix in set(df_new.index):
hd = df_new.loc[ix]['cuff_id']
seq = df_new.loc[ix]['seq']
print('>' + hd, file=f)
print(seq, file=f)
# new gene protein seq ret end
mcscanout_list = [
'Vr2Cc.collinearity.kaks.recentpeakWGD',
'Vr2Gm.collinearity.kaks.recentpeakWGD',
'Vr2Wd.collinearity.kaks.recentpeakWGD',
'Vra2Vrr.collinearity.kaks.recentpeakWGD'
]
file_GMAA = 'GMAA.fasta'
file_VRGA = 'VRPA.fasta'
file_matchGM = 'match.GM.txt'
file_matchVRG = 'match.Vrg.txt'
#file_group = 'groups.primary.txt'
file_fa = 'all.fas'
Outfile_seed = open('seed_orthologs.txt', 'w')
dicHD2seq = kang.Fasta2dic(file_fa)
dicGMAA2seq = kang.Fasta2dic(file_GMAA)
dicVRGA2seq = kang.Fasta2dic(file_VRGA)
dicGMA2B = {}
for line in open(file_matchGM):
cell = line.strip().split('\t')
strA = cell[0]
strB = cell[1]
dicGMA2B[strA] = strB
dicVRGA2B = {}
for line in open(file_matchVRG):
cell = line.strip().split('\t')
strA = cell[0]
strB = cell[1]
filename_align = 'out.align.txt'
filename_pos = 'out.pos.txt'
file_ref_fa = '/ref/analysis/juntaehwan/ref/Cannuum/Pepper.v.1.55.total.chr.fa.seeders.fa'
file_gff = '/ref/analysis/juntaehwan/ref/Cannuum/Pepper_1.55.gene_models.gff3'
file_ref_annotation = '/ref/analysis/juntaehwan/ref/Pepper.v.1.55.proteins.annotated.fasta'
list_vcf = [
'/ref/analysis/juntaehwan/data/YCM334_samtools.raw.vcf',
'/ref/analysis/juntaehwan/data/Taeahn_samtools.raw.vcf'
]
#'/ref/analysis/juntaehwan/data/Perennial.SRR2751913.cv.vcf','/ref/analysis/juntaehwan/data/Dempsey.SRR2751914.cv.sambamba.vcf']
list_vcf_label = ['YCM334', 'TAEAHN'] #,'PERENN','DEMPSE']
target_genelist = [x.strip() for x in open('nbslrr.txt').readlines()]
dic_annotation_fa = kang.Fasta2dic_all(file_ref_annotation)
dic_ref_fa = kang.Fasta2dic(file_ref_fa)
dic_annot = {}
for line in dic_annotation_fa.keys():
cell = line.split()
try:
dic_annot[cell[0]] = ' '.join(cell[1:])
except IndexError:
dic_annot[cell[0]] = 'None'
print('gff parsing')
df_gff = pd.read_csv(file_gff, sep='\t', header=None)
mask = (df_gff[2] == 'CDS')
df_gff_cds = df_gff[mask]
mask = (df_gff[2] == 'gene')
df_gff_gene = df_gff[mask]
df_gff_cds['ID'] = df_gff_cds[8].apply(lambda x: x.split(';')[0].split(':')[1])
df_gff_gene['ID'] = df_gff_gene[8].apply(
file_cdhitfa = 'transcripts.fasta.transdecoder.cds.cdhit'
Gene_list = []
Transcript_list = []
for line in open(file_cdhitpfam):
if line[0] == '#':
continue
cell = line.split()
Gene_list.append(cell[3].replace('m', 'g'))
Transcript_list.append(cell[3])
Gene_list = set(Gene_list)
Transcript_list = set(Transcript_list)
dicFa = kang.Fasta2dic(file_cdhitfa)
dicFa_new = {}
for gene in dicFa:
if gene in Transcript_list:
dicFa_new[gene] = dicFa[gene]
kang.dic2fa(dicFa_new, file_cdhitfa + '.pfamfilt.fa')
file_gff = 'transcripts.fasta.transdecoder.gff3'
Outfile = open(file_gff + '.cdhit.pfamfilt.gff3', 'w')
for line in open(file_gff):
if line.strip() == '':
continue
cell = line.strip().split('\t')
strT = cell[2]
info = cell[-1]
dicinfo = dict(
#!/usr/bin/python3
import kang, sys
dicHD2seq = kang.Fasta2dic(sys.argv[1])
Outfile = open(sys.argv[2], 'w')
for strHD in dicHD2seq:
seq = dicHD2seq[strHD]
if len(seq) < 5:
continue
print('>' + strHD, file=Outfile)
print(kang.translation(seq), file=Outfile)
#!/usr/bin/python3
import kang, sys
file_joo = sys.argv[1] #'joinmap.ml.n10.txt_ordered.txt'
dicHD2Seq = kang.Fasta2dic('superscaf.fa')
dicLG2Seq = {}
LGIncludedSC = []
Outfile_chr = open(file_joo + '_' + 'Pseudo_chr.fa', 'w')
Outfile_scaff = open(file_joo + '_' + 'non_anchored_scaffolds.fa', 'w')
for line in open(file_joo):
if line[0] == '#' or line.strip() == '':
continue
cell = line.strip().split('\t')
strLG = cell[0]
print(cell)
strSC = cell[1].replace('*', '')
if 'SS' in strSC:
strSC = strSC.replace('SS', 'SuperScaf_')
else:
strSC = strSC.replace('s', 'scaffold_')
LGIncludedSC.append(strSC)
strD = cell[2]
if strD == 'F':
strSeq = dicHD2Seq[strSC]
elif strD == 'R':
strSeq = kang.rev_comp(dicHD2Seq[strSC])
else:
strSeq = dicHD2Seq[strSC]
try:
dicLG2Seq[strLG] += 'N' * 500 + strSeq
#!/usr/bin/python3
import glob, kang
fa_list = glob.glob('*.prealn.fa')
orthologs = 'orthologs.txt'
dicAHD2seq = kang.Fasta2dic('all.fa')
dicA2B = {}
for line in open(orthologs):
cell = line.strip().split()
strA = cell[0]
strB = cell[1]
try:
dicA2B[strA].append(strB)
except KeyError:
dicA2B[strA] = [strB]
try:
dicA2B[strB].append(strA)
except KeyError:
dicA2B[strB] = [strA]
for file_fa in fa_list:
dicHD2seq = kang.Fasta2dic(file_fa)
dicSPCS2GN = {}
for strHD in dicHD2seq:
spcs = strHD.split('|')[0]
gn = strHD.split('|')[1]
dicSPCS2GN[spcs] = strHD
try:
add_list = dicA2B[dicSPCS2GN['VRA']]
from __future__ import print_function
import pandas as pd
import numpy as np
import sys
sys.path.append('/ref/analysis/pipelines/')
import kang
from tqdm import tqdm
import glob
file_stringtie_fa = sys.argv[1] #'/ref/analysis/Cre/braker/braker.try5_mario/guided/transcripts.fasta'
dic_stringtie_fa = kang.Fasta2dic(file_stringtie_fa)
#main_dir = './'
file_ag = sys.argv[2:6] #main_dir+'transcripts.fasta.augustus.ath.complete.gff3.nosharp.genome.v1.gff'
file_td = sys.argv[6] #main_dir+'selected_mRNA_v4.gff'
ag_predictions = []
for efile_ag in file_ag:
df_ag = pd.read_csv(efile_ag,sep='\t')
df_ag['ID'] = df_ag['Name'].apply(lambda x : x.replace('Name=',''))
df_ag.set_index('ID',inplace=True)
ag_predictions.append(df_ag)
df_td = pd.read_csv(file_td,sep='\t')
#df_td['ID'] = df_td['Name'].apply(lambda x : x.split('|')[0].replace('Name=',''))
df_td['ID'] = df_td['Name'].apply(lambda x : x.split('::')[1])
df_td.set_index('ID',inplace=True)
#!/usr/bin/python3
import subprocess, glob, kang, os
file_orth = 'orthologs.txt'
Outfile = open(file_orth + '.shared', 'w')
dicHD2seq = kang.Fasta2dic('../cds/all.cds.fa')
dicA2B = {}
for line in open(file_orth):
cell = line.strip().split('\t')
strA = cell[0]
strB = cell[1]
if strA.split('|')[0] == 'VAG':
try:
dicA2B[strA].append(strB)
except KeyError:
dicA2B[strA] = [strA, strB]
else:
try:
dicA2B[strB].append(strA)
except KeyError:
dicA2B[strB] = [strB, strA]
def convert_axt(in_file, out_file):
Outfile = open(out_file, 'w')
dicHD2seq = kang.Fasta2dic(in_file)
print(out_file, file=Outfile)
for strHD in dicHD2seq:
print(dicHD2seq[strHD], file=Outfile)
Outfile.close()
