#README
These are a set of wrappers compossing a pipline which finds transposable elements in a genome assembly. The pipline includes the following steps:
-
MAKE A DENOVO LIB WITH REPEAT-MODELER. Input: genome assembly, Output: a library containing partially classified consensus sequences of de-novo repeat clusters. Program: RepeatModeler and all its many dependencies.
-
ADD CLASSIFICATIONS FROM THE ONLINE CENSOR TEXT OUTPUT TO THE REPEAT LIB. Input: A) a library containing partially classified consensus sequences of de-novo repeat clusters. B) text file containing the online censor results. Output: a library containing more classified consensus sequences of de-novo repeat clusters (still some unknow classifications)
-
SEARCH FOR TEs IN THE GENOME ASSEMBLY WITH REPEAT-MASKER USING THE DENOVO LIB. Input: genome assembly. Output: text file with a .out suffix containing classified TE loci with some redundancies. Program: RepeatMasker, which is a dependency of RepeatModeler.
-
ELIMINATE REDUNDANCIES IN THE REPEAT-MASKER RESULTS. Input: text file with a .out suffix containing classified TE loci with some redundancies, or a directory with several .out files. Output: elem_stored.csv files, one per contig. Also generate other files per contig. Puts them in the same folder as the .out files used as input. Program: One Code To Find Them All.
-
INDEPENDENT SEARCH FOR LTR ELEMENTS BASED ON SECONDARY STRUCTURE. Input: genome assembly, Output: text file with loci. Program: LTRharvest
-
INDEPENDENT SEARCH FOR ELEMENTS BASED ON CODING SEQUENCES. Input: genome assembly, Output: text file with loci. Program: TransposonPSI
-
ELIMINATE REDUNDANCIES AMONG PROGRAMS
-
Read OneCodeTo... results. Input: Directory containing elem_stored.csv files. Output: Dictionary, Integer: num of elements in Dictionary.
-
Read LTRharvest results and eliminate redundancies chosing the longer match between the programs. Input: Dictionary, Integer: num of elements in Dictionary. Output: Dictionary, Integer: num of elements in Dictionary.
-
Read TransposonPSI results and eliminate redundancies chosing the longer match between the programs. Input: Dictionary, Integer: num of elements in Dictionary. Output: Dictionary, Integer: num of elements in Dictionary.
-
-
PRINT NON-REDUNDANT OUTPUT FILE. Input: Dictionary. Output: gff3 file.
RepeatModeler will produce consensus sequeces representing clusters of denovo repeat sequences, partialy classified by RepeatMasker
from TE import *
make_repeatmodeler_database(name='a_database',
input_filename='genome_assembly_file')
# use the BuildDatabase keyword to specify the path to your executable
run_repeatmodeler('a_database')
# use the RepeatModeler keyword to specify the path to your executable
The default engine is ncbi and the default lib is eukaryota. RepeatModeler should make a folder in the CWD with the file 'consensi.fa.classified' in it. It wil write alot of temp files so don't run in Dropbox.
Copy and paste the results from the webpage into a text file, here named 'Censor_results'.
censor_classifications = parse_online_censor('Censor_results')
print_online_censor(censor_classifications,
'censor_classifications_file')
put_censor_classification_in_repeatmodeler_lib('consensi.fa.classified',
censor_classifications,
'consensi.fa.censor')
Some contig names are too long to be parsed in RepeatMasker. However it is possible to replace the names with aliases and have the translations in a file using the first function in this section. It is important to remember to use the aliased genome assembly in the other programs as well, so that redundancies can be resolved.
code_sequence_ids('genome_assembly',
'names_translations',
'coded_genome_assembly',
'prefix_for_aliases')
run_repeat_masker('coded_genome_assembly', lib = 'consensi.fa.censor', species=None)
Aliases: if you give 'Mflo' as a prefix, the contig aliases will be 'Mflo_0', 'Mflo_1' ...
The run_repeat_masker function accepts all the RepeatMasker keywards.
RepeatModeler will write temporary files in a new folder in the CWD so do not run in Dropbox. The output files (most importantly the .out files) will be in the same directory as the input genome assembly.
Two type of rdundancies are possible: 1) within a run, the same locus may have one classification and subsections of it may have other classifications. 2) you may want to make an additional run of RepeatMasker using the eukaryota library instead of the denovo lib. Both types are handled in this stage. You need to put the .out files of all the RepeatMasker runs in one directory and point the function to that directory.
run_OneCodeToFindThemAll('/path/to/RM/.out/files/',
'name_of_intermediate_file',
'octfta_output_filename',
'coded_genome_assembly',
build_dictionary = 'build_dictionary.pl',
octfta = 'one_code_to_find_them_all.pl'
)
As before, the default path specified in the build_dictionary keyword is the local path on my machine
LTRharvest will search for LTR secondary structures and TransposonPSI will do a blastx search against TE protein CDDs
run_LTRharvest('coded_genome_asembly',
'name_of_intermideate_file',
'ltrharvest_output')
run_TransposonPSI('coded_genome_asembly',
TPSI = 'perl transposonPSI.pl')
The path for TransposonPSI is pointed to by the TPSI keyword.
The data structure is a dictionary with the following structure:
TEs = { 'taken': { 'element0': {'ref': {'record': 'the line from the program's output',
'program': 'the program's name'},
'contig': 'the contig's name',
'start': integer,
'end': integer,
'length': integer,
'lower_tx_level': 'element',
'higher_tx_level': 'class or order or family'
},
'element1': {...},
'element2': {...},
...
},
'discarded': {...}
}
The internal structure repeats itself within the 'discarded' key. The element number is unique across the taken and discarded elements.
\# puting RepeatMasker results as parsed by OneCode... in the data structure
TEs, serial = parse_ocfa_elem_stored('/path/to/RM/.elem_stored.csv'/files/')
\# adding LTRharvest results to the data structure
TEs, serial = integrate_ltrharvest_to_RM_TEs('ltrharvest_output',
'coded_genome_assembly',
serial)
\# adding TransposonPSI results to the data structure
TEs = integrate_TransposonPSI_to_RM_TEs('NameOfInput.TPSI.allHits.chains.bestPerLocus',
'coded_genome_asembly',
TEs,
serial)
Redundencies are resolved by taking the longer match across the programs
Don't forget to replace the coded contig names with the real ones, based on the content of the file names_translations. You can either preprocess the TEs dictionary or postprocess the gff file. There is no function to do this.
def write_gff(TEs, 'output.gff3', max_RM_OC_score=False)
One Code concatenates the scores of the TEs it assembles. It does not compute a composite score. By default, the concatenated scores will be written to the gff3 file, although most gff tools don't supprt that.
However, if max_RM_OC_score==True, only the highset score will be retained, in which case the file will be completely complient with the schema.
from Bio.Blast.Applications import NcbitblastnCommandline, NcbipsiblastCommandline
from Bio.Blast import NCBIXML
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio import SeqFeature
from Bio.SeqFeature import SeqFeature, FeatureLocation
from Bio.Alphabet import IUPAC
from Bio.Blast import NCBIXML
import re, os, inspect, subprocess, warnings, sys
def code_sequence_ids(in_fasta_file_name, codes_log_filename, out_fasta_file_name, genome_code):
from Bio import SeqIO
infile = SeqIO.parse(in_fasta_file_name, 'fasta')
codes = open(codes_log_filename, 'wt')
contig_ids_coded = {}
coded_contigs = []
count = 1
for record in infile:
contig_ids_coded[genome_code+'_'+str(count)] = record.id
record.id = genome_code+'_'+str(count)
record.description = ''
count += 1
coded_contigs.append(record)
for code in contig_ids_coded.keys():
codes.write(code + '\\t' + contig_ids_coded[code] + '\\n')
SeqIO.write(coded_contigs, out_fasta_file_name, 'fasta')
codes.close()
return contig_ids_coded
def run_repeat_masker(query, RepeatMasker = '/home/amir/homeWork/RM_package/RepeatMasker/RepeatMasker', engine=False, parallel=2,
slow=False, quick=False, rush=False, nolow=False, noint=False, norna=True, alu=False, div=False,
lib=False, cutoff=255, species='eukaryota', is_only=False, is_clip=False, no_is=False, gc=False,
gccalc=False, frag=60000, nocut=False, alignments=True, inv=False, lcambig=True,
small=False, xsmall=False, poly=False, source=False, html=False, ace=False, gff=False, u=False,
xm=False, no_id=True, excln=True, noisy=False):
frame = inspect.currentframe()
args, _, _, values = inspect.getargvalues(frame)
del values['frame']
# compose the command line
args_wo_values = ['slow','quick','rush','nolow','noint','norna','alu','is_only','is_clip','no_is'
'gccalc','nocut','nocut','alignments','inv','lcambig','small','xsmall','poly',
'source','html','ace','gff','u','xm','no_id','excln','noisy']
cline = RepeatMasker + ' '
for arg in values.keys():
if not arg in ['RepeatMasker','query']:
if not arg in args_wo_values and not values[arg] == False:
cline = cline + '-' + arg + ' ' + str(values[arg]) + ' '
elif values[arg] == True:
cline = cline + '-' + arg + ' '
cline = cline + query
# execute the command
print cline
return os.system(cline), query + '.out'
def make_repeatmodeler_database(name, input_filename, BuildDatabase='/home/amir/homeWork/RM_package/RepeatModeler/BuildDatabase',
dir=False, engine='ncbi', batch=False):
frame = inspect.currentframe()
args, _, _, values = inspect.getargvalues(frame)
del values['frame']
# compose the command line
cline = BuildDatabase + ' '
for arg in values.keys():
if not arg in ['BuildDatabase']:
if not values[arg] == False and not values[arg] == True and not arg == 'input_filename':
cline = cline + '-' + arg + ' ' + str(values[arg]) + ' '
cline = cline + input_filename
# execute the command
print cline
return os.system(cline)
def run_repeatmodeler(database, RepeatModeler='perl /home/amir/homeWork/RM_package/RepeatModeler/RepeatModeler', engine='ncbi',
species='eukaryota'):
frame = inspect.currentframe()
args, _, _, values = inspect.getargvalues(frame)
del values['frame']
cline = (RepeatModeler + ' -engine ' + engine + ' -database ' + database +
' > ' + database + '.out')
print cline
return os.system(cline)
def find_result_dirs_per_genome_code():
"""CWD should be a direcrtory containing a set of subdirs with
RepeatModeler results. This returns a dict with the genome codes
as keys and the dir name as values"""
RM_results_subdirectories = {}
for sub in os.walk('.').next()[1]:
if sub[0:2] == 'RM':
folder_name = sub
Genome_code = open('./'+sub+'/round-1/sampleDB-1.fa','r').readlines()[0].split()[1].split('-')[0]
RM_results_subdirectories[Genome_code]=folder_name
return RM_results_subdirectories
#print find_results_dirs_per_genome_code()
# {'Gros': 'RM_6449.WedMay210843002014_Gros'}
#Parse online CENSOR results
def parse_online_censor(filename, pident_cutoff, score_cutoff):
censor_classifications = {}
keep_parsing = True
lines = open(filename,'r').readlines()
for line in lines:
if "Masked Sequence" in line:
keep_parsing = False
parse_this_line = True
NOT = ('[GIRI]', 'Home ', 'Map of Hits', 'SVG', 'Name ')
for n in NOT:
if n in line or line[0]=='\t' or line[0]==' ' or line[0]=='\n':
parse_this_line = False
if keep_parsing and parse_this_line:
l = line.rstrip()
name = l.split('\t')[0][:-1]
Class = l.split('\t')[6][:-1]
score = int(l.split('\t')[-1])
pident = float(l.split('\t')[-3])
if (name in censor_classifications.keys() and
score >= score_cutoff and pident >= pident_cutoff):
if score > censor_classifications[name]['score']:
censor_classifications[name]['score'] = score
censor_classifications[name]['Class'] = Class
censor_classifications[name]['pident'] = pident
elif score >= score_cutoff and pident >= pident_cutoff:
censor_classifications[name] = {'score': score,
'Class': Class,
'pident': pident}
return censor_classifications
def print_online_censor(censor_classifications, filename):
import csv
with open(filename, 'wb') as csvfile:
linewriter = csv.writer(csvfile, delimiter='\t',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
linewriter.writerow(['Name','calss','score'])
for name in censor_classifications.keys():
line = [name, censor_classifications[name]['Class'], censor_classifications[name]['score']]
linewriter.writerow(line)
def put_censor_classification_in_repeatmodeler_lib(input_filename, censor_classifications, output_filename):
from Bio import SeqIO
RM_lib = SeqIO.parse(input_filename, 'fasta')
RM_CENCOR_lib = []
for record in RM_lib:
if 'Unknown' in record.id and record.id in censor_classifications.keys():
classification = censor_classifications[record.id]['Class']
record.id = record.id.split('#')[0]+'#'+classification
record.description = (' ').join(record.description.split(' ')[1:])
RM_CENCOR_lib.append(record)
else:
RM_CENCOR_lib.append(record)
SeqIO.write(RM_CENCOR_lib,output_filename,'fasta')
def run_OneCodeToFindThemAll(pooled_RM_outputs_dir, #The directory containing repeatmasker .out files or a path to a specific .out file
ltr_dict_filename, # name of intermediate file
output_filename,
genome_assembly,
unknown=False,
strict=True,
build_dictionary = '/home/amir/homeWork/RM_package/OCtFtA/build_dictionary.pl',
octfta = '/home/amir/homeWork/RM_package/OCtFtA/one_code_to_find_them_all.pl',
):
cline = build_dictionary+' --rm '+pooled_RM_outputs_dir+' --unknown > '+ ltr_dict_filename
os.system(cline)
cline = octfta+' --rm '+pooled_RM_outputs_dir+' --ltr '+ltr_dict_filename+' --fasta '+genome_assembly
if unknown:
cline += ' --unknown'
if strict:
cline += ' --strict'
cline +=' > '+output_filename
os.system(cline)
def run_LTRharvest(input_filename, index_name, output_name):
cline = 'gt suffixerator -db '+input_filename+' -indexname '+index_name+' -tis -suf -lcp -des -ssp -sds -dna'
os.system(cline)
cline = 'gt ltrharvest -index '+index_name+' -mintsd 5 -maxtsd 100 > '+output_name
os.system(cline)
def run_TransposonPSI(input_filename,
TPSI = 'perl /home/amir/homeWork/RM_package/TransposonPSI_08222010/transposonPSI.pl'):
cline = (TPSI+' '+input_filename+' nuc')
os.system(cline)
def parse_ocfa_elem_stored(pooled_RM_outputs_dir):
# Get all the '.elem_stored.csv' file names
from glob import glob
filenames = glob(pooled_RM_outputs_dir + '*.elem_sorted.csv')
# An empty dict to hold final TE list
TEs = {'taken': {}, 'discarded': {}}
serial = 1
# Get all the elements in the OCTFTA output
for filename in filenames:
taken_elements = {}
discarded_elements = {}
for line in open(filename, 'r').readlines():
if line[:3] == '###':
reference = {'program': 'RMOCFA',
'record': line}
contig = line.split('\t')[4]
start = line.split('\t')[5]
end = line.split('\t')[6]
length = line.split('\t')[7]
element = line.split('\t')[9]
family = line.split('\t')[10]
max_score = max([int(i) for i in line.split('\t')[0][3:].split('/')])
take = True
# make sure the locus is not covered and if it is check which match is better
for element in taken_elements:
prex_el_line = taken_elements[element]['ref']['record']
prex_el_score = max([int(i) for i in prex_el_line.split('\t')[0][3:].split('/')])
prex_el_contig = taken_elements[element]['contig']
prex_el_start = taken_elements[element]['start']
prex_el_end = taken_elements[element]['end']
if (contig == prex_el_contig and
(prex_el_start < start < prex_el_end or
prex_el_start < end < prex_el_end or
start < prex_el_start < end or
start < prex_el_end <end)):
if max_score > prex_el_score:
discarded_elements[element] = taken_elements.pop(element, None)
else:
take = False
if take:
taken_elements['element' + str(serial)] = {'ref': reference,
'contig': contig,
'start': int(start),
'end': int(end),
'length': int(length),
'lower_tx_level': element,
'higher_tx_level': family}
serial += 1
TEs['taken'].update(taken_elements)
TEs['discarded'].update(discarded_elements)
return TEs, serial
Get loci from the LTRharvest output only if they are longer than ones found with repeatmasker for the same locus
def integrate_ltrharvest_to_RM_TEs(LTRharvest_output_filename,genome_path, TEs_from_RMOCFA, serial, sim_cutoff=85, l_cutoff=4000 ):
import re
from Bio import SeqIO
lines = open(LTRharvest_output_filename, 'r').readlines()
contig_names = [record.id for record in SeqIO.parse(genome_path, 'fasta')]
line_count = 0
for line in lines:
if not line[0] == '#' and len(line) > 1:
# correct contig name:
## get the true contig name based on the sequence number in the LTRarvest output:
from Bio import SeqIO
corrected_sequence_name = None
line_serial = int(line.rstrip().split(' ')[-1])
try:
corrected_sequence_name = contig_names[line_serial]
except:
raise RuntimeError('Could not find contig for seq number ' + str(line_serial))
## Parse the LTRharvest results line
reference = {'program': 'LTRharvest',
'record': line}
contig = corrected_sequence_name
start = int(line.split(' ')[0])
end = int(line.split(' ')[1])
length = int(line.split(' ')[2])
lower_tx_level = '?'
higher_tx_level = 'LTR'
sim = float(line.split(' ')[-2])
l = int(line.split(' ')[2])
TE = {'ref': reference,
'contig': contig,
'start': int(start),
'end': int(end),
'length': int(length),
'lower_tx_level': lower_tx_level,
'higher_tx_level': higher_tx_level}
## Check if the locus is already covered by the repeatmasker results
## If it is, check if the ltr hit is longer (then place in taken, and move the rm hit to discarded)
## or shorter (then place the ltr hit in discraded)
placed = False
for key in TEs_from_RMOCFA['taken'].keys():
if ( TEs_from_RMOCFA['taken'][key]['contig'] == contig and
(TEs_from_RMOCFA['taken'][key]['start']< start <TEs_from_RMOCFA['taken'][key]['end'] or
TEs_from_RMOCFA['taken'][key]['start']< end <TEs_from_RMOCFA['taken'][key]['end'] or
start < TEs_from_RMOCFA['taken'][key]['start'] < end or
start < TEs_from_RMOCFA['taken'][key]['end'] < end)):
### since it is, keep the longer output (either repeatmasker or LTRharvest)
### use the repeatmasker classification either way
### put the looser in the 'discarded' dictionary
if TEs_from_RMOCFA['taken'][key]['length'] < length and sim >= sim_cutoff and l >= l_cutoff:
#TE['element'] = TEs_from_RMOCFA['taken'][key]['lower_tx_level']
#TE['family'] = TEs_from_RMOCFA['taken'][key]['higher_tx_level']
TEs_from_RMOCFA['discarded'][key] = TEs_from_RMOCFA['taken'].pop(key, None)
TEs_from_RMOCFA['taken']['element'+str(serial)] = TE
else:
TEs_from_RMOCFA['discarded']['element'+str(serial)] = TE
placed = True
break
if not placed and sim >= sim_cutoff and l >= l_cutoff:
### Since it is not, add the LTRharvest TE to the 'taken' dict:
TEs_from_RMOCFA['taken']['element'+str(serial)] = TE
serial +=1
else:
TEs_from_RMOCFA['discarded']['element'+str(serial)] = TE
serial +=1
#if line_count%100 == 0:
# print str(line_count)
line_count += 1
return TEs_from_RMOCFA, serial
Get loci from the TransposonPSI output only if they are longer than ones found with repeatmasker for the same locus
def integrate_TransposonPSI_to_RM_TEs(TransposonPSI_output_filename,genome_path, TEs_from_RMOCFA, serial, score_cutoff=100):
import re
lines = open(TransposonPSI_output_filename, 'r').readlines()
line_count = 0
for line in lines:
if line[0] == '#':
## Parse the TransposonPSI results line
reference = {'program': 'TransposonPSI',
'record': line}
contig = line.split('\t')[3]
start = int(line.split('\t')[4].split('-')[0])
end = int(line.split('\t')[4].split('-')[1])
length = end-start+1
lower_tx_level = '?'
higher_tx_level = line.split('\t')[1]
score = line.split('\t')[-1].rstrip()
TE = {'ref': reference,
'contig': contig,
'start': int(start),
'end': int(end),
'length': int(length),
'lower_tx_level': lower_tx_level,
'higher_tx_level': higher_tx_level}
## Check if the locus is already covered by previous results
placed = False
for key in TEs_from_RMOCFA['taken'].keys():
if ( TEs_from_RMOCFA['taken'][key]['contig'] == contig and
(TEs_from_RMOCFA['taken'][key]['start']< start <TEs_from_RMOCFA['taken'][key]['end'] or
TEs_from_RMOCFA['taken'][key]['start']< end <TEs_from_RMOCFA['taken'][key]['end']or
start < TEs_from_RMOCFA['taken'][key]['start'] < end or
start < TEs_from_RMOCFA['taken'][key]['end'] < end)):
### since it is, keep the longer output
### put the looser in the 'discarded' dictionary
if TEs_from_RMOCFA['taken'][key]['length'] < length and score >= score_cutoff:
TEs_from_RMOCFA['discarded'][key] = TEs_from_RMOCFA['taken'].pop(key, None)
TEs_from_RMOCFA['taken']['element'+str(serial)] = TE
else:
TEs_from_RMOCFA['discarded']['element'+str(serial)] = TE
placed = True
break
if not placed and score >= score_cutoff:
### Since it is not, add the TransposonPSI TE to the 'taken' dict:
TEs_from_RMOCFA['taken']['element'+str(serial)] = TE
else:
TEs_from_RMOCFA['discarded']['element'+str(serial)] = TE
serial +=1
#if line_count%100 == 0:
# print str(line_count)
line_count += 1
return TEs_from_RMOCFA
def write_gff(TEs, gff_filename, max_RM_OC_score=False):
gff_pattern = "%s\t%s\ttransposable_element\t%i\t%i\t%s\t%s\t.\tID=%s;Name=%s;Note=%s\n"
#%(contig, program, start, end, score, strand, ID, name, note)
# Make the regions bit for the top of the file
regions = {}
for e in TEs['taken']:
record = TEs['taken'][e]
contig = record['contig']
start, end = record['start'], record['end']
if start > end:
start, end = end, start
# Each contig has to be included once and encopass all the TEs
# that are on it
if not contig in regions:
regions[contig] = [start,end]
else:
if start < regions[contig][0]:
regions[contig][0] = start
if end > regions[contig][1]:
regions[contig][1] = end
regions = sorted(regions.items(), key = lambda i: i[0])
regions = ["##sequence-region %s %i %i\n"%(j[0],j[1][0],j[1][1]) for j in regions]
# Write the file
with open(gff_filename,'wt') as gff:
# Write the regions
gff.write('##gff-version 3\n')
for l in regions:
gff.write(l)
# Write the matches
for e in TEs['taken']:
record = TEs['taken'][e]
contig = record['contig']
program = record['ref']['program']
if program == 'RMOCFA':
program = 'Repeatmasker-OneCode'
start, end = record['start'], record['end']
# make sure start is the smaller coordinate
if start > end:
start, end = end, start
ID = e
name = record['higher_tx_level']
note = record['lower_tx_level']
if 'element' in note:
note = '?'
score, strand = '.', '.'
ref = record['ref']['record'].rstrip()
if program == 'TransposonPSI':
score, strand = ref.split('\t')[-1], ref.split('\t')[-2]
elif program == 'Repeatmasker-OneCode':
score, strand = ref.split('\t')[0], ref.split('\t')[8]
if max_RM_OC_score:
score = max([int(s) for s in score.split('/')])
strand = strand.replace('C','-')
score = score.replace('#','')
gff.write(gff_pattern%(contig, program, start, end, score, strand, ID, name, note))
def genome_codes_list(genomes_directory, mode='++', code_file = 'genome_assembly_files.csv'):
""" return a list of codes
genomes_directory: path to the directory containing the genome assemblies
code file: contains code and genome assembly file names (wo path).
It is formated as follows:
<code><space><assembly filename><space><mode><newline>
mode is any symbol grouping the filenames. It is nested.
examples:
mode = '$'
code1 filename1 $ -> will be read
code2 filename2 $$ -> will be read
code3 filename3 $+ -> will be read
code4 filename4 + -> will not be read
mode = '$$'
with the same example as above, only code2 will be read.
"""
codes = []
for line in open(genomes_directory+code_file,'r').readlines():
if mode in line:
codes.append(line.split()[0])
return codes
def genomes_dict(genomes_directory, mode='++', code_file = 'genome_assembly_files.csv'):
""" returns a dict, codes as keys, file names as values """
genomes = {}
for line in open(genomes_directory+code_file,'r').readlines():
if mode in line:
genomes[line.split()[0]] = line.split()[1]
return genomes
def assembly_of(code, genomes_directory, generator=True, code_file = 'genome_assembly_files.csv', mode='++'):
""" returns a list of SeqRecords
which are the contig of the assembly.
"""
from Bio import SeqIO
records = SeqIO.parse(genomes_directory+genomes_dict(genomes_directory=genomes_directory, code_file=code_file, mode='++')[code],'fasta')
if not generator:
records = list(records)
return records
def codes_with_no_censor_lib(folders):
""" return codes with no denovo lib """
import os
missing_censor_results = []
for path in codes_with_folders(folders=folders):
pass
if not os.path.isfile(path+'consensi.fa.censor'):
missing_censor_results.append(path.split('/')[-2])
return missing_censor_results
def codes_with_censor_lib(folders):
""" return codes that have a censor denovo lib """
import os
missing_censor_results = []
for path in codes_with_folders(folders=folders):
pass
if os.path.isfile(path+'consensi.fa.censor'):
missing_censor_results.append(path.split('/')[-2])
return missing_censor_results
def make_non_redundant_lib(redundant_lib, non_redundant_lib,
cluster_identity=0.9, uclust = 'uclust'):
""" makes a non redundant lib """
import os
cline = uclust+' --sort '+redundant_lib+' --output seqs_sorted.fasta'
os.system(cline)
cline = uclust+' --input seqs_sorted.fasta --uc results.uc --id '+str(cluster_identity)
os.system(cline)
cline = uclust+' --uc2fasta results.uc --input seqs_sorted.fasta --types S --output results.fasta'
os.system(cline)
from Bio import SeqIO
from Bio.Seq import Seq
records = list(SeqIO.parse('results.fasta', 'fasta'))
for r in records:
r.id = r.id.split('|')[2]
r.description = ' '.join(r.description.split()[1:])
alpha = r.seq.alphabet
r.seq = Seq(str(r.seq).replace('-',''), alphabet=alpha)
SeqIO.write(records,non_redundant_lib,'fasta')
return non_redundant_lib