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multitrim.py
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multitrim.py
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#!/usr/bin/env python3
import sys
import os
import subprocess
import tempfile
import argparse
import multiprocessing
import re
import shutil
from datetime import datetime
#Reads a file with adapters and uses them as the starting set for adapter identification. By default, uses the current MiGA adapter list as of Feb. 23, 2021
def read_adapters(adapters_fasta):
cleanup = False
if adapters_fasta == "internal":
adapters, adapters_fasta = generate_adapters_temporary_file()
cleanup = True
else:
adapters = {}
current_seq = ""
current_id = ""
adapt = open(adapters_fasta, "r")
for line in adapt:
if line.startswith(">"):
if len(current_seq) > 0:
adapters[current_id] = current_seq
current_id = line.strip()[1:]
current_seq = ""
else:
current_seq += line.strip()
adapters[current_id] = current_seq
adapt.close()
return adapters, adapters_fasta, cleanup
#Only contains adapters we already recognize as part of a kit. It will need updated as new ones may be added.
def family_detection(adapter_seqs):
#Currently acceptable fams:
'''
singleend
pairedend
dpnII
smallrna
multiplex
pcr
dpnIIgex
otherrna
trueseq
rnapcr
trueseq2
nextera
cre-loxp
truseq1
pcr_primer
nextera_junction
'''
#There are some repeats in adapters. All are added - this meant to make the program as conservative as possible.
fam_to_id_to_seq = {}
#MiGA adapters
fam_to_id_to_seq['singleend'] = {'Illumina_Single_End_Apapter_1': 'ACACTCTTTCCCTACACGACGCTGTTCCATCT', 'Illumina_Single_End_Apapter_2': 'CAAGCAGAAGACGGCATACGAGCTCTTCCGATCT', 'Illumina_Single_End_PCR_Primer_1': 'AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT', 'Illumina_Single_End_PCR_Primer_2': 'CAAGCAGAAGACGGCATACGAGCTCTTCCGATCT', 'Illumina_Single_End_Sequencing_Primer': 'ACACTCTTTCCCTACACGACGCTCTTCCGATCT'}
fam_to_id_to_seq['pairedend'] = {'Illumina_Paired_End_Adapter_1': 'ACACTCTTTCCCTACACGACGCTCTTCCGATCT', 'Illumina_Paired_End_Adapter_2': 'CTCGGCATTCCTGCTGAACCGCTCTTCCGATCT', 'Illumina_Paried_End_PCR_Primer_1': 'AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT', 'Illumina_Paired_End_PCR_Primer_2': 'CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT', 'Illumina_Paried_End_Sequencing_Primer_1': 'ACACTCTTTCCCTACACGACGCTCTTCCGATCT', 'Illumina_Paired_End_Sequencing_Primer_2': 'CGGTCTCGGCATTCCTACTGAACCGCTCTTCCGATCT'}
fam_to_id_to_seq['dpnII'] = {'Illumina_DpnII_expression_Adapter_1': 'ACAGGTTCAGAGTTCTACAGTCCGAC', 'Illumina_DpnII_expression_Adapter_2': 'CAAGCAGAAGACGGCATACGA', 'Illumina_DpnII_expression_PCR_Primer_1': 'CAAGCAGAAGACGGCATACGA', 'Illumina_DpnII_expression_PCR_Primer_2': 'AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA', 'Illumina_DpnII_expression_Sequencing_Primer': 'CGACAGGTTCAGAGTTCTACAGTCCGACGATC', 'Illumina_NlaIII_expression_Adapter_1': 'ACAGGTTCAGAGTTCTACAGTCCGACATG', 'Illumina_NlaIII_expression_Adapter_2': 'CAAGCAGAAGACGGCATACGA', 'Illumina_NlaIII_expression_PCR_Primer_1': 'CAAGCAGAAGACGGCATACGA', 'Illumina_NlaIII_expression_PCR_Primer_2': 'AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA', 'Illumina_NlaIII_expression_Sequencing_Primer': 'CCGACAGGTTCAGAGTTCTACAGTCCGACATG'}
fam_to_id_to_seq['smallrna'] = {'Illumina_Small_RNA_Adapter_1': 'GTTCAGAGTTCTACAGTCCGACGATC', 'Illumina_Small_RNA_Adapter_2': 'TCGTATGCCGTCTTCTGCTTGT', 'Illumina_Small_RNA_RT_Primer': 'CAAGCAGAAGACGGCATACGA', 'Illumina_Small_RNA_PCR_Primer_1': 'CAAGCAGAAGACGGCATACGA', 'Illumina_Small_RNA_PCR_Primer_2': 'AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA', 'Illumina_Small_RNA_Sequencing_Primer': 'CGACAGGTTCAGAGTTCTACAGTCCGACGATC'}
fam_to_id_to_seq['multiplex'] = {'Illumina_Multiplexing_Adapter_1': 'GATCGGAAGAGCACACGTCT', 'Illumina_Multiplexing_Adapter_2': 'ACACTCTTTCCCTACACGACGCTCTTCCGATCT', 'Illumina_Multiplexing_PCR_Primer_1.01': 'AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT', 'Illumina_Multiplexing_PCR_Primer_2.01': 'GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT', 'Illumina_Multiplexing_Read1_Sequencing_Primer': 'ACACTCTTTCCCTACACGACGCTCTTCCGATCT', 'Illumina_Multiplexing_Index_Sequencing_Primer': 'GATCGGAAGAGCACACGTCTGAACTCCAGTCAC', 'Illumina_Multiplexing_Read2_Sequencing_Primer': 'GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT'}
fam_to_id_to_seq['pcr'] = {'Illumina_PCR_Primer_Index_1': 'CAAGCAGAAGACGGCATACGAGATCGTGATGTGACTGGAGTTC', 'Illumina_PCR_Primer_Index_2': 'CAAGCAGAAGACGGCATACGAGATACATCGGTGACTGGAGTTC', 'Illumina_PCR_Primer_Index_3': 'CAAGCAGAAGACGGCATACGAGATGCCTAAGTGACTGGAGTTC', 'Illumina_PCR_Primer_Index_4': 'CAAGCAGAAGACGGCATACGAGATTGGTCAGTGACTGGAGTTC', 'Illumina_PCR_Primer_Index_5': 'CAAGCAGAAGACGGCATACGAGATCACTGTGTGACTGGAGTTC', 'Illumina_PCR_Primer_Index_6': 'CAAGCAGAAGACGGCATACGAGATATTGGCGTGACTGGAGTTC', 'Illumina_PCR_Primer_Index_7': 'CAAGCAGAAGACGGCATACGAGATGATCTGGTGACTGGAGTTC', 'Illumina_PCR_Primer_Index_8': 'CAAGCAGAAGACGGCATACGAGATTCAAGTGTGACTGGAGTTC', 'Illumina_PCR_Primer_Index_9': 'CAAGCAGAAGACGGCATACGAGATCTGATCGTGACTGGAGTTC', 'Illumina_PCR_Primer_Index_10': 'CAAGCAGAAGACGGCATACGAGATAAGCTAGTGACTGGAGTTC', 'Illumina_PCR_Primer_Index_11': 'CAAGCAGAAGACGGCATACGAGATGTAGCCGTGACTGGAGTTC', 'Illumina_PCR_Primer_Index_12': 'CAAGCAGAAGACGGCATACGAGATTACAAGGTGACTGGAGTTC'}
fam_to_id_to_seq['dpnIIgex'] = {'Illumina_DpnII_Gex_Adapter_1': 'GATCGTCGGACTGTAGAACTCTGAAC', 'Illumina_DpnII_Gex_Adapter_1.01': 'ACAGGTTCAGAGTTCTACAGTCCGAC', 'Illumina_DpnII_Gex_Adapter_2': 'CAAGCAGAAGACGGCATACGA', 'Illumina_DpnII_Gex_Adapter_2.01': 'TCGTATGCCGTCTTCTGCTTG', 'Illumina_DpnII_Gex_PCR_Primer_1': 'CAAGCAGAAGACGGCATACGA', 'Illumina_DpnII_Gex_PCR_Primer_2': 'AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA', 'Illumina_DpnII_Gex_Sequencing_Primer': 'CGACAGGTTCAGAGTTCTACAGTCCGACGATC', 'Illumina_NlaIII_Gex_Adapter_1.01': 'TCGGACTGTAGAACTCTGAAC', 'Illumina_NlaIII_Gex_Adapter_1.02': 'ACAGGTTCAGAGTTCTACAGTCCGACATG', 'Illumina_NlaIII_Gex_Adapter_2.01': 'CAAGCAGAAGACGGCATACGA', 'Illumina_NlaIII_Gex_Adapter_2.02': 'TCGTATGCCGTCTTCTGCTTG', 'Illumina_NlaIII_Gex_PCR_Primer_1': 'CAAGCAGAAGACGGCATACGA', 'Illumina_NlaIII_Gex_PCR_Primer_2': 'AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA', 'Illumina_NlaIII_Gex_Sequencing_Primer': 'CCGACAGGTTCAGAGTTCTACAGTCCGACATG'}
fam_to_id_to_seq['otherrna'] = {'Illumina_5p_RNA_Adapter': 'GTTCAGAGTTCTACAGTCCGACGATC', 'Illumina_RNA_Adapter1': 'TCGTATGCCGTCTTCTGCTTGT', 'Illumina_Small_RNA_3p_Adapter_1': 'ATCTCGTATGCCGTCTTCTGCTTG'}
fam_to_id_to_seq['trueseq'] = {'TruSeq_Universal_Adapter': 'AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT', 'TruSeq_Adapter_Index_1': 'GATCGGAAGAGCACACGTCTGAACTCCAGTCACATCACGATCTCGTATGCCGTCTTCTGCTTG', 'TruSeq_Adapter_Index_2': 'GATCGGAAGAGCACACGTCTGAACTCCAGTCACCGATGTATCTCGTATGCCGTCTTCTGCTTG', 'TruSeq_Adapter_Index_3': 'GATCGGAAGAGCACACGTCTGAACTCCAGTCACTTAGGCATCTCGTATGCCGTCTTCTGCTTG', 'TruSeq_Adapter_Index_4': 'GATCGGAAGAGCACACGTCTGAACTCCAGTCACTGACCAATCTCGTATGCCGTCTTCTGCTTG', 'TruSeq_Adapter_Index_5': 'GATCGGAAGAGCACACGTCTGAACTCCAGTCACACAGTGATCTCGTATGCCGTCTTCTGCTTG', 'TruSeq_Adapter_Index_6': 'GATCGGAAGAGCACACGTCTGAACTCCAGTCACGCCAATATCTCGTATGCCGTCTTCTGCTTG', 'TruSeq_Adapter_Index_7': 'GATCGGAAGAGCACACGTCTGAACTCCAGTCACCAGATCATCTCGTATGCCGTCTTCTGCTTG', 'TruSeq_Adapter_Index_8': 'GATCGGAAGAGCACACGTCTGAACTCCAGTCACACTTGAATCTCGTATGCCGTCTTCTGCTTG', 'TruSeq_Adapter_Index_9': 'GATCGGAAGAGCACACGTCTGAACTCCAGTCACGATCAGATCTCGTATGCCGTCTTCTGCTTG', 'TruSeq_Adapter_Index_10': 'GATCGGAAGAGCACACGTCTGAACTCCAGTCACTAGCTTATCTCGTATGCCGTCTTCTGCTTG', 'TruSeq_Adapter_Index_11': 'GATCGGAAGAGCACACGTCTGAACTCCAGTCACGGCTACATCTCGTATGCCGTCTTCTGCTTG', 'TruSeq_Adapter_Index_12': 'GATCGGAAGAGCACACGTCTGAACTCCAGTCACCTTGTAATCTCGTATGCCGTCTTCTGCTTG'}
fam_to_id_to_seq['rnapcr'] = {'Illumina_RNA_RT_Primer': 'GCCTTGGCACCCGAGAATTCCA', 'Illumina_RNA_PCR_Primer': 'AATGATACGGCGACCACCGAGATCTACACGTTCAGAGTTCTACAGTCCGA', 'RNA_PCR_Primer_Index_1': 'CAAGCAGAAGACGGCATACGAGATCGTGATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_2': 'CAAGCAGAAGACGGCATACGAGATACATCGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_3': 'CAAGCAGAAGACGGCATACGAGATGCCTAAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_4': 'CAAGCAGAAGACGGCATACGAGATTGGTCAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_5': 'CAAGCAGAAGACGGCATACGAGATCACTGTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_6': 'CAAGCAGAAGACGGCATACGAGATATTGGCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_7': 'CAAGCAGAAGACGGCATACGAGATGATCTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_8': 'CAAGCAGAAGACGGCATACGAGATTCAAGTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_9': 'CAAGCAGAAGACGGCATACGAGATCTGATCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_10': 'CAAGCAGAAGACGGCATACGAGATAAGCTAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_11': 'CAAGCAGAAGACGGCATACGAGATGTAGCCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_12': 'CAAGCAGAAGACGGCATACGAGATTACAAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_13': 'CAAGCAGAAGACGGCATACGAGATTTGACTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_14': 'CAAGCAGAAGACGGCATACGAGATGGAACTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_15': 'CAAGCAGAAGACGGCATACGAGATTGACATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_16': 'CAAGCAGAAGACGGCATACGAGATGGACGGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_17': 'CAAGCAGAAGACGGCATACGAGATCTCTACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_18': 'CAAGCAGAAGACGGCATACGAGATGCGGACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_19': 'CAAGCAGAAGACGGCATACGAGATTTTCACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_20': 'CAAGCAGAAGACGGCATACGAGATGGCCACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_21': 'CAAGCAGAAGACGGCATACGAGATCGAAACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_22': 'CAAGCAGAAGACGGCATACGAGATCGTACGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_23': 'CAAGCAGAAGACGGCATACGAGATCCACTCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_24': 'CAAGCAGAAGACGGCATACGAGATGCTACCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_25': 'CAAGCAGAAGACGGCATACGAGATATCAGTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_26': 'CAAGCAGAAGACGGCATACGAGATGCTCATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_27': 'CAAGCAGAAGACGGCATACGAGATAGGAATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_28': 'CAAGCAGAAGACGGCATACGAGATCTTTTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_29': 'CAAGCAGAAGACGGCATACGAGATTAGTTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_30': 'CAAGCAGAAGACGGCATACGAGATCCGGTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_31': 'CAAGCAGAAGACGGCATACGAGATATCGTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_32': 'CAAGCAGAAGACGGCATACGAGATTGAGTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_33': 'CAAGCAGAAGACGGCATACGAGATCGCCTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_34': 'CAAGCAGAAGACGGCATACGAGATGCCATGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_35': 'CAAGCAGAAGACGGCATACGAGATAAAATGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_36': 'CAAGCAGAAGACGGCATACGAGATTGTTGGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_37': 'CAAGCAGAAGACGGCATACGAGATATTCCGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_38': 'CAAGCAGAAGACGGCATACGAGATAGCTAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_39': 'CAAGCAGAAGACGGCATACGAGATGTATAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_40': 'CAAGCAGAAGACGGCATACGAGATTCTGAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_41': 'CAAGCAGAAGACGGCATACGAGATGTCGTCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_42': 'CAAGCAGAAGACGGCATACGAGATCGATTAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_43': 'CAAGCAGAAGACGGCATACGAGATGCTGTAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_44': 'CAAGCAGAAGACGGCATACGAGATATTATAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_45': 'CAAGCAGAAGACGGCATACGAGATGAATGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_46': 'CAAGCAGAAGACGGCATACGAGATTCGGGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_47': 'CAAGCAGAAGACGGCATACGAGATCTTCGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA', 'RNA_PCR_Primer_Index_48': 'CAAGCAGAAGACGGCATACGAGATTGCCGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA'}
fam_to_id_to_seq['abi'] = {'ABI_Dynabead_EcoP_Oligo': 'CTGATCTAGAGGTACCGGATCCCAGCAGT', 'ABI_Solid3_Adapter_A': 'CTGCCCCGGGTTCCTCATTCTCTCAGCAGCATG', 'ABI_Solid3_Adapter_B': 'CCACTACGCCTCCGCTTTCCTCTCTATGGGCAGTCGGTGAT', 'ABI_Solid3_5_AMP_Primer': 'CCACTACGCCTCCGCTTTCCTCTCTATG', 'ABI_Solid3_3_AMP_Primer': 'CTGCCCCGGGTTCCTCATTCT', 'ABI_Solid3_EF1_alpha_Sense_Primer': 'CATGTGTGTTGAGAGCTTC', 'ABI_Solid3_EF1_alpha_Antisense_Primer': 'GAAAACCAAAGTGGTCCAC', 'ABI_Solid3_GAPDH_Forward_Primer': 'TTAGCACCCCTGGCCAAGG', 'ABI_Solid3_GAPDH_Reverse_Primer': 'CTTACTCCTTGGAGGCCATG'}
fam_to_id_to_seq['trueseq2'] = {'TruSeq2_SE': 'AGATCGGAAGAGCTCGTATGCCGTCTTCTGCTTG', 'TruSeq2_PE_f': 'AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT', 'TruSeq2_PE_r': 'AGATCGGAAGAGCGGTTCAGCAGGAATGCCGAG', 'TruSeq3_IndexedAdapter': 'AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC', 'TruSeq3_UniversalAdapter': 'AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA'}
fam_to_id_to_seq['nextera'] = {'Nextera_PE_PrefixNX/1': 'AGATGTGTATAAGAGACAG', 'Nextera_PE_PrefixNX/2': 'AGATGTGTATAAGAGACAG', 'Nextera_PE_Trans1': 'TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG', 'Nextera_PE_Trans1_rc': 'CTGTCTCTTATACACATCTGACGCTGCCGACGA', 'Nextera_PE_Trans2': 'GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG', 'Nextera_PE_Trans2_rc': 'CTGTCTCTTATACACATCTCCGAGCCCACGAGAC'}
#FaQCs adapters for safety
fam_to_id_to_seq['cre-loxp'] = {'cre-loxp-forward' : 'TCGTATAACTTCGTATAATGTATGCTATACGAAGTTATTACG', 'cre-loxp-reverse' : 'AGCATATTGAAGCATATTACATACGATATGCTTCAATAATGC'}
fam_to_id_to_seq['truseq1'] = {'TruSeq-adapter-1' : 'GGGGTAGTGTGGATCCTCCTCTAGGCAGTTGGGTTATTCTAGAAGCAGATGTGTTGGCTGTTTCTGAAACTCTGGAAAA', 'TruSeq-adapter-3' : 'CAACAGCCGGTCAAAACATCTGGAGGGTAAGCCATAAACACCTCAACAGAAAA'}
fam_to_id_to_seq['pcr_primer'] = {'PCR-primer-1' : 'CGATAACTTCGTATAATGTATGCTATACGAAGTTATTACG', 'PCR-primer-2' : 'GCATAACTTCGTATAGCATACATTATACGAAGTTATACGA'}
fam_to_id_to_seq['nextera_junction'] = {'Nextera-junction-adapter-1' : 'CTGTCTCTTATACACATCTAGATGTGTATAAGAGACAG'}
fam_to_id_to_seq['Nextera-primer-adapter'] = {'Nextera-primer-adapter-1' : 'GATCGGAAGAGCACACGTCTGAACTCCAGTCAC', 'Nextera-primer-adapter-2' : 'GATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT'}
detected_fams = []
#for each user sequence
for seq in adapter_seqs.values():
#for each family
for family in fam_to_id_to_seq:
#Check if the sequence appears as a value in the current family; add it to the detected family list if it's not already there.
if seq in fam_to_id_to_seq[family].values() and family not in detected_fams:
detected_fams.append(family)
#If a family was detected, add ALL sequences from that family to the final list, except the ones that the user already supplied.
for fam in detected_fams:
for id in fam_to_id_to_seq[fam]:
sequence = fam_to_id_to_seq[fam][id]
#User seqs came in with adapter_seqs, so we want to skip adding the preset one; it would be redundant, change names. Just add the others.
if sequence not in adapter_seqs.values():
adapter_seqs[id] = sequence
#add '>' to the start of each seq.
easy_print = {}
for id in adapter_seqs:
easy_print[">"+id] = adapter_seqs[id]
return easy_print
#This contains code which generates a complete list of illumina adapters from scratch
def generate_adapters_temporary_file():
#print("Preparing adapter file for you.")
adapters_dict = {}
'''
I identify the adapter families here with comments. Any adapter recognized in one of these during preprocessing will include
all of the members of its family in final, e.g. seeing Illumina_Single_End_Apapter_1 will include the following:
Illumina_Single_End_Apapter_1, Illumina_Single_End_Apapter_2, Illumina_Single_End_PCR_Primer_1, Illumina_Single_End_PCR_Primer_2, and Illumina_Single_End_Sequencing_Primer
in the final filtering fasta
'''
#Single end family
adapters_dict["Illumina_Single_End_Apapter_1"] = "ACACTCTTTCCCTACACGACGCTGTTCCATCT"
adapters_dict["Illumina_Single_End_Apapter_2"] = "CAAGCAGAAGACGGCATACGAGCTCTTCCGATCT"
adapters_dict["Illumina_Single_End_PCR_Primer_1"] = "AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT"
adapters_dict["Illumina_Single_End_PCR_Primer_2"] = "CAAGCAGAAGACGGCATACGAGCTCTTCCGATCT"
adapters_dict["Illumina_Single_End_Sequencing_Primer"] = "ACACTCTTTCCCTACACGACGCTCTTCCGATCT"
#Paired end family
adapters_dict["Illumina_Paired_End_Adapter_1"] = "ACACTCTTTCCCTACACGACGCTCTTCCGATCT"
adapters_dict["Illumina_Paired_End_Adapter_2"] = "CTCGGCATTCCTGCTGAACCGCTCTTCCGATCT"
adapters_dict["Illumina_Paried_End_PCR_Primer_1"] = "AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT"
adapters_dict["Illumina_Paired_End_PCR_Primer_2"] = "CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT"
adapters_dict["Illumina_Paried_End_Sequencing_Primer_1"] = "ACACTCTTTCCCTACACGACGCTCTTCCGATCT"
adapters_dict["Illumina_Paired_End_Sequencing_Primer_2"] = "CGGTCTCGGCATTCCTACTGAACCGCTCTTCCGATCT"
#DpnII family
adapters_dict["Illumina_DpnII_expression_Adapter_1"] = "ACAGGTTCAGAGTTCTACAGTCCGAC"
adapters_dict["Illumina_DpnII_expression_Adapter_2"] = "CAAGCAGAAGACGGCATACGA"
adapters_dict["Illumina_DpnII_expression_PCR_Primer_1"] = "CAAGCAGAAGACGGCATACGA"
adapters_dict["Illumina_DpnII_expression_PCR_Primer_2"] = "AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA"
adapters_dict["Illumina_DpnII_expression_Sequencing_Primer"] = "CGACAGGTTCAGAGTTCTACAGTCCGACGATC"
adapters_dict["Illumina_NlaIII_expression_Adapter_1"] = "ACAGGTTCAGAGTTCTACAGTCCGACATG"
adapters_dict["Illumina_NlaIII_expression_Adapter_2"] = "CAAGCAGAAGACGGCATACGA"
adapters_dict["Illumina_NlaIII_expression_PCR_Primer_1"] = "CAAGCAGAAGACGGCATACGA"
adapters_dict["Illumina_NlaIII_expression_PCR_Primer_2"] = "AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA"
adapters_dict["Illumina_NlaIII_expression_Sequencing_Primer"] = "CCGACAGGTTCAGAGTTCTACAGTCCGACATG"
#Small RNA family
adapters_dict["Illumina_Small_RNA_Adapter_1"] = "GTTCAGAGTTCTACAGTCCGACGATC"
adapters_dict["Illumina_Small_RNA_Adapter_2"] = "TCGTATGCCGTCTTCTGCTTGT"
adapters_dict["Illumina_Small_RNA_RT_Primer"] = "CAAGCAGAAGACGGCATACGA"
adapters_dict["Illumina_Small_RNA_PCR_Primer_1"] = "CAAGCAGAAGACGGCATACGA"
adapters_dict["Illumina_Small_RNA_PCR_Primer_2"] = "AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA"
adapters_dict["Illumina_Small_RNA_Sequencing_Primer"] = "CGACAGGTTCAGAGTTCTACAGTCCGACGATC"
#Multiplexing Family
adapters_dict["Illumina_Multiplexing_Adapter_1"] = "GATCGGAAGAGCACACGTCT"
adapters_dict["Illumina_Multiplexing_Adapter_2"] = "ACACTCTTTCCCTACACGACGCTCTTCCGATCT"
adapters_dict["Illumina_Multiplexing_PCR_Primer_1.01"] = "AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT"
adapters_dict["Illumina_Multiplexing_PCR_Primer_2.01"] = "GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT"
adapters_dict["Illumina_Multiplexing_Read1_Sequencing_Primer"] = "ACACTCTTTCCCTACACGACGCTCTTCCGATCT"
adapters_dict["Illumina_Multiplexing_Index_Sequencing_Primer"] = "GATCGGAAGAGCACACGTCTGAACTCCAGTCAC"
adapters_dict["Illumina_Multiplexing_Read2_Sequencing_Primer"] = "GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT"
#PCR primer family
adapters_dict["Illumina_PCR_Primer_Index_1"] = "CAAGCAGAAGACGGCATACGAGATCGTGATGTGACTGGAGTTC"
adapters_dict["Illumina_PCR_Primer_Index_2"] = "CAAGCAGAAGACGGCATACGAGATACATCGGTGACTGGAGTTC"
adapters_dict["Illumina_PCR_Primer_Index_3"] = "CAAGCAGAAGACGGCATACGAGATGCCTAAGTGACTGGAGTTC"
adapters_dict["Illumina_PCR_Primer_Index_4"] = "CAAGCAGAAGACGGCATACGAGATTGGTCAGTGACTGGAGTTC"
adapters_dict["Illumina_PCR_Primer_Index_5"] = "CAAGCAGAAGACGGCATACGAGATCACTGTGTGACTGGAGTTC"
adapters_dict["Illumina_PCR_Primer_Index_6"] = "CAAGCAGAAGACGGCATACGAGATATTGGCGTGACTGGAGTTC"
adapters_dict["Illumina_PCR_Primer_Index_7"] = "CAAGCAGAAGACGGCATACGAGATGATCTGGTGACTGGAGTTC"
adapters_dict["Illumina_PCR_Primer_Index_8"] = "CAAGCAGAAGACGGCATACGAGATTCAAGTGTGACTGGAGTTC"
adapters_dict["Illumina_PCR_Primer_Index_9"] = "CAAGCAGAAGACGGCATACGAGATCTGATCGTGACTGGAGTTC"
adapters_dict["Illumina_PCR_Primer_Index_10"] = "CAAGCAGAAGACGGCATACGAGATAAGCTAGTGACTGGAGTTC"
adapters_dict["Illumina_PCR_Primer_Index_11"] = "CAAGCAGAAGACGGCATACGAGATGTAGCCGTGACTGGAGTTC"
adapters_dict["Illumina_PCR_Primer_Index_12"] = "CAAGCAGAAGACGGCATACGAGATTACAAGGTGACTGGAGTTC"
#DpnII Gex family
adapters_dict["Illumina_DpnII_Gex_Adapter_1"] = "GATCGTCGGACTGTAGAACTCTGAAC"
adapters_dict["Illumina_DpnII_Gex_Adapter_1.01"] = "ACAGGTTCAGAGTTCTACAGTCCGAC"
adapters_dict["Illumina_DpnII_Gex_Adapter_2"] = "CAAGCAGAAGACGGCATACGA"
adapters_dict["Illumina_DpnII_Gex_Adapter_2.01"] = "TCGTATGCCGTCTTCTGCTTG"
adapters_dict["Illumina_DpnII_Gex_PCR_Primer_1"] = "CAAGCAGAAGACGGCATACGA"
adapters_dict["Illumina_DpnII_Gex_PCR_Primer_2"] = "AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA"
adapters_dict["Illumina_DpnII_Gex_Sequencing_Primer"] = "CGACAGGTTCAGAGTTCTACAGTCCGACGATC"
adapters_dict["Illumina_NlaIII_Gex_Adapter_1.01"] = "TCGGACTGTAGAACTCTGAAC"
adapters_dict["Illumina_NlaIII_Gex_Adapter_1.02"] = "ACAGGTTCAGAGTTCTACAGTCCGACATG"
adapters_dict["Illumina_NlaIII_Gex_Adapter_2.01"] = "CAAGCAGAAGACGGCATACGA"
adapters_dict["Illumina_NlaIII_Gex_Adapter_2.02"] = "TCGTATGCCGTCTTCTGCTTG"
adapters_dict["Illumina_NlaIII_Gex_PCR_Primer_1"] = "CAAGCAGAAGACGGCATACGA"
adapters_dict["Illumina_NlaIII_Gex_PCR_Primer_2"] = "AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA"
adapters_dict["Illumina_NlaIII_Gex_Sequencing_Primer"] = "CCGACAGGTTCAGAGTTCTACAGTCCGACATG"
#Other RNA family
adapters_dict["Illumina_5p_RNA_Adapter"] = "GTTCAGAGTTCTACAGTCCGACGATC"
adapters_dict["Illumina_RNA_Adapter1"] = "TCGTATGCCGTCTTCTGCTTGT"
adapters_dict["Illumina_Small_RNA_3p_Adapter_1"] = "ATCTCGTATGCCGTCTTCTGCTTG"
#TrueSeq family
adapters_dict["TruSeq_Universal_Adapter"] = "AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT"
adapters_dict["TruSeq_Adapter_Index_1"] = "GATCGGAAGAGCACACGTCTGAACTCCAGTCACATCACGATCTCGTATGCCGTCTTCTGCTTG"
adapters_dict["TruSeq_Adapter_Index_2"] = "GATCGGAAGAGCACACGTCTGAACTCCAGTCACCGATGTATCTCGTATGCCGTCTTCTGCTTG"
adapters_dict["TruSeq_Adapter_Index_3"] = "GATCGGAAGAGCACACGTCTGAACTCCAGTCACTTAGGCATCTCGTATGCCGTCTTCTGCTTG"
adapters_dict["TruSeq_Adapter_Index_4"] = "GATCGGAAGAGCACACGTCTGAACTCCAGTCACTGACCAATCTCGTATGCCGTCTTCTGCTTG"
adapters_dict["TruSeq_Adapter_Index_5"] = "GATCGGAAGAGCACACGTCTGAACTCCAGTCACACAGTGATCTCGTATGCCGTCTTCTGCTTG"
adapters_dict["TruSeq_Adapter_Index_6"] = "GATCGGAAGAGCACACGTCTGAACTCCAGTCACGCCAATATCTCGTATGCCGTCTTCTGCTTG"
adapters_dict["TruSeq_Adapter_Index_7"] = "GATCGGAAGAGCACACGTCTGAACTCCAGTCACCAGATCATCTCGTATGCCGTCTTCTGCTTG"
adapters_dict["TruSeq_Adapter_Index_8"] = "GATCGGAAGAGCACACGTCTGAACTCCAGTCACACTTGAATCTCGTATGCCGTCTTCTGCTTG"
adapters_dict["TruSeq_Adapter_Index_9"] = "GATCGGAAGAGCACACGTCTGAACTCCAGTCACGATCAGATCTCGTATGCCGTCTTCTGCTTG"
adapters_dict["TruSeq_Adapter_Index_10"] = "GATCGGAAGAGCACACGTCTGAACTCCAGTCACTAGCTTATCTCGTATGCCGTCTTCTGCTTG"
adapters_dict["TruSeq_Adapter_Index_11"] = "GATCGGAAGAGCACACGTCTGAACTCCAGTCACGGCTACATCTCGTATGCCGTCTTCTGCTTG"
adapters_dict["TruSeq_Adapter_Index_12"] = "GATCGGAAGAGCACACGTCTGAACTCCAGTCACCTTGTAATCTCGTATGCCGTCTTCTGCTTG"
#RNA PCR family
adapters_dict["Illumina_RNA_RT_Primer"] = "GCCTTGGCACCCGAGAATTCCA"
adapters_dict["Illumina_RNA_PCR_Primer"] = "AATGATACGGCGACCACCGAGATCTACACGTTCAGAGTTCTACAGTCCGA"
adapters_dict["RNA_PCR_Primer_Index_1"] = "CAAGCAGAAGACGGCATACGAGATCGTGATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_2"] = "CAAGCAGAAGACGGCATACGAGATACATCGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_3"] = "CAAGCAGAAGACGGCATACGAGATGCCTAAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_4"] = "CAAGCAGAAGACGGCATACGAGATTGGTCAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_5"] = "CAAGCAGAAGACGGCATACGAGATCACTGTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_6"] = "CAAGCAGAAGACGGCATACGAGATATTGGCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_7"] = "CAAGCAGAAGACGGCATACGAGATGATCTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_8"] = "CAAGCAGAAGACGGCATACGAGATTCAAGTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_9"] = "CAAGCAGAAGACGGCATACGAGATCTGATCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_10"] = "CAAGCAGAAGACGGCATACGAGATAAGCTAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_11"] = "CAAGCAGAAGACGGCATACGAGATGTAGCCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_12"] = "CAAGCAGAAGACGGCATACGAGATTACAAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_13"] = "CAAGCAGAAGACGGCATACGAGATTTGACTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_14"] = "CAAGCAGAAGACGGCATACGAGATGGAACTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_15"] = "CAAGCAGAAGACGGCATACGAGATTGACATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_16"] = "CAAGCAGAAGACGGCATACGAGATGGACGGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_17"] = "CAAGCAGAAGACGGCATACGAGATCTCTACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_18"] = "CAAGCAGAAGACGGCATACGAGATGCGGACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_19"] = "CAAGCAGAAGACGGCATACGAGATTTTCACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_20"] = "CAAGCAGAAGACGGCATACGAGATGGCCACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_21"] = "CAAGCAGAAGACGGCATACGAGATCGAAACGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_22"] = "CAAGCAGAAGACGGCATACGAGATCGTACGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_23"] = "CAAGCAGAAGACGGCATACGAGATCCACTCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_24"] = "CAAGCAGAAGACGGCATACGAGATGCTACCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_25"] = "CAAGCAGAAGACGGCATACGAGATATCAGTGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_26"] = "CAAGCAGAAGACGGCATACGAGATGCTCATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_27"] = "CAAGCAGAAGACGGCATACGAGATAGGAATGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_28"] = "CAAGCAGAAGACGGCATACGAGATCTTTTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_29"] = "CAAGCAGAAGACGGCATACGAGATTAGTTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_30"] = "CAAGCAGAAGACGGCATACGAGATCCGGTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_31"] = "CAAGCAGAAGACGGCATACGAGATATCGTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_32"] = "CAAGCAGAAGACGGCATACGAGATTGAGTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_33"] = "CAAGCAGAAGACGGCATACGAGATCGCCTGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_34"] = "CAAGCAGAAGACGGCATACGAGATGCCATGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_35"] = "CAAGCAGAAGACGGCATACGAGATAAAATGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_36"] = "CAAGCAGAAGACGGCATACGAGATTGTTGGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_37"] = "CAAGCAGAAGACGGCATACGAGATATTCCGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_38"] = "CAAGCAGAAGACGGCATACGAGATAGCTAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_39"] = "CAAGCAGAAGACGGCATACGAGATGTATAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_40"] = "CAAGCAGAAGACGGCATACGAGATTCTGAGGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_41"] = "CAAGCAGAAGACGGCATACGAGATGTCGTCGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_42"] = "CAAGCAGAAGACGGCATACGAGATCGATTAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_43"] = "CAAGCAGAAGACGGCATACGAGATGCTGTAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_44"] = "CAAGCAGAAGACGGCATACGAGATATTATAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_45"] = "CAAGCAGAAGACGGCATACGAGATGAATGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_46"] = "CAAGCAGAAGACGGCATACGAGATTCGGGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_47"] = "CAAGCAGAAGACGGCATACGAGATCTTCGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
adapters_dict["RNA_PCR_Primer_Index_48"] = "CAAGCAGAAGACGGCATACGAGATTGCCGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA"
#ABI family
adapters_dict["ABI_Dynabead_EcoP_Oligo"] = "CTGATCTAGAGGTACCGGATCCCAGCAGT"
adapters_dict["ABI_Solid3_Adapter_A"] = "CTGCCCCGGGTTCCTCATTCTCTCAGCAGCATG"
adapters_dict["ABI_Solid3_Adapter_B"] = "CCACTACGCCTCCGCTTTCCTCTCTATGGGCAGTCGGTGAT"
adapters_dict["ABI_Solid3_5_AMP_Primer"] = "CCACTACGCCTCCGCTTTCCTCTCTATG"
adapters_dict["ABI_Solid3_3_AMP_Primer"] = "CTGCCCCGGGTTCCTCATTCT"
adapters_dict["ABI_Solid3_EF1_alpha_Sense_Primer"] = "CATGTGTGTTGAGAGCTTC"
adapters_dict["ABI_Solid3_EF1_alpha_Antisense_Primer"] = "GAAAACCAAAGTGGTCCAC"
adapters_dict["ABI_Solid3_GAPDH_Forward_Primer"] = "TTAGCACCCCTGGCCAAGG"
adapters_dict["ABI_Solid3_GAPDH_Reverse_Primer"] = "CTTACTCCTTGGAGGCCATG"
#TrueSeq2 family
adapters_dict["TruSeq2_SE"] = "AGATCGGAAGAGCTCGTATGCCGTCTTCTGCTTG"
adapters_dict["TruSeq2_PE_f"] = "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT"
adapters_dict["TruSeq2_PE_r"] = "AGATCGGAAGAGCGGTTCAGCAGGAATGCCGAG"
adapters_dict["TruSeq3_IndexedAdapter"] = "AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC"
adapters_dict["TruSeq3_UniversalAdapter"] = "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA"
#Nextera Family
adapters_dict["Nextera_PE_PrefixNX/1"] = "AGATGTGTATAAGAGACAG"
adapters_dict["Nextera_PE_PrefixNX/2"] = "AGATGTGTATAAGAGACAG"
adapters_dict["Nextera_PE_Trans1"] = "TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG"
adapters_dict["Nextera_PE_Trans1_rc"] = "CTGTCTCTTATACACATCTGACGCTGCCGACGA"
adapters_dict["Nextera_PE_Trans2"] = "GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG"
adapters_dict["Nextera_PE_Trans2_rc"] = "CTGTCTCTTATACACATCTCCGAGCCCACGAGAC"
all_adapters = tempfile.NamedTemporaryFile(mode = "w", delete = False)
for adapt in adapters_dict:
print(">"+adapt, file = all_adapters)
print(adapters_dict[adapt], file = all_adapters)
name = all_adapters.name
all_adapters.close()
return adapters_dict, name
#FaCQs supports external adapter sequences, but has no option to EXCLUDE its own internal adapters while doing so.
#This function returns a dict of ID:adapter for the FaQCs internal sequences so that Multitrim doesn't break should a FaQCs adapter
#appear in parse_adapters
def faqcs_internal_adapters():
adapters_dict = {}
#Below are the adapters present in FaQCs by default.
#Cre-loxp family
adapters_dict["cre-loxp-forward"] = "TCGTATAACTTCGTATAATGTATGCTATACGAAGTTATTACG"
adapters_dict["cre-loxp-reverse"] = "AGCATATTGAAGCATATTACATACGATATGCTTCAATAATGC"
#TruSeq 1 family
adapters_dict["TruSeq-adapter-1"] = "GGGGTAGTGTGGATCCTCCTCTAGGCAGTTGGGTTATTCTAGAAGCAGATGTGTTGGCTGTTTCTGAAACTCTGGAAAA"
adapters_dict["TruSeq-adapter-3"] = "CAACAGCCGGTCAAAACATCTGGAGGGTAAGCCATAAACACCTCAACAGAAAA"
#PCR primers
adapters_dict["PCR-primer-1"] = "CGATAACTTCGTATAATGTATGCTATACGAAGTTATTACG"
adapters_dict["PCR-primer-2"] = "GCATAACTTCGTATAGCATACATTATACGAAGTTATACGA"
#Nextera Junction family
adapters_dict["Nextera-junction-adapter-1"] = "CTGTCTCTTATACACATCTAGATGTGTATAAGAGACAG"
#Nextera-primer-adapter family; these are copies of earlier adapters in this list, but I want to make sure they're detectable since they're internal to FaQCs
adapters_dict["Nextera-primer-adapter-1"] = "GATCGGAAGAGCACACGTCTGAACTCCAGTCAC"
adapters_dict["Nextera-primer-adapter-2"] = "GATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT"
return(adapters_dict)
#Get file names right up front for ease of use
def names_pe(forward, reverse, outdir = ".", prefix = ""):
forward_basename = os.path.basename(os.path.normpath(forward))
if forward_basename.endswith(".gz"):
forward_basename = forward_basename[:-3]
forward_basename = os.path.splitext(forward_basename)[0]
reverse_basename = os.path.basename(os.path.normpath(reverse))
if reverse_basename.endswith(".gz"):
reverse_basename = reverse_basename[:-3]
reverse_basename = os.path.splitext(reverse_basename)[0]
pre_qc_f = outdir + "/" + prefix + "1.pre_trim_QC_" + forward_basename
pre_qc_r = outdir + "/" + prefix + "2.pre_trim_QC_" + reverse_basename
post_qc_f = outdir + "/" + prefix + "1.post_trim_QC_" + forward_basename
post_qc_r = outdir + "/" + prefix + "2.post_trim_QC_" + reverse_basename
post_trim_reads_f = outdir + "/" + prefix + "1.post_trim_" + forward_basename + ".fq"
post_trim_reads_r = outdir + "/" + prefix + "2.post_trim_" + reverse_basename + ".fq"
return pre_qc_f, pre_qc_r, post_qc_f, post_qc_r, post_trim_reads_f, post_trim_reads_r
#Get file names right up front for ease of use
def names_se(reads, outdir = ".", prefix = ""):
base_name = os.path.basename(os.path.normpath(reads))
if base_name.endswith(".gz"):
base_name = base_name[:-3]
base_name = os.path.splitext(base_name)[0]
pre_qc = outdir + "/" + prefix + "unpaired.pre_trim_QC_" + base_name
post_qc = outdir + "/" + prefix + "unpaired.post_trim_QC_" + base_name
post_trim_reads = outdir + "/" + prefix + "unpaired.post_trim_" + base_name + ".fq"
return pre_qc, post_qc, post_trim_reads
#DSRC needs its own. Whoops.
def do_falco(read_name_tool):
'''
Falco does not support naming files, but does support selecting output directory.
As we are possibly generating multiple falco reports simultaneously,
we get around this issue by generating the generically named files in a temp dir
and then move the results to the final location with an appropriate rename.
'''
#temp directory
loc = tempfile.mkdtemp()
reads = read_name_tool[0]
output_name = read_name_tool[1]
falco_path = read_name_tool[2]
#falco command
command = [falco_path, "--quiet", "-o", loc, reads]
#command = [falco_path, "-o", loc, reads]
#run the command
#Working perfectly, the falco call should not produce any output. Until falco has bugs patched, it's not working perfectly
#subprocess.call(command, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
subprocess.call(command)
#move the results and rename
#I'm just gonna move the html.
#shutil.move(loc+"/fastqc_data.txt", output_name + ".data.txt")
shutil.move(loc+"/fastqc_report.html", output_name + ".html")
#Cleanup
shutil.rmtree(loc)
return None
#do all QC at once - now old
def falco_qc_pe(pre_trim_reads_f, pre_trim_reads_r, post_trim_reads_f, post_trim_reads_r, pre_name_f, post_name_f, pre_name_r, post_name_r, threads, falco_binary):
pre_forward = [pre_trim_reads_f, pre_name_f, falco_binary]
pre_reverse = [pre_trim_reads_r, pre_name_r, falco_binary]
post_forward = [post_trim_reads_f+".gz", post_name_f, falco_binary]
post_reverse = [post_trim_reads_r+".gz", post_name_r, falco_binary]
commands = [pre_forward, pre_reverse, post_forward, post_reverse]
print("Generating QC reports.")
pool = multiprocessing.Pool(min(4, threads))
pool.map(do_falco, commands)
pool.close()
#do all QC at once - now old
def falco_qc_se(pre_trim_reads, post_trim_reads, pre_name, post_name, threads, falco_binary):
pre = [pre_trim_reads, pre_name, falco_binary]
post = [post_trim_reads+".gz", post_name, falco_binary]
commands = [pre, post]
print("Generating QC reports.")
pool = multiprocessing.Pool(min(2, threads))
pool.map(do_falco, commands)
pool.close()
def do_seqtk(read_tool):
sample = read_tool[0]
seqtk_path = read_tool[1]
print("Subsampling:", sample)
#-s 100 specifies seed as 100. The number chosen is arbitrary, and I only spcify it so that results are deterministic and reproducible.
command = [seqtk_path, "sample", "-s", "100", sample, "100000"]
temp = tempfile.NamedTemporaryFile("w", delete=False)
ps = subprocess.run(command, stdout=subprocess.PIPE, universal_newlines = True)
temp.write(ps.stdout)
name = temp.name
temp.close()
return name
#Subsample reads; identify adapters with FaQCs
def adapter_identification_pe(artificial_artifacts, seqtk_binary, faqcs_binary, forward = "", reverse = "", threads = 1, output = ".", minimum_presence = 0.1, prefix = "", phred_fmt = "33"):
#seqtk forward and reverse
subsample_f = [forward, seqtk_binary]
subsample_r = [reverse, seqtk_binary]
seqtk_commands = [subsample_f, subsample_r]
pool = multiprocessing.Pool(min(2, threads))
seqtk_samples = pool.map(do_seqtk, seqtk_commands)
pool.close()
#FaQCs PE with adapter file
faqcs_subset_command = [faqcs_binary, "-t", str(threads), "--qc_only", "-d", output, "--artifactFile", artificial_artifacts, "--ascii", phred_fmt]
#proper naming
if prefix != "":
faqcs_subset_command.append("--prefix")
faqcs_subset_command.append(prefix + "Subsample_Adapter_Detection")
pdf_name = prefix + "Subsample_Adapter_Detection_qc_report.pdf"
else :
faqcs_subset_command.append("--prefix")
faqcs_subset_command.append("Subsample_Adapter_Detection")
pdf_name = "Subsample_Adapter_Detection_qc_report.pdf"
#forward strand
faqcs_subset_command.append("-1")
faqcs_subset_command.append(seqtk_samples[0])
#reverse strand
faqcs_subset_command.append("-2")
faqcs_subset_command.append(seqtk_samples[1])
print("Detecting adapters now... ", end = "")
ps = subprocess.Popen(faqcs_subset_command)
ps.wait()
os.remove(output + "/" + pdf_name)
#Adapter detection from output of FaQCs
detection_report = open(output + "/" + prefix + "Subsample_Adapter_Detection.stats.txt")
detected_adapters = {}
begin_assessment = False
for line in detection_report:
if not begin_assessment:
if line.strip().startswith("Reads with Adapters/Primers:"):
begin_assessment = True
else:
segment = line.strip().split()
detected_adapters[segment[0]] = float(re.findall("\d+\.\d+", segment[3])[0])
detection_report.close()
clean_detection = []
for adapter in detected_adapters:
if detected_adapters[adapter] >= minimum_presence:
clean_detection.append(adapter)
#Cleans up after itself.
for item in seqtk_samples:
os.remove(item)
print("Detection done!")
#Return adapter file
return clean_detection
#Subsample reads; identify adapters with FaQCs
def adapter_identification_se(artificial_artifacts, seqtk_binary, faqcs_binary, unpaired = "", threads = 1, output = ".", minimum_presence = 0.1, prefix = "", phred_fmt = "33"):
#seqtk forward and reverse
subsample = [unpaired, seqtk_binary]
seqtk_samples = do_seqtk(subsample)
#FaQCs SE with adapter file
faqcs_subset_command = [faqcs_binary, "-t", str(threads), "--qc_only", "-d", output, "--artifactFile", artificial_artifacts, "--ascii", phred_fmt]
#proper naming
if prefix != "":
faqcs_subset_command.append("--prefix")
faqcs_subset_command.append(prefix + "Subsample_Adapter_Detection")
pdf_name = prefix + "Subsample_Adapter_Detection_qc_report.pdf"
else :
faqcs_subset_command.append("--prefix")
faqcs_subset_command.append("Subsample_Adapter_Detection")
pdf_name = "Subsample_Adapter_Detection_qc_report.pdf"
#forward strand
faqcs_subset_command.append("-u")
faqcs_subset_command.append(seqtk_samples)
print("Detecting adapters now... ", end = "")
ps = subprocess.Popen(faqcs_subset_command)
ps.wait()
os.remove(output + "/" + pdf_name)
#Adapter detection from output of FaQCs
detection_report = open(output + "/" + prefix + "Subsample_Adapter_Detection.stats.txt")
detected_adapters = {}
begin_assessment = False
for line in detection_report:
if not begin_assessment:
if line.strip().startswith("Reads with Adapters/Primers:"):
begin_assessment = True
else:
segment = line.strip().split()
detected_adapters[segment[0]] = float(re.findall("\d+\.\d+", segment[3])[0])
detection_report.close()
clean_detection = []
for adapter in detected_adapters:
if detected_adapters[adapter] >= minimum_presence:
clean_detection.append(adapter)
#Cleans up after itself.
os.remove(seqtk_samples)
print("Detection done!")
#Return adapter file
return clean_detection
#gets adapter families for later use
def parse_adapters(full_list, detected_adapters, output, prefix = ""):
print("Creating specific adapters file for you.")
#detected adapters is just a list of the user's detected adapters by ID.
faqcs_internal_adapter_list = faqcs_internal_adapters()
found = False
detected_seqs = {}
for id in detected_adapters:
found = False
if id in full_list:
found = True
print("Adapter sequence:", id, "detected.")
detected_seqs[id] = full_list[id]
if id in faqcs_internal_adapter_list:
found = True
print("Adapter sequence:", id, "detected. This adapter is part of a non-optional internal list used by FaQCs and will be included.")
detected_seqs[id] = faqcs_internal_adapter_list[id]
#Skip adapter if it cannot be found. Should never happen, now that FaQCs' adapters will always be found and other seqs must be from internal or supplied sequences file
if not found:
print("Adapter sequence:", id, "not found in Multitrim's adapter list! It will NOT be included in trimming.")
adapters_by_family = family_detection(detected_seqs)
#This is a file I don't want to be temporary. It both helps identify the adapters present in a dataset and provides a fasta for a user to reuse
subset = open(output + "/" + prefix + "detected_adapters.fasta", "w")
for adapter in adapters_by_family:
print(adapter, file = subset)
print(adapters_by_family[adapter], file = subset)
subset.close()
return(output+"/"+ prefix + "detected_adapters.fasta")
#paired end version of the full trim; trims using detected adapters with FaQCs -q 27, then fastp --cut_right window 3 qual 20
def full_trim_pe(forward_in, reverse_in, forward_out, reverse_out, directory, adapters, threads, faqcs, fastp, score, minlen, window, window_qual, prefix, compressor, compress_level, phred_fmt = "33", advanced = False, skip_fastp = False, skip_faqcs = False):
'''
Command structure:
The primary purpose is to issue a FaQCs call on the untrimmed reads, then a subsequent fastp call on the outputs from the FaQCs call.
Additionally, supports using only one of the two tools. Commands will be built even if the tool is to be skipped, but the call will never be issued.
'''
faqcs_command = [faqcs, "-t", str(threads), "-1", forward_in, "-2", reverse_in, "--artifactFile", adapters, "-q", str(score), "--min_L", str(minlen), "--prefix", "reads", "--trim_only", "-d", directory, "--ascii", phred_fmt]
fastp_command = [fastp, "--thread", str(threads), "--adapter_fasta", adapters, "-l", str(minlen), "--json", directory + "/" + prefix + "post_trim_fastp.json", "--html", directory + "/" + prefix + "post_trim_fastp.html"]
#Args can be added to fastp command with no consequences if fastp is skipped; command simply won't issue so they will be silent
if skip_faqcs:
#This handles taking the input reads directly
fastp_command.append("-i")
fastp_command.append(forward_in)
fastp_command.append("-I")
fastp_command.append(reverse_in)
else:
#FaQCs goes first; this is how I coerce FaQCs reads to look afterwards
fastp_command.append("-i")
fastp_command.append(directory+"/reads.1.trimmed.fastq")
fastp_command.append("-I")
fastp_command.append(directory+"/reads.2.trimmed.fastq")
#Outputs are the same regardless of inputs
fastp_command.append("-o")
fastp_command.append(forward_out)
fastp_command.append("-O")
fastp_command.append(reverse_out)
if int(window) > 0:
fastp_command.append("--cut_right")
fastp_command.append("--cut_right_window_size")
fastp_command.append(str(window))
fastp_command.append("--cut_right_mean_quality")
fastp_command.append(str(window_qual))
if phred_fmt != "33":
fastp_command.append("--phred64")
if advanced:
fastp_command.append("--trim_poly_g")
fastp_command.append("--low_complexity_filter")
time_format = "%d/%m/%Y %H:%M:%S"
#Manage issuing of commands
if not skip_faqcs:
timer = datetime.now()
printable_time = timer.strftime(time_format)
print("Trimming with FaQCs. Started at:", printable_time)
subprocess.run(faqcs_command, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
os.remove(directory + "/" + "reads.stats.txt")
if not skip_fastp:
timer = datetime.now()
printable_time = timer.strftime(time_format)
print("Trimming with Fastp. Started at:", printable_time)
subprocess.run(fastp_command, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
os.remove(directory + "/" + prefix + "post_trim_fastp.json")
os.remove(directory + "/" + prefix + "post_trim_fastp.html")
#We want to rename the non-fastp files, then pass all files and threads to compress with pigz under the nice, neat names
if skip_fastp:
#rename FaQCs files to correct names; compress
#remove this one in any event. We don't want any unpaireds with paired end
os.remove(directory+"/reads.unpaired.trimmed.fastq")
shutil.move(directory+"/reads.1.trimmed.fastq", forward_out)
shutil.move(directory+"/reads.2.trimmed.fastq", reverse_out)
#compress_commands = [[directory+"/reads.1.trimmed.fastq", forward_out], [directory+"/reads.2.trimmed.fastq", reverse_out]]
#might as well be parallel
#pool = multiprocessing.Pool(min(2, threads))
#pool.map(compress_faqcs, compress_commands)
#pool.close()
elif not skip_faqcs:
#remove FaQCs files if fastp has results or skip if FaQCs not done.
os.remove(directory+"/reads.1.trimmed.fastq")
os.remove(directory+"/reads.2.trimmed.fastq")
#remove this one in any event. We don't want any unpaireds with paired end - the call has to be duplicated, unfortunately.
os.remove(directory+"/reads.unpaired.trimmed.fastq")
compress_results([forward_out, reverse_out], threads, compressor, compress_level)
return None
#single end version of the full trim; trims using detected adapters with FaQCs -q 27, then fastp --cut_right window 3 qual 20
def full_trim_se(reads_in, reads_out, directory, adapters, threads, faqcs, fastp, score, minlen, window, window_qual, prefix, compressor, compress_level, phred_fmt = "33", advanced = False, skip_fastp = False, skip_faqcs = False):
'''
Command structure:
The primary purpose is to issue a FaQCs call on the untrimmed reads, then a subsequent fastp call on the outputs from the FaQCs call.
Additionally, supports using only one of the two tools. Commands will be built even if the tool is to be skipped, but the call will never be issued.
'''
faqcs_command = [faqcs, "-t", str(threads), "-u", reads_in, "--artifactFile", adapters, "-q", str(score), "--min_L", str(minlen), "--prefix", "reads", "--trim_only", "-d", directory, "--ascii", phred_fmt]
fastp_command = [fastp, "--thread", str(threads), "--adapter_fasta", adapters, "-l", str(minlen), "--json", directory + "/" + prefix + "post_trim_fastp.json", "--html", directory + "/" + prefix + "post_trim_fastp.html"]
#Args can be added to fastp command with no consequences if fastp is skipped; command simply won't issue so they will be silent
if skip_faqcs:
#This handles taking the input reads directly
fastp_command.append("-i")
fastp_command.append(reads_in)
else:
#FaQCs goes first; this is how I coerce FaQCs reads to look afterwards
fastp_command.append("-i")
fastp_command.append(directory+"/reads.unpaired.trimmed.fastq")
#Outputs are the same regardless of inputs
fastp_command.append("-o")
fastp_command.append(reads_out)
if int(window) > 0:
fastp_command.append("--cut_right")
fastp_command.append("--cut_right_window_size")
fastp_command.append(str(window))
fastp_command.append("--cut_right_mean_quality")
fastp_command.append(str(window_qual))
if phred_fmt != "33":
fastp_command.append("--phred64")
if advanced:
fastp_command.append("--trim_poly_g")
fastp_command.append("--low_complexity_filter")
time_format = "%d/%m/%Y %H:%M:%S"
#Manage issuing of commands
if not skip_faqcs:
timer = datetime.now()
printable_time = timer.strftime(time_format)
print("Trimming with FaQCs. Started at:", printable_time)
subprocess.run(faqcs_command, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
os.remove(directory + "/" + "reads.stats.txt")
if not skip_fastp:
timer = datetime.now()
printable_time = timer.strftime(time_format)
print("Trimming with Fastp. Started at:", printable_time)
subprocess.run(fastp_command, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
os.remove(directory + "/" + prefix + "post_trim_fastp.json")
os.remove(directory + "/" + prefix + "post_trim_fastp.html")
if skip_fastp:
#compress the result
#remove this one in any event. We don't want any unpaireds with paired end
shutil.move(directory+"/reads.unpaired.trimmed.fastq", reads_out)
elif not skip_faqcs:
#remove FaQCs files if fastp has results or skip if FaQCs not run.
os.remove(directory+"/reads.unpaired.trimmed.fastq")
compress_results([reads_out], threads, compressor, compress_level)
return None
#compress results using selected compressor.
def compress_results(output_files, threads, compressor, level):
#Just for printing feedback.
pretty_compressor = ["GZIP", "PIGZ", "DSRC-2"][["gzip", "pigz", "dsrc"].index(compressor)]
time_format = "%d/%m/%Y %H:%M:%S"
timer = datetime.now()
printable_time = timer.strftime(time_format)
print("Beginning compression of trimmed reads using", pretty_compressor, "at:", printable_time)
#These get the file sizes, runtimes, compression ratio
if compressor == "gzip":
gzip_compress_module(output_files, threads, level)
if compressor == "pigz":
pigz_compress_module(output_files, threads, level)
'''
if compressor == "dsrc":
dsrc_compress_module(output_files, threads, level)
'''
print("Outputs compressed!")
return None
#gzip is NOT threaded, so we open up to 4 threads and compress each input simultaneously, feeding the results to falco as we go.
def gzip_compress_module(outputs, threads, level):
#Get the gzip set up for each file
gzip_arguments = []
for file in outputs:
gzip_arguments.append(["gzip", "-f"+str(level), file])
#Don't open more threads than you have to.
num_files = len(outputs)
#Run args
pool = multiprocessing.Pool(min(threads, num_files))
pool.map(do_gzip_pretty, gzip_arguments)
pool.close()
return None
#The particular parallelization for this is a bother.
def do_gzip_pretty(compress_argument):
file = compress_argument[2]
start_time = datetime.now()
initial_size = os.path.getsize(file)
subprocess.call(compress_argument)
final_size = os.path.getsize(file+".gz")
end_time = datetime.now()
pretty_print_file_size(file, initial_size, final_size, start_time, end_time)
return None
#pigz is threaded, so compression happens 1 file at a time using all threads, then falco QC 4 using the gzip approach above since the result is in gzip format
def pigz_compress_module(outputs, threads, level):
for file in outputs:
start_time = datetime.now()
initial_size = os.path.getsize(file)
pigz_argument = ["pigz", "-f", "-"+str(level), "-p", str(threads), file]
subprocess.call(pigz_argument)
final_size = os.path.getsize(file+".gz")
end_time = datetime.now()
pretty_print_file_size(file, initial_size, final_size, start_time, end_time)
return None
#Unfinished, Has more moving parts to take care of.
#DSRC-2 is threaded, but the compressed format is not supported by falco. Thus, we run QC, THEN compress each file 1 at a time using all threads.
def dsrc_compress_module(inputs, outputs, threads, level):
print("DSRC-2 will also produce QC reports at this time!")
#DSRC only accepts up to 64 threads
if threads > 64:
threads = 64
#DSRC-formatted args
threads = "-t"+str(threads)
level = "-m"+str(level)
#falco goes here for DSRC-2, must be uncompressed files.
num_files = min(threads, len(inputs)+len(outputs))
for file in files:
output_file_name = file+".dsrc"
start_time = datetime.now()
initial_size = os.path.getsize(files[i])
compress_command = ["dsrc", "c", threads, level, file, output_file_name]
subprocess.run(compress_command)
ending_size = os.path.getsize(output_file_name)
end_time = datetime.now()
pretty_print_file_size(files[i], initial_size, ending_size, start_time, end_time)
print("Compression and QC complete!")
#Unfinished.
#Function for checking if an input file is a DSRC archive - these have to be decompressed for trimming, since the tools don't directly support such archives.
def check_is_dsrc(file):
#We're going to make a file in a temporary directory and use it
base_name = os.path.basename(file)
loc = tempfile.mkdtemp()
tempout = loc + "/" + base_name
is_dsrc = False
#Attempt to DSRC decompress into the temp file
try:
#Multiple reasons this could fail, including tool absence. All should be handled by this except.
dsrc_decomp = ["dsrc", "d", file, tempout]
subprocess.run(dsrc_decomp, stdout = subprocess.DEVNULL, stderr = subprocess.DEVNULL)
#DSRC only creates the file if it's successful in opening the file and DSRC can be called in the first place.
is_dsrc = os.path.exists(tempout)
#If the file cannot be decompressed, delete the temp file and return self.
except:
shutil.rmtree(loc)
if is_dsrc:
dsrc_file = tempout
else:
dsrc_file = file
return is_dsrc, dsrc_file
#Convert a file's size in bytes to human-readable format.
def humansize(nbytes):
suffixes = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']
i = 0
while nbytes >= 1024 and i < len(suffixes)-1:
nbytes /= 1024.
i += 1
f = ('%.2f' % nbytes).rstrip('0').rstrip('.')
return '%s %s' % (f, suffixes[i])
#Print well-formatted compression time info.
def pretty_print_file_size(name, start, end, start_time, end_time):
runtime = end_time - start_time
try:
hours = runtime.hours
except:
hours = 0
try:
minutes = runtime.minutes
except:
minutes = 0
try:
seconds = runtime.seconds
except:
seconds = 0
runtime = '%02d:%02d:%02d' % (hours, minutes, seconds)
print(name, "compressed! Compression took:", runtime, "and the file was compressed to", str(round((end/start)*100, 2)), "percent of original size from", humansize(start), "to", humansize(end))
return None
#Stolen from a SO thread on how to issue usage information on an error.
class MyParser(argparse.ArgumentParser):
def error(self, message):
sys.stderr.write('error: %s\n' % message)
self.print_help()
sys.exit(2)
#Option parsing
def gather_opts():
parser = MyParser(description=''' This program is designed to facilitate effective trimming of your reads.
It will help to identify the presence of adapters in your reads, trim those adapters and the reads efficiently,
and produce several bfore and after quality reports in addition to the trimmed reads. This is a pipeline incorporating
FaQCs, falco, and seqtk commands, in addition to several python operations which exist to facilitate adapter finding and
subsetting. --user and --UNLIMITED_POWER are jokes, but you should usually use --UNLIMITED_POWER.''')
#Use all available cores.
parser.add_argument("--max", dest = "Sheev", action = 'store_true', help = "Attempts to detect and use all available processors for threading.")
#Or this many threads. Laaaaame
parser.add_argument("--threads", "-t", dest = "threads", default = 1, help = "Number of threads to use for parallel processes. Default 1")
#file inputs
parser.add_argument("--forward", "-1", dest = "f", default = "", help = "Forward Strand Reads (use -u for unpaired reads)")
parser.add_argument("--reverse", "-2", dest = "r", default = "", help = "Reverse Strand Reads (use -u for unpaired reads)")
parser.add_argument("--unpaired", "-u", dest = "u", default = "", help = "Unpaired Reads")
#final out directory
parser.add_argument("--output", "-o", dest = "outdir", default = ".", help = "Directory to send final outputs.")
#naming convention
parser.add_argument("--prefix", "-p", dest = "pref", default = "", help = "Prefix to place on outputs.")
#Adapter detection opts
parser.add_argument("--min_adapt_pres", "-m", dest = "minpres", default = 0.1, help = "Minimum presence of an adapter for it to be considered present in a set of reads. Default 0.1, so an adapter is considered present if detected in 0.1 percent of reads.")
parser.add_argument("--adapters", "-a", dest = "adapter_fasta", default = "internal", help = "Supply a custom set of adapters for adapter detection. Detected adapters can come only from this set. Multitrim uses the MiGA adapter set by defualt.")
#parser.add_argument("--kits", dest = "adapter_families", default = "internal", help = "Supply a 2-column, comma separated list of adapter IDs and kits of origin. When an adapter is detected, all adapters in the same seq. prep kit are also considered detected when using the default MiGA adapters.")