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offTargetAnalysis.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 12 11:39:35 2021
@author: klaus
"""
from multiprocessing import Pool
from Bio.Seq import Seq
from Bio import SeqIO
import pandas as pd
import re
import os
import numpy as np
import math
import translatePattern as tp
import gregexprindex as gr
import biostrings as bio
import getSeqFromBed as gSFB
import findgRNAs as f
import filtergRNAs as filtergRNAs
import searchHits2
import writeHits2 as wrtHts2
import buildFeatureVectorForScoring as bFVFS
import getOfftargetScore
import getOfftargetScore2
import filterOffTarget
import uniqueREs
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
pd.set_option("display.max_colwidth", None)
pd.set_option("display.width", None)
pd.set_option('expand_frame_repr', True)
def offTargetAnalysis(inputFilePath, outputDir, BSgenomeName=None, txdb=None, orgAnn=None, chrom_acc = None,
corrected_seq = None, target_start = None, target_end = None,
targeted_seq_length_change = None, gRNAoutputName = None, format = "fasta",
header = False, findgRNAs = True, exportAllgRNAs = "all",
findgRNAsWithREcutOnly = False, annotatePaired = True,
REpatternFile = os.getcwd() + os.sep + os.sep.join(["extdata", "NEBenzymes.fa"]),
minREpatternSize = 4, overlap_gRNA_positions = [17, 18], findPairedgRNAOnly = False,
paired_orientation = "PAMout", enable_multicore = False, n_cores_max = 6,
min_gap = 0, max_gap = 20, gRNA_name_prefix = "", PAM_size = 3, gRNA_size = 20,
PAM = "NGG", chromToSearch = ["all"],
chromToExclude = ["chr17_ctg5_hap1", "chr4_ctg9_hap1", "chr6_apd_hap1",
"chr6_cox_hap2", "chr6_dbb_hap3", "chr6_mann_hap4",
"chr6_mcf_hap5", "chr6_qbl_hap6","chr6_ssto_hap7"],
max_mismatch = 3, PAM_pattern = "NRG$", allowed_mismatch_PAM = 1, gRNA_pattern = "",
baseEditing = False, targetBase = "C", editingWindow = [4, 8],
editingWindow_offtargets = [4, 8], primeEditing = False, PBS_length = 13,
RT_template_length = [8, 28],
RT_template_pattern = "D$", bp_after_target_end = 15,
primeEditingPaired_output = "pairedgRNAsForPE.xls", min_score = 0, topN = 1000,
topN_OfftargetTotalScore = 10, annotateExon = True, ignore_strand = True,
fetchSequence = True, upstream = 200, downstream = 200, upstream_search = 0,
downstream_search = 0, weights = [0, 0, 0.014, 0, 0, 0.395, 0.317, 0, 0.389, 0.079,
0.445, 0.508, 0.613, 0.851, 0.732, 0.828, 0.615,
0.804, 0.685, 0.583],
baseBeforegRNA = 4, baseAfterPAM = 3, featureWeightMatrixFile = pd.read_csv(os.getcwd() + os.sep + os.sep.join(["extdata", "DoenchNBT2014.csv"])),
useScore = True, useEfficacyFromInputSeq = False, outputUniqueREs = True,
gRNA_backbone = "GUUUUAGAGCUAGAAAUAGCAAGUUAAAAUAAGGCUAGUCCGUUAUCAACUUGAAAAAGUGGCACCGAGUCGGUGCUUUUUU",
temperature = 37, overwrite = False, scoring_method = "Hsu-Zhang",
subPAM_activity = {"AA" : 0,
"AC" : 0,
"AG" : 0.259259259,
"AT" : 0,
"CA" : 0,
"CC" : 0,
"CG" : 0.107142857,
"CT" : 0,
"GA" : 0.069444444,
"GC" : 0.022222222,
"GG" : 1,
"GT" : 0.016129032,
"TA" : 0,
"TC" : 0,
"TG" : 0.038961039,
"TT" : 0},
subPAM_position = [22, 23], PAM_location = "3prime", rule_set = "Root_RuleSet1_2014",
calculategRNAefficacyForOfftargets = True,
mismatch_activity_file = pd.read_csv(os.getcwd() + os.sep + os.sep.join(["extdata", "NatureBiot2016SuppTable19DoenchRoot.csv"])),
method_indelFreq = "Lindel", baseBeforegRNA_indelFreq = 13, baseAfterPAM_indelFreq = 24):
if type(inputFilePath) == str:
if os.path.isfile(inputFilePath) == True:
inputFilePath = pd.read_csv(inputFilePath, sep = ">", header = None)
inputFilePathseq = inputFilePath[0].dropna().reset_index(drop=True)
inputFilePathname = inputFilePath[1].dropna().reset_index(drop=True)
inputFilePath = pd.concat([inputFilePathseq, inputFilePathname],1).rename(columns={0:"seq", 1:"names"})
else:
inputFilePath = inputFilePath.upper()
if inputFilePath.count("A") + inputFilePath.count("T") + inputFilePath.count("G") + inputFilePath.count("C") == len(inputFilePath):
inputFilePath = pd.concat([pd.DataFrame([inputFilePath], columns=(["seq"])), pd.DataFrame(["NA"], columns = (["names"]))], 1)
else:
raise Exception("inputFilePath has to be a DNA string of characters!")
else:
raise Exception("inputFilePath has to be a DNA string or string of characters!")
print("Validating input ...\n")
if rule_set == "DeepCpf1":
baseBeforegRNA = 8
baseAfterPAM = 26
if scoring_method == "CFDscore" and subPAM_activity["TT"] < 1:
subPAM_activity = { "AA" : 0,
"AC" : 0,
"AG" : 0,
"AT" : 0.1,
"CA" : 0,
"CC" : 0,
"CG" : 0,
"CT" : 0.05,
"GA" : 0,
"GC" : 0,
"GG" : 0,
"GT" : 0.05,
"TA" : 0.2,
"TC" : 0.1,
"TG" : 0.1,
"TT" : 1}
elif rule_set in ["Root_RuleSet1_2014", "Root_RuleSet2_2016", "CRISPRscan"]:
if PAM_location == "3prime":
baseBeforegRNA = 4
baseAfterPAM = 3
else:
baseBeforegRNA = 4 + PAM_size
baseAfterPAM = 3 + gRNA_size
mismatch_activity = mismatch_activity_file
required_col = ["Mismatch.Type", "Position", "Percent.Active"]
if scoring_method == "CFDscore":
mismatch_activity = mismatch_activity_file
required_col = ["Mismatch.Type", "Position", "Percent.Active"]
if len(mismatch_activity.columns.intersection(required_col)) != len(required_col):
raise Exception("Please rename the mismatch activity file column to contain at least these 3 column names: Mismatch.Type, Position, Percent.Active\n")
elif scoring_method == "Hsu-Zhang":
if len(weights) != gRNA_size:
raise Exception("Please make sure the size of weights vector equals to the gRNA_size!\n")
if findgRNAsWithREcutOnly == True and findgRNAs == True and os.path.isfile(os.getcwd() + os.sep + os.sep.join(["extdata", "NEBenzymes.fa"])) != True:
raise Exception("Please specify an REpattern file as fasta file with restriction enzyme recognition sequences!")
pairOutputFile = outputDir + os.sep+ "pairedgRNAs.xls"
REcutDetailFile = outputDir + os.sep + "REcutDetails.xls"
bedFile = outputDir + os.sep + "gRNAspycrisprcas9.bed"
if gRNAoutputName == None:
gRNAoutputName = "inputseq"
if format =="bed":
if os.path.isfile(os.getcwd() + os.sep + os.sep.join(["extdata", BSgenomeName + ".2bit"])) != True:
raise Exception("BSgenomeName is required as BSgenome object when input file is in bed format!")
inputFilePath = gSFB.getSeqFromBed(inputFilePath, header = header, BSgenomeName = BSgenomeName)
#### format for filtergRNAs
format = "fasta"
if findgRNAs == True:
print("Searching for gRNAs ...\n")
efficacyFile = outputDir + os.sep + "gRNAefficacy.xls"
if chromToSearch == "" or useEfficacyFromInputSeq == True:
potential_gRNAs = f.findgRNAs(inputFilePath,
overlap_gRNA_positions = overlap_gRNA_positions,
baseEditing = baseEditing, targetBase = targetBase, editingWindow = editingWindow,
primeEditing = primeEditing,
findPairedgRNAOnly = findPairedgRNAOnly,
annotatePaired = annotatePaired,
paired_orientation = paired_orientation,
pairOutputFile = pairOutputFile, PAM = PAM,
PAM_location = PAM_location,
gRNA_pattern = gRNA_pattern, PAM_size = PAM_size,
gRNA_size = gRNA_size, min_gap = min_gap,
max_gap = max_gap, name_prefix = gRNA_name_prefix,
format = format, featureWeightMatrixFile = featureWeightMatrixFile,
baseBeforegRNA = baseBeforegRNA, baseAfterPAM = baseAfterPAM,
calculategRNAEfficacy = True, efficacyFile = efficacyFile, corrected_seq = "T",
targeted_seq_length_change = 0, target_start = 46, target_end = 46,
rule_set = rule_set, chrom_acc = chrom_acc)
else:
potential_gRNAs = f.findgRNAs(inputFilePath,
overlap_gRNA_positions = overlap_gRNA_positions,
baseEditing = baseEditing, targetBase = targetBase, editingWindow = editingWindow,
primeEditing = primeEditing,
PBS_length = PBS_length,
RT_template_length = RT_template_length,
RT_template_pattern = RT_template_pattern,
corrected_seq = corrected_seq,
targeted_seq_length_change = targeted_seq_length_change,
bp_after_target_end = bp_after_target_end,
target_start = target_start,
target_end = target_end,
primeEditingPaired_output = outputDir + primeEditingPaired_output,
findPairedgRNAOnly = findPairedgRNAOnly,
annotatePaired = annotatePaired,
paired_orientation = paired_orientation,
enable_multicore = enable_multicore,
n_cores_max = n_cores_max,
pairOutputFile = pairOutputFile, PAM = PAM,
gRNA_pattern = gRNA_pattern, PAM_size = PAM_size,
PAM_location = PAM_location,
gRNA_size = gRNA_size, min_gap = min_gap,
max_gap = max_gap, name_prefix = gRNA_name_prefix,
efficacyFile = efficacyFile, featureWeightMatrixFile = featureWeightMatrixFile,
format = format, rule_set = rule_set, chrom_acc = chrom_acc)
potential_gRNAs["names"] = potential_gRNAs["names"].str.replace(">", "")
if len(potential_gRNAs) == 0:
return(print("no gRNAs found!"))
if len(potential_gRNAs) > 0 and exportAllgRNAs == "fasta" or exportAllgRNAs == "all":
saveFasta = open(outputDir + "allgRNAs.fa", "w+")
for i in range(len(potential_gRNAs)):
if ">" in potential_gRNAs["names"][i]:
saveFasta.write(potential_gRNAs["names"][i] +"\n")
else:
saveFasta.write(">" + potential_gRNAs["names"][i] +"\n")
saveFasta.write(potential_gRNAs["seq"][i]+"\n")
saveFasta.close()
if len(potential_gRNAs) > 0 and exportAllgRNAs == "genbank" or exportAllgRNAs == "all":
if type(inputFilePath) == pd.core.frame.DataFrame:
subjects = []
subjects.append(inputFilePath.iloc[0][0].upper())
subjects = bio.DNAStringSet(subjects)
else:
subjects = bio.DNAStringSet(inputFilePath.upper())
subjects["names"] = inputFilePath["names"]
locuses = list(subjects["names"])
names_gRNA = list(potential_gRNAs["names"])
for i in range(len(locuses)):
thisLocus = re.sub("'", "", locuses[i])
thisLocus = re.sub(" ", "", thisLocus)
thisLocus = re.sub(">", "", thisLocus)
thisSeq = subjects.iloc[i][1].lower()
n_bp = len(thisSeq)
temp = []
for j in range(len(names_gRNA)):
temp.append(re.sub(">", "", names_gRNA[j]).split(thisLocus + "_gR"))
locus = "LOCUS " + thisLocus + " " + str(n_bp) + " bp dna linear UNK"
definition = "DEFINITION pycrisprcas9 output for " + gRNAoutputName +" sequence"
accession = "ACCESSION unknown"
features = "FEATURES Location/Qualifiers"
header = pd.DataFrame([locus, definition, accession, features], index=(["locus", "definition", "accession", "features"]))
found_gRNA = 0
for j in range(len(temp)):
if len(temp[j]) > 1:
found_gRNA = found_gRNA + 1
if found_gRNA == 1:
thisFile = thisLocus + ".gbk"
savegbk = open(outputDir + thisFile, "w+")
for k in range(len(header)):
savegbk.write(header[0][k]+"\n")
savegbk.close()
temp1 = []
for l in range(len(temp[j])):
if len(gr.gregexpr_index("f", temp[j][l])) > 0:
temp1.append(re.sub("f", "", temp[j][l]))
isForward = True
else:
temp1.append(re.sub("r", "", temp[j][l]))
isForward = False
feature = temp1[0]
location = temp1[1]
if isForward ==True:
Start = location
End = int(Start) + max(overlap_gRNA_positions) - min(overlap_gRNA_positions)
savegbk = open(outputDir + thisFile, "a+")
savegbk.write(" misc_bind " + str(Start) + ".." + str(End) + "\n")
savegbk.write(" /note=\"gRNAf" + str(feature) + "\"" + "\n")
savegbk.close()
else:
End = location
Start = int(End) - max(overlap_gRNA_positions) + min(overlap_gRNA_positions)
savegbk = open(outputDir + thisFile, "a+")
savegbk.write(" misc_bind complement(" + str(Start) + ".." + End + ")" +"\n")
savegbk.write(" /note=\"gRNAr" + feature + "\"" + "\n")
savegbk.close()
if found_gRNA > 0:
savegbk = open(outputDir + thisFile, "a+")
savegbk.write("ORIGIN" + "\n")
savegbk.close()
seq_lines = math.floor(len(thisSeq) / 60) + 1
for k in range(1, seq_lines+1):
line_start = (k - 1) * 60 + 1
line_end = min(line_start + 59, len(thisSeq))
n_leading_spaces = 9 - len(str(line_start))
leading_spaces = " " * n_leading_spaces
seq_thisLine = thisSeq[line_start-1:line_end]
len_thisLine = len(seq_thisLine)
n_seg = math.floor(len_thisLine /10) + 1
for m in range(1, n_seg+1):
seg_start = (m -1) * 10 + 1
seg_end = min(seg_start + 9, len_thisLine)
if m == 1:
seq_thisLine_formatted = seq_thisLine[seg_start-1:seg_end]
else:
seq_thisLine_formatted = seq_thisLine_formatted + " " + seq_thisLine[seg_start-1:seg_end]
savegbk = open(outputDir + thisFile, "a+")
savegbk.write(leading_spaces + str(line_start) + " " + seq_thisLine_formatted +"\n")
savegbk.close()
savegbk = open(outputDir + thisFile, "a+")
savegbk.write("//" + "\n")
savegbk.close()
if findPairedgRNAOnly == True and len(potential_gRNAs) >0:
gRNAs_RE = filtergRNAs.filtergRNAs(potential_gRNAs, pairOutputFile = pairOutputFile,
findgRNAsWithREcutOnly = findgRNAsWithREcutOnly, REpatternFile = REpatternFile,
format = format, minREpatternSize = minREpatternSize,
overlap_gRNA_positions = overlap_gRNA_positions)
REcutDetails = gRNAs_RE[1]
REcutDetails.sort_values('ForwardgRNAName').to_csv(REcutDetailFile, index = False)
elif len(potential_gRNAs) >0:
gRNAs_RE = filtergRNAs.filtergRNAs(potential_gRNAs, findgRNAsWithREcutOnly = findgRNAsWithREcutOnly,
REpatternFile = REpatternFile, format = format,
minREpatternSize = minREpatternSize,
overlap_gRNA_positions = overlap_gRNA_positions)
REcutDetails = gRNAs_RE[1]
REcutDetails.sort_values('REcutgRNAName').to_csv(REcutDetailFile, index = False)
if findgRNAsWithREcutOnly == True:
gRNAs = gRNAs_RE[0]
else:
gRNAs = potential_gRNAs
if annotatePaired == True or findPairedgRNAOnly == True:
pairedInformation = pd.read_csv(pairOutputFile, sep="\t")
else:
if type(inputFilePath) != pd.core.frame.DataFrame:
if os.path.isfile(outputDir + inputFilePath) != True:
raise Exception("inputfile specified as ", inputFilePath, " does not exists!")
if format == "fasta" or format == "fastq":
potential_gRNAs = pd.read_csv(inputFilePath)
else:
raise Exception("format needs to be either fasta,fastq or bed!")
else:
potential_gRNAs = inputFilePath
if len(potential_gRNAs.columns) == 0:
for i in range(len(potential_gRNAs)):
potential_gRNAs = potential_gRNAs.rename(columns={potential_gRNAs.columns[i]:"gRNAs" + str(i+1)})
gRNAs_RE = filtergRNAs.filtergRNAs(potential_gRNAs, REpatternFile = REpatternFile, format = format,
minREpatternSize = minREpatternSize,
overlap_gRNA_positions = overlap_gRNA_positions)
REcutDetails = gRNAs_RE[1]
REcutDetails.sort_values("REcutgRNAName").to_csv(REcutDetailFile, index = False)
if findgRNAsWithREcutOnly == True:
gRNAs = gRNAs_RE[0]
else:
gRNAs = potential_gRNAs
pairedInformation = ""
if len(chromToSearch) == 1 and chromToSearch == [""]:
print("Done. Please check output files in directory ", outputDir, "\n")
return gRNAs
if len(gRNAs) != 0:
for i in range(len(gRNAs)):
gRNAs.loc[i, ("names")] = re.sub("\t", "", gRNAs.loc[i, ("names")])
gRNAs.loc[i, ("names")] = re.sub("\n", "", gRNAs.loc[i, ("names")])
gRNAs.loc[i, ("names")] = re.sub(" ", "", gRNAs.loc[i, ("names")])
hits = searchHits2.searchHits2(gRNAs = gRNAs, PAM = PAM, PAM_pattern = PAM_pattern, outputDir = outputDir,
BSgenomeName = BSgenomeName, chromToSearch = chromToSearch,
chromToExclude = chromToExclude, max_mismatch = max_mismatch,
PAM_size = PAM_size, gRNA_size = gRNA_size, allowed_mismatch_PAM = allowed_mismatch_PAM,
PAM_location = PAM_location, baseEditing = baseEditing, targetBase = targetBase,
editingWindow = editingWindow_offtargets)
if np.shape(hits)[0] > 0:
print("Building feature vectors for scoring ...\n")
featureVectors = bFVFS.buildFeatureVectorForScoring(hits = hits, canonical_PAM = PAM, gRNA_size = gRNA_size,
subPAM_position = subPAM_position, PAM_location = PAM_location, PAM_size = PAM_size)
print("Calculating scores ...\n")
if scoring_method == "CFDscore":
scores = getOfftargetScore2.getOfftargetScore2(featureVectors, subPAM_activity = subPAM_activity, mismatch_activity_file = mismatch_activity_file)
else:
scores = getOfftargetScore.getOfftargetScore(featureVectors, weights = weights)
print("Annotating, filtering and generating reports ...\n")
offTargets = filterOffTarget.filterOffTarget(scores = scores, outputDir = outputDir,
BSgenomeName = BSgenomeName, fetchSequence = fetchSequence, txdb = txdb,
orgAnn = orgAnn, ignore_strand = ignore_strand, min_score = min_score, topN = topN,
topN_OfftargetTotalScore = topN_OfftargetTotalScore, upstream = upstream,
downstream = downstream, annotateExon = annotateExon, baseBeforegRNA = baseBeforegRNA,
baseAfterPAM = baseAfterPAM, gRNA_size = gRNA_size, PAM_location = PAM_location,
PAM_size = PAM_size, featureWeightMatrixFile = featureWeightMatrixFile, rule_set = rule_set,
chrom_acc = chrom_acc, calculategRNAefficacyForOfftargets = calculategRNAefficacyForOfftargets)
print("Done annotating\n")
summary = pd.read_csv(outputDir + "Summary.xls", keep_default_na=False)
if np.shape(summary)[1] == 1:
summary1 = []
for i in range(np.shape(offTargets[1])[1]):
summary0= []
for j in range(np.shape(offTargets[1])[0]):
summary0.append(offTargets[1].iloc[j][i])
summary1.append(summary0)
summary = pd.DataFrame(summary1, index=offTargets[1].columns)
for i in range(len(summary.columns)):
if "topOfftarget" in summary.columns[i]:
y = summary.loc[:, summary.columns[i]]
for j in range(len(y)):
if y[j] == "NA":
y[j] = ""
summary.loc[j, summary.columns[i]] = y[j]
if findgRNAs == True and (annotatePaired == True or findPairedgRNAOnly == True):
print("Add paired information...\n")
PairedgRNAName = []
for i in range(np.shape(summary)[0]):
a = ""
for j in range(len(pairedInformation[pairedInformation["ForwardgRNAName"] == summary.loc[i, ("names")]]["ReversegRNAName"].drop_duplicates().values)):
a = a + pairedInformation[pairedInformation["ForwardgRNAName"] == summary.loc[i, ("names")]]["ReversegRNAName"].drop_duplicates().values[j]
if len(pairedInformation[pairedInformation["ForwardgRNAName"] == summary.loc[i, ("names")]]["ReversegRNAName"].drop_duplicates().values) > 1:
a = a + " "
for k in range(len(pairedInformation[pairedInformation["ReversegRNAName"] == summary.loc[i, ("names")]]["ForwardgRNAName"].drop_duplicates().values)):
a = a + pairedInformation[pairedInformation["ReversegRNAName"] == summary.loc[i, ("names")]]["ForwardgRNAName"].drop_duplicates().values[k]
if len(pairedInformation[pairedInformation["ReversegRNAName"] == summary.loc[i, ("names")]]["ForwardgRNAName"].drop_duplicates().values) > 1 :
a = a + " "
PairedgRNAName.append(a)
print("Add RE information...\n")
if findPairedgRNAOnly == True and findgRNAs == True:
REname = []
for i in range(np.shape(summary)[0]):
a = ""
for j in range(len(REcutDetails[REcutDetails["ForwardREcutgRNAName"] == summary.loc[i, ("names")]]["ForwardREname"].drop_duplicates().values)):
a = a + REcutDetails[REcutDetails["ForwardREcutgRNAName"] == summary.loc[i, ("names")]]["ForwardREname"].drop_duplicates().values[j]
if len(REcutDetails[REcutDetails["ForwardREcutgRNAName"] == summary.loc[i, ("names")]]["ForwardREname"].drop_duplicates().values) > 1 and len(REcutDetails[REcutDetails["ForwardREcutgRNAName"] == summary.loc[i, ("names")]]["ForwardREname"].drop_duplicates().values) != j+1:
a = a + " "
for k in range(len(REcutDetails[REcutDetails["ReverseREcutgRNAName"] == summary.loc[i, ("names")]]["ReverseREname"].drop_duplicates().values)):
a = a + REcutDetails[REcutDetails["ReverseREcutgRNAName"] == summary.loc[i, ("names")]]["ReverseREname"].drop_duplicates().values[k]
if len(REcutDetails[REcutDetails["ReverseREcutgRNAName"] == summary.loc[i, ("names")]]["ReverseREname"].drop_duplicates().values) > 1 and len(REcutDetails[REcutDetails["ReverseREcutgRNAName"] == summary.loc[i, ("names")]]["ReverseREname"].drop_duplicates().values) != k+1:
a = a + " "
REname.append(a)
summary = pd.concat([summary, pd.DataFrame(PairedgRNAName, columns=(["PairedgRNAName"])), pd.DataFrame(REname, columns=(["REname"]))], 1)
else:
REname = []
for i in range(np.shape(summary)[0]):
a = ""
for j in range(len(REcutDetails[REcutDetails["REcutgRNAName"] == summary.loc[i, ("names")]]["REname"].values)):
a = a + re.sub("NA", "", REcutDetails[REcutDetails["REcutgRNAName"] == summary.loc[i, ("names")]]["REname"].values[j])
if len(REcutDetails[REcutDetails["REcutgRNAName"] == summary.loc[i, ("names")]]["REname"].values) > 1 and len(REcutDetails[REcutDetails["REcutgRNAName"] == summary.loc[i, ("names")]]["REname"].values) != j+1:
a = a + " "
REname.append(a)
summary = pd.concat([summary, pd.DataFrame(REname, columns=(["REname"]))], 1)
seq = list(summary["gRNAsPlusPAM"])
print("write gRNAs to bed file...\n")
on_target = offTargets[0]
#for i in range(len(on_target)):
on_target = on_target[on_target.loc[:, ("n.mismatch")] == 0].drop_duplicates()
on_target = on_target[on_target.loc[:, ("isCanonicalPAM")] == 1].drop_duplicates().sort_values("name", ascending=False).reset_index(drop=True)
if np.shape(on_target)[0] > 0:
gRNA_bed = pd.concat([on_target["chrom"], on_target["chromStart"],
on_target["chromEnd"], on_target["name"],
on_target["gRNAefficacy"] * 1000,
on_target["strand"],
on_target["chromStart"],
on_target["chromEnd"]], 1).drop_duplicates().sort_values("name", ascending=False).reset_index(drop=True)
if useScore != True:
gRNA_bed = pd.concat([gRNA_bed, pd.DataFrame(["255,0,0"]*np.shape(gRNA_bed)[0])], 1)
for i in range(np.shape(gRNA_bed)[0]):
if gRNA_bed.loc[i, ("strand")] == "-":
gRNA_bed.iloc[i, 8] = "0,255,0"
for i in range(np.shape(gRNA_bed)[0]):
gRNA_bed.iloc[i, 1] = gRNA_bed.iloc[i, 1].astype(int) - 1
gRNA_bed.iloc[i, 2] = gRNA_bed.iloc[i, 2].astype(int)
if gRNA_bed.iloc[i, 5] == "+":
gRNA_bed.iloc[i, 6] = gRNA_bed.iloc[i, 1] + min(overlap_gRNA_positions) - 1
gRNA_bed.iloc[i, 7] = gRNA_bed.iloc[i, 1] + max(overlap_gRNA_positions)
else:
gRNA_bed.iloc[i, 6] = gRNA_bed.iloc[i, 2] - max(overlap_gRNA_positions)
gRNA_bed.iloc[i, 7] = gRNA_bed.iloc[i, 2] - min(overlap_gRNA_positions) + 1
savebed = open(bedFile, "w+")
savebed.write("track name=\"gRNA sites\" description=\"pycrisprcas9\" visibility=2 useScore=1 itemRgb=\"On\""+"\n")
savebed.close()
gRNA_bed.to_csv(bedFile, mode = "a+", sep = "\t", header = None, index = False)
on_target = pd.concat([on_target["name"], on_target["forViewInUCSC"], on_target["extendedSequence"], on_target["gRNAefficacy"]], 1).drop_duplicates().rename(columns={"name": "names"})
if useEfficacyFromInputSeq == True:
on_target = on_target.iloc[:,:2]
inputEfficacy = pd.read_csv(efficacyFile, sep="\t")
inputEfficacy = inputEfficacy[["name","extendedSequence","gRNAefficacy"]]
on_target = on_target.merge(inputEfficacy.rename(columns={"name":"names"}), how="inner", on="names").sort_values("names", ascending=False).reset_index(drop=True)
if np.shape(on_target)[0] > 0:
summary = on_target.merge(summary, how="inner", on="names").drop_duplicates().sort_values("names").reset_index(drop=True)
summary.to_csv(outputDir + "Summary.xls", index = False)
print("Scan for REsites in flanking region...\n")
if outputUniqueREs == True:
REs_isUnique100 = uniqueREs.uniqueREs(REcutDetails = REcutDetails, summary = summary, offTargets=offTargets[0], BSgenomeName = BSgenomeName, scanUpstream = 100, scanDownstream = 100)
REs_isUnique50 = uniqueREs.uniqueREs(REcutDetails = REcutDetails, summary = summary, offTargets=offTargets[0], BSgenomeName = BSgenomeName, scanUpstream = 50, scanDownstream = 50)
summary = pd.concat([summary, pd.DataFrame(REs_isUnique100, columns=(["uniqREin200"])), pd.DataFrame(REs_isUnique50, columns=(["uniqREin100"]))], 1)
else:
REs_isUnique100 = ""
REs_isUnique50 = ""
else:
print("No on-target found for the input gRNAs with your search criteria!")
gRNA_bed = ""
REs_isUnique100 = ""
REs_isUnique50 = ""
if "id" in summary.columns:
summary.drop("id", axis = 1, inplace = True)
if "id" in offTargets[0].columns:
offTargets[0].drop("id", axis = 1, inplace = True)
gRNAs_notInGenome = set(gRNAs["names"]).difference(set(summary["names"]))
if len(gRNAs_notInGenome) > 0:
dat2 = pd.DataFrame(np.array([ ["NA"] * np.shape(summary)[1]] * len(gRNAs_notInGenome)))
for i in range(np.shape(summary)[1]):
dat2 = dat2.rename(columns={i:summary.columns.values[i]})
for i in range(len(dat2["names"])):
dat2.loc[i, ("names")] = list(gRNAs_notInGenome)[i]
for j in range(len(gRNAs["names"])):
if gRNAs.loc[j, ("names")] == dat2.loc[i, ("names")]:
dat2.loc[i, ("gRNAsPlusPAM")] = gRNAs.loc[j, ("seq")][0:gRNA_size] + PAM
summary = pd.concat([summary, dat2], 0).reset_index(drop = True)
if np.shape(on_target)[0] == 0:
summary.sort_values("names").reset_index(drop=True).to_csv(outputDir + "Summary.xls", index = False)
else:
summary.sort_values("forViewInUCSC").reset_index(drop=True).to_csv(outputDir + "Summary.xls", index = False)
print("Done. Please check output files in directory \n", outputDir, "\n")
return {"on_target": on_target, "summary": summary, "offtarget": offTargets[0], "gRNAs.bedFormat": gRNA_bed, "REcutDetails": REcutDetails, "REs_isUnique100": REs_isUnique100, "REs_isUnique50": REs_isUnique50}
else:
x = []
if PAM_location == "3prime":
for i in range(np.shape(gRNAs)[0]):
x.append(gRNAs.loc[i, ("seq")][0:gRNA_size] + PAM)
else:
for i in range(np.shape(gRNAs)[0]):
x.append(PAM + gRNAs.loc[i, ("seq")][0:gRNA_size])
summary = pd.concat([gRNAs["names"], pd.DataFrame(x, columns=(["gRNAsPlusPAM"])), pd.DataFrame(["NA"]*len(gRNAs), columns=(["top5OfftargetTotalScore"])),
pd.DataFrame(["NA"] * len(gRNAs), columns=(["top10OfftargetTotalScore"])),
pd.DataFrame(["NA"] * len(gRNAs), columns=(["top1Hit.onTarget.MMdistance2PAM "]))], 1)
summary.to_csv(outputDir + "Summary.xls", index = False)
return summary