Source code for deeptools.writeBedGraph

import os
import sys
import shutil
import numpy as np
import pyBigWig

# own modules
from deeptools import mapReduce
from deeptools.utilities import getCommonChrNames
import deeptools.countReadsPerBin as cr
from deeptools import bamHandler
from deeptools import utilities

debug = 0
old_settings = np.seterr(all='ignore')

[docs] def writeBedGraph_wrapper(args): """ Passes the arguments to writeBedGraph_worker. This is a step required given the constrains from the multiprocessing module. The args var, contains as first element the 'self' value from the WriteBedGraph object """ return WriteBedGraph.writeBedGraph_worker(*args)
[docs] class WriteBedGraph(cr.CountReadsPerBin): r"""Reads bam files coverages and writes a bedgraph or bigwig file Extends the CountReadsPerBin object such that the coverage of bam files is writen to multiple bedgraph files at once. The bedgraph files are later merge into one and converted into a bigwig file if necessary. The constructor arguments are the same as for CountReadsPerBin. However, when calling the `run` method, the following parameters have to be passed Examples -------- Given the following distribution of reads that cover 200 on a chromosome named '3R':: 0 100 200 |------------------------------------------------------------| A =============== =============== B =============== =============== =============== =============== >>> import tempfile >>> test_path = os.path.dirname(os.path.abspath(__file__)) + "/test/test_data/" >>> outFile = tempfile.NamedTemporaryFile() >>> bam_file = test_path + "testA.bam" For the example a simple scaling function is going to be used. This function takes the coverage found at each region and multiplies it to the scaling factor. In this case the scaling factor is 1.5 >>> function_to_call = scaleCoverage >>> funcArgs = {'scaleFactor': 1.5} Restrict process to a region between positions 0 and 200 of chromosome 3R >>> region = '3R:0:200' Set up such that coverage is computed for consecutive bins of length 25 bp >>> bin_length = 25 >>> step_size = 25 >>> num_sample_sites = 0 #overruled by step_size >>> c = WriteBedGraph([bam_file], binLength=bin_length, region=region, stepSize=step_size) >>>, funcArgs, >>> f = open(, 'r') >>> f.readlines() ['3R\t0\t100\t0\n', '3R\t100\t200\t1.5\n'] >>> f.close() >>> outFile.close() """
[docs] def run(self, func_to_call, func_args, out_file_name, blackListFileName=None, format="bedgraph", smoothLength=0): r""" Given a list of bamfiles, a function and a function arguments, this method writes a bedgraph file (or bigwig) file for a partition of the genome into tiles of given size and a value for each tile that corresponds to the given function and that is related to the coverage underlying the tile. Parameters ---------- func_to_call : str function name to be called to convert the list of coverages computed for each bam file at each position into a single value. An example is a function that takes the ratio between the coverage of two bam files. func_args : dict dict of arguments to pass to `func`. E.g. {'scaleFactor':1.0} out_file_name : str name of the file to save the resulting data. smoothLength : int Distance in bp for smoothing the coverage per tile. """ self.__dict__["smoothLength"] = smoothLength getStats = len(self.mappedList) < len(self.bamFilesList) bam_handles = [] for x in self.bamFilesList: if getStats: bam, mapped, unmapped, stats = bamHandler.openBam(x, returnStats=True, nThreads=self.numberOfProcessors) self.mappedList.append(mapped) self.statsList.append(stats) else: bam = bamHandler.openBam(x) bam_handles.append(bam) genome_chunk_length = getGenomeChunkLength(bam_handles, self.binLength, self.mappedList) # check if both bam files correspond to the same species # by comparing the chromosome names: chrom_names_and_size, non_common = getCommonChrNames(bam_handles, verbose=False) if self.region: # in case a region is used, append the tilesize self.region += ":{}".format(self.binLength) for x in list(self.__dict__.keys()): if x in ["mappedList", "statsList"]: continue sys.stderr.write("{}: {}\n".format(x, self.__getattribute__(x))) res = mapReduce.mapReduce([func_to_call, func_args], writeBedGraph_wrapper, chrom_names_and_size, self_=self, genomeChunkLength=genome_chunk_length, region=self.region, blackListFileName=blackListFileName, numberOfProcessors=self.numberOfProcessors) # Determine the sorted order of the temp files chrom_order = dict() for i, _ in enumerate(chrom_names_and_size): chrom_order[_[0]] = i res = [[chrom_order[x[0]], x[1], x[2], x[3]] for x in res] res.sort() if format == 'bedgraph': out_file = open(out_file_name, 'wb') for r in res: if r[3]: _foo = open(r[3], 'rb') shutil.copyfileobj(_foo, out_file) _foo.close() os.remove(r[3]) out_file.close() else: bedGraphToBigWig(chrom_names_and_size, [x[3] for x in res], out_file_name)
[docs] def writeBedGraph_worker(self, chrom, start, end, func_to_call, func_args, bed_regions_list=None): r"""Writes a bedgraph based on the read coverage found on bamFiles The given func is called to compute the desired bedgraph value using the funcArgs Parameters ---------- chrom : str Chrom name start : int start coordinate end : int end coordinate func_to_call : str function name to be called to convert the list of coverages computed for each bam file at each position into a single value. An example is a function that takes the ratio between the coverage of two bam files. func_args : dict dict of arguments to pass to `func`. smoothLength : int Distance in bp for smoothing the coverage per tile. bed_regions_list: list List of tuples of the form (chrom, start, end) corresponding to bed regions to be processed. If not bed file was passed to the object constructor then this list is empty. Returns ------- A list of [chromosome, start, end, temporary file], where the temporary file contains the bedgraph results for the region queried. Examples -------- >>> test_path = os.path.dirname(os.path.abspath(__file__)) + "/test/test_data/" >>> bamFile1 = test_path + "testA.bam" >>> bin_length = 50 >>> number_of_samples = 0 # overruled by step_size >>> func_to_call = scaleCoverage >>> funcArgs = {'scaleFactor': 1.0} >>> c = WriteBedGraph([bamFile1], bin_length, number_of_samples, stepSize=50) >>> tempFile = c.writeBedGraph_worker( '3R', 0, 200, func_to_call, funcArgs) >>> f = open(tempFile[3], 'r') >>> f.readlines() ['3R\t0\t100\t0\n', '3R\t100\t200\t1\n'] >>> f.close() >>> os.remove(tempFile[3]) """ if start > end: raise NameError("start position ({0}) bigger " "than end position ({1})".format(start, end)) coverage, _ = self.count_reads_in_region(chrom, start, end) _file = open(utilities.getTempFileName(suffix='.bg'), 'w') previous_value = None line_string = "{}\t{}\t{}\t{:g}\n" for tileIndex in range(coverage.shape[0]): if self.smoothLength is not None and self.smoothLength > 0: vector_start, vector_end = self.getSmoothRange(tileIndex, self.binLength, self.smoothLength, coverage.shape[0]) tileCoverage = np.mean(coverage[vector_start:vector_end, :], axis=0) else: tileCoverage = coverage[tileIndex, :] if self.skipZeroOverZero and np.sum(tileCoverage) == 0: continue value = func_to_call(tileCoverage, func_args) """ # uncomment these lines if fixed step bedgraph is required if not np.isnan(value): writeStart = start + tileIndex * self.binLength writeEnd = min(writeStart + self.binLength, end) _file.write(line_string.format(chrom, writeStart, writeEnd, value)) continue """ if previous_value is None: writeStart = start + tileIndex * self.binLength writeEnd = min(writeStart + self.binLength, end) previous_value = value elif previous_value == value: writeEnd = min(writeEnd + self.binLength, end) elif previous_value != value: if not np.isnan(previous_value): _file.write( line_string.format(chrom, writeStart, writeEnd, previous_value)) previous_value = value writeStart = writeEnd writeEnd = min(writeStart + self.binLength, end) # write remaining value if not a nan if previous_value is not None and writeStart != end and not np.isnan(previous_value): _file.write(line_string.format(chrom, writeStart, end, previous_value)) tempfilename = _file.close() return chrom, start, end, tempfilename
[docs] def bedGraphToBigWig(chromSizes, bedGraphFiles, bigWigPath): """ Takes a sorted list of bedgraph files and write them to a single bigWig file using pyBigWig. The order of bedGraphFiles must match that of chromSizes! """ bw =, "w") assert bw is not None bw.addHeader(chromSizes, maxZooms=10) lastChrom = None starts = [] ends = [] vals = [] for bg in bedGraphFiles: if bg is not None: f = open(bg) for line in f: interval = line.split() # Buffer up to a million entries if interval[0] != lastChrom or len(starts) == 1000000: if lastChrom is not None: bw.addEntries([lastChrom] * len(starts), starts, ends=ends, values=vals) lastChrom = interval[0] starts = [int(interval[1])] ends = [int(interval[2])] vals = [float(interval[3])] else: starts.append(int(interval[1])) ends.append(int(interval[2])) vals.append(float(interval[3])) f.close() os.remove(bg) if len(starts) > 0: bw.addEntries([lastChrom] * len(starts), starts, ends=ends, values=vals) bw.close()
[docs] def getGenomeChunkLength(bamHandles, tile_size, mappedList): """ Tries to estimate the length of the genome sent to the workers based on the density of reads per bam file and the number of bam files. The chunk length should be a multiple of the tileSize """ genomeLength = sum(bamHandles[0].lengths) max_reads_per_bp = max([float(x) / genomeLength for x in mappedList]) # 2e6 is an empirical estimate genomeChunkLength = int(min(5e6, int(2e6 / (max_reads_per_bp * len(bamHandles))))) genomeChunkLength -= genomeChunkLength % tile_size return genomeChunkLength
[docs] def scaleCoverage(tile_coverage, args): """ tileCoverage should be an list with only one element """ return args['scaleFactor'] * tile_coverage[0]
[docs] def ratio(tile_coverage, args): """ tileCoverage should be an list of two elements """ return float(tile_coverage[0]) / tile_coverage[1]