Mercurial > repos > mvdbeek > dapars
changeset 0:bb84ee2f2137 draft
planemo upload for repository https://github.com/mvdbeek/dapars commit 868f8f2f7ac5d70c39b7d725ff087833b0f24f52-dirty
| author | mvdbeek |
|---|---|
| date | Tue, 27 Oct 2015 10:14:33 -0400 |
| parents | |
| children | 1b20ba32b4c5 |
| files | LICENSE README.md RELEASE_HISTORY dapars.py dapars.xml filter_utr.py tool_dependencies.xml |
| diffstat | 7 files changed, 853 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/LICENSE Tue Oct 27 10:14:33 2015 -0400 @@ -0,0 +1,340 @@ + GNU GENERAL PUBLIC LICENSE + Version 2, June 1991 + + Copyright (C) 1989, 1991 Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The licenses for most software are designed to take away your +freedom to share and change it. By contrast, the GNU General Public +License is intended to guarantee your freedom to share and change free +software--to make sure the software is free for all its users. This +General Public License applies to most of the Free Software +Foundation's software and to any other program whose authors commit to +using it. (Some other Free Software Foundation software is covered by +the GNU Lesser General Public License instead.) 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.md Tue Oct 27 10:14:33 2015 -0400 @@ -0,0 +1,28 @@ +DaPars(Dynamic analysis of Alternative PolyAdenylation from RNA-seq) +====== + +**Current version**: 0.9.1 + +[**Full Documentations**](http://lilab.research.bcm.edu/dldcc-web/lilab/zheng/DaPars_Documentation/html/DaPars.html) +Description +----- +The dynamic usage of the 3’untranslated region (3’UTR) resulting from alternative polyadenylation (APA) is emerging as a pervasive mechanism for regulating mRNA diversity, stability and translation. Though RNA-seq provides the whole-transcriptome information and a lot of tools for analyzing gene/isoform expression are available, very few tool focus on the analysis of 3'UTR from standard RNA-seq. DaPars is the first de novo tool that directly infers the dynamic alternative polyadenylation (APA) usage by comparing standard RNA-seq. Given the annotated gene model, DaPars can infer the de novo proximal APA sites as well as the long and short 3'UTR expression levels. Finally, the dynamic APA usages between two conditions will be identified. + + + + + + + + +Citation +----- +*Please cite the following articles if you use DaPars in your research*: +* Masamha, C.P., Xia, Z., Yang, J., Albrecht, T.R., Li, M., Shyu, A., Li, W., Wagner, E.J. 2014. CFIm25 links Alternative Polyadenylation to Glioblastoma Tumor Suppression. Nature, 510:412-416. + +* Xia, Z., Donehower, L.A., Wheeler, D.A., Cooper, T.A., Neilson, J.R., Wagner E.J., Li, W. 2014. Dynamic Analyses of Alternative Polyadenylation from RNA-Seq Reveal 3'-UTR Landscape Across 7 Tumor Types. Nature Communications, 5:5274. + +Mailing list +----------- +If you have questions, requests, or bugs to report, please email the [DaPars mailing list](https://groups.google.com/forum/#!forum/DaPars) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/RELEASE_HISTORY Tue Oct 27 10:14:33 2015 -0400 @@ -0,0 +1,9 @@ +DaPars v0.9.1 (12-Feb-2015) + +Fixed some minor bugs. +Improved documentation. + + + +DaPars v0.9.0 (2013) +DaPars is released.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/dapars.py Tue Oct 27 10:14:33 2015 -0400 @@ -0,0 +1,322 @@ +import argparse +import os +import csv +import numpy as np +from collections import OrderedDict, namedtuple +import filter_utr +import subprocess +from multiprocessing import Pool +import warnings + + +def parse_args(): + """ + Returns floating point values except for input files. + My initial approach will not filter anything. (FDR. fold_change, PDUI, Num_least ...) + :param argv: + :return: + """ + parser = argparse.ArgumentParser(prog='DaPars', description='Determines the usage of proximal polyA usage') + parser.add_argument("-c", "--control_alignments", nargs="+", required=True, + help="Alignment files in BAM format from control condition") + parser.add_argument("-t", "--treatment_alignments", nargs="+", required=True, + help="Alignment files in BAM format from treatment condition") + parser.add_argument("-u", "--utr_bed_file", required=True, type=file, + help="Bed file describing longest 3UTR positions") + parser.add_argument("-o", "--output_file", required=True, type=argparse.FileType('w'), + help="file containing output") + parser.add_argument("-cpu", required=False, type=int, default=1, + help="Number of CPU cores to use.") + parser.add_argument("-s", "--search_start", required=False, type=int, default=50, + help="Start search for breakpoint n nucleotides downstream of UTR start") + parser.add_argument("-ct", "--coverage_threshold", required=False, type=float, default=20, + help="minimum coverage in each aligment to be considered for determining breakpoints") + parser.add_argument("-b", "--breakpoint_bed", required=False, type=argparse.FileType('w'), + help="Write bedfile with coordinates of breakpoint positions to supplied path.") + parser.add_argument("-v", "--version", action='version', version='%(prog)s 0.1.4') + return parser.parse_args() + + +class UtrFinder(): + """ + This seems to be the main caller. + """ + + def __init__(self, args): + self.control_alignments = [file for file in args.control_alignments] + self.treatment_alignments = [file for file in args.treatment_alignments] + self.n_cpus = args.cpu + self.search_start = args.search_start + self.coverage_threshold = args.coverage_threshold + self.utr = args.utr_bed_file + self.gtf_fields = filter_utr.get_gtf_fields() + self.result_file = args.output_file + self.all_alignments = self.control_alignments + self.treatment_alignments + self.alignment_names = { file: os.path.basename(file) for file in self.all_alignments } + self.num_samples = len(self.all_alignments) + self.utr_dict = self.get_utr_dict(0.2) + self.dump_utr_dict_to_bedfile() + print "Established dictionary of 3\'UTRs" + self.coverage_files = self.run_bedtools_coverage() + self.utr_coverages = self.read_coverage_result() + print "Established dictionary of 3\'UTR coverages" + self.coverage_weights = self.get_coverage_weights() + self.result_tuple = self.get_result_tuple() + self.result_d = self.calculate_apa_ratios() + self.write_results() + if args.breakpoint_bed: + self.bed_output = args.breakpoint_bed + self.write_bed() + + + def dump_utr_dict_to_bedfile(self): + w = csv.writer(open("tmp_bedfile.bed", "w"), delimiter="\t") + for gene, utr in self.utr_dict.iteritems(): + w.writerow([utr["chr"], utr["new_start"]-1, utr["new_end"], gene, ".", utr["strand"]]) + + def run_bedtools_coverage(self): + """ + Use bedtools coverage to generate pileup data for all alignment files for the regions specified in utr_dict. + """ + coverage_files = [] + cmds = [] + for alignment_file in self.all_alignments: + cmd = "sort -k1,1 -k2,2n tmp_bedfile.bed | " + cmd = cmd + "bedtools coverage -d -s -abam {alignment_file} -b stdin |" \ + " cut -f 4,7,8 > coverage_file_{alignment_name}".format( + alignment_file = alignment_file, alignment_name= self.alignment_names[alignment_file] ) + cmds.append(cmd) + pool = Pool(self.n_cpus) + subprocesses = [subprocess.Popen([cmd], shell=True) for cmd in cmds] + [p.wait() for p in subprocesses] + coverage_files = ["gene_position_coverage_{alignment_name}".format( + alignment_name = self.alignment_names[alignment_file]) for alignment_file in self.all_alignments ] + return coverage_files + + def read_coverage_result(self): + """ + Read coverages back in and store as dictionary of numpy arrays + """ + coverage_dict = { gene: { name: np.zeros(utr_d["new_end"]+1-utr_d["new_start"]) for name in self.alignment_names.itervalues() } for gene, utr_d in self.utr_dict.iteritems() } + for alignment_name in self.alignment_names.itervalues(): + with open("coverage_file_{alignment_name}".format(alignment_name = alignment_name)) as coverage_file: + for line in coverage_file: + gene, position, coverage= line.strip().split("\t") + coverage_dict[gene][alignment_name][int(position)-1] = coverage + for utr_d in self.utr_dict.itervalues(): + if utr_d["strand"] == "-": + for alignment_name in self.alignment_names.values(): + coverage_dict[gene][alignment_name] = coverage_dict[gene][alignment_name][::-1] + return coverage_dict + + def get_utr_dict(self, shift): + utr_dict = OrderedDict() + for line in self.utr: + if not line.startswith("#"): + filter_utr.get_feature_dict( line=line, gtf_fields=self.gtf_fields, utr_dict=utr_dict, feature="UTR" ) + gene, utr_d = utr_dict.popitem() + utr_d = utr_d[0] + end_shift = int(round(abs(utr_d["start"] - utr_d["end"]) * shift)) + if utr_d["strand"] == "+": + utr_d["new_end"] = utr_d["end"] - end_shift + utr_d["new_start"] = utr_d["start"] + else: + utr_d["new_end"] = utr_d["end"] + utr_d["new_start"] = utr_d["start"] + end_shift + if utr_d["new_start"] + 50 < utr_d["new_end"]: + utr_dict[gene] = utr_d + return utr_dict + + def get_utr_coverage(self): + """ + Returns a dict: + { UTR : [coverage_aligment1, ...]} + """ + utr_coverages = {} + for utr, utr_d in self.utr_dict.iteritems(): + if utr_d["chr"] in self.available_chromosomes: + if utr_d["strand"] == "+": + is_reverse = False + else: + is_reverse = True + utr_coverage = [] + for bam in self.all_alignments: + bp_coverage = get_bp_coverage(bam, utr_d["chr"], utr_d["new_start"], utr_d["new_end"], is_reverse) + utr_coverage.append(bp_coverage) + utr_coverages[utr] = utr_coverage + return utr_coverages + + def get_coverage_weights(self): + """ + Return weights for normalizing coverage. + utr_coverage is still confusing. + """ + coverage_per_alignment = [] + for utr in self.utr_coverages.itervalues(): # TODO: be smarter about this. + utr_coverage = [] + for vector in utr.itervalues(): + utr_coverage.append(np.sum(vector)) + coverage_per_alignment.append(utr_coverage) + coverages = np.array([ sum(x) for x in zip(*coverage_per_alignment) ]) + coverage_weights = coverages / np.mean(coverages) # TODO: proabably median is better suited? + return coverage_weights + + def get_result_tuple(self): + static_desc = ["chr", "start", "end", "strand", "gene", "breakpoint", "control_mean_percent", "treatment_mean_percent" ] + samples_desc = [] + for statistic in ["coverage_long", "coverage_short", "percent_long"]: + for i, sample in enumerate(self.control_alignments): + samples_desc.append("control_{i}_{statistic}".format(i=i, statistic = statistic)) + for i, sample in enumerate(self.treatment_alignments): + samples_desc.append("treatment_{i}_{statistic}".format(i=i, statistic = statistic)) + return namedtuple("result", static_desc + samples_desc) + + def calculate_apa_ratios(self): + result_d = OrderedDict() + arg_d = {"result_tuple": self.result_tuple, + "coverage_weights":self.coverage_weights, + "num_samples":self.num_samples, + "num_control":len(self.control_alignments), + "num_treatment":len(self.treatment_alignments), + "result_d":result_d} + pool = Pool(self.n_cpus) + tasks = [ (self.utr_coverages[utr], utr, utr_d, self.result_tuple._fields, self.coverage_weights, self.num_samples, + len(self.control_alignments), len(self.treatment_alignments), result_d, self.search_start, self.coverage_threshold) for utr, utr_d in self.utr_dict.iteritems() ] + processed_tasks = [ pool.apply_async(calculate_all_utr, t) for t in tasks] + result = [res.get() for res in processed_tasks] + for d in result: + if isinstance(d, dict): + t = self.result_tuple(**d) + result_d[d["gene"]] = t + return result_d + + def write_results(self): + w = csv.writer(self.result_file, delimiter='\t') + header = list(self.result_tuple._fields) + header[0] = "#chr" + w.writerow(header) # field header + w.writerows( self.result_d.values()) + + def write_bed(self): + w = csv.writer(self.bed_output, delimiter='\t') + bed = [(result.chr, result.breakpoint, result.breakpoint+1, result.gene, 0, result.strand) for result in self.result_d.itervalues()] + w.writerows(bed) + +def calculate_all_utr(utr_coverage, utr, utr_d, result_tuple_fields, coverage_weights, num_samples, num_control, + num_treatment, result_d, search_start, coverage_threshold): + res = dict(zip(result_tuple_fields, result_tuple_fields)) + if utr_d["strand"] == "+": + is_reverse = False + else: + is_reverse = True + mse, breakpoint, abundances = estimate_coverage_extended_utr(utr_coverage, + utr_d["new_start"], + utr_d["new_end"], + is_reverse, + coverage_weights, + search_start, + coverage_threshold) + if not str(mse) == "Na": + long_coverage_vector = abundances[0] + short_coverage_vector = abundances[1] + num_non_zero = sum((np.array(long_coverage_vector) + np.array(short_coverage_vector)) > 0) # TODO: This introduces bias + if num_non_zero == num_samples: + percentage_long = [] + for i in range(num_samples): + ratio = float(long_coverage_vector[i]) / (long_coverage_vector[i] + short_coverage_vector[i]) # long 3'UTR percentage + percentage_long.append(ratio) + for i in range(num_control): + res["control_{i}_coverage_long".format(i=i)] = float(long_coverage_vector[i]) + res["control_{i}_coverage_short".format(i=i)] = float(short_coverage_vector[i]) + res["control_{i}_percent_long".format(i=i)] = percentage_long[i] + for k in range(num_treatment): + i = k + num_control + res["treatment_{i}_coverage_long".format(i=k)] = float(long_coverage_vector[i]) + res["treatment_{i}_coverage_short".format(i=k)] = float(short_coverage_vector[i]) + res["treatment_{i}_percent_long".format(i=k)] = percentage_long[i] + control_mean_percent = np.mean(np.array(percentage_long[:num_control])) + treatment_mean_percent = np.mean(np.array(percentage_long[num_control:])) + res["chr"] = utr_d["chr"] + res["start"] = utr_d["start"] + res["end"] = utr_d["end"] + res["strand"] = utr_d["strand"] + if is_reverse: + breakpoint = utr_d["new_end"] - breakpoint + else: + breakpoint = utr_d["new_start"] + breakpoint + res["breakpoint"] = breakpoint + res["control_mean_percent"] = control_mean_percent + res["treatment_mean_percent"] = treatment_mean_percent + res["gene"] = utr + return res + + +def estimate_coverage_extended_utr(utr_coverage, UTR_start, + UTR_end, is_reverse, coverage_weigths, search_start, coverage_threshold): + """ + We are searching for a breakpoint in coverage?! + utr_coverage is a list with items corresponding to numpy arrays of coverage for a sample. + """ + search_point_end = int(abs((UTR_end - UTR_start)) * 0.1) # TODO: This is 10% of total UTR end. Why? + num_samples = len(utr_coverage) + ##read coverage filtering + normalized_utr_coverage = [coverage/ coverage_weigths[i] for i, coverage in enumerate( utr_coverage.values() )] + start_coverage = [np.mean(coverage[0:99]) for coverage in utr_coverage.values()] # filters threshold on mean coverage over first 100 nt + is_above_threshold = sum(np.array(start_coverage) >= coverage_threshold) >= num_samples # This filters on the raw threshold. Why? + is_above_length = UTR_end - UTR_start >= 150 + if (is_above_threshold) and (is_above_length): + if not is_reverse: + search_region = range(UTR_start + search_start, UTR_end - search_point_end + 1) + else: + search_region = range(UTR_end - search_start, UTR_start + search_point_end - 1, -1) + search_end = UTR_end - UTR_start - search_point_end + normalized_utr_coverage = np.array(normalized_utr_coverage) + breakpoints = range(search_start, search_end + 1) + mse_list = [ estimate_mse(normalized_utr_coverage,bp, num_samples) for bp in breakpoints ] + if len(mse_list) > 0: + min_ele_index = mse_list.index(min(mse_list)) + breakpoint = breakpoints[min_ele_index] + UTR_abundances = estimate_abundance(normalized_utr_coverage, breakpoint, num_samples) + select_mean_squared_error = mse_list[min_ele_index] + selected_break_point = breakpoint + else: + select_mean_squared_error = 'Na' + UTR_abundances = 'Na' + selected_break_point = 'Na' + else: + select_mean_squared_error = 'Na' + UTR_abundances = 'Na' + selected_break_point = 'Na' + + return select_mean_squared_error, selected_break_point, UTR_abundances + + +def estimate_mse(cov, bp, num_samples): + """ + get abundance of long utr vs short utr with breakpoint specifying the position of long and short utr. + """ + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=RuntimeWarning) + long_utr_vector = cov[:num_samples, bp:] + short_utr_vector = cov[:num_samples, 0:bp] + mean_long_utr = np.mean(long_utr_vector, 1) + mean_short_utr = np.mean(short_utr_vector, 1) + square_mean_centered_short_utr_vector = (short_utr_vector[:num_samples] - mean_short_utr[:, np.newaxis] )**2 + square_mean_centered_long_utr_vector = (long_utr_vector[:num_samples] - mean_long_utr[:, np.newaxis])**2 + mse = np.mean(np.append(square_mean_centered_short_utr_vector[:num_samples], square_mean_centered_long_utr_vector[:num_samples])) + return mse + +def estimate_abundance(cov, bp, num_samples): + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=RuntimeWarning) + long_utr_vector = cov[:num_samples, bp:] + short_utr_vector = cov[:num_samples, 0:bp] + mean_long_utr = np.mean(long_utr_vector, 1) + mean_short_utr = np.mean(short_utr_vector, 1) + return mean_long_utr, mean_short_utr + + +if __name__ == '__main__': + args = parse_args() + find_utr = UtrFinder(args) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/dapars.xml Tue Oct 27 10:14:33 2015 -0400 @@ -0,0 +1,49 @@ +<tool id="dapars" name="dapars" version="0.1.4"> + <description>infer de-novo alternative polyadenylation from rna-seq</description> + <requirements> + <requirement type="package" version="1.9">numpy</requirement> + <requirement type="package" version="2.22">bedtools</requirement> + </requirements> + <stdio> + <exit_code range="1:" /> + </stdio> + <command interpreter="python"><![CDATA[ + dapars.py -c + #for $c in $controls: + "$c" + #end for + -t + #for $t in $treatments: + "$t" + #end for + -u "$utr" + -o "$apa_sites" + -cpu \${GALAXY_SLOTS:-4} + --coverage_threshold "$coverage_threshold" + --search_start "$search_start" + #if $make_breakpoint: + -b "$breakpoint_bed" + #end if + ]]></command> + <inputs> + <param type="data" name="utr" format="gtf" label="GFF file containing 3prime UTRs" help="featureType of the UTRs + must be UTR, and the attribute group must have geneid in first position."/> + <param type="data" name="controls" format="bam,sam" multiple="True" label="Control alignment files" help="Select control alignment files" /> + <param type="data" name="treatments" format="bam,sam" multiple="True" label="Treatment alignment files" help="Select treatment alignment files" /> + <param type="integer" name="search_start" value="100" optional="False" min="1" label="Search start" help="Search start in nucleotides downstream of the start of the UTR. Necessary to correct for proximal drops in coverage. Select 200 for humans. Genomes with short UTRs may require more prpximal search start points."/> + <param type="float" name="coverage_threshold" value="20" optional="False" label="Coverage threshold" help="Skip the analysis of UTRs whose mean coverage is below the Coverage Threshold in any of the alignment files."/> + <param name="make_breakpoint" type="boolean" checked="False" label="Output bedfile with breakpoint positions?"/> + </inputs> + <outputs> + <data name="apa_sites" format="tabular" /> + <data name="breakpoint_bed" format="bed6"> + <filter>(make_breakpoint == True)</filter> + </data> + </outputs> + <help><![CDATA[ + TODO: Fill in help. + ]]></help> + <citations> + <citation type="doi">10.1038/ncomms6274</citation> + </citations> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/filter_utr.py Tue Oct 27 10:14:33 2015 -0400 @@ -0,0 +1,96 @@ +#!/usr/bin/env python +# Filter out UTRs and, if a gene has multiple UTRs, return a single UTR with minimum start and maximum end coordinate +# usage: python filter_utr.py input.gtf output.gtf + +import sys +from collections import OrderedDict + +def get_gtf_fields(): + return [ "chr", "source", "feature", "start", "end", "score", "strand", "frame", "group" ] + +def get_feature_dict(line, gtf_fields, utr_dict, feature="UTR"): + """ + Return a dictionary with lines of a GTF if the line describes a UTR. + Key is the first attribute of the group. + """ + if line.split("\t")[2] == feature: + fields = line.strip().split("\t") + fields[3] = int(fields[3]) + fields[4] = int(fields[4]) + gene_id = fields[-1].split("\"")[1] + if not gene_id in utr_dict: + utr_dict[gene_id] = [OrderedDict(zip(gtf_fields, fields))] + else: + utr_dict[gene_id].append(OrderedDict(zip(gtf_fields, fields))) + +def remove_five_prime_utrs(utr_dict, start_codon_dict): + for gene, features in start_codon_dict.iteritems(): + is_reverse = features[0]["strand"] == "-" + if is_reverse: + stop_codon_end = min([feature["start"] for feature in features]) + else: + stop_codon_end = max([feature["end"] for feature in features])# get last start codon + to_remove = [] + if gene in utr_dict: + for utr in utr_dict[gene]: + start = utr["start"] + end = utr["end"] + if is_reverse: + if end >= stop_codon_end: + to_remove.append(utr) + else: + if start <= stop_codon_end: + to_remove.append(utr) + [utr_dict[gene].remove(utr) for utr in to_remove ] + + +def get_longest_utr(utr_dict): + """ + Start of the composite utr is the most 5p start, end is the most 3p end. + """ + gtf = [] + for gene_id, values in utr_dict.iteritems(): + if not values: + continue + if len(values) == 1: + values[0]["start"] = str(values[0]["start"]) + values[0]["end"] = str(values[0]["end"]) + gtf.append( "\t".join( values[0].values() ) ) + else: + start = min( [fields["start"] for fields in values] ) + end = max( [fields["end"] for fields in values] ) + values[0]["start"] = str(start) + values[0]["end"] = str(end) + gtf.append( "\t".join( values[0].values() ) ) + return gtf + +def main(): + utr_dict = OrderedDict() + start_codon_dict = {} + header = [] + gtf_fields = get_gtf_fields() + with open(sys.argv[1]) as input: + for line in input: + if line.startswith("#"): + header.append( line.strip() ) + else: + get_feature_dict(line, gtf_fields, utr_dict, feature="UTR") + get_feature_dict(line, gtf_fields, start_codon_dict, feature="CDS") + remove_five_prime_utrs(utr_dict, start_codon_dict) + gtf = header + get_longest_utr(utr_dict) + if len(sys.argv) == 3: + with open(sys.argv[2], "w") as output: + [output.write(line + "\n") for line in gtf] + else: + for line in gtf: + print(line) + +if __name__ == "__main__": + main() + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_dependencies.xml Tue Oct 27 10:14:33 2015 -0400 @@ -0,0 +1,9 @@ +<?xml version="1.0"?> +<tool_dependency> + <package name="bedtools" version="2.22"> + <repository changeset_revision="8359d121547e" name="package_bedtools_2_22" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + </package> + <package name="numpy" version="1.9"> + <repository changeset_revision="816d3480b0b1" name="package_numpy_1_9" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + </package> +</tool_dependency>