Exemple #1
0
 def __init__(self, config):
     self.config = config
     self.stage_config = config["stage"][self.stage]
     self.ribo = self.stage_config["ribo"]
     self.picard = BroadRunner(config["program"]["picard"], None, {"algorithm": {}})
     self.ref = prepare_ref_file(self.stage_config["ref"],
                                 self.config)
def main(config_file):
    with open(config_file) as in_handle:
        config = yaml.load(in_handle)

    # make the needed directories
    map(safe_makedir, config["dir"].values())

    # specific for thesis pipeline
    in_dir = config["dir"]["data"]
    id_file = config["id_file"]
    curr_files = input_files_from_dir(in_dir, id_file)
    logger.info("Running pipeline on %s." % (curr_files))

    for stage in config["run"]:
        if stage == "fastqc":
            logger.info("Running fastqc on %s." % (curr_files))
            stage_runner = fastqc.FastQC(config)
            view.map(stage_runner, curr_files, block=False)

        if stage == "cutadapt":
            logger.info("Running cutadapt on %s." % (curr_files))
            stage_runner = trim.Cutadapt(config)
            curr_files = view.map(stage_runner, curr_files)

        if stage == "bowtie":
            logger.info("Running bowtie on %s." % (curr_files))
            bowtie = Bowtie(config)
            curr_files = view.map(bowtie, curr_files)
            mapped = view.map(sam.only_mapped, curr_files)
            unmapped = view.map(sam.only_unmapped, curr_files)
            curr_files = mapped
            bam_files = view.map(sam.sam2bam, mapped)
            bam_sorted = view.map(sam.bamsort, bam_files)
            view.map(sam.bamindex, bam_sorted)


        if stage == "coverage":
            logger.info("Calculating RNASeq metrics on %s." % (curr_files))
            nrun = len(curr_files)
            ref = prepare_ref_file(config["stage"][stage]["ref"], config)
            ribo = config["stage"][stage]["ribo"]
            picard = BroadRunner(config["program"]["picard"])
            out_dir = os.path.join(config["dir"]["results"], stage)
            safe_makedir(out_dir)
            out_files = [replace_suffix(os.path.basename(x),
                                        "metrics") for x in curr_files]
            out_files = [os.path.join(out_dir, x) for x in out_files]
            out_files = view.map(picardrun.picard_rnaseq_metrics,
                                 [picard] * nrun,
                                 curr_files,
                                 [ref] * nrun,
                                 [ribo] * nrun,
                                 out_files)

    stop_cluster()
def main(config_file):
    with open(config_file) as in_handle:
        config = yaml.load(in_handle)

    # make the needed directories
    map(safe_makedir, config["dir"].values())

    # specific for thesis pipeline
    in_dir = config["dir"]["data"]
    id_file = config["id_file"]
    curr_files = input_files_from_dir(in_dir, id_file)
    logger.info("Running pipeline on %s." % (curr_files))

    for stage in config["run"]:
        if stage == "fastqc":
            logger.info("Running fastqc on %s." % (curr_files))
            stage_runner = fastqc.FastQC(config)
            view.map(stage_runner, curr_files, block=False)

        if stage == "cutadapt":
            logger.info("Running cutadapt on %s." % (curr_files))
            stage_runner = trim.Cutadapt(config)
            curr_files = view.map(stage_runner, curr_files)

        if stage == "bowtie":
            logger.info("Running bowtie on %s." % (curr_files))
            bowtie = Bowtie(config)
            curr_files = view.map(bowtie, curr_files)
            mapped = view.map(sam.only_mapped, curr_files)
            unmapped = view.map(sam.only_unmapped, curr_files)
            curr_files = mapped
            bam_files = view.map(sam.sam2bam, mapped)
            bam_sorted = view.map(sam.bamsort, bam_files)
            view.map(sam.bamindex, bam_sorted)

        if stage == "coverage":
            logger.info("Calculating RNASeq metrics on %s." % (curr_files))
            nrun = len(curr_files)
            ref = prepare_ref_file(config["stage"][stage]["ref"], config)
            ribo = config["stage"][stage]["ribo"]
            picard = BroadRunner(config["program"]["picard"])
            out_dir = os.path.join(config["dir"]["results"], stage)
            safe_makedir(out_dir)
            out_files = [
                replace_suffix(os.path.basename(x), "metrics")
                for x in curr_files
            ]
            out_files = [os.path.join(out_dir, x) for x in out_files]
            out_files = view.map(picardrun.picard_rnaseq_metrics,
                                 [picard] * nrun, curr_files, [ref] * nrun,
                                 [ribo] * nrun, out_files)

    stop_cluster()
Exemple #4
0
def run_with_config(input_file, config, stage, out_file=None):

    if out_file is None:
        out_dir = os.path.join(config["dir"].get("results", None), stage)
        out_file = os.path.join(out_dir, _get_outfilename(input_file))

    safe_makedir(out_dir)
    if "annotation" not in config:
        logger.error("annotation must appear in the config file, see example "
                     "configuration files.")
        exit(1)
    ref = prepare_ref_file(config["annotation"], config)
    out_file = run(input_file, ref, out_file)
    return out_file
Exemple #5
0
def run_with_config(input_file, config, stage, out_file=None):

    if out_file is None:
        out_dir = os.path.join(config["dir"].get("results", None), stage)
        out_file = os.path.join(out_dir, _get_outfilename(input_file))

    safe_makedir(out_dir)
    if "annotation" not in config:
        logger.error("annotation must appear in the config file, see example "
                     "configuration files.")
        exit(1)
    ref = prepare_ref_file(config["annotation"], config)
    out_file = run(input_file, ref, out_file)
    return out_file
Exemple #6
0
def main(config_file):
    """ this assumes that we are keeping the same order of the files
    throughout """
    with open(config_file) as in_handle:
        config = yaml.load(in_handle)

    # make the needed directories
    map(safe_makedir, config["dir"].values())

    input_dict = config["input"]
    curr_files = _make_current_files(input_dict.keys())
    input_meta = input_dict.values()

    for stage in config["run"]:
        if stage == "fastqc":
            _emit_stage_message(stage, curr_files)
            fastqc_config = _get_stage_config(config, stage)
            fastqc_args = zip(*product(curr_files, [fastqc_config], [config]))
            view.map(fastqc.run, *fastqc_args)

        if stage == "cutadapt":
            _emit_stage_message(stage, curr_files)
            cutadapt_config = _get_stage_config(config, stage)
            cutadapt_args = zip(
                *product(curr_files, [cutadapt_config], [config]))
            cutadapt_outputs = view.map(cutadapt_tool.run, *cutadapt_args)
            curr_files = _make_current_files(cutadapt_outputs)

        if stage == "tophat":
            _emit_stage_message(stage, curr_files)
            tophat_config = _get_stage_config(config, stage)
            tophat_args = zip(*product(curr_files, [None], [config["ref"]],
                                       ["tophat"], [config]))
            tophat_outputs = view.map(tophat.run_with_config, *tophat_args)
            bamfiles = view.map(sam.sam2bam, tophat_outputs)
            bamsort = view.map(sam.bamsort, bamfiles)
            view.map(sam.bamindex, bamsort)
            final_bamfiles = bamsort
            curr_files = tophat_outputs

        if stage == "htseq-count":
            _emit_stage_message(stage, curr_files)
            htseq_config = _get_stage_config(config, stage)
            htseq_args = zip(*product(curr_files, [config], [stage]))
            htseq_outputs = view.map(htseq_count.run_with_config, *htseq_args)
            combined_out = os.path.join(config["dir"]["results"], stage,
                                        "all_combined.counts")
            combined_out = htseq_count.combine_counts(htseq_outputs,
                                                      None,
                                                      out_file=combined_out)

        if stage == "rseqc":
            _emit_stage_message(stage, curr_files)
            rseqc_config = _get_stage_config(config, stage)
            rseq_args = zip(*product(curr_files, [config]))
            view.map(rseqc.bam_stat, *rseq_args)
            view.map(rseqc.genebody_coverage, *rseq_args)
            view.map(rseqc.junction_annotation, *rseq_args)
            view.map(rseqc.junction_saturation, *rseq_args)
            RPKM_args = zip(*product(final_bamfiles, [config]))
            RPKM_count_out = view.map(rseqc.RPKM_count, *RPKM_args)
            RPKM_count_fixed = view.map(rseqc.fix_RPKM_count_file,
                                        RPKM_count_out)
            annotate_args = zip(*product(RPKM_count_fixed, ["gene_id"],
                                         ["ensembl_transcript_id"], ["mouse"]))
            view.map(annotate.annotate_table_with_biomart, *annotate_args)
            view.map(rseqc.RPKM_saturation, *RPKM_args)

        if stage == "coverage":
            logger.info("Calculating RNASeq metrics on %s." % (curr_files))
            nrun = len(curr_files)
            ref = prepare_ref_file(config["stage"][stage]["ref"], config)
            ribo = config["stage"][stage]["ribo"]
            picard = BroadRunner(config["program"]["picard"])
            out_dir = os.path.join(config["dir"]["results"], stage)
            safe_makedir(out_dir)
            out_files = [
                replace_suffix(os.path.basename(x), "metrics")
                for x in curr_files
            ]
            out_files = [os.path.join(out_dir, x) for x in out_files]
            out_files = view.map(picardrun.picard_rnaseq_metrics,
                                 [picard] * nrun, curr_files, [ref] * nrun,
                                 [ribo] * nrun, out_files)

        if stage == "deseq":
            _emit_stage_message(stage, curr_files)
            deseq_config = _get_stage_config(config, stage)
            out_dir = os.path.join(config["dir"]["results"], stage)
            safe_makedir(out_dir)
            for test in deseq_config["tests"]:
                indexes = [
                    _find_file_index_for_test(input_meta, condition)
                    for condition in test
                ]
                files = [htseq_outputs[x] for x in indexes]
                conditions = [input_meta[x]["condition"] for x in indexes]
                combined_out = os.path.join(
                    out_dir, "_".join(conditions) + "_combined.counts")
                logger.info("Combining %s to %s." % (files, combined_out))
                count_file = htseq_count.combine_counts(files,
                                                        None,
                                                        out_file=combined_out)
                out_file = os.path.join(out_dir,
                                        "_".join(conditions) + "_deseq.txt")
                logger.info("Running deseq on %s with conditions %s "
                            "and writing to %s" %
                            (count_file, conditions, out_file))
                view.map(deseq.run, [count_file], [conditions], [out_file])
                #deseq.run(count_file, conditions, out_file=out_file)

    # end gracefully
    stop_cluster()
def main(config_file):
    """ this assumes that we are keeping the same order of the files
    throughout """
    with open(config_file) as in_handle:
        config = yaml.load(in_handle)

    # make the needed directories
    map(safe_makedir, config["dir"].values())

    input_dir = config["input_dir"]
    results_dir = config["dir"].get("results", "results")
    input_files = glob.glob(os.path.join(input_dir, "*.fq"))
    curr_files = _make_current_files(input_files)
    conditions = [os.path.basename(x).split("_")[0] for x in input_files]

    for stage in config["run"]:
        if stage == "fastqc":
            _emit_stage_message(stage, curr_files)
            fastqc_config = _get_stage_config(config, stage)
            fastqc_args = zip(*product(curr_files, [fastqc_config], [config]))
            fastqc_out = view.map(fastqc.run, *fastqc_args)
            logger.info("fastqc outfiles: %s" % (fastqc_out))

        if stage == "cutadapt":
            _emit_stage_message(stage, curr_files)
            cutadapt_config = _get_stage_config(config, stage)
            cutadapt_args = zip(
                *product(curr_files, [cutadapt_config], [config]))
            cutadapt_outputs = view.map(cutadapt_tool.run, *cutadapt_args)
            curr_files = _make_current_files(cutadapt_outputs)

        if stage == "tophat":
            _emit_stage_message(stage, curr_files)
            tophat_config = _get_stage_config(config, stage)
            tophat_args = zip(*product(curr_files, [None], [config["ref"]],
                                       ["tophat"], [config]))
            tophat_outputs = view.map(tophat.run_with_config, *tophat_args)
            # convert to bam, sort and index
            bamfiles = view.map(sam.sam2bam, tophat_outputs)
            sorted_bf = view.map(sam.bamsort, bamfiles)
            view.map(sam.bamindex, sorted_bf)
            curr_files = sorted_bf

        if stage == "rseqc":
            _emit_stage_message(stage, curr_files)
            rseqc_config = _get_stage_config(config, stage)
            rseq_args = zip(*product(curr_files, [config]))
            view.map(rseqc.bam2bigwig, *rseq_args, block=False)
            view.map(rseqc.bam_stat, *rseq_args, block=False)
            view.map(rseqc.clipping_profile, *rseq_args, block=False)
            view.map(rseqc.genebody_coverage, *rseq_args, block=False)
            view.map(rseqc.junction_annotation, *rseq_args, block=False)
            view.map(rseqc.junction_saturation, *rseq_args, block=False)
            view.map(rseqc.RPKM_count, *rseq_args, block=False)
            view.map(rseqc.RPKM_saturation, *rseq_args, block=False)
            curr_files = tophat_outputs

        if stage == "coverage":
            logger.info("Calculating RNASeq metrics on %s." % (curr_files))
            nrun = len(curr_files)
            ref = prepare_ref_file(config["stage"][stage]["ref"], config)
            ribo = config["stage"][stage]["ribo"]
            picard = BroadRunner(config["program"]["picard"])
            out_dir = os.path.join(results_dir, stage)
            safe_makedir(out_dir)
            out_files = [
                replace_suffix(os.path.basename(x), "metrics")
                for x in curr_files
            ]
            out_files = [os.path.join(out_dir, x) for x in out_files]
            out_files = view.map(picardrun.picard_rnaseq_metrics,
                                 [picard] * nrun, curr_files, [ref] * nrun,
                                 [ribo] * nrun, out_files)

        if stage == "htseq-count":
            _emit_stage_message(stage, curr_files)
            htseq_config = _get_stage_config(config, stage)
            htseq_args = zip(*product(curr_files, [config], [stage]))
            htseq_outputs = view.map(htseq_count.run_with_config, *htseq_args)
            combined_out = os.path.join(config["dir"]["results"], stage,
                                        "all_combined.counts")
            combined_out = htseq_count.combine_counts(htseq_outputs,
                                                      None,
                                                      out_file=combined_out)

        if stage == "deseq":
            _emit_stage_message(stage, curr_files)
            deseq_config = _get_stage_config(config, stage)
            out_dir = os.path.join(config["dir"]["results"], stage)
            safe_makedir(out_dir)
            for comparison in deseq_config["comparisons"]:
                comparison_name = "_vs_".join(comparison)
                out_dir = os.path.join(results_dir, stage, comparison_name)
                safe_makedir(out_dir)
                indexes = [
                    x for x, y in enumerate(conditions) if y in comparison
                ]
                htseq_files = [htseq_outputs[index] for index in indexes]
                htseq_columns = [conditions[index] for index in indexes]
                out_file = os.path.join(out_dir,
                                        comparison_name + ".counts.txt")
                combined_out = htseq_count.combine_counts(
                    htseq_files, htseq_columns, out_file)
                deseq_conds = [conditions[index] for index in indexes]
                deseq_out = os.path.join(out_dir,
                                         comparison_name + ".deseq.txt")
                logger.info("Running deseq on %s with conditions %s "
                            "and writing to %s" %
                            (combined_out, conditions, deseq_out))
                view.map(deseq.run, [combined_out], [deseq_conds], [deseq_out])
                annotated_file = view.map(annotate.annotate_table_with_biomart,
                                          [deseq_out], ["id"],
                                          ["ensembl_gene_id"], ["zebrafish"])

    # end gracefully
    stop_cluster()
def main(config_file):
    """ this assumes that we are keeping the same order of the files
    throughout """
    with open(config_file) as in_handle:
        config = yaml.load(in_handle)

    # make the needed directories
    map(safe_makedir, config["dir"].values())

    input_dict = config["input"]
    curr_files = _make_current_files(input_dict.keys())
    input_meta = input_dict.values()

    for stage in config["run"]:
        if stage == "fastqc":
            _emit_stage_message(stage, curr_files)
            fastqc_config = _get_stage_config(config, stage)
            fastqc_args = zip(*product(curr_files, [fastqc_config],
                                       [config]))
            view.map(fastqc.run, *fastqc_args)

        if stage == "cutadapt":
            _emit_stage_message(stage, curr_files)
            cutadapt_config = _get_stage_config(config, stage)
            cutadapt_args = zip(*product(curr_files, [cutadapt_config],
                                         [config]))
            cutadapt_outputs = view.map(cutadapt_tool.run, *cutadapt_args)
            curr_files = _make_current_files(cutadapt_outputs)

        if stage == "tophat":
            _emit_stage_message(stage, curr_files)
            tophat_config = _get_stage_config(config, stage)
            tophat_args = zip(*product(curr_files, [None], [config["ref"]],
                                       ["tophat"], [config]))
            tophat_outputs = view.map(tophat.run_with_config, *tophat_args)
            bamfiles = view.map(sam.sam2bam, tophat_outputs)
            bamsort = view.map(sam.bamsort, bamfiles)
            view.map(sam.bamindex, bamsort)
            final_bamfiles = bamsort
            curr_files = tophat_outputs


        if stage == "htseq-count":
            _emit_stage_message(stage, curr_files)
            htseq_config = _get_stage_config(config, stage)
            htseq_args = zip(*product(curr_files, [config], [stage]))
            htseq_outputs = view.map(htseq_count.run_with_config,
                                     *htseq_args)
            combined_out = os.path.join(config["dir"]["results"], stage,
                                        "all_combined.counts")
            combined_out = htseq_count.combine_counts(htseq_outputs, None,
                                                      out_file=combined_out)

        if stage == "rseqc":
            _emit_stage_message(stage, curr_files)
            rseqc_config = _get_stage_config(config, stage)
            rseq_args = zip(*product(curr_files, [config]))
            view.map(rseqc.bam_stat, *rseq_args)
            view.map(rseqc.genebody_coverage, *rseq_args)
            view.map(rseqc.junction_annotation, *rseq_args)
            view.map(rseqc.junction_saturation, *rseq_args)
            RPKM_args = zip(*product(final_bamfiles, [config]))
            RPKM_count_out = view.map(rseqc.RPKM_count, *RPKM_args)
            RPKM_count_fixed = view.map(rseqc.fix_RPKM_count_file,
                                        RPKM_count_out)
            annotate_args = zip(*product(RPKM_count_fixed,
                                         ["gene_id"],
                                         ["ensembl_transcript_id"],
                                         ["mouse"]))
            view.map(annotate.annotate_table_with_biomart,
                     *annotate_args)
            view.map(rseqc.RPKM_saturation, *RPKM_args)

        if stage == "coverage":
            logger.info("Calculating RNASeq metrics on %s." % (curr_files))
            nrun = len(curr_files)
            ref = prepare_ref_file(config["stage"][stage]["ref"], config)
            ribo = config["stage"][stage]["ribo"]
            picard = BroadRunner(config["program"]["picard"])
            out_dir = os.path.join(config["dir"]["results"], stage)
            safe_makedir(out_dir)
            out_files = [replace_suffix(os.path.basename(x),
                                        "metrics") for x in curr_files]
            out_files = [os.path.join(out_dir, x) for x in out_files]
            out_files = view.map(picardrun.picard_rnaseq_metrics,
                                 [picard] * nrun,
                                 curr_files,
                                 [ref] * nrun,
                                 [ribo] * nrun,
                                 out_files)

        if stage == "deseq":
            _emit_stage_message(stage, curr_files)
            deseq_config = _get_stage_config(config, stage)
            out_dir = os.path.join(config["dir"]["results"], stage)
            safe_makedir(out_dir)
            for test in deseq_config["tests"]:
                indexes = [_find_file_index_for_test(input_meta,
                                                     condition) for
                                                     condition in test]
                files = [htseq_outputs[x] for x in indexes]
                conditions = [input_meta[x]["condition"] for x in indexes]
                combined_out = os.path.join(out_dir,
                                            "_".join(conditions) +
                                            "_combined.counts")
                logger.info("Combining %s to %s." % (files, combined_out))
                count_file = htseq_count.combine_counts(files, None,
                                                        out_file=combined_out)
                out_file = os.path.join(out_dir, "_".join(conditions) +
                                        "_deseq.txt")
                logger.info("Running deseq on %s with conditions %s "
                            "and writing to %s" % (count_file,
                                                   conditions,
                                                   out_file))
                view.map(deseq.run, [count_file], [conditions], [out_file])
                #deseq.run(count_file, conditions, out_file=out_file)

    # end gracefully
    stop_cluster()
Exemple #9
0
def main(config_file):
    with open(config_file) as in_handle:
        config = yaml.load(in_handle)

    # make the needed directories
    map(safe_makedir, config["dir"].values())

    # specific for thesis pipeline
    input_dirs = config["input_dirs"]

    results_dir = config["dir"].get("results", "results")
    input_files = _find_input_files(config)
    conditions = _group_input_by_condition(input_files)
    logger.info("Input_files: %s" % (input_files))
    logger.info("Condition groups %s" %(conditions))
    htseq_outdict = {}

    for condition, curr_files in conditions.items():
        condition_dir = os.path.join(results_dir, condition)
        safe_makedir(condition_dir)
        config["dir"]["results"] = condition_dir

        for stage in config["run"]:
            if stage == "fastqc":
                logger.info("Running fastqc on %s." % (curr_files))
                stage_runner = FastQC(config)
                view.map(stage_runner, curr_files)

            if stage == "cutadapt":
                logger.info("Running cutadapt on %s." % (curr_files))
                stage_runner = Cutadapt(config)
                curr_files = view.map(stage_runner, curr_files)

            if stage == "tophat":
                logger.info("Running tophat on %s." % (curr_files))
                stage_runner = Tophat(config)
                tophat_outputs = view.map(stage_runner, curr_files)
                bamfiles = view.map(sam.sam2bam, tophat_outputs)
                bamsort = view.map(sam.bamsort, bamfiles)
                view.map(sam.bamindex, bamsort)
                final_bamfiles = bamsort
                curr_files = tophat_outputs

            if stage == "htseq-count":
                _emit_stage_message(stage, curr_files)
                htseq_config = _get_stage_config(config, stage)
                htseq_args = zip(*product(curr_files, [config], [stage]))
                htseq_outputs = view.map(htseq_count.run_with_config,
                                         *htseq_args)
                htseq_outdict[condition] = htseq_outputs

            if stage == "coverage":
                logger.info("Calculating RNASeq metrics on %s." % (curr_files))
                nrun = len(curr_files)
                ref = prepare_ref_file(config["stage"][stage]["ref"], config)
                ribo = config["stage"][stage]["ribo"]
                picard = BroadRunner(config["program"]["picard"])
                out_dir = os.path.join(results_dir, stage)
                safe_makedir(out_dir)
                out_files = [replace_suffix(os.path.basename(x),
                                            "metrics") for x in curr_files]
                out_files = [os.path.join(out_dir, x) for x in out_files]
                out_files = view.map(picardrun.picard_rnaseq_metrics,
                                     [picard] * nrun,
                                     curr_files,
                                     [ref] * nrun,
                                     [ribo] * nrun,
                                     out_files)

            if stage == "rseqc":
                _emit_stage_message(stage, curr_files)
                rseqc_config = _get_stage_config(config, stage)
                rseq_args = zip(*product(curr_files, [config]))
                view.map(rseqc.bam_stat, *rseq_args)
                view.map(rseqc.genebody_coverage, *rseq_args)
                view.map(rseqc.junction_annotation, *rseq_args)
                view.map(rseqc.junction_saturation, *rseq_args)
                RPKM_args = zip(*product(final_bamfiles, [config]))
                RPKM_count_out = view.map(rseqc.RPKM_count, *RPKM_args)
                RPKM_count_fixed = view.map(rseqc.fix_RPKM_count_file,
                                            RPKM_count_out)
                """
                                annotate_args = zip(*product(RPKM_count_fixed,
                                             ["gene_id"],
                                             ["ensembl_gene_id"],
                                             ["human"]))
                view.map(annotate.annotate_table_with_biomart,
                         *annotate_args)
                         """
                view.map(rseqc.RPKM_saturation, *rseq_args)
                curr_files = tophat_outputs

    # combine htseq-count files and run deseq on them
    conditions, htseq_files = dict_to_vectors(htseq_outdict)
    deseq_config = _get_stage_config(config, "deseq")
    cell_types = _group_input_by_cell_type(htseq_files)
    for cell_type, files in cell_types.items():
        for comparison in deseq_config["comparisons"]:
            comparison_name = "_vs_".join(comparison)
            deseq_dir = os.path.join(results_dir, "deseq", cell_type,
                                     comparison_name)
            safe_makedir(deseq_dir)
            out_file = os.path.join(deseq_dir, comparison_name + ".counts.txt")
            files_by_condition = _group_input_by_condition(files)
            _emit_stage_message("deseq", files_by_condition)
            c, f = dict_to_vectors(files_by_condition)
            combined_out = htseq_count.combine_counts(f,
                                                      None,
                                                      out_file)
            deseq_out = os.path.join(deseq_dir, comparison_name)
            logger.info("Running deseq on %s with conditions %s "
                        "and writing ot %s" % (combined_out,
                                               conditions,
                                               deseq_out))
            deseq_out = view.map(deseq.run, [combined_out], [c], [deseq_out])
            annotate.annotate_table_with_biomart(deseq_out[0],
                                                 "id",
                                                 "ensembl_gene_id",
                                                 "human")

    # end gracefully
    stop_cluster()
Exemple #10
0
def main(config_file):
    with open(config_file) as in_handle:
        config = yaml.load(in_handle)

    # make the needed directories
    map(safe_makedir, config["dir"].values())

    stage_dict = {"download_encode": _download_encode,
                  "fastqc": _run_fastqc}

    curr_files = config["encode_file"]

    results_dir = config["dir"].get("results", "results")

    for cell_type in config["cell_types"]:
        cell_type_dir = os.path.join(results_dir, cell_type)
        safe_makedir(cell_type_dir)
        config["dir"]["results"] = cell_type_dir
        in_files = glob.glob(os.path.join(config["dir"]["data"],
                                          cell_type, "*"))
        curr_files = in_files
        for stage in config["run"]:
            if stage == "fastqc":
                _emit_stage_message(stage, curr_files)
                fastqc_config = _get_stage_config(config, stage)
                fastqc_args = zip(*product(curr_files, [fastqc_config],
                                           [config]))
                view.map(fastqc.run, *fastqc_args)

            if stage == "cutadapt":
                _emit_stage_message(stage, curr_files)
                cutadapt_config = _get_stage_config(config, stage)
                cutadapt_args = zip(*product(curr_files, [cutadapt_config],
                                             [config]))
                cutadapt_outputs = view.map(cutadapt_tool.run, *cutadapt_args)
                curr_files = cutadapt_outputs

            if stage == "tophat":
                _emit_stage_message(stage, curr_files)
                tophat_config = _get_stage_config(config, stage)
                tophat_args = zip(*product(curr_files, [None], [config["ref"]],
                                           ["tophat"], [config]))
                tophat_outputs = view.map(tophat.run_with_config, *tophat_args)

                picard = BroadRunner(config["program"]["picard"])
                # convert to bam
                #args = zip(*product([picard], tophat_outputs))
                #bamfiles = view.map(picardrun.picard_formatconverter,
                #                    *args)
                bamfiles = view.map(sam.sam2bam, tophat_outputs)
                sorted_bf = view.map(sam.bamsort, bamfiles)
                view.map(sam.bamindex, sorted_bf)
                curr_files = sorted_bf

            if stage == "rseqc":
                _emit_stage_message(stage, curr_files)
                rseqc_config = _get_stage_config(config, stage)
                rseq_args = zip(*product(curr_files, [config]))
                view.map(rseqc.bam2bigwig, *rseq_args, block=False)
                view.map(rseqc.bam_stat, *rseq_args, block=False)
                view.map(rseqc.clipping_profile, *rseq_args, block=False)
                view.map(rseqc.genebody_coverage, *rseq_args, block=False)
                view.map(rseqc.junction_annotation, *rseq_args, block=False)
                view.map(rseqc.junction_saturation, *rseq_args, block=False)
                RPKM_count_files = view.map(rseqc.RPKM_count,
                                            *rseq_args)
                dirs_to_process = list(set(map(os.path.dirname,
                                               RPKM_count_files)))
                logger.info("Count files: %s" % (RPKM_count_files))
                logger.info("dirnames to process: %s" % (dirs_to_process))
                RPKM_merged = view.map(rseqc.merge_RPKM, dirs_to_process)

                view.map(rseqc.RPKM_saturation, *rseq_args, block=False)
                curr_files = tophat_outputs

            if stage == "htseq-count":
                _emit_stage_message(stage, curr_files)
                htseq_config = _get_stage_config(config, stage)
                htseq_args = zip(*product(curr_files, [config], [stage]))
                htseq_outputs = view.map(htseq_count.run_with_config,
                                         *htseq_args)
                column_names = in_files
                out_file = os.path.join(config["dir"]["results"], stage,
                                        cell_type + ".combined.counts")
                combined_out = htseq_count.combine_counts(htseq_outputs,
                                                          column_names,
                                                          out_file)
                rpkm = htseq_count.calculate_rpkm(combined_out,
                                                  config["annotation"]["file"])
                rpkm_file = os.path.join(config["dir"]["results"], stage,
                                         cell_type + ".rpkm.txt")
                rpkm.to_csv(rpkm_file, sep="\t")

            if stage == "coverage":
                logger.info("Calculating RNASeq metrics on %s." % (curr_files))
                nrun = len(curr_files)
                ref = prepare_ref_file(config["stage"][stage]["ref"],
                                              config)
                ribo = config["stage"][stage]["ribo"]
                picard = BroadRunner(config["program"]["picard"])
                out_dir = os.path.join(config["dir"]["results"], stage)
                safe_makedir(out_dir)
                out_files = [replace_suffix(os.path.basename(x),
                                            "metrics") for x in curr_files]
                out_files = [os.path.join(out_dir, x) for x in out_files]
                out_files = view.map(picardrun.picard_rnaseq_metrics,
                                     [picard] * nrun,
                                     curr_files,
                                     [ref] * nrun,
                                     [ribo] * nrun,
                                     out_files)

    # end gracefully, wait for jobs to finish, then exit
    view.wait()
    stop_cluster()
def main(config_file):
    with open(config_file) as in_handle:
        config = yaml.load(in_handle)

    # make the needed directories
    map(safe_makedir, config["dir"].values())

    # specific for thesis pipeline
    in_dir = config["dir"]["data"]
    curr_files = input_files_from_dir(in_dir)

    for stage in config["run"]:
        if stage == "fastqc":
            stage_runner = fastqc.FastQCStage(config)
            view.map(stage_runner, curr_files)

        if stage == "cutadapt":
            stage_runner = trim.Cutadapt(config)
            curr_files = view.map(stage_runner, curr_files)

        if stage == "tophat":
            _emit_stage_message(stage, curr_files)
            tophat_config = _get_stage_config(config, stage)
            tophat_outputs = view.map(tophat.run_with_config,
                                      first, [None] * len(curr_files),
                                      [config["ref"]] * len(curr_files),
                                      ["tophat"] * len(curr_files),
                                      [config] * len(curr_files))
            bamfiles = view.map(sam.sam2bam, tophat_outputs)
            bamsort = view.map(sam.bamsort, bamfiles)
            view.map(sam.bamindex, bamsort)
            final_bamfiles = bamsort
            curr_files = tophat_outputs

        if stage == "coverage":
            logger.info("Calculating RNASeq metrics on %s." % (curr_files))
            nrun = len(curr_files)
            ref = prepare_ref_file(config["stage"][stage]["ref"], config)
            ribo = config["stage"][stage]["ribo"]
            picard = BroadRunner(config["program"]["picard"])
            out_dir = os.path.join(results_dir, stage)
            safe_makedir(out_dir)
            out_files = [replace_suffix(os.path.basename(x),
                                        "metrics") for x in curr_files]
            out_files = [os.path.join(out_dir, x) for x in out_files]
            out_files = view.map(picardrun.picard_rnaseq_metrics,
                                 [picard] * nrun,
                                 curr_files,
                                 [ref] * nrun,
                                 [ribo] * nrun,
                                 out_files)

        if stage == "rseqc":
            _emit_stage_message(stage, curr_files)
            rseqc_config = _get_stage_config(config, stage)
            rseq_args = zip(*product(curr_files, [config]))
            view.map(rseqc.bam_stat, *rseq_args)
            view.map(rseqc.genebody_coverage, *rseq_args)
            view.map(rseqc.junction_annotation, *rseq_args)
            view.map(rseqc.junction_saturation, *rseq_args)
            RPKM_args = zip(*product(final_bamfiles, [config]))
            RPKM_count_out = view.map(rseqc.RPKM_count, *RPKM_args)
            RPKM_count_fixed = view.map(rseqc.fix_RPKM_count_file,
                                        RPKM_count_out)
            """
i                                annotate_args = zip(*product(RPKM_count_fixed,
                                             ["gene_id"],
                                             ["ensembl_gene_id"],
                                             ["human"]))
                view.map(annotate.annotate_table_with_biomart,
                         *annotate_args)
                         """
                view.map(rseqc.RPKM_saturation, *rseq_args)
                curr_files = tophat_outputs
Exemple #12
0
def main(config_file):
    with open(config_file) as in_handle:
        config = yaml.load(in_handle)

    # make the needed directories
    map(safe_makedir, config["dir"].values())

    # specific for thesis pipeline
    input_dirs = config["input_dirs"]

    results_dir = config["dir"].get("results", "results")
    input_files = _find_input_files(config)
    conditions = _group_input_by_condition(input_files)
    logger.info("Input_files: %s" % (input_files))
    logger.info("Condition groups %s" % (conditions))
    htseq_outdict = {}

    for condition, curr_files in conditions.items():
        condition_dir = os.path.join(results_dir, condition)
        safe_makedir(condition_dir)
        config["dir"]["results"] = condition_dir

        for stage in config["run"]:
            if stage == "fastqc":
                _emit_stage_message(stage, curr_files)
                fastqc_config = _get_stage_config(config, stage)
                fastqc_args = zip(
                    *product(curr_files, [fastqc_config], [config]))
                view.map(fastqc.run, *fastqc_args)

            if stage == "cutadapt":
                _emit_stage_message(stage, curr_files)
                cutadapt_config = _get_stage_config(config, stage)
                cutadapt_args = zip(
                    *product(curr_files, [cutadapt_config], [config]))
                cutadapt_outputs = view.map(cutadapt_tool.run, *cutadapt_args)
                curr_files = cutadapt_outputs
                logger.info("Fixing mate pair information.")
                pairs = combine_pairs(curr_files)
                first = [x[0] for x in pairs]
                second = [x[1] for x in pairs]
                logger.info("Forward: %s" % (first))
                logger.info("Reverse: %s" % (second))
                fixed = view.map(fastq.fix_mate_pairs_with_config, first,
                                 second, [config] * len(first))
                curr_files = list(flatten(fixed))

            if stage == "sickle":
                _emit_stage_message(stage, curr_files)
                pairs = combine_pairs(curr_files)
                first = [x[0] for x in pairs]
                second = [x[1] for x in pairs]
                fixed = view.map(sickle.run_with_config, first, second,
                                 [config] * len(first))
                curr_files = list(flatten(fixed))

            if stage == "tophat":
                _emit_stage_message(stage, curr_files)
                tophat_config = _get_stage_config(config, stage)
                pairs = combine_pairs(curr_files)
                first = [x[0] for x in pairs]
                second = [x[1] for x in pairs]
                logger.info("first %s" % (first))
                logger.info("second %s" % (second))

                #tophat_args = zip(*product(first, second, [config["ref"]],
                #                           ["tophat"], [config]))
                tophat_outputs = view.map(tophat.run_with_config, first,
                                          second, [config["ref"]] * len(first),
                                          ["tophat"] * len(first),
                                          [config] * len(first))
                bamfiles = view.map(sam.sam2bam, tophat_outputs)
                bamsort = view.map(sam.bamsort, bamfiles)
                view.map(sam.bamindex, bamsort)
                final_bamfiles = bamsort
                curr_files = tophat_outputs

            if stage == "htseq-count":
                _emit_stage_message(stage, curr_files)
                htseq_config = _get_stage_config(config, stage)
                htseq_args = zip(*product(curr_files, [config], [stage]))
                htseq_outputs = view.map(htseq_count.run_with_config,
                                         *htseq_args)
                htseq_outdict[condition] = htseq_outputs

            if stage == "coverage":
                logger.info("Calculating RNASeq metrics on %s." % (curr_files))
                nrun = len(curr_files)
                ref = prepare_ref_file(config["stage"][stage]["ref"], config)
                ribo = config["stage"][stage]["ribo"]
                picard = BroadRunner(config["program"]["picard"])
                out_dir = os.path.join(results_dir, stage)
                safe_makedir(out_dir)
                out_files = [
                    replace_suffix(os.path.basename(x), "metrics")
                    for x in curr_files
                ]
                out_files = [os.path.join(out_dir, x) for x in out_files]
                out_files = view.map(picardrun.picard_rnaseq_metrics,
                                     [picard] * nrun, curr_files, [ref] * nrun,
                                     [ribo] * nrun, out_files)

            if stage == "rseqc":
                _emit_stage_message(stage, curr_files)
                rseqc_config = _get_stage_config(config, stage)
                rseq_args = zip(*product(curr_files, [config]))
                view.map(rseqc.bam_stat, *rseq_args)
                view.map(rseqc.genebody_coverage, *rseq_args)
                view.map(rseqc.junction_annotation, *rseq_args)
                view.map(rseqc.junction_saturation, *rseq_args)
                RPKM_args = zip(*product(final_bamfiles, [config]))
                RPKM_count_out = view.map(rseqc.RPKM_count, *RPKM_args)
                RPKM_count_fixed = view.map(rseqc.fix_RPKM_count_file,
                                            RPKM_count_out)
                """
                                annotate_args = zip(*product(RPKM_count_fixed,
                                             ["gene_id"],
                                             ["ensembl_gene_id"],
                                             ["human"]))
                view.map(annotate.annotate_table_with_biomart,
                         *annotate_args)
                         """
                view.map(rseqc.RPKM_saturation, *rseq_args)
                curr_files = tophat_outputs

    # combine htseq-count files and run deseq on them
    conditions, htseq_files = dict_to_vectors(htseq_outdict)
    deseq_config = _get_stage_config(config, "deseq")
    cell_types = _group_input_by_cell_type(htseq_files)
    for cell_type, files in cell_types.items():
        for comparison in deseq_config["comparisons"]:
            comparison_name = "_vs_".join(comparison)
            deseq_dir = os.path.join(results_dir, "deseq", cell_type,
                                     comparison_name)
            safe_makedir(deseq_dir)
            out_file = os.path.join(deseq_dir, comparison_name + ".counts.txt")
            files_by_condition = _group_input_by_condition(files)
            _emit_stage_message("deseq", files_by_condition)
            c, f = dict_to_vectors(files_by_condition)
            combined_out = htseq_count.combine_counts(f, None, out_file)
            deseq_out = os.path.join(deseq_dir, comparison_name)
            logger.info("Running deseq on %s with conditions %s "
                        "and writing ot %s" %
                        (combined_out, conditions, deseq_out))
            deseq_out = view.map(deseq.run, [combined_out], [c], [deseq_out])
            annotate.annotate_table_with_biomart(deseq_out[0], "id",
                                                 "ensembl_gene_id", "human")
            #annotated_file = view.map(annotate.annotate_table_with_biomart,
            #                          [deseq_out],
            #                          ["id"],
            #                          ["ensembl_gene_id"],
            #                          ["human"])

    # end gracefully
    stop_cluster()
Exemple #13
0
def main(config_file):
    """ this assumes that we are keeping the same order of the files
    throughout """
    with open(config_file) as in_handle:
        config = yaml.load(in_handle)

    # make the needed directories
    map(safe_makedir, config["dir"].values())

    input_dir = config["input_dir"]
    results_dir = config["dir"].get("results", "results")
    input_files = glob.glob(os.path.join(input_dir, "*.fq"))
    curr_files = _make_current_files(input_files)
    conditions = [os.path.basename(x).split("_")[0] for x in input_files]

    for stage in config["run"]:
        if stage == "fastqc":
            _emit_stage_message(stage, curr_files)
            fastqc_config = _get_stage_config(config, stage)
            fastqc_args = zip(*product(curr_files, [fastqc_config],
                                       [config]))
            fastqc_out = view.map(fastqc.run, *fastqc_args)
            logger.info("fastqc outfiles: %s" % (fastqc_out))

        if stage == "cutadapt":
            _emit_stage_message(stage, curr_files)
            cutadapt_config = _get_stage_config(config, stage)
            cutadapt_args = zip(*product(curr_files, [cutadapt_config],
                                         [config]))
            cutadapt_outputs = view.map(cutadapt_tool.run, *cutadapt_args)
            curr_files = _make_current_files(cutadapt_outputs)

        if stage == "tophat":
            _emit_stage_message(stage, curr_files)
            tophat_config = _get_stage_config(config, stage)
            tophat_args = zip(*product(curr_files, [None], [config["ref"]],
                                       ["tophat"], [config]))
            tophat_outputs = view.map(tophat.run_with_config, *tophat_args)
            # convert to bam, sort and index
            bamfiles = view.map(sam.sam2bam, tophat_outputs)
            sorted_bf = view.map(sam.bamsort, bamfiles)
            view.map(sam.bamindex, sorted_bf)
            curr_files = sorted_bf

        if stage == "rseqc":
            _emit_stage_message(stage, curr_files)
            rseqc_config = _get_stage_config(config, stage)
            rseq_args = zip(*product(curr_files, [config]))
            view.map(rseqc.bam2bigwig, *rseq_args, block=False)
            view.map(rseqc.bam_stat, *rseq_args, block=False)
            view.map(rseqc.clipping_profile, *rseq_args, block=False)
            view.map(rseqc.genebody_coverage, *rseq_args, block=False)
            view.map(rseqc.junction_annotation, *rseq_args, block=False)
            view.map(rseqc.junction_saturation, *rseq_args, block=False)
            view.map(rseqc.RPKM_count, *rseq_args, block=False)
            view.map(rseqc.RPKM_saturation, *rseq_args, block=False)
            curr_files = tophat_outputs

        if stage == "coverage":
            logger.info("Calculating RNASeq metrics on %s." % (curr_files))
            nrun = len(curr_files)
            ref = prepare_ref_file(config["stage"][stage]["ref"],
                                          config)
            ribo = config["stage"][stage]["ribo"]
            picard = BroadRunner(config["program"]["picard"])
            out_dir = os.path.join(results_dir, stage)
            safe_makedir(out_dir)
            out_files = [replace_suffix(os.path.basename(x),
                                        "metrics") for x in curr_files]
            out_files = [os.path.join(out_dir, x) for x in out_files]
            out_files = view.map(picardrun.picard_rnaseq_metrics,
                                 [picard] * nrun,
                                 curr_files,
                                 [ref] * nrun,
                                 [ribo] * nrun,
                                 out_files)

        if stage == "htseq-count":
            _emit_stage_message(stage, curr_files)
            htseq_config = _get_stage_config(config, stage)
            htseq_args = zip(*product(curr_files, [config], [stage]))
            htseq_outputs = view.map(htseq_count.run_with_config,
                                     *htseq_args)
            combined_out = os.path.join(config["dir"]["results"], stage,
                                        "all_combined.counts")
            combined_out = htseq_count.combine_counts(htseq_outputs, None,
                                                      out_file=combined_out)

        if stage == "deseq":
            _emit_stage_message(stage, curr_files)
            deseq_config = _get_stage_config(config, stage)
            out_dir = os.path.join(config["dir"]["results"], stage)
            safe_makedir(out_dir)
            for comparison in deseq_config["comparisons"]:
                comparison_name = "_vs_".join(comparison)
                out_dir = os.path.join(results_dir, stage,
                                       comparison_name)
                safe_makedir(out_dir)
                indexes = [x for x, y in enumerate(conditions) if y
                           in comparison]
                htseq_files = [htseq_outputs[index] for index in indexes]
                htseq_columns = [conditions[index] for index in indexes]
                out_file = os.path.join(out_dir,
                                        comparison_name + ".counts.txt")
                combined_out = htseq_count.combine_counts(htseq_files,
                                                          htseq_columns,
                                                          out_file)
                deseq_conds = [conditions[index] for index in indexes]
                deseq_out = os.path.join(out_dir,
                                         comparison_name + ".deseq.txt")
                logger.info("Running deseq on %s with conditions %s "
                            "and writing to %s" % (combined_out,
                                                   conditions,
                                                   deseq_out))
                view.map(deseq.run, [combined_out], [deseq_conds], [deseq_out])
                annotated_file = view.map(annotate.annotate_table_with_biomart,
                                          [deseq_out],
                                          ["id"],
                                          ["ensembl_gene_id"],
                                          ["zebrafish"])


    # end gracefully
    stop_cluster()