示例#1
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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"]

    for stage in config["run"]:
        if stage == "download_encode":
            curr_files = _download_encode(config["encode_file"], config)
        elif stage == "fastqc":
            _run_fastqc(curr_files, config)
        elif stage == "trim":
            _run_trim(curr_files, config)
        elif stage == "align":
            _run_tophat(curr_files, config)


    cell_types = _get_cell_types(config["encode_file"])
    logger.info("files: %s" % (curr_files))
    logger.info("types: %s" % (cell_types))

    # end gracefully
    stop_cluster()
示例#2
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def _run_fastqc(curr_files, config):
    logger.info("Running fastqc on %s" % (str(curr_files)))
    nfiles = len(curr_files)
    fastqc_config = config["stage"]["fastqc"]
    out_files = view.map(fastqc.run, curr_files,
                         [fastqc_config] * nfiles,
                         [config] * nfiles)
    return out_files
示例#3
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文件: slim.py 项目: Xiuying/projects
def main(config_file):
    with open(config_file) as in_handle:
        config = yaml.load(in_handle)
    setup_logging(config)
    from rkinf.log import logger
    start_cluster(config)

    from rkinf.cluster import view
    input_files = [os.path.join(config["dir"]["data"], x) for x in
                   config["input"]]
    results_dir = config["dir"]["results"]

    map(safe_makedir, config["dir"].values())

    curr_files = input_files

    for stage in config["run"]:
        if stage == "fastqc":
            nfiles = len(curr_files)
            logger.info("Running %s on %s" % (stage, str(curr_files)))
            fastqc_config = _get_stage_config(config, stage)
            fastqc_outputs = view.map(fastqc.run, curr_files,
                                      [fastqc_config] * nfiles,
                                      [config] * nfiles)

        if stage == "cutadapt":
            nfiles = len(curr_files)
            cutadapt_config = _get_stage_config(config, stage)
            cutadapt_outputs = view.map(cutadapt_tool.run,
                                        curr_files,
                                        [cutadapt_config] * nfiles,
                                        [config] * nfiles)
            curr_files = cutadapt_outputs

        if stage == "novoalign":
            nfiles = len(curr_files)
            novoalign_config = _get_stage_config(config, stage)
            #db = novoindex.run(config["ref"],
            #                   _get_stage_config(config, "novoindex"),
            #                   config)
            db = config["genome"]["file"]
            novoalign_outputs = view.map(novoalign.run, curr_files,
                                         [db] * nfiles,
                                         [novoalign_config] * nfiles,
                                         [config] * nfiles)
            curr_files = novoalign_outputs

        if stage == "htseq-count":
            nfiles = len(curr_files)
            htseq_config = _get_stage_config(config, stage)
            htseq_outputs = view.map(htseq_count.run_with_config,
                                     curr_files,
                                     [config] * nfiles,
                                     [stage] * nfiles)
            combined_out = htseq_count.combine_counts(htseq_outputs,
                                                      "combined.counts")

    stop_cluster()
示例#4
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def main(config_file):
    with open(config_file) as in_handle:
        config = yaml.load(in_handle)
    setup_logging(config)
    from rkinf.log import logger
    start_cluster(config)

    from rkinf.cluster import view
    input_files = [
        os.path.join(config["dir"]["data"], x) for x in config["input"]
    ]
    results_dir = config["dir"]["results"]

    map(safe_makedir, config["dir"].values())

    curr_files = input_files

    for stage in config["run"]:
        if stage == "fastqc":
            nfiles = len(curr_files)
            logger.info("Running %s on %s" % (stage, str(curr_files)))
            fastqc_config = _get_stage_config(config, stage)
            fastqc_outputs = view.map(fastqc.run, curr_files,
                                      [fastqc_config] * nfiles,
                                      [config] * nfiles)

        if stage == "cutadapt":
            nfiles = len(curr_files)
            cutadapt_config = _get_stage_config(config, stage)
            cutadapt_outputs = view.map(cutadapt_tool.run, curr_files,
                                        [cutadapt_config] * nfiles,
                                        [config] * nfiles)
            curr_files = cutadapt_outputs

        if stage == "novoalign":
            nfiles = len(curr_files)
            novoalign_config = _get_stage_config(config, stage)
            #db = novoindex.run(config["ref"],
            #                   _get_stage_config(config, "novoindex"),
            #                   config)
            db = config["genome"]["file"]
            novoalign_outputs = view.map(novoalign.run, curr_files,
                                         [db] * nfiles,
                                         [novoalign_config] * nfiles,
                                         [config] * nfiles)
            curr_files = novoalign_outputs

        if stage == "htseq-count":
            nfiles = len(curr_files)
            htseq_config = _get_stage_config(config, stage)
            htseq_outputs = view.map(htseq_count.run_with_config, curr_files,
                                     [config] * nfiles, [stage] * nfiles)
            combined_out = htseq_count.combine_counts(htseq_outputs,
                                                      "combined.counts")

    stop_cluster()
示例#5
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def _combine_and_write(dataframes, out_file):
    from rkinf.log import logger
    import pandas as pd
    from bcbio.utils import file_exists
    logger.info("Writing combined file to %s." % (out_file))
    if file_exists(out_file):
        return out_file
    merged = pd.concat(dataframes)
    df_subset = merged[TO_KEEP]
    df_subset.to_csv(out_file, index=False, sep="\t")
    return out_file
示例#6
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def _download_encode(input_file, config):
    """ grab the encode files they listed in their file """
    NAME_FIELD = 0
    if not os.path.exists(input_file):
        logger.info("Error %s does not exist, aborting." % (input_file))
        exit(-1)

    with open(input_file) as in_handle:
        reader = csv.reader(in_handle, delimiter="\t")
        files = [x[NAME_FIELD] for x in reader]
    logger.info("Downloading %s." % (files))
    data_dir = config["dir"].get("data", "data")
    out_files = view.map(_download_ref, files, [data_dir] * len(files))

    return out_files
示例#7
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def _run_trim(curr_files, config):
    logger.info("Trimming poor quality ends from %s" % (str(curr_files)))
    nfiles = len(curr_files)
    min_length = str(config["stage"]["trim"].get("min_length", 20))
    pair = str(config["stage"]["trim"].get("pair", "se"))
    platform = str(config["stage"]["trim"].get("platform", "sanger"))
    out_dir = os.path.join(config["dir"]["results"], "trimmed")
    safe_makedir(out_dir)
    out_files = [append_stem(os.path.basename(x), "trim") for
                 x in curr_files]
    out_files = [os.path.join(out_dir, x) for x in out_files]
    out_files = view.map(sickle.run, curr_files,
                         [pair] * nfiles,
                         [platform] * nfiles,
                         [min_length] * nfiles,
                         out_files)
    return out_files
示例#8
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def _annotate_df(in_file, join_column, organism, out_file=None):
    from rkinf.log import logger
    from rkinf.utils import append_stem
    from rpy2 import robjects
    ORG_TO_ENSEMBL = {"opossum": {"gene_ensembl": "mdomestica_gene_ensembl",
                                  "gene_symbol": "hgnc_symbol"},
                      "mouse": {"gene_ensembl": "mmusculus_gene_ensembl",
                                "gene_symbol": "mgi_symbol"},
                      "human": {"gene_ensembl": "hsapiens_gene_ensembl",
                                "gene_symbol": "hgnc_symbol"},
                      "taz": {"gene_ensembl": "sharrisii_gene_ensembl",
                              "gene_symbol": "hgnc_symbol"}}

    if organism not in ORG_TO_ENSEMBL:
        logger.error("organism not supported")
        exit(1)

    logger.info("Annotating %s." % (organism))
    if not out_file:
        out_file = append_stem(in_file, "annotated")
    if os.path.exists(out_file):
        return out_file
    # use biomaRt to annotate the data file
    r = robjects.r
    r.assign('join_column', join_column)
    r.assign('in_file', in_file)
    r.assign('out_file', out_file)
    r.assign('ensembl_gene', ORG_TO_ENSEMBL[organism]["gene_ensembl"])
    r.assign('gene_symbol', ORG_TO_ENSEMBL[organism]["gene_symbol"])
    r('''
    library(biomaRt)
    ensembl = useMart("ensembl", dataset = ensembl_gene)
    d = read.table(in_file, header=TRUE)
    a = getBM(attributes=c("ensembl_transcript_id", "ensembl_gene_id",
                gene_symbol, "description"),
                filters=c("ensembl_transcript_id"), values=d[,join_column],
                mart=ensembl)
    m = merge(d, a, by.x=join_column, by.y="ensembl_transcript_id")
    write.table(m, out_file, quote=FALSE, row.names=FALSE, sep="\t")
    ''')

    return out_file
示例#9
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def main(config_file):
    with open(config_file) as in_handle:
        config = yaml.load(in_handle)
    setup_logging(config)
    start_cluster(config)

    # after the cluster is up, import a view to it
    from rkinf.cluster import view
    in_file = config.get("query")

    # de-parallelize for now
    blast_results = []
    for stage in config["run"]:
        if config["stage"][stage]["program"] == "blastn":
            blastn_config = config["stage"][stage]
            blast_results = [blastn.run(in_file, ref, blastn_config, config) for
                             ref in config["refs"]]

    for identity in config["min_identity"]:
        filtered_results = []
        for blast_result in blast_results:
            filtered_results.append(blastn.filter_results_by_length(
                blast_result, identity))

        fasta_hits = set()
        for filtered_result in filtered_results:
            fasta_hits.update(blastn.get_id_of_hits(filtered_result))

        def in_set_predicate(x):
            return x.id in fasta_hits

        outfile = os.path.join(build_results_dir(blastn_config, config),
                               append_stem(os.path.basename(in_file),
                                           str(identity) + "_filt"))

        fasta_filtered = fasta.filter_fasta(in_file,
                                            in_set_predicate,
                                            outfile)

        trimmed = _trim(fasta_filtered, filtered_results)
        org_names = [x["name"] for x in config["refs"]]
        logger.info(trimmed)
        logger.info(filtered_results)
        logger.info(org_names)
        combined = _make_combined_csv(trimmed, filtered_results, org_names)

    stop_cluster()
示例#10
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def main(config_file):
    with open(config_file) as in_handle:
        config = yaml.load(in_handle)
    setup_logging(config)
    start_cluster(config)

    # after the cluster is up, import a view to it
    from rkinf.cluster import view
    in_file = config.get("query")
    org_names = [x["name"] for x in config["refs"]]

    curr_files = in_file

    for stage in config["run"]:
        if stage == "blastn":
            logger.info("Running %s on %s." % (stage, curr_files))
            blastn_config = config["stage"][stage]
            refs = config["refs"]
            args = zip(*itertools.product([curr_files], refs,
                                          [blastn_config], [config]))
            blastn_results = view.map(blastn.run, *args)
            curr_files = blastn_results

        if stage == "annotate":
            logger.info("Running %s on %s." % (stage, curr_files))
            # annotate the data frames
            args = zip(*itertools.product(curr_files, ["sseqid"],
                                          org_names))
            annotated = view.map(_annotate_df, *args)
            curr_files = annotated

        if stage == "combine":
            out_fname = os.path.join(os.path.dirname(curr_files[0]),
                                                     append_stem(in_file,
                                                                 "combined"))
            logger.info("Combining %s into %s." % (curr_files, out_fname))
            org_names = [x["name"] for x in config["refs"]]
            #       combined = _make_combined_csv(curr_files, org_names, out_fname)

    stop_cluster()
示例#11
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def main(config_file):

    with open(config_file) as in_handle:
        config = yaml.load(in_handle)
    setup_logging(config)
    start_cluster(config)

    # after the cluster is up, import the view to i
    from rkinf.cluster import view
    input_files = config["input"]
    results_dir = config["dir"]["results"]

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

    curr_files = input_files

    ## qc steps
    for stage in config["run"]:
        if stage == "fastqc":
            # run the basic fastqc
            logger.info("Running %s on %s" % (stage, str(curr_files)))
            fastqc_config = config["stage"][stage]
            fastqc_outputs = view.map(fastqc.run, curr_files,
                                      [fastqc_config] * len(curr_files),
                                      [config] * len(curr_files))
            # this does nothing for now, not implemented yet
            summary_file = _combine_fastqc(fastqc_outputs)

        if stage == "trim":
            logger.info("Trimming poor quality ends "
                        " from %s" % (str(curr_files)))
            nlen = len(curr_files)
            min_length = str(config["stage"][stage].get("min_length", 20))

            # trim low quality ends of reads
            # do this dirty for now
            out_dir = os.path.join(results_dir, "trimmed")
            safe_makedir(out_dir)
            out_files = [append_stem(os.path.basename(x), "trim") for
                         x in curr_files]
            out_files = [os.path.join(out_dir, x) for x in out_files]
            # XXX remove the magic number of 10 the length of the
            # minimum read to keep
            out_files = view.map(sickle.run, curr_files,
                                 ["se"] * nlen,
                                 ["sanger"] * nlen,
                                 [min_length] * nlen,
                                 out_files)
            curr_files = out_files

        if stage == "tagdust":
            input_files = curr_files
            # remove tags matching the other miRNA tested
            logger.info("Running %s on %s." % (stage, input_files))
            tagdust_config = config["stage"][stage]
            tagdust_outputs = view.map(tagdust.run, input_files,
                                       [tagdust_config] * len(input_files),
                                       [config] * len(input_files))
            curr_files = [x[0] for x in tagdust_outputs]

        if stage == "filter_length":
            # filter out reads below or above a certain length
            filter_config = config["stage"][stage]
            min_length = filter_config.get("min_length", 0)
            max_length = filter_config.get("max_length", MAX_READ_LENGTH)

            # length predicate
            def length_filter(x):
                return min_length < len(x.seq) < max_length

            # filter the input reads based on length
            # parallelizing this doesn't seem to work
            # ipython can't accept closures as an argument to view.map()
            """
            filtered_fastq = view.map(filter_seqio, tagdust_outputs,
                                      [lf] * len(tagdust_outputs),
                                      ["filt"] * len(tagdust_outputs),
                                      ["fastq"] * len(tagdust_outputs))"""
            out_files = [append_stem(os.path.basename(input_file[0]),
                         "filt") for input_file in tagdust_outputs]
            out_dir = os.path.join(config["dir"]["results"],
                                   "length_filtered")
            safe_makedir(out_dir)
            out_files = [os.path.join(out_dir, x) for x in out_files]

            filtered_fastq = [filter_seqio(x[0], length_filter, y, "fastq")
                              for x, y in zip(tagdust_outputs, out_files)]

            curr_files = filtered_fastq

        if stage == "count_ends":
            logger.info("Compiling nucleotide counts at 3' and 5' ends.")
            # count the nucleotide at the end of each read
            def count_ends(x, y):
                """ keeps a running count of an arbitrary set of keys
                during the reduce step """
                x[y] = x.get(y, 0) + 1
                return x

            def get_3prime_end(x):
                return str(x.seq[-1])

            def get_5prime_end(x):
                return str(x.seq[0])

            def output_counts(end_function, count_file):
                # if the count_file already exists, skip
                outdir = os.path.join(config["dir"]["results"], stage)
                safe_makedir(outdir)
                count_file = os.path.join(outdir, count_file)
                if os.path.exists(count_file):
                    return count_file
                # outputs a tab file of the counts at the end
                # of the fastq files kj
                counts = [reduce(count_ends,
                                 apply_seqio(x, end_function, kind="fastq"),
                                 {}) for x in curr_files]
                df = pd.DataFrame(counts,
                                  index=map(_short_name, curr_files))
                df = df.astype(float)
                total = df.sum(axis=1)
                df = df.div(total, axis=0)
                df["total"] = total
                df.to_csv(count_file, sep="\t")

            output_counts(get_3prime_end, "3prime_counts.tsv")
            output_counts(get_5prime_end, "5prime_counts.tsv")

        if stage == "tophat":
            tophat_config = config["stage"][stage]
            logger.info("Running tophat on %s" % (str(curr_files)))
            nlen = len(curr_files)
            pair_file = None
            ref_file = tophat_config["annotation"]
            out_base = os.path.join(results_dir, "mirna")
            align_dir = os.path.join(results_dir, "tophat")
            config = config
            tophat_files = view.map(tophat.align,
                                    curr_files,
                                    [pair_file] * nlen,
                                    [ref_file] * nlen,
                                    [out_base] * nlen,
                                    [align_dir] * nlen,
                                    [config] * nlen)
            curr_files = tophat_files

        if stage == "novoalign":
            logger.info("Running novoalign on %s" % (str(curr_files)))
            # align
            ref = config["genome"]["file"]
            novoalign_config = config["stage"][stage]
            aligned_outputs = view.map(novoalign.run, curr_files,
                                       [ref] * len(curr_files),
                                       [novoalign_config] * len(curr_files),
                                       [config] * len(curr_files))
            # convert sam to bam, sort and index
            picard = BroadRunner(config["program"]["picard"])
            bamfiles = view.map(picardrun.picard_formatconverter,
                                [picard] * len(aligned_outputs),
                                aligned_outputs)
            sorted_bf = view.map(picardrun.picard_sort,
                                 [picard] * len(bamfiles),
                                 bamfiles)
            # these files are the new starting point for the downstream
            # analyses, so copy them over into the data dir and setting
            # them to read only
            data_dir = os.path.join(config["dir"]["data"], stage)
            safe_makedir(data_dir)
            view.map(shutil.copy, sorted_bf, [data_dir] * len(sorted_bf))
            new_files = [os.path.join(data_dir, x) for x in
                         map(os.path.basename, sorted_bf)]
            [os.chmod(x, stat.S_IREAD | stat.S_IRGRP) for x in new_files]
            # index the bam files for later use
            view.map(picardrun.picard_index, [picard] * len(new_files),
                     new_files)

            curr_files = new_files

        if stage == "new_coverage":
            logger.info("Calculating RNASeq metrics on %s." % (curr_files))
            nrun = len(curr_files)
            ref = blastn.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, "new_coverage")
            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)
            curr_files = out_files

        if stage == "coverage":
            gtf = blastn.prepare_ref_file(config["annotation"], config)
            logger.info("Calculating coverage of features in %s for %s"
                        % (gtf, str(sorted_bf)))
            out_files = [replace_suffix(x, "counts.bed") for
                         x in sorted_bf]
            out_dir = os.path.join(results_dir, stage)
            safe_makedir(out_dir)
            logger.info(out_files)
            out_files = [os.path.join(out_dir,
                                      os.path.basename(x)) for x in out_files]
            logger.info(out_files)
            view.map(bedtools.count_overlaps, sorted_bf,
                     [gtf] * len(sorted_bf),
                     out_files)

    stop_cluster()