view graphprot_predict_wrapper.py @ 3:ace92c9a4653 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/graphprot commit efcac98677c3ea9039c1c61eaa9e58f78287ccb3"

#!/usr/bin/env python3

import argparse as ap
import os
import subprocess
import sys

import gplib


"""

TOOL DEPENDENCIES
=================

GraphProt 1.1.7
Best install via:
https://anaconda.org/bioconda/graphprot
Tested with: miniconda3, conda 4.7.12


Script: What's my job this time, master?
Author: It'll be a though one.
Script: I take this as a given.
Author: Oh yeah?
Script: ... I'm ready.


OUTPUT FILES
============

    data_id.avg_profile
    data_id.avg_profile.peaks.bed
--conf-out
    data_id.avg_profile.p50.peaks.bed
--gen-site-bed
    data_id.avg_profile.genomic_peaks.bed
--conf-out --gen-site-bed
    data_id.avg_profile.p50.genomic_peaks.bed
--ws-pred
    data_id.predictions
--ws-pred --conf-out
    data_id.predictions
    data_id.p50.predictions


EXAMPLE CALLS
=============

flake8 coming out of hotel room. FB enters.
FB: Who is this f*** ???


python graphprot_predict_wrapper.py --model test2.model --params test2.params
--fasta gp_data/test10_predict.fa --data-id test2pred --gp-output
python graphprot_predict_wrapper.py --model test2.model --params test2.params
--fasta gp_data/test10_predict.fa --data-id test2pred
--gen-site-bed gp_data/test10_predict.bed

python graphprot_predict_wrapper.py --model test2.model --params test2.params
--fasta gp_data/test10_predict.fa --data-id test2pred
--gen-site-bed gp_data/test10_predict.bed --conf-out

python graphprot_predict_wrapper.py --model test2.model --params test2.params
--fasta gp_data/test10_predict.fa --data-id test2pred --conf-out --ws-pred

python graphprot_predict_wrapper.py --model test-data/test.model
--params test-data/test.params --fasta test-data/test_predict.fa
--data-id predtest

python graphprot_predict_wrapper.py --model test-data/test.model
--params test-data/test.params --fasta test-data/test_predict.fa
--data-id predtest --gen-site-bed test-data/test_predict.bed
--sc-thr 0.0 --max-merge-dist 0 --conf-out  --ap-extlr 5

python graphprot_predict_wrapper.py --data-id GraphProt
--fasta test-data/test_predict.fa --model test-data/test.model
--params test-data/test.params --gen-site-bed test-data/test_predict.bed
--sc-thr 0.0 --max-merge-dist 0 --conf-out  --ap-extlr 5

"""


###############################################################################

def setup_argument_parser():
    """Setup argparse parser."""
    help_description = """
    Galaxy wrapper script for GraphProt (-action predict and -action
    predict_profile) to compute whole site or position-wise scores for input
    FASTA sequences.
    By default, profile predictions are calculated, followed by average
    profiles computions and peak regions extraction from average profiles.
    If --ws-pred is set, whole site score predictions on input sequences
    will be run instead.
    If --conf-out is set, sites or peak regions with a score >= the median
    score of positive training sites will be output.
    If --gen-site-bed .bed file is provided, peak regions will be output
    with genomic coordinates too.

    """
    # Define argument parser.
    p = ap.ArgumentParser(add_help=False,
                          prog="graphprot_predict_wrapper.py",
                          description=help_description,
                          formatter_class=ap.MetavarTypeHelpFormatter)

    # Argument groups.
    p_man = p.add_argument_group("REQUIRED ARGUMENTS")
    p_opt = p.add_argument_group("OPTIONAL ARGUMENTS")

    # Required arguments.
    p_opt.add_argument("-h", "--help",
                       action="help",
                       help="Print help message")
    p_man.add_argument("--fasta",
                       dest="in_fa",
                       type=str,
                       required=True,
                       help="Sequences .fa file to predict"
                            " on (option -fasta)")
    p_man.add_argument("--model",
                       dest="in_model",
                       type=str,
                       required=True,
                       help="GraphProt model file to use for predictions"
                            " (option -model)")
    p_man.add_argument("--params",
                       dest="in_params",
                       type=str,
                       required=True,
                       help="Parameter file for given model")
    p_man.add_argument("--data-id",
                       dest="data_id",
                       type=str,
                       required=True,
                       help="Data ID (option -prefix)")
    # ---> I'm  a conditional argument <---
    p_opt.add_argument("--ws-pred",
                       dest="ws_pred",
                       default=False,
                       action="store_true",
                       help="Run a whole site prediction instead "
                            "of calculating profiles (default: false)")
    # Additional arguments.
    p_opt.add_argument("--sc-thr",
                       dest="score_thr",
                       type=float,
                       default=0,
                       help="Score threshold for extracting "
                            "average profile peak regions (default: 0)")
    p_opt.add_argument("--max-merge-dist",
                       dest="max_merge_dist",
                       type=int,
                       default=0,
                       choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                       help="Maximum merge distance for nearby peak regions"
                            " (default: report all non-overlapping regions)")
    p_opt.add_argument("--gen-site-bed",
                       dest="genomic_sites_bed",
                       type=str,
                       help=".bed file specifying the genomic regions of "
                            "the input .fa sequences. Corrupt .bed "
                            "information will be punished (default: false)")
    p_opt.add_argument("--conf-out",
                       dest="conf_out",
                       default=False,
                       action="store_true",
                       help="Output filtered peak regions BED file or "
                            "predictions file (if --ws-pred) using the median "
                            "positive training site score for filtering "
                            "(default: false)")
    p_opt.add_argument("--gp-output",
                       dest="gp_output",
                       default=False,
                       action="store_true",
                       help="Print output produced by GraphProt "
                            "(default: false)")
    p_opt.add_argument("--ap-extlr",
                       dest="ap_extlr",
                       type=int,
                       default=5,
                       choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                       help="Define average profile up- and downstream "
                            "extension to produce the average profile. "
                            "The mean over small sequence windows "
                            "(window length = --ap-extlr*2 + 1) is used to "
                            "get position scores, thus the average profile "
                            "is more smooth than the initial profile output "
                            "by GraphProt (default: 5)")
    return p


###############################################################################

if __name__ == '__main__':

    # Setup argparse.
    parser = setup_argument_parser()
    # Read in command line arguments.
    args = parser.parse_args()

    """
    Do all sorts of sanity checking.

    """
    # Check for Linux.
    assert "linux" in sys.platform, "please use Linux"
    # Check tool availability.
    assert gplib.is_tool("GraphProt.pl"), "GraphProt.pl not in PATH"
    # Check file inputs.
    assert os.path.exists(args.in_fa), \
        "input .fa file \"%s\" not found" % (args.in_fa)
    assert os.path.exists(args.in_model), \
        "input .model file \"%s\" not found" % (args.in_model)
    assert os.path.exists(args.in_params), \
        "input .params file \"%s\" not found" % (args.in_params)
    # Count .fa entries.
    c_in_fa = gplib.count_fasta_headers(args.in_fa)
    assert c_in_fa, "input .fa file \"%s\" no headers found" % (args.in_fa)
    print("# input .fa sequences:   %i" % (c_in_fa))
    # Read in FASTA sequences to check for uppercase sequences.
    seqs_dic = gplib.read_fasta_into_dic(args.in_fa)
    c_uc_nt = gplib.seqs_dic_count_uc_nts(seqs_dic)
    assert c_uc_nt, "no uppercase nucleotides in input .fa sequences. "\
                    "Please change sequences to uppercase (keep in mind "\
                    "GraphProt only scores uppercase regions (according "\
                    "to its viewpoint concept)"
    if not args.ws_pred:
        # Check for lowercase sequences.
        c_lc_nt = gplib.seqs_dic_count_lc_nts(seqs_dic)
        assert not c_lc_nt, "lowercase nucleotides in input .fa not allowed"\
            " in profile predictions, since GraphProt only scores "\
            "uppercase regions (according to its viewpoint concept)"
    # Check .bed.
    if args.genomic_sites_bed:
        # An array of checks, marvelous.
        assert \
            os.path.exists(args.genomic_sites_bed), \
            "genomic .bed file \"%s\" not found" % (args.genomic_sites_bed)
        # Check .bed for content.
        assert gplib.count_file_rows(args.genomic_sites_bed), \
            "genomic .bed file \"%s\" is empty" % (args.genomic_sites_bed)
        # Check .bed for 6-column format.
        assert gplib.bed_check_six_col_format(args.genomic_sites_bed), \
            "genomic .bed file \"%s\" appears to not "\
            "be in 6-column .bed format" % (args.genomic_sites_bed)
        # Check for unique column 4 IDs.
        assert gplib.bed_check_unique_ids(args.genomic_sites_bed), \
            "genomic .bed file \"%s\" column 4 IDs not unique" % \
            (args.genomic_sites_bed)
        # Read in .bed regions, compare to FASTA sequences.
        seq_len_dic = gplib.get_seq_lengths_from_seqs_dic(seqs_dic)
        reg_len_dic = gplib.bed_get_region_lengths(args.genomic_sites_bed)
        for seq_id in seq_len_dic:
            seq_l = seq_len_dic[seq_id]
            assert seq_id in reg_len_dic, \
                "sequence ID \"%s\" missing in input .bed \"%s\"" % \
                (seq_id, args.genomic_sites_bed)
            reg_l = reg_len_dic[seq_id]
            assert seq_l == reg_l, \
                "sequence length differs from .bed region length (%i != %i)" \
                % (seq_l, reg_l)
    # Read in model parameters.
    param_dic = gplib.graphprot_get_param_dic(args.in_params)
    # Create GraphProt parameter string.
    param_string = gplib.graphprot_get_param_string(args.in_params)

    """
    Run predictions.

    """
    if args.ws_pred:
        # Do whole site prediction.
        print("Starting whole site predictions on input .fa file"
              " (-action predict) ... ")
        check_cmd = "GraphProt.pl -action predict -prefix " \
            + args.data_id + " -fasta " + args.in_fa + " " \
            + param_string + " -model " + args.in_model
        output = subprocess.getoutput(check_cmd)
        assert output, "the following call of GraphProt.pl "\
            "produced no output:\n%s" % (check_cmd)
        if args.gp_output:
            print(output)
        ws_predictions_file = args.data_id + ".predictions"
        assert os.path.exists(ws_predictions_file), \
            "Whole site prediction output .predictions file \"%s\" not found" \
            % (ws_predictions_file)
        if args.conf_out:
            # Filter by pos_train_ws_pred_median median.
            assert "pos_train_ws_pred_median" in param_dic, \
                "whole site top scores median information "\
                "missing in .params file"
            pos_tr_ws_pred_med = \
                float(param_dic["pos_train_ws_pred_median"])
            # Filtered file.
            filt_ws_predictions_file = args.data_id + ".p50.predictions"
            print("Extracting p50 sites from whole site predictions"
                  " (score threshold = %f) ... " % (pos_tr_ws_pred_med))
            gplib.graphprot_filter_predictions_file(ws_predictions_file,
                                                    filt_ws_predictions_file,
                                                    sc_thr=pos_tr_ws_pred_med)
    else:
        # Do profile prediction.
        print("Starting profile predictions on on input .fa file "
              "(-action predict_profile) ... ")
        check_cmd = "GraphProt.pl -action predict_profile -prefix " \
                    + args.data_id + " -fasta " + args.in_fa + " " + \
                    param_string + " -model " + args.in_model
        output = subprocess.getoutput(check_cmd)
        assert output, "the following call of GraphProt.pl produced "\
                       "no output:\n%s" % (check_cmd)
        if args.gp_output:
            print(output)
        profile_predictions_file = args.data_id + ".profile"
        assert os.path.exists(profile_predictions_file), \
            "Profile prediction output .profile file \"%s\" not found" % \
            (profile_predictions_file)

        # Profile prediction output files.
        avg_prof_file = args.data_id + ".avg_profile"
        avg_prof_peaks_file = args.data_id + ".avg_profile.peaks.bed"
        avg_prof_gen_peaks_file = args.data_id + \
            ".avg_profile.genomic_peaks.bed"
        avg_prof_peaks_p50_file = args.data_id + \
            ".avg_profile.p50.peaks.bed"
        avg_prof_gen_peaks_p50_file = args.data_id + \
            ".avg_profile.p50.genomic_peaks.bed"

        # Get sequence IDs in order from input .fa file.
        seq_ids_list = gplib.fasta_read_in_ids(args.in_fa)
        # Calculate average profiles.
        print("Getting average profile from profile "
              "(extlr for smoothing: %i) ... " % (args.ap_extlr))
        gplib.graphprot_profile_calc_avg_profile(profile_predictions_file,
                                                 avg_prof_file,
                                                 ap_extlr=args.ap_extlr,
                                                 seq_ids_list=seq_ids_list,
                                                 method=2)
        # Extract peak regions on sequences with threshold score 0.
        print("Extracting peak regions from average profile "
              "(score threshold = 0) ... ")
        killpep8 = args.max_merge_dist
        gplib.graphprot_profile_extract_peak_regions(avg_prof_file,
                                                     avg_prof_peaks_file,
                                                     max_merge_dist=killpep8,
                                                     sc_thr=args.score_thr)
        # Convert peaks to genomic coordinates.
        if args.genomic_sites_bed:
            print("Converting peak regions to genomic coordinates ... ")
            killit = args.genomic_sites_bed
            gplib.bed_peaks_to_genomic_peaks(avg_prof_peaks_file,
                                             avg_prof_gen_peaks_file,
                                             print_rows=False,
                                             genomic_sites_bed=killit)
        # Extract peak regions with threshold score p50.
        if args.conf_out:
            sc_id = "pos_train_avg_profile_median_%i" % (args.ap_extlr)
            # Filter by pos_tr_ws_pred_med median.
            assert sc_id in param_dic, "average profile extlr %i median "\
                "information missing in .params file" % (args.ap_extlr)
            p50_sc_thr = float(param_dic[sc_id])
            print("Extracting p50 peak regions from average profile "
                  "(score threshold = %f) ... " % (p50_sc_thr))
            despair = avg_prof_peaks_p50_file
            pain = args.max_merge_dist
            gplib.graphprot_profile_extract_peak_regions(avg_prof_file,
                                                         despair,
                                                         max_merge_dist=pain,
                                                         sc_thr=p50_sc_thr)
            # Convert peaks to genomic coordinates.
            if args.genomic_sites_bed:
                print("Converting p50 peak regions to "
                      "genomic coordinates ... ")
                madness = args.genomic_sites_bed
                gplib.bed_peaks_to_genomic_peaks(avg_prof_peaks_p50_file,
                                                 avg_prof_gen_peaks_p50_file,
                                                 genomic_sites_bed=madness)
    # Done.
    print("Script: I'm done.")
    print("Author: ... ")