view amplicon_analysis_pipeline.py @ 4:86a12d75ebe4 draft default tip

planemo upload for repository https://github.com/pjbriggs/Amplicon_analysis-galaxy commit 7be61b7ed35ca3deaad68d2eae384c8cd365bcb8

#!/usr/bin/env python
#
# Wrapper script to run Amplicon_analysis_pipeline.sh
# from Galaxy tool

import sys
import os
import argparse
import subprocess
import glob

class PipelineCmd(object):
    def __init__(self,cmd):
        self.cmd = [str(cmd)]
    def add_args(self,*args):
        for arg in args:
            self.cmd.append(str(arg))
    def __repr__(self):
        return ' '.join([str(arg) for arg in self.cmd])

def ahref(target,name=None,type=None):
    if name is None:
        name = os.path.basename(target)
    ahref = "<a href='%s'" % target
    if type is not None:
        ahref += " type='%s'" % type
    ahref += ">%s</a>" % name
    return ahref

def check_errors():
    # Errors in Amplicon_analysis_pipeline.log
    with open('Amplicon_analysis_pipeline.log','r') as pipeline_log:
        log = pipeline_log.read()
        if "Names in the first column of Metatable.txt and in the second column of Final_name.txt do not match" in log:
            print_error("""*** Sample IDs don't match dataset names ***

The sample IDs (first column of the Metatable file) don't match the
supplied sample names for the input Fastq pairs.
""")
    # Errors in pipeline output
    with open('pipeline.log','r') as pipeline_log:
        log = pipeline_log.read()
        if "Errors and/or warnings detected in mapping file" in log:
            with open("Metatable_log/Metatable.log","r") as metatable_log:
                # Echo the Metatable log file to the tool log
                print_error("""*** Error in Metatable mapping file ***

%s""" % metatable_log.read())
        elif "No header line was found in mapping file" in log:
            # Report error to the tool log
            print_error("""*** No header in Metatable mapping file ***

Check you've specified the correct file as the input Metatable""")

def print_error(message):
    width = max([len(line) for line in message.split('\n')]) + 4
    sys.stderr.write("\n%s\n" % ('*'*width))
    for line in message.split('\n'):
        sys.stderr.write("* %s%s *\n" % (line,' '*(width-len(line)-4)))
    sys.stderr.write("%s\n\n" % ('*'*width))

def clean_up_name(sample):
    # Remove extensions and trailing "_L[0-9]+_001" from
    # Fastq pair names
    sample_name = '.'.join(sample.split('.')[:1])
    split_name = sample_name.split('_')
    if split_name[-1] == "001":
        split_name = split_name[:-1]
    if split_name[-1].startswith('L'):
        try:
            int(split_name[-1][1:])
            split_name = split_name[:-1]
        except ValueError:
            pass
    return '_'.join(split_name)

def list_outputs(filen=None):
    # List the output directory contents
    # If filen is specified then will be the filename to
    # write to, otherwise write to stdout
    if filen is not None:
        fp = open(filen,'w')
    else:
        fp = sys.stdout
    results_dir = os.path.abspath("RESULTS")
    fp.write("Listing contents of output dir %s:\n" % results_dir)
    ix = 0
    for d,dirs,files in os.walk(results_dir):
        ix += 1
        fp.write("-- %d: %s\n" % (ix,
                                  os.path.relpath(d,results_dir)))
        for f in files:
            ix += 1
            fp.write("---- %d: %s\n" % (ix,
                                        os.path.relpath(f,results_dir)))
    # Close output file
    if filen is not None:
        fp.close()

if __name__ == "__main__":
    # Command line
    print "Amplicon analysis: starting"
    p = argparse.ArgumentParser()
    p.add_argument("metatable",
                   metavar="METATABLE_FILE",
                   help="Metatable.txt file")
    p.add_argument("fastq_pairs",
                   metavar="SAMPLE_NAME FQ_R1 FQ_R2",
                   nargs="+",
                   default=list(),
                   help="Triplets of SAMPLE_NAME followed by "
                   "a R1/R2 FASTQ file pair")
    p.add_argument("-g",dest="forward_pcr_primer")
    p.add_argument("-G",dest="reverse_pcr_primer")
    p.add_argument("-q",dest="trimming_threshold")
    p.add_argument("-O",dest="minimum_overlap")
    p.add_argument("-L",dest="minimum_length")
    p.add_argument("-l",dest="sliding_window_length")
    p.add_argument("-P",dest="pipeline",
                   choices=["Vsearch","DADA2"],
                   type=str,
                   default="Vsearch")
    p.add_argument("-S",dest="use_silva",action="store_true")
    p.add_argument("-H",dest="use_homd",action="store_true")
    p.add_argument("-r",dest="reference_data_path")
    p.add_argument("-c",dest="categories_file")
    args = p.parse_args()

    # Build the environment for running the pipeline
    print "Amplicon analysis: building the environment"
    metatable_file = os.path.abspath(args.metatable)
    os.symlink(metatable_file,"Metatable.txt")
    print "-- made symlink to Metatable.txt"

    # Link to Categories.txt file (if provided)
    if args.categories_file is not None:
        categories_file = os.path.abspath(args.categories_file)
        os.symlink(categories_file,"Categories.txt")
        print "-- made symlink to Categories.txt"

    # Link to FASTQs and construct Final_name.txt file
    sample_names = []
    print "-- making Final_name.txt"
    with open("Final_name.txt",'w') as final_name:
        fastqs = iter(args.fastq_pairs)
        for sample_name,fqr1,fqr2 in zip(fastqs,fastqs,fastqs):
            sample_name = clean_up_name(sample_name)
            print "   %s" % sample_name
            r1 = "%s_R1_.fastq" % sample_name
            r2 = "%s_R2_.fastq" % sample_name
            os.symlink(fqr1,r1)
            os.symlink(fqr2,r2)
            final_name.write("%s\n" % '\t'.join((r1,sample_name)))
            final_name.write("%s\n" % '\t'.join((r2,sample_name)))
            sample_names.append(sample_name)

    # Reference database
    if args.pipeline == "Vsearch":
        if args.use_silva:
            ref_database = "silva"
        elif args.use_homd:
            ref_database = "homd"
        else:
            ref_database = "gg"
    elif args.pipeline == "DADA2":
        ref_database = "silva"

    # Construct the pipeline command
    print "Amplicon analysis: constructing pipeline command"
    pipeline = PipelineCmd("Amplicon_analysis_pipeline.sh")
    if args.forward_pcr_primer:
        pipeline.add_args("-g",args.forward_pcr_primer)
    if args.reverse_pcr_primer:
        pipeline.add_args("-G",args.reverse_pcr_primer)
    if args.trimming_threshold:
        pipeline.add_args("-q",args.trimming_threshold)
    if args.minimum_overlap:
        pipeline.add_args("-O",args.minimum_overlap)
    if args.minimum_length:
        pipeline.add_args("-L",args.minimum_length)
    if args.sliding_window_length:
        pipeline.add_args("-l",args.sliding_window_length)
    if args.reference_data_path:
        pipeline.add_args("-r",args.reference_data_path)
    pipeline.add_args("-P",args.pipeline)
    if args.pipeline == "Vsearch":
        if ref_database == "silva":
            pipeline.add_args("-S")
        elif ref_database == "homd":
            pipeline.add_args("-H")

    # Echo the pipeline command to stdout
    print "Running %s" % pipeline

    # Run the pipeline
    with open("pipeline.log","w") as pipeline_out:
        try:
            subprocess.check_call(pipeline.cmd,
                                  stdout=pipeline_out,
                                  stderr=subprocess.STDOUT)
            exit_code = 0
            print "Pipeline completed ok"
        except subprocess.CalledProcessError as ex:
            # Non-zero exit status
            sys.stderr.write("Pipeline failed: exit code %s\n" %
                             ex.returncode)
            exit_code = ex.returncode
        except Exception as ex:
            # Some other problem
            sys.stderr.write("Unexpected error: %s\n" % str(ex))
            exit_code = 1

    # Write out the list of outputs
    outputs_file = "Pipeline_outputs.txt"
    list_outputs(outputs_file)

    # Check for log file
    log_file = "Amplicon_analysis_pipeline.log"
    if os.path.exists(log_file):
        print "Found log file: %s" % log_file
        if exit_code == 0:
            # Create an HTML file to link to log files etc
            # NB the paths to the files should be correct once
            # copied by Galaxy on job completion
            with open("pipeline_outputs.html","w") as html_out:
                html_out.write("""<html>
<head>
<title>Amplicon analysis pipeline: log files</title>
<head>
<body>
<h1>Amplicon analysis pipeline: log files</h1>
<ul>
""")
                html_out.write(
                    "<li>%s</li>\n" %
                    ahref("Amplicon_analysis_pipeline.log",
                          type="text/plain"))
                html_out.write(
                    "<li>%s</li>\n" %
                    ahref("pipeline.log",type="text/plain"))
                html_out.write(
                    "<li>%s</li>\n" %
                    ahref("Pipeline_outputs.txt",
                          type="text/plain"))
                html_out.write(
                    "<li>%s</li>\n" %
                    ahref("Metatable.html"))
                html_out.write("""<ul>
</body>
</html>
""")
        else:
            # Check for known error messages
            check_errors()
            # Write pipeline stdout to tool stderr
            sys.stderr.write("\nOutput from pipeline:\n")
            with open("pipeline.log",'r') as log:
                sys.stderr.write("%s" % log.read())
            # Write log file contents to tool log
            print "\nAmplicon_analysis_pipeline.log:"
            with open(log_file,'r') as log:
                print "%s" % log.read()
    else:
        sys.stderr.write("ERROR missing log file \"%s\"\n" %
                         log_file)

    # Handle FastQC boxplots
    print "Amplicon analysis: collating per base quality boxplots"
    with open("fastqc_quality_boxplots.html","w") as quality_boxplots:
        # PHRED value for trimming
        phred_score = 20
        if args.trimming_threshold is not None:
            phred_score = args.trimming_threshold
        # Write header for HTML output file
        quality_boxplots.write("""<html>
<head>
<title>Amplicon analysis pipeline: Per-base Quality Boxplots (FastQC)</title>
<head>
<body>
<h1>Amplicon analysis pipeline: Per-base Quality Boxplots (FastQC)</h1>
""")
        # Look for raw and trimmed FastQC output for each sample
        for sample_name in sample_names:
            # Replace underscores with hyphens in sample names
            sample_name = sample_name.replace('_','-')
            # Write HTML file with links to the FastQC boxplots
            fastqc_dir = os.path.join(sample_name,"FastQC")
            quality_boxplots.write("<h2>%s</h2>" % sample_name)
            for d in ("Raw","cutdapt_sickle/Q%s" % phred_score):
                quality_boxplots.write("<h3>%s</h3>" % d)
                fastqc_html_files = glob.glob(
                    os.path.join(fastqc_dir,d,"*_fastqc.html"))
                if not fastqc_html_files:
                    quality_boxplots.write("<p>No FastQC outputs found</p>")
                    continue
                # Pull out the per-base quality boxplots
                for f in fastqc_html_files:
                    boxplot = None
                    with open(f) as fp:
                        for line in fp.read().split(">"):
                            try:
                                line.index("alt=\"Per base quality graph\"")
                                boxplot = line + ">"
                                break
                            except ValueError:
                                pass
                    if boxplot is None:
                        boxplot = "Missing plot"
                    quality_boxplots.write("<h4>%s</h4><p>%s</p>" %
                                           (os.path.basename(f),
                                            boxplot))
            quality_boxplots.write("""</body>
</html>
""")

    # Handle DADA2 error rate plot PDFs
    if args.pipeline == "DADA2":
        print("Amplicon analysis: collecting error rate plots")
        error_rate_plots_dir = os.path.abspath(
            os.path.join("DADA2_OTU_tables",
                         "Error_rate_plots"))
        error_rate_plot_pdfs = [os.path.basename(pdf)
                                for pdf in
                                sorted(glob.glob(
                                    os.path.join(error_rate_plots_dir,"*.pdf")))]
        error_rate_plots_html = os.path.join(error_rate_plots_dir,
                                             "error_rate_plots.html")
        with open(error_rate_plots_html,"w") as error_rate_plots_out:
            error_rate_plots_out.write("""<html>
<head>
<title>Amplicon analysis pipeline: DADA2 Error Rate Plots</title>
<head>
<body>
<h1>Amplicon analysis pipeline: DADA2 Error Rate Plots</h1>
""")
            error_rate_plots_out.write("<ul>\n")
            for pdf in error_rate_plot_pdfs:
                error_rate_plots_out.write("<li>%s</li>\n" % ahref(pdf))
            error_rate_plots_out.write("<ul>\n")
            error_rate_plots_out.write("""</body>
</html>
""")

    # Handle additional output when categories file was supplied
    if args.categories_file is not None:
        # Alpha diversity boxplots
        print "Amplicon analysis: indexing alpha diversity boxplots"
        boxplots_dir = os.path.abspath(
            os.path.join("RESULTS",
                         "%s_%s" % (args.pipeline,
                                    ref_database),
                         "Alpha_diversity",
                         "Alpha_diversity_boxplot",
                         "Categories_shannon"))
        print "Amplicon analysis: gathering PDFs from %s" % boxplots_dir
        boxplot_pdfs = [os.path.basename(pdf)
                        for pdf in
                        sorted(glob.glob(
                            os.path.join(boxplots_dir,"*.pdf")))]
        with open("alpha_diversity_boxplots.html","w") as boxplots_out:
            boxplots_out.write("""<html>
<head>
<title>Amplicon analysis pipeline: Alpha Diversity Boxplots (Shannon)</title>
<head>
<body>
<h1>Amplicon analysis pipeline: Alpha Diversity Boxplots (Shannon)</h1>
""")
            boxplots_out.write("<ul>\n")
            for pdf in boxplot_pdfs:
                boxplots_out.write("<li>%s</li>\n" % ahref(pdf))
            boxplots_out.write("<ul>\n")
            boxplots_out.write("""</body>
</html>
""")

    # Finish
    print "Amplicon analysis: finishing, exit code: %s" % exit_code
    sys.exit(exit_code)