Mercurial > repos > iuc > concoct_coverage_table
changeset 0:7e01297a3b4a draft
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/concoct commit 40a09cbfd6052f7b0295946621db1bdf58228b09"
| author | iuc |
|---|---|
| date | Sun, 13 Mar 2022 08:45:32 +0000 |
| parents | |
| children | 052e6bc06ea3 |
| files | coverage_table.py coverage_table.xml cut_up_fasta.py extract_fasta_bins.py macros.xml merge_cut_up_clustering.py static/images/pipeline.png test-data/input.fasta.gz test-data/input1.fa.gz test-data/input1.tabular test-data/input_coverage_table.fasta.gz test-data/input_coverage_table.tabular test-data/input_merge_clusters.tabular test-data/inputcluster1.tabular test-data/output_coverage_table.tabular test-data/output_merge_clusters.tabular test-data/process_log.txt |
| diffstat | 17 files changed, 2247 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/coverage_table.py Sun Mar 13 08:45:32 2022 +0000 @@ -0,0 +1,83 @@ +#!/usr/bin/env python + +import argparse +import gzip +from functools import partial + +from Bio import SeqIO + + +def generate_coverage_table(input_fasta, input_tabular, gzipped, output): + # Read input file into a dict and return everything + # in the table format required by CONCOCT. + gc_and_len_dict = get_gc_and_len_dict(input_fasta, gzipped) + assert(len(gc_and_len_dict) > 0) + bed_coverage_dict = get_bed_coverage_dict(input_tabular) + + with open(output, 'w') as fh: + # Output the header. + fh.write("contig\tlength") + t = tuple(range(len(bed_coverage_dict))) + fh.write("\tcov_mean_sample_%d\n" % len(t)) + # Output the content. + for acc in gc_and_len_dict: + # Fasta stats. + fh.write("%s\t%s" % (acc, gc_and_len_dict[acc]['length'])) + # Mean + try: + # Coverage mean + fh.write("\t%f" % (bed_coverage_dict[acc]["cov_mean"])) + except KeyError: + # No reads mapped to this contig + fh.write("\t0") + fh.write("\n") + + +def get_bed_coverage_dict(input_tabular): + # Ddetermine mean coverage and percentage covered + # for each contig, returning a dict with fasta id + # as key and percentage covered and cov_mean as keys + # for the inner dict. + out_dict = {} + + with open(input_tabular, 'r') as fh: + for line in fh: + line = line.rstrip('\r\n') + cols = line.split('\t') + try: + d = out_dict[cols[0]] + except KeyError: + d = {} + out_dict[cols[0]] = d + if int(cols[1]) == 0: + d["percentage_covered"] = 100 - float(cols[4]) * 100.0 + else: + d["cov_mean"] = d.get("cov_mean", 0) + int(cols[1]) * float(cols[4]) + return out_dict + + +def get_gc_and_len_dict(input_fasta, gzipped): + # Creates a dictionary with the fasta id as key + # and GC and length as keys for the inner dictionary. + if gzipped: + _open = partial(gzip.open, mode='rt') + else: + _open = open + + out_dict = {} + with _open(input_fasta) as input_fh: + for rec in SeqIO.parse(input_fh, "fasta"): + out_dict[rec.id] = {} + out_dict[rec.id]["length"] = len(rec.seq) + return out_dict + + +parser = argparse.ArgumentParser(description=__doc__) +parser.add_argument('--input_tabular', action='store', dest='input_tabular', help='bedtools genomeCoverageBed bed file') +parser.add_argument('--input_fasta', action='store', dest='input_fasta', help='Contigs fasta file') +parser.add_argument("--gzipped", action="store_true", dest="gzipped", default=False, help="input_fasta is gzipped") +parser.add_argument('--output', action='store', dest='output', help='Output file') + +args = parser.parse_args() + +generate_coverage_table(args.input_fasta, args.input_tabular, args.gzipped, args.output)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/coverage_table.xml Sun Mar 13 08:45:32 2022 +0000 @@ -0,0 +1,40 @@ +<tool id="concoct_coverage_table" name="CONCOCT coverage table" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@"> + <description></description> + <macros> + <import>macros.xml</import> + </macros> + <expand macro="requirements"/> + <command detect_errors="exit_code"><![CDATA[ +python '$__tool_directory__/coverage_table.py' +--input_fasta '$input_fasta' +#if $input_fasta.is_of_type('fasta.gz'): + --gzipped +#end if +--input_tabular '$input_tabular' +--output '$output' + ]]></command> + <inputs> + <param name="input_fasta" type="data" format="fasta,fasta.gz" label="Contigs fasta file"/> + <param name="input_tabular" type="data" format="tabular" label="Tabular bedtools Genome Coverage histogram file" help="Set the bedtools Genome Coverage Output type to be Data suitable for Histogram"/> + </inputs> + <outputs> + <data name="output" format="tabular"/> + </outputs> + <tests> + <test expect_num_outputs="1"> + <param name="input_fasta" value="input_coverage_table.fasta.gz" ftype="fasta.gz"/> + <param name="input_tabular" value="input_coverage_table.tabular" ftype="tabular"/> + <output name="output" file="output_coverage_table.tabular" ftype="tabular"/> + </test> + </tests> + <help><![CDATA[ +**What it does** + +Accepts an assembled (and possibly cut by the Cut fasta contigs tool) fasta contigs file and a tabular coverage histogram +file (produced by the bedtools Genomve Coverage tool) and outputs a tabular coverage file for use as the input to the +CONCOCT metagenome binning tool. + +@HELP_OVERVIEW@ + ]]></help> + <expand macro="citations"/> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cut_up_fasta.py Sun Mar 13 08:45:32 2022 +0000 @@ -0,0 +1,58 @@ +#!/usr/bin/env python + +import argparse +import gzip +from functools import partial + +from Bio import SeqIO + + +def cut_up_fasta(input_fasta, chunk_size, overlap, merge_last, output_fasta, output_bed, gzipped): + if gzipped: + _open = partial(gzip.open, mode='rt') + else: + _open = open + + fasta_fh = open(output_fasta, 'w') + + if output_bed is not None: + bed_fh = open(output_bed, 'w') + + with _open(input_fasta) as input_fh: + for record in SeqIO.parse(input_fh, "fasta"): + if (not merge_last and len(record.seq) > chunk_size) or (merge_last and len(record.seq) >= 2 * chunk_size): + for index, split_seq in enumerate(chunks(record.seq, chunk_size, overlap, merge_last)): + fasta_fh.write(f">{record.id}.concoct_part_{index}\n{split_seq}\n") + if output_bed is not None: + bed_fh.write(f"{record.id}\t{chunk_size * index}\t{chunk_size * index + len(split_seq)}\t{record.id}.concoct_part_{index}\n") + else: + fasta_fh.write(f">{record.id}.concoct_part_0\n{record.seq}\n") + if output_bed is not None: + bed_fh.write(f"{record.id}\t0\t{len(record.seq)}\t{record.id}.concoct_part_0\n") + if output_bed is not None: + bed_fh.close() + + +def chunks(seq, chunk_size, overlap_size, merge_last): + # Yield successive chunk_size-sized chunks from seq + # with given overlap overlap_size between the chunks. + assert chunk_size > overlap_size + if merge_last: + for i in range(0, len(seq) - chunk_size + 1, chunk_size - overlap_size): + yield seq[i:i + chunk_size] if i + chunk_size + chunk_size - overlap_size <= len(seq) else seq[i:] + else: + for i in range(0, len(seq), chunk_size - overlap_size): + yield seq[i:i + chunk_size] + + +parser = argparse.ArgumentParser() +parser.add_argument("--input_fasta", action="store", dest="input_fasta", help="Fasta files with contigs") +parser.add_argument("--gzipped", action="store_true", dest="gzipped", help="Input file is gzipped") +parser.add_argument("--chunk_size", action="store", dest="chunk_size", type=int, help="Chunk size\n") +parser.add_argument("--overlap_size", action="store", dest="overlap_size", type=int, help="Overlap size\n") +parser.add_argument("--merge_last", default=False, action="store_true", dest="merge_last", help="Concatenate final part to last contig\n") +parser.add_argument("--output_bed", action="store", dest="output_bed", default=None, help="BED file to be created with exact regions of the original contigs corresponding to the newly created contigs") +parser.add_argument("--output_fasta", action="store", dest="output_fasta", help="Output fasta file with cut contigs") + +args = parser.parse_args() +cut_up_fasta(args.input_fasta, args.chunk_size, args.overlap_size, args.merge_last, args.output_fasta, args.output_bed, args.gzipped)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/extract_fasta_bins.py Sun Mar 13 08:45:32 2022 +0000 @@ -0,0 +1,49 @@ +#!/usr/bin/env python + +import argparse +import gzip +import os +import sys +from collections import defaultdict +from functools import partial + +import pandas as pd +from Bio import SeqIO + +parser = argparse.ArgumentParser(description=__doc__) + +parser.add_argument('--gzipped', action='store_true', dest='gzipped', help='Input files are gzipped') +parser.add_argument("--input_fasta", action="store", dest="input_fasta", help="Input Fasta file") +parser.add_argument("--input_cluster", action="store", dest="input_cluster", help="Concoct output cluster file") +parser.add_argument("--output_path", help="Output directory") + +args = parser.parse_args() + +all_seqs = {} +if args.gzipped: + _open = partial(gzip.open, mode='rt') +else: + _open = open + +with _open(args.input_fasta) as fh: + for seq in SeqIO.parse(fh, "fasta"): + all_seqs[seq.id] = seq + +# Make sure we're reading the file as tabular! +df = pd.read_csv(args.input_cluster, sep='\t') +try: + assert df.columns[0] == 'contig_id' + assert df.columns[1] == 'cluster_id' +except AssertionError: + sys.stderr.write("ERROR! Header line was not 'contig_id, cluster_id', please adjust your input file. Exiting!\n") + sys.exit(-1) + +cluster_to_contigs = defaultdict(list) +for i, row in df.iterrows(): + cluster_to_contigs[row['cluster_id']].append(row['contig_id']) + +for cluster_id, contig_ids in cluster_to_contigs.items(): + output_file = os.path.join(args.output_path, "{0}.fa".format(cluster_id)) + seqs = [all_seqs[contig_id] for contig_id in contig_ids] + with open(output_file, 'w') as ofh: + SeqIO.write(seqs, ofh, 'fasta')
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Sun Mar 13 08:45:32 2022 +0000 @@ -0,0 +1,26 @@ +<macros> + <token name="@TOOL_VERSION@">1.0.0</token> + <token name="@VERSION_SUFFIX@">0</token> + <token name="@PROFILE@">21.01</token> + <xml name="requirements"> + <requirements> + <requirement type="package" version="1.79">biopython</requirement> + <requirement type="package" version="@TOOL_VERSION@">concoct</requirement> + <requirement type="package" version="0.19.2">pandas</requirement> + </requirements> + </xml> + <token name="@HELP_OVERVIEW@"> + +The intended use of the CONCOCT tools is shown in the following image. + +.. image:: pipeline.png + +More information may be found on the CONCOCT homepage:: https://github.com/BinPro/CONCOCT + + </token> + <xml name="citations"> + <citations> + <citation type="doi">10.1038/nmeth.3103</citation> + </citations> + </xml> +</macros>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/merge_cut_up_clustering.py Sun Mar 13 08:45:32 2022 +0000 @@ -0,0 +1,56 @@ +#!/usr/bin/env python + +import argparse +import re +import sys +from collections import Counter +from collections import defaultdict + + +CONTIG_PART_EXPR = re.compile(r'(.*)\.concoct_part_([0-9]*)') + + +def original_contig_name_special(contig_id): + try: + original_id, part_index = CONTIG_PART_EXPR.match(contig_id).group(1, 2) + return original_id, part_index + except AttributeError: + # No matches for concoct_part regex. + return contig_id, 0 + + +parser = argparse.ArgumentParser() +parser.add_argument("--input", action="store", dest="input", help="Tabular file with cut up clusters") +parser.add_argument("--output", action="store", dest="output", help="Output file with merged clusters") + +args = parser.parse_args() + +# Get cut up clusters +all_seqs = {} +all_originals = defaultdict(dict) +with open(args.input, 'r') as ifh: + for i, line in enumerate(ifh): + if i == 0: + if 'contig_id' not in line: + sys.stderr.write("ERROR nvalid clustering file, 'contig_id' is not found in the header.") + sys.exit(-1) + # Skip header. + continue + line = line.rstrip('\r\n') + contig_id, cluster_id = line.split('\t') + original_contig_name, part_id = original_contig_name_special(contig_id) + all_originals[original_contig_name][part_id] = cluster_id + +# Merge cut up clusters. +with open(args.output, 'w') as ofh: + ofh.write("contig_id\tcluster_id\n") + for original_contig_id, part_ids_d in all_originals.items(): + if len(part_ids_d) > 1: + c = Counter(part_ids_d.values()) + cluster_id = c.most_common(1)[0][0] + c_string = [(a, b) for a, b in c.items()] + # Here if len(c.values()) > 1, + # then no cluster for contig. + else: + cluster_id = list(part_ids_d.values())[0] + ofh.write(f"{original_contig_id}\t{cluster_id}\n")
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0.840234 0.454141 1.247525 4.491684 4.999008 2.860339 1.297345 +NODE_236_length_61287_cov_8.171537.0 10000 2.158700 4.476100 2.940000 0 0 0 3.387300 0 4.136800 1.190000 0.400000 1.010000 3.907600 5.274900 2.967500 1.619300 +NODE_236_length_61287_cov_8.171537.1 10000 2.028300 4.395400 2.637700 0 0 0 3.448900 0 4.137700 1.299300 0.340000 1.140000 3.906500 4.687300 2.740000 1.439500 +NODE_236_length_61287_cov_8.171537.2 10000 1.629700 4.597100 3.068600 0 0 0 3.538100 0 4.504500 0.939100 0.370000 1.359700 4.058600 4.566500 2.937400 1.449900 +NODE_236_length_61287_cov_8.171537.3 10000 1.898600 4.264900 2.769300 0 0 0 4.075400 0 4.266100 1.279200 0.378200 1.476900 3.545200 4.672600 2.719400 1.908900 +NODE_236_length_61287_cov_8.171537.4 10000 1.639600 4.673400 3.238300 0 0 0 3.337300 0 4.317000 0.880000 0.319900 1.138200 4.167100 4.907500 2.907900 1.659100 +NODE_236_length_61287_cov_8.171537.5 11357 1.787356 4.083122 3.095975 0 0 0 4.213967 0 4.143965 1.275072 0.536674 1.240468 4.044466 4.628253 2.833846 1.715066 +NODE_238_length_184_cov_14.336957 254 0.787403 0 0 0 1.574802 1.181103 0 0.787401 0 0.787401 1.181100 0 0 0 0 0 +NODE_239_length_121_cov_19.338842 191 0.523560 0.523560 1.570680 0 0 0 1.047120 0 1.570681 0 0 0 0 3.664924 0 1.047120 +NODE_23_length_107_cov_17.261683 177 3.954801 3.327683 4.836154 0 0 0 2.248587 0 4.988696 0.564972 0 2.209042 4.474582 6.169499 1.694916 1.129944 +NODE_240_length_214_cov_4.116822 284 0.704225 0 1.056339 0 0 0 0 0 0 1.056338 0.704224 0 0.704225 0 0 1.408450 +NODE_242_length_125_cov_17.591999 195 0 1.025641 0.512821 0 0 0 1.538460 0 0.512821 0 0 0 1.025641 1.025640 0 0 +NODE_243_length_73_cov_15.643836 143 1.398602 0 0 0 1.398602 1.398602 0 0 0 0 3.496500 0 0 0 0 0 +NODE_246_length_206288_cov_6.775057.0 10000 2.016900 0.060000 0.819300 0.090000 5.538900 5.376700 0.588900 2.457400 0 3.078400 5.616800 1.110000 0 0.300000 0.110000 0.060000 +NODE_246_length_206288_cov_6.775057.1 10000 1.547200 0.030000 0.599500 0.140000 5.696100 5.978800 0.569800 1.719300 0.009900 2.626800 5.316600 1.318600 0 0.319800 0.230000 0 +NODE_246_length_206288_cov_6.775057.10 10000 1.776300 0.060000 0.709900 0.080000 6.151200 6.399200 0.600000 2.090000 0 3.019800 5.966300 1.567500 0 0.450000 0.159900 0 +NODE_246_length_206288_cov_6.775057.11 10000 1.707900 0.050000 0.680000 0.079200 5.775100 5.875100 0.400000 2.697500 0 2.679300 4.992500 1.559000 0 0.427900 0.120000 0 +NODE_246_length_206288_cov_6.775057.12 10000 1.770000 0.130000 0.680000 0.020000 5.967900 4.954500 0.420000 1.949200 0 2.205600 5.256300 1.602600 0 0.400000 0.189800 0 +NODE_246_length_206288_cov_6.775057.13 10000 1.368200 0.040000 0.629100 0.140000 6.364000 5.872900 0.260000 1.919000 0 2.858000 5.215100 1.487900 0 0.409100 0.250000 0 +NODE_246_length_206288_cov_6.775057.14 10000 1.519100 0.040000 0.780000 0.020000 6.066300 6.024000 0.299700 2.318200 0 2.706400 5.514900 1.787700 0 0.280000 0.260000 0 +NODE_246_length_206288_cov_6.775057.15 10000 1.517700 0.100000 0.668300 0.060000 6.016100 4.997700 0.480000 2.290000 0 2.547800 4.837900 1.620000 0 0.310000 0.149100 0 +NODE_246_length_206288_cov_6.775057.16 10000 1.937400 0.050000 0.710000 0 5.833700 4.857500 0.240000 1.998900 0 2.627600 5.225200 1.320000 0 0.220000 0.180000 0 +NODE_246_length_206288_cov_6.775057.17 10000 1.458800 0.070000 0.758600 0.040000 5.416100 6.212700 0.360000 2.259200 0 2.927600 5.205300 1.610000 0 0.280000 0.180000 0 +NODE_246_length_206288_cov_6.775057.18 10000 1.617500 0.090000 0.980000 0.060000 6.303000 6.396800 0.398800 2.126900 0 2.628300 5.746800 1.528600 0 0.330000 0.309700 0 +NODE_246_length_206288_cov_6.775057.19 16358 1.795635 0.079472 0.788117 0.036679 5.620613 6.077637 0.396870 2.187616 0 2.510636 5.137795 1.606923 0 0.385133 0.140604 0 +NODE_246_length_206288_cov_6.775057.2 10000 1.739800 0.090000 0.779200 0.080000 6.272700 5.946100 0.300000 2.519200 0 2.898000 4.937800 1.648700 0 0.390000 0.189200 0 +NODE_246_length_206288_cov_6.775057.3 10000 1.797400 0.160000 0.569900 0.080000 6.195800 5.305100 0.378200 2.318000 0 2.485400 4.937600 1.559800 0 0.350000 0.049400 0 +NODE_246_length_206288_cov_6.775057.4 10000 1.537600 0.020000 0.508100 0 6.157100 6.135400 0.180000 2.427500 0 2.747500 4.943800 1.519000 0 0.280000 0.180000 0 +NODE_246_length_206288_cov_6.775057.5 10000 1.879300 0.040000 1.029500 0.280000 6.475200 6.084000 0.500000 3.205600 0.210000 2.799300 5.198200 2.445500 0 0.509700 0.849300 0.459900 +NODE_246_length_206288_cov_6.775057.6 10000 1.689300 0.060000 0.769000 0.020000 5.736400 6.167200 0.449000 2.058600 0 2.598700 5.423600 1.458300 0 0.380000 0.140000 0 +NODE_246_length_206288_cov_6.775057.7 10000 1.438200 0.100000 0.678700 0.040000 6.535800 5.674300 0.390000 2.375200 0 3.034700 5.145900 1.499400 0 0.468800 0.100000 0 +NODE_246_length_206288_cov_6.775057.8 10000 1.738100 0.019400 0.679800 0.060000 5.993100 5.795800 0.350000 2.029300 0 3.274900 5.829700 1.530000 0 0.408800 0.160000 0 +NODE_246_length_206288_cov_6.775057.9 10000 1.589800 0.120000 0.749000 0.100000 6.382400 5.938600 0.469400 2.298800 0 3.099600 5.901400 1.349000 0 0.390000 0.198700 0 +NODE_247_length_147_cov_4.074830 217 0.921659 0 0.460829 0 0 0 0.460829 0 0 0 0 0 0.460829 0 0.460829 1.843316 +NODE_248_length_650_cov_2.520000 720 0.694445 0.263889 1.250000 0 0 0 0.277778 0 0.277778 0.972223 0 0.555555 0.694444 0 0.138889 2.218057 +NODE_249_length_152_cov_4.210526 222 0.450450 1.351350 1.351353 0 0 0 0.450450 0 0 0.450450 0.900900 0.450450 0.450450 0 0.450450 3.567567 +NODE_24_length_115_cov_8.556521 185 1.081080 2.621622 3.632429 0 0 0 2.075676 0 4.772976 1.081081 0 2.605405 3.135135 3.102702 1.016215 1.621623 +NODE_250_length_251161_cov_8.263253.0 10000 2.300000 5.045200 4.533100 0 0 0 4.447400 0 4.544800 2.350000 0.980000 1.659700 4.807100 4.436100 3.209600 3.583800 +NODE_250_length_251161_cov_8.263253.1 10000 1.548600 4.458100 2.588600 0 0 0 3.748000 0 4.565000 0.889100 0.370000 1.098700 4.176600 5.054400 2.259100 1.449200 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4.158469 0 2.715847 0.502732 0.546448 1.568306 3.683064 2.601093 1.005465 0.546448 +NODE_254_length_191233_cov_8.270398.0 10000 1.438900 5.056200 3.207600 0 0 0 4.118500 0 4.606800 1.129500 0.250000 1.120000 4.013300 5.088500 2.866900 1.248500 +NODE_254_length_191233_cov_8.270398.1 10000 1.529300 4.077000 3.059100 0 0 0 4.108900 0 4.221300 1.009700 0.240000 1.129400 3.835100 4.448500 2.608800 1.830000 +NODE_254_length_191233_cov_8.270398.10 10000 2.049900 4.227300 2.988400 0 0 0 3.405900 0 4.016400 1.448100 0.569900 1.218600 4.097800 4.867900 2.976600 1.887100 +NODE_254_length_191233_cov_8.270398.11 10000 2.067900 4.644800 3.338500 0 0 0 4.284900 0 4.566100 1.246900 0.550000 1.348500 4.584300 4.404900 3.196700 2.427300 +NODE_254_length_191233_cov_8.270398.12 10000 1.489900 4.314200 3.087900 0 0 0 4.216400 0 4.036000 1.108800 0.340000 1.316100 4.037900 4.525200 2.834900 1.856800 +NODE_254_length_191233_cov_8.270398.13 10000 1.899300 4.357300 3.443800 0 0 0 3.847200 0 4.273400 1.249400 0.280000 1.160000 4.266600 4.796900 2.618300 1.939500 +NODE_254_length_191233_cov_8.270398.14 10000 2.637600 5.067000 3.467400 0 0 0 4.265800 0 4.416700 2.329400 0.420000 1.549000 4.795900 5.035400 2.715000 3.445500 +NODE_254_length_191233_cov_8.270398.15 10000 1.827000 4.536000 2.968300 0 0 0 3.587500 0 4.325600 1.307500 0.370000 1.218800 4.918000 5.306100 3.225200 2.049600 +NODE_254_length_191233_cov_8.270398.16 10000 1.939400 4.778600 2.776300 0 0 0 3.496800 0 4.277200 0.919700 0.200000 1.228800 3.864900 4.857800 2.808500 1.630000 +NODE_254_length_191233_cov_8.270398.17 10000 1.668700 4.275400 2.978100 0 0 0 4.047300 0 4.408700 0.968600 0.239700 0.949400 3.745200 4.714500 2.697000 1.507900 +NODE_254_length_191233_cov_8.270398.18 11303 1.882509 4.382906 3.174469 0 0 0 4.081393 0 4.119083 1.335573 0.353712 1.237460 4.322480 4.898871 2.704239 1.449438 +NODE_254_length_191233_cov_8.270398.2 10000 2.219900 4.766800 2.957200 0 0 0 4.237600 0 4.406700 1.379600 0.338600 1.180000 4.106100 4.938300 3.097300 1.569800 +NODE_254_length_191233_cov_8.270398.3 10000 1.997000 4.448200 2.728100 0 0 0 4.124900 0 4.576300 0.949700 0.480000 1.028700 4.744900 4.926300 2.487000 1.609700 +NODE_254_length_191233_cov_8.270398.4 10000 1.598600 4.305700 3.276600 0 0 0 4.377700 0 3.837800 0.899100 0.300000 1.058600 4.116900 4.785700 2.898200 1.508400 +NODE_254_length_191233_cov_8.270398.5 10000 1.799200 4.536300 3.108000 0 0 0 3.558300 0 4.385000 1.020000 0.350000 1.399000 4.178500 4.336100 2.857800 1.519100 +NODE_254_length_191233_cov_8.270398.6 10000 1.767700 4.935800 2.938900 0 0 0 4.194800 0 4.533100 0.949400 0.298900 1.250000 4.289100 4.097800 2.699700 1.380000 +NODE_254_length_191233_cov_8.270398.7 10000 1.969800 4.558000 3.164900 0 0 0 3.754700 0 4.387900 1.028500 0.460000 1.359500 4.426700 4.604500 2.697200 1.649300 +NODE_254_length_191233_cov_8.270398.8 10000 1.700000 3.986200 3.137500 0 0 0 3.698000 0 4.124100 0.979400 0.379900 1.178900 3.617500 4.915900 2.748100 1.210000 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5.045600 3.249700 1.440000 +NODE_261_length_321461_cov_8.199894.11 10000 1.919600 4.318100 3.083800 0 0 0 4.178900 0 5.137600 0.670000 0.260000 0.980000 4.576600 4.976200 2.998600 1.629700 +NODE_261_length_321461_cov_8.199894.12 10000 1.627000 4.104200 3.037200 0 0 0 3.927600 0 4.576600 1.069400 0.260000 1.188500 4.028400 4.632200 3.009100 1.427200 +NODE_261_length_321461_cov_8.199894.13 10000 1.698900 4.537200 3.057700 0 0 0 4.183300 0 4.527800 1.118600 0.400000 1.046300 3.859000 4.865300 3.266800 1.508500 +NODE_261_length_321461_cov_8.199894.14 10000 1.896800 4.205500 3.148900 0 0 0 3.587400 0 4.698200 0.998600 0.299500 0.968900 3.915200 4.946600 2.546500 1.679200 +NODE_261_length_321461_cov_8.199894.15 10000 1.848100 4.377100 2.958600 0 0 0 4.086000 0 4.282200 0.880000 0.388100 1.210000 4.723500 5.207000 2.787200 1.928400 +NODE_261_length_321461_cov_8.199894.16 10000 1.869700 4.804000 3.156900 0 0 0 3.605600 0 4.785000 1.019800 0.309900 1.129700 4.138100 4.036300 2.639600 1.649000 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/process_log.txt Sun Mar 13 08:45:32 2022 +0000 @@ -0,0 +1,10 @@ +Results created at +Successfully loaded composition data. +Successfully loaded coverage data. +Performed PCA, resulted in 154 dimensions +Wrote PCA transformed file. +Wrote PCA components file. +PCA transformed data. +Will call vbgmm with parameters: outdir/, 400, 1000, 1 +Wrote assign file. +CONCOCT Finished