Mercurial > repos > mheinzl > fsd
changeset 0:9736b9d04a0b draft
planemo upload for repository https://github.com/monikaheinzl/galaxyProject/tree/master/tools/fsd commit f674213e798956531c935e7b9eb7f444286d0a5e-dirty
| author | mheinzl |
|---|---|
| date | Wed, 25 Apr 2018 08:59:17 -0400 |
| parents | |
| children | 770a38352a51 |
| files | fsd.py fsd.xml test-data/Test_data.tabular test-data/output_file.csv test-data/output_file.pdf |
| diffstat | 5 files changed, 680 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/fsd.py Wed Apr 25 08:59:17 2018 -0400 @@ -0,0 +1,431 @@ +#!/usr/bin/env python + +# Family size distribution of SSCSs +# +# Author: Monika Heinzl, Johannes-Kepler University Linz (Austria) +# Contact: monika.heinzl@edumail.at +# +# Takes at least one TABULAR file with tags before the alignment to the SSCS, but up to 4 files can be provided, as input. +# The program produces a plot which shows the distribution of family sizes of the all SSCSs from the input files and +# a CSV file with the data of the plot, as well as a TXT file with all tags of the DCS and their family sizes. +# If only one file is provided, then a family size distribution, which is separated after SSCSs without a partner and DCSs, is produced. +# Whereas a family size distribution with multiple data in one plot is produced, when more than one file (up to 4) is given. + +# USAGE: python FSD_Galaxy_1.4_commandLine_FINAL.py filename --inputFile2 filename2 --inputFile3 filename3 --inputFile4 filename4 / +# --title_file outputFileName --sep "characterWhichSeparatesCSVFile" + +import numpy +import matplotlib.pyplot as plt +from matplotlib.backends.backend_pdf import PdfPages +import argparse +import sys +import os +import re +from Cheetah.Template import Template + +def readFileReferenceFree(file): + with open(file, 'r') as dest_f: + data_array = numpy.genfromtxt(dest_f, skip_header=0, delimiter='\t', comments='#', dtype='string') + return(data_array) + +def make_argparser(): + parser = argparse.ArgumentParser(description='Family Size Distribution of duplex sequencing data') + parser.add_argument('inputFile', + help='Tabular File with three columns: ab or ba, tag and family size.') + parser.add_argument('--inputName1') + parser.add_argument('--inputFile2',default=None, + help='Tabular File with three columns: ab or ba, tag and family size.') + parser.add_argument('--inputName2') + parser.add_argument('--inputFile3',default=None, + help='Tabular File with three columns: ab or ba, tag and family size.') + parser.add_argument('--inputName3') + parser.add_argument('--inputFile4',default=None, + help='Tabular File with three columns: ab or ba, tag and family size.') + parser.add_argument('--inputName4') + parser.add_argument('--sep', default=",", + help='Separator in the csv file.') + parser.add_argument('--output_csv', default="data.csv",type=str, + help='Name of the pdf and csv file.') + parser.add_argument('--output_pdf', default="data.pdf",type=str, + help='Name of the pdf and csv file.') + return parser + +def compare_read_families(argv): + parser = make_argparser() + args=parser.parse_args(argv[1:]) + + firstFile = args.inputFile + name1 = args.inputName1 + secondFile = args.inputFile2 + name2 = args.inputName2 + thirdFile = args.inputFile3 + name3 = args.inputName3 + fourthFile = args.inputFile4 + name4 = args.inputName4 + + title_file = args.output_csv + title_file2 = args.output_pdf + sep = args.sep + + if type(sep) is not str or len(sep)>1: + print("Error: --sep must be a single character.") + exit(4) + + plt.rc('figure', figsize=(11.69, 8.27)) # A4 format + plt.rcParams['patch.edgecolor'] = "black" + plt.rcParams['axes.facecolor'] = "E0E0E0" # grey background color + plt.rcParams['xtick.labelsize'] = 12 + plt.rcParams['ytick.labelsize'] = 12 + + list_to_plot = [] + label = [] + data_array_list = [] + + with open(title_file, "w") as output_file, PdfPages(title_file2) as pdf: + fig = plt.figure() + plt.subplots_adjust(bottom=0.25) + if firstFile != str(None): + file1 = readFileReferenceFree(firstFile) + integers = numpy.array(file1[:, 0]).astype(int) ## keep original family sizes + + # for plot: replace all big family sizes by 22 + data1 = numpy.array(file1[:, 0]).astype(int) + bigFamilies = numpy.where(data1 > 20)[0] + data1[bigFamilies] = 22 + + name1 = name1.split(".tabular")[0] + list_to_plot.append(data1) + label.append(name1) + data_array_list.append(file1) + + legend = "\n\n\n{}".format(name1) + plt.text(0.1, 0.11, legend, size=12, transform=plt.gcf().transFigure) + legend1 = "singletons:\nabsolute nr.\n{:,}".format(numpy.bincount(data1)[1]) + plt.text(0.4, 0.11, legend1, size=12, transform=plt.gcf().transFigure) + + legend3 = "rel. freq\n{:.3f}".format(float(numpy.bincount(data1)[1]) / len(data1)) + plt.text(0.5, 0.11, legend3, size=12, transform=plt.gcf().transFigure) + + legend4 = "family size > 20:\nabsolute nr.\n{:,}".format( + numpy.bincount(data1)[len(numpy.bincount(data1)) - 1].astype(int)) + plt.text(0.6, 0.11, legend4, size=12, transform=plt.gcf().transFigure) + + legend5 = "rel. freq\n{:.3f}".format(float(numpy.bincount(data1)[len(numpy.bincount(data1)) - 1]) / len(data1)) + plt.text(0.7, 0.11, legend5, size=12, transform=plt.gcf().transFigure) + + legend6 = "total length\n{:,}".format(len(data1)) + plt.text(0.8, 0.11, legend6, size=12, transform=plt.gcf().transFigure) + + if secondFile != str(None): + file2 = readFileReferenceFree(secondFile) + data2 = numpy.asarray(file2[:, 0]).astype(int) + bigFamilies2 = numpy.where(data2 > 20)[0] + data2[bigFamilies2] = 22 + + list_to_plot.append(data2) + name2 = name2.split(".tabular")[0] + label.append(name2) + data_array_list.append(file2) + + plt.text(0.1, 0.09, name2, size=12, transform=plt.gcf().transFigure) + + legend1 = "{:,}".format(numpy.bincount(data2)[1]) + plt.text(0.4, 0.09, legend1, size=12, transform=plt.gcf().transFigure) + + legend3 = "{:.3f}".format(float(numpy.bincount(data2)[1]) / len(data2)) + plt.text(0.5, 0.09, legend3, size=12, transform=plt.gcf().transFigure) + + legend4 = "{:,}".format(numpy.bincount(data2)[len(numpy.bincount(data2)) - 1].astype(int)) + plt.text(0.6, 0.09, legend4, size=12, transform=plt.gcf().transFigure) + + legend5 = "{:.3f}".format(float(numpy.bincount(data2)[len(numpy.bincount(data2)) - 1]) / len(data2)) + plt.text(0.7, 0.09, legend5, size=12, transform=plt.gcf().transFigure) + + legend6 = "{:,}".format(len(data2)) + plt.text(0.8, 0.09, legend6, size=12, transform=plt.gcf().transFigure) + + if thirdFile != str(None): + file3 = readFileReferenceFree(thirdFile) + + data3 = numpy.asarray(file3[:, 0]).astype(int) + bigFamilies3 = numpy.where(data3 > 20)[0] + data3[bigFamilies3] = 22 + + list_to_plot.append(data3) + name3 = name3.split(".tabular")[0] + label.append(name3) + data_array_list.append(file3) + + plt.text(0.1, 0.07, name3, size=12, transform=plt.gcf().transFigure) + + legend1 = "{:,}".format(numpy.bincount(data3)[1]) + plt.text(0.4, 0.07, legend1, size=12, transform=plt.gcf().transFigure) + + legend3 = "{:.3f}".format(float(numpy.bincount(data3)[1]) / len(data3)) + plt.text(0.5, 0.07, legend3, size=12, transform=plt.gcf().transFigure) + + legend4 = "{:,}".format(numpy.bincount(data3)[len(numpy.bincount(data3)) - 1].astype(int)) + plt.text(0.6, 0.07, legend4, size=12, transform=plt.gcf().transFigure) + + legend5 = "{:.3f}".format(float(numpy.bincount(data3)[len(numpy.bincount(data3)) - 1]) / len(data3)) + plt.text(0.7, 0.07, legend5, size=12, transform=plt.gcf().transFigure) + + legend6 = "{:,}".format(len(data3)) + plt.text(0.8, 0.07, legend6, size=12, transform=plt.gcf().transFigure) + + if fourthFile != str(None): + file4 = readFileReferenceFree(fourthFile) + + data4 = numpy.asarray(file4[:, 0]).astype(int) + bigFamilies4 = numpy.where(data4 > 20)[0] + data4[bigFamilies4] = 22 + + list_to_plot.append(data4) + name4 = name4.split(".tabular")[0] + label.append(name4) + data_array_list.append(file4) + + plt.text(0.1, 0.05, name4, size=12, transform=plt.gcf().transFigure) + + legend1 = "{:,}".format(numpy.bincount(data4)[1]) + plt.text(0.4, 0.05, legend1, size=12, transform=plt.gcf().transFigure) + + legend4 = "{:.3f}".format(float(numpy.bincount(data4)[1]) / len(data4)) + plt.text(0.5, 0.05, legend4, size=12, transform=plt.gcf().transFigure) + + legend4 = "{:,}".format(numpy.bincount(data4)[len(numpy.bincount(data4)) - 1].astype(int)) + plt.text(0.6, 0.05, legend4, size=12, transform=plt.gcf().transFigure) + + legend5 = "{:.3f}".format(float(numpy.bincount(data4)[len(numpy.bincount(data4)) - 1]) / len(data4)) + plt.text(0.7, 0.05, legend5, size=12, transform=plt.gcf().transFigure) + + legend6 = "{:,}".format(len(data4)) + plt.text(0.8, 0.05, legend6, size=12, transform=plt.gcf().transFigure) + + maximumX = numpy.amax(numpy.concatenate(list_to_plot)) + minimumX = numpy.amin(numpy.concatenate(list_to_plot)) + + counts = plt.hist(list_to_plot, bins=range(minimumX, maximumX + 1), stacked=False, edgecolor="black", + linewidth=1, label=label, align="left", alpha=0.7, rwidth=0.8) + + ticks = numpy.arange(minimumX - 1, maximumX, 1) + ticks1 = map(str, ticks) + ticks1[len(ticks1) - 1] = ">20" + plt.xticks(numpy.array(ticks), ticks1) + + plt.legend(loc='upper right', fontsize=14, frameon=True, bbox_to_anchor=(0.9, 1)) + plt.title("Family Size Distribution", fontsize=14) + plt.xlabel("No. of Family Members", fontsize=14) + plt.ylabel("Absolute Frequency", fontsize=14) + plt.margins(0.01, None) + plt.grid(b=True, which="major", color="#424242", linestyle=":") + pdf.savefig(fig) + plt.close() + + # write data to CSV file + output_file.write("Values from family size distribution with all datasets\n") + output_file.write("\nFamily size") + for i in label: + output_file.write("{}{}".format(sep, i)) + output_file.write("{}sum".format(sep)) + output_file.write("\n") + j = 0 + for fs in counts[1][0:len(counts[1]) - 1]: + if fs == 21: + fs = ">20" + else: + fs = "={}".format(fs) + output_file.write("FS{}{}".format(fs, sep)) + values_of_fs = [] + if len(label) == 1: + output_file.write("{}{}".format(int(counts[0][j]), sep)) + values_of_fs.append(int(counts[0][j])) + else: + for n in range(len(label)): + output_file.write("{}{}".format(int(counts[0][n][j]), sep)) + values_of_fs.append(int(counts[0][n][j])) + output_file.write("{}\n".format(sum(values_of_fs))) + j += 1 + output_file.write("sum{}".format(sep)) + values_for_sum = [] + if len(label) == 1: + output_file.write("{}{}".format(int(sum(counts[0])), sep)) + values_for_sum.append(int(sum(counts[0]))) + else: + for i in counts[0]: + output_file.write("{}{}".format(int(sum(i)), sep)) + values_for_sum.append(int(sum(i))) + + output_file.write("{}\n".format(sum(values_for_sum))) + +### Family size distribution after DCS and SSCS + for dataset, data, name_file in zip(list_to_plot, data_array_list, label): + maximumX = numpy.amax(dataset) + minimumX = numpy.amin(dataset) + + tags = numpy.array(data[:, 2]) + seq = numpy.array(data[:, 1]) + data = numpy.array(dataset) + + # find all unique tags and get the indices for ALL tags, but only once + u, index_unique, c = numpy.unique(numpy.array(seq), return_counts=True, return_index=True) + d = u[c > 1] + + # get family sizes, tag for duplicates + duplTags_double = data[numpy.in1d(seq, d)] + duplTags = duplTags_double[0::2] # ab of DCS + duplTagsBA = duplTags_double[1::2] # ba of DCS + + duplTags_double_tag = tags[numpy.in1d(seq, d)] + duplTags_double_seq = seq[numpy.in1d(seq, d)] + + # get family sizes for SSCS with no partner + ab = numpy.where(tags == "ab")[0] + abSeq = seq[ab] + ab = data[ab] + ba = numpy.where(tags == "ba")[0] + baSeq = seq[ba] + ba = data[ba] + + dataAB = ab[numpy.in1d(abSeq, d, invert=True)] + dataBA = ba[numpy.in1d(baSeq, d, invert=True)] + + # write DCS tags to file + # with open("DCS information_{}.txt".format(firstFile), "w") as file: + # for t, s, f in zip(duplTags_double_tag, duplTags_double_seq, duplTags_double): + # file.write("{}\t{}\t{}\n".format(t, s, f)) + + list1 = [duplTags_double, dataAB, dataBA] # list for plotting + + ## information for family size >= 3 + dataAB_FS3 = dataAB[dataAB >= 3] + dataBA_FS3 = dataBA[dataBA >= 3] + ab_FS3 = ab[ab >= 3] + ba_FS3 = ba[ba >= 3] + + duplTags_FS3 = duplTags[(duplTags >= 3) & (duplTagsBA >= 3)] # ab+ba with FS>=3 + duplTags_FS3_BA = duplTagsBA[(duplTags >= 3) & (duplTagsBA >= 3)] # ba+ab with FS>=3 + duplTags_double_FS3 = len(duplTags_FS3)+len(duplTags_FS3_BA) # both ab and ba strands with FS>=3 + + fig = plt.figure() + + plt.subplots_adjust(bottom=0.3) + counts = plt.hist(list1, bins=range(minimumX, maximumX + 1), stacked=True, + label=["duplex", "ab", "ba"], edgecolor="black", linewidth=1, + align="left", color=["#FF0000", "#5FB404", "#FFBF00"]) + # tick labels of x axis + ticks = numpy.arange(minimumX - 1, maximumX, 1) + ticks1 = map(str, ticks) + ticks1[len(ticks1) - 1] = ">20" + plt.xticks(numpy.array(ticks), ticks1) + singl = counts[0][2][0] # singletons + last = counts[0][2][len(counts[0][0]) - 1] # large families + + plt.legend(loc='upper right', fontsize=14, bbox_to_anchor=(0.9, 1), frameon=True) + plt.title(name1, fontsize=14) + plt.xlabel("No. of Family Members", fontsize=14) + plt.ylabel("Absolute Frequency", fontsize=14) + plt.margins(0.01, None) + plt.grid(b=True, which="major", color="#424242", linestyle=":") + + ## extra information beneath the plot + legend = "SSCS ab= \nSSCS ba= \nDCS (total)= \nlength of dataset=" + plt.text(0.1, 0.09, legend, size=12, transform=plt.gcf().transFigure) + + legend = "absolute numbers\n\n{:,}\n{:,}\n{:,} ({:,})\n{:,}" \ + .format(len(dataAB), len(dataBA), len(duplTags), len(duplTags_double), + (len(dataAB) + len(dataBA) + len(duplTags))) + plt.text(0.35, 0.09, legend, size=12, transform=plt.gcf().transFigure) + + legend = "relative frequencies\nunique\n{:.3f}\n{:.3f}\n{:.3f}\n{:,}" \ + .format(float(len(dataAB)) / (len(dataAB) + len(dataBA) + len(duplTags)), + float(len(dataBA)) / (len(dataAB) + len(dataBA) + len(duplTags)), + float(len(duplTags)) / (len(dataAB) + len(dataBA) + len(duplTags)), + (len(dataAB) + len(dataBA) + len(duplTags))) + plt.text(0.54, 0.09, legend, size=12, transform=plt.gcf().transFigure) + + legend = "total\n{:.3f}\n{:.3f}\n{:.3f} ({:.3f})\n{:,}" \ + .format(float(len(dataAB)) / (len(ab) + len(ba)), float(len(dataBA)) / (len(ab) + len(ba)), + float(len(duplTags)) / (len(ab) + len(ba)), + float(len(duplTags_double)) / (len(ab) + len(ba)), (len(ab) + len(ba))) + plt.text(0.64, 0.09, legend, size=12, transform=plt.gcf().transFigure) + + legend1 = "\nsingletons:\nfamily size > 20:" + plt.text(0.1, 0.03, legend1, size=12, transform=plt.gcf().transFigure) + + legend4 = "{:,}\n{:,}".format(singl.astype(int), last.astype(int)) + plt.text(0.35, 0.03, legend4, size=12, transform=plt.gcf().transFigure) + + legend3 = "{:.3f}\n{:.3f}".format(singl / len(data),last / len(data)) + plt.text(0.54, 0.03, legend3, size=12, transform=plt.gcf().transFigure) + + pdf.savefig(fig) + plt.close() + + # write same information to a csv file + count = numpy.bincount(integers) # original counts of family sizes + output_file.write("\nDataset:{}{}\n".format(sep, name_file)) + output_file.write("max. family size:{}{}\n".format(sep, max(integers))) + output_file.write("absolute frequency:{}{}\n".format(sep, count[len(count) - 1])) + output_file.write("relative frequency:{}{:.3f}\n\n".format(sep, float(count[len(count) - 1]) / sum(count))) + + output_file.write("{}singletons:{}{}family size > 20:\n".format(sep, sep, sep)) + output_file.write( + "{}absolute nr.{}rel. freq{}absolute nr.{}rel. freq{}total length\n".format(sep, sep, sep, sep, sep)) + output_file.write("{}{}{}{}{:.3f}{}{}{}{:.3f}{}{}\n\n".format(name_file, sep, singl.astype(int), sep, + singl / len(data), sep,last.astype(int), sep, + last / len(data), sep, len(data))) + + ## information for FS >= 1 + output_file.write( + "The unique frequencies were calculated from the dataset where the tags occured only once (=ab without DCS, ba without DCS)\n" \ + "Whereas the total frequencies were calculated from the whole dataset (=including the DCS).\n\n") + output_file.write("FS >= 1{}{}unique:{}total:\n".format(sep, sep, sep)) + output_file.write("nr./rel. freq of ab={}{}{}{:.3f}{}{:.3f}\n".format(sep, len(dataAB), sep, + float(len(dataAB)) / (len(dataAB) + len(dataBA) + len( duplTags)), sep, + float(len(dataAB)) / (len(ab) + len(ba)))) + output_file.write("nr./rel. freq of ba={}{}{}{:.3f}{}{:.3f}\n".format(sep, len(dataBA), sep, + float(len(dataBA)) / (len(dataBA) + len(dataBA) + len(duplTags)), sep, + float(len(dataBA)) / (len(ba) + len(ba)))) + output_file.write( + "nr./rel. freq of DCS (total)={}{} ({}){}{:.3f}{}{:.3f} ({:.3f})\n".format(sep, len(duplTags), len(duplTags_double), sep, + float(len(duplTags)) / ( len(dataAB) + len( dataBA) + len(duplTags)), + sep, float(len(duplTags)) / ( len(ab) + len(ba)), + float( len(duplTags_double)) / (len(ab) + len(ba)))) + output_file.write( + "length of dataset={}{}{}{}{}{}\n".format(sep, (len(dataAB) + len(dataBA) + len(duplTags)), sep, + (len(dataAB) + len(dataBA) + len(duplTags)), sep,(len(ab) + len(ba)))) + ## information for FS >= 3 + output_file.write("FS >= 3{}{}unique:{}total:\n".format(sep, sep, sep)) + output_file.write("nr./rel. freq of ab={}{}{}{:.3f}{}{:.3f}\n".format(sep, len(dataAB_FS3), sep, + float(len(dataAB_FS3)) / (len(dataAB_FS3) + len(dataBA_FS3) + len(duplTags_FS3)), + sep, float(len(dataAB_FS3)) / ( len(ab_FS3) + len(ba_FS3)))) + output_file.write("nr./rel. freq of ba={}{}{}{:.3f}{}{:.3f}\n".format(sep, len(dataBA_FS3), sep, + float(len(dataBA_FS3)) / ( len(dataBA_FS3) + len(dataBA_FS3) + len(duplTags_FS3)), + sep,float(len(dataBA_FS3)) / (len(ba_FS3) + len(ba_FS3)))) + output_file.write( + "nr./rel. freq of DCS (total)={}{} ({}){}{:.3f}{}{:.3f} ({:.3f})\n".format(sep, len(duplTags_FS3),duplTags_double_FS3, + sep, float(len( duplTags_FS3)) / (len(dataBA_FS3) + len(duplTags_FS3)), + sep, float(len(duplTags_FS3)) / (len(ab_FS3) + len(ba_FS3)), + float(duplTags_double_FS3) / (len(ab_FS3) + len(ba_FS3)))) + output_file.write( + "length of dataset={}{}{}{}{}{}\n".format(sep, (len(dataAB_FS3) + len(dataBA_FS3) + len(duplTags_FS3)), sep, + (len(dataAB_FS3) + len(dataBA_FS3) + len(duplTags_FS3)), sep, + (len(ab_FS3) + len(ba_FS3)))) + + output_file.write("\nValues from family size distribution\n") + output_file.write("{}duplex{}ab{}ba{}sum\n".format(sep, sep, sep, sep)) + for dx, ab, ba, fs in zip(counts[0][0], counts[0][1], counts[0][2], counts[1]): + if fs == 21: + fs = ">20" + else: + fs = "={}".format(fs) + ab1 = ab - dx + ba1 = ba - ab + output_file.write( + "FS{}{}{}{}{}{}{}{}{}\n".format(fs, sep, int(dx), sep, int(ab1), sep, int(ba1), sep, int(ba))) + + print("Files successfully created!") + +if __name__ == '__main__': + sys.exit(compare_read_families(sys.argv))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/fsd.xml Wed Apr 25 08:59:17 2018 -0400 @@ -0,0 +1,83 @@ +<?xml version="1.0" encoding="UTF-8"?> +<tool id="fsd" name="Duplex Sequencing Analysis:" version="0.0.1"> + <description>Family size distribution of tags</description> + <requirements> + <requirement type="package" version="1.4">matplotlib</requirement> + </requirements> + + <command> + python FSD.py $file1 --inputName1 $file1.name --inputFile2 $file2 --inputName2 $file2.name --inputFile3 $file3 --inputName3 $file3.name --inputFile4 $file4 --inputName4 $file4.name --sep $separator --output_csv $output_csv --output_pdf $output_pdf + </command> + <inputs> + <param name="file1" type="data" format="tabular" label="Dataset 1: input tags" optional="false"/> + <param name="file2" type="data" format="tabular" label="Dataset 2: input tags" optional="true" /> + <param name="file3" type="data" format="tabular" label="Dataset 3: input tags" optional="true" /> + <param name="file4" type="data" format="tabular" label="Dataset 4: input tags" optional="true" help="Input in tabular format with the family size, tags and the direction of the strand ('ab' or 'ba') for each family. Name of the files can have max. 34 charcters, blanks are not allowed!"/> + <param name="separator" type="text" label="Separator of the CSV file." help="can be a single character" value=","/> + </inputs> + <outputs> + <data name="output_pdf" format="pdf" /> + <data name="output_csv" format="csv"/> + </outputs> + <!-- <tests> + <test> + <param name="file1" value="Test_data.tabular"/> + <param name="file2" value="None"/> + <param name="file3" value="None"/> + <param name="file4" value="None"/> + <output name="output_pdf" file="output_file.pdf"/> + <output name="output_csv" file="output_file.csv"/> + </test> + </tests> + --> + <help> <![CDATA[ + +**What it does** + + This tool will create a distribution of family sizes of each tag, which is separated after families tags that have only the forward (ab) strand, the reverse (ba) strand or both strands (ab+ba) of the DCS and a family size distribution without separation is created. If multiple files are provided as input, the family size distribution without separation contains all datasets in one plot and for each dataset a distribution with separation after single ab, ba strands and DCSs is produced. + + +**Input** + + This tools expects a tabular file with the tags of all families, their sizes and information about forward (ab) and reverse (ba) strands. + + **!!! Name of the files can have max. 34 charcters, blanks are not allowed !!!** + + +-----+----------------------------+----+ + | 1 | AAAAAAAAAAAATGTTGGAATCTT | ba | + +-----+----------------------------+----+ + | 10 | AAAAAAAAAAAGGCGGTCCACCCC | ab | + +-----+----------------------------+----+ + | 28 | AAAAAAAAAAATGGTATGGACCGA | ab | + +-----+----------------------------+----+ + + + + + +**Output** + + The output is a PDF file with the plot and a CSV with the data of the plot. + + +**About Author** + + Author: Monika Heinzl + + Department: Institute of Bioinformatics, Johannes Kepler University Linz, Austria + + Contact: monika.heinzl@edumail.at + + ]]> + + </help> + <citations> + <citation type="bibtex"> + @misc{duplex, + author = {Heinzl, Monika}, + year = {2018}, + title = {Duplex analysis} + } + </citation> + </citations> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Test_data.tabular Wed Apr 25 08:59:17 2018 -0400 @@ -0,0 +1,112 @@ +1 AAAAAAAAAAAAAACCAAAACTTC ba +1 AAAAAAAAAAAAACCAGGCGTCGA ba +1 AAAAAAAAAAAAAGCTCCACGTTG ba +1 AAAAAAAAAAAAATCGTGGTTTGT ba +1 AAAAAAAAAAAAATTCACCCTTGT ba +7 AAAAAAAAAAAACACACTTAACTT ba +1 AAAAAAAAAAAACAGTGTTGAGAC ba +4 AAAAAAAAAAAACCGCTCCTCACA ba +1 AAAAAAAAAAAAGGCAACACAGAA ab +2 AAAAAAAAAAAATCTTTCTTTGAG ab +1 AAAAAAAAAAAATTGGGTTCCTTA ab +1 AAAAAAAAAAAGAGTCGCACCCAG ba +4 AAAAAAAAAAAGATCGTGGTTTGT ba +1 AAAAAAAAAAAGCGCAACACAGAA ab +3 AAAAAAAAAAAGGGCAACACAGAA ab +1 AAAAAAAAAAAGTAGCCCTAAACG ab +1 AAAAAAAAAAAGTCTTTCTTTGAG ab +1 AAAAAAAAAAATATCATAGACTCT ab +6 AAAAAAAAAAATATTCACCCTTGT ba +1 AAAAAAAAAAATATTCGAAAGTTA ba +3 AAAAAAAAAAATCACACTTAACTT ba +1 AAAAAAAAAAATCCGCTCCTCACA ba +1 AAAAAAAAAAATTAACTAAACTTA ab +1 AAAAAAAAAACAAATTCTATTATT ab +1 AAAAAAAAAACTCCCAGATTTTTT ab +1 AAAAAAAAAACTTCTGCTTGGCGG ba +11 AAAAAAAAAAGAATCGTGGTTTGT ba +5 AAAAAAAAAAGATAGCCCTAAACG ab +1 AAAAAAAAAAGCAATAATGCCAGT ab +2 AAAAAAAAAAGTACCGCACTCTCA ba +1 AAAAAAAAAAGTTCTTTCTTTGAG ab +1 AAAAAAAAAATAACTTCAATAATG ba +2 AAAAAAAAAATAATCATAGACTCT ab +1 AAAAAAAAAATAGTCTCACATTTA ab +1 AAAAAAAAAATATAACCTTTGGCG ab +3 AAAAAAAAACAAAATTCTATTATT ab +1 AAAAAAAAACAAGTACGCGGCATT ab +1 AAAAAAAAACAAGTACGCGGTATT ab +1 AAAAAAAAACAATATCGAATTAAC ab +3 AAAAAAAAACACGGTGAGACAAGG ba +1 AAAAAAAAACACGTTTCTCCCCTT ba +1 AAAAAAAAACATATCGTCCCGAGC ba +1 AAAAAAAAACCTACCTGAGGCCCC ab +3 AAAAAAAAACCTTATTACAGCGGA ab +1 AAAAAAAAACGATTCTCTGTATCT ba +1 AAAAAAAAACGTACCGCACTCTCA ba +4 AAAAAAAAACTACCCAGATTTTTT ba +1 AAAAAAAAACTAGATGAGACGACC ba +4 AAAAAAAAACTGTCTGCTTGGCGG ba +1 AAAAAAAAAGAAGTTTAATTTTAA ab +1 AAAAAAAAAGAATGCCTAAGACGA ba +6 AAAAAAAAAGACCGGCCTTAGACA ba +1 AAAAAAAAAGATATCGTGGTTTGT ba +1 AAAAAAAAAGCAATACTCAAGCTG ba +6 AAAAAAAAAGCAATGTCTAAGCCT ba +1 AAAAAAAAAGCACTGTCTAAGCCT ab +2 AAAAAAAAAGCTAATAATGCCAGT ab +1 AAAAAAAAAGTTTCGTGAAGGTCC ba +1 AAAAAAAAATAAAGGTCCGAATCT ab +1 AAAAAAAAATAAATGAGAGTGTAA ba +8 AAAAAAAAATAAGTCTCACATTTA ab +1 AAAAAAAAATAATAACCTCTGGCG ab +10 AAAAAAAAATAATAACCTTTGGCG ab +1 AAAAAAAAATAATCCCCTTTGTCG ab +6 AAAAAAAAATACGCAAACGCTGAG ab +4 AAAAAAAAATAGATCATAGACTCT ab +10 AAAAAAAAATAGATCATAGACTCT ba +10 AAAAAAAAATAGTAGGATTTCATG ba +7 AAAAAAAAATATGAATACCCTCGT ba +1 AAAAAAAAATATGCCACTTGATCC ba +1 AAAAAAAAATATTCTGCCACTTGA ba +3 AAAAAAAAATCAAACCAAGAGGAC ba +1 AAAAAAAAATCAGTACCCCTAAAC ab +12 AAAAAAAAATCCTAGTTAATGAAG ba +1 AAAAAAAAATCGATTCTTTATGCG ab +1 AAAAAAAAATGTCTGAAAATATCT ab +4 AAAAAAAAATGTCTGAAAATATCT ba +1 AAAAAAAAATTTCCGCAGACCGTT ba +8 AAAAAAAAATTTGGGCTACTACAA ba +1 AAAAAAAACAAAATTAGAACCCTT ab +1 AAAAAAAACAAACCGCTCCTCACA ba +5 AAAAAAAACAACGTACGCGGTATT ab +4 AAAAAAAACAATATCGTTGATATG ba +4 AAAAAAAACAATCACGTTAATAGG ab +1 AAAAAAAACAGAATCGTGGTTTGT ba +1 AAAAAAAACCAAATCGTTGATATG ba +9 AAAAAAAACCAAGTCCAGGCATCT ba +2 AAAAAAAACCACGGTGAGACAAGG ba +1 AAAAAAAACCGCCCAACTGCCGGT ab +5 AAAAAAAACCTCTCAACCCCAAAT ba +7 AAAAAAAACCTCTTGCGATGTTGT ab +1 AAAAAAAACCTCTTGCGCTGTTGT ab +1 AAAAAAAACCTCTTGTGATGTTGT ab +12 AAAAAAAACCTGAGCAATGGTTCC ab +3 AAAAAAAACCTTGACCCTCACATG ba +6 AAAAAAAACCTTGCACTCGTCCTA ba +9 AAAAAAAACGAAATAAAAAAACCT ba +1 AAAAAAAACGACCGGCCTTAGACA ba +4 AAAAAAAACGCCACCACCCCCTTT ab +12 AAAAAAAACGCCACGGGCACTATT ba +13 AAAAAAAACGTATCAGTAGATCCT ab +1 AAAAAAAACTAGTAGGATTTCATG ba +3 AAAAAAAACTATAGAAAATCCATT ba +1 AAAAAAAACTATTCTATTTCCGAT ba +13 AAAAAAAACTGATCTGCTTGGCGG ba +8 AAAAAAAACTTGCGAATAGCATCG ba +4 AAAAAAAACTTGTTATCAAAACGT ab +1 AAAAAAAAGAAAAGTTCAACACGC ba +1 AAAAAAAAGAAGTTCGCCCTCCGA ab +13 AAAAAAAAGAGAGTTTAGTCATGG ab +1 AAAAAAAAGAGAGTTTAGTCATGG ba +1 AAAAAAAAGAGAGTTTAGTCCTGG ab \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output_file.csv Wed Apr 25 08:59:17 2018 -0400 @@ -0,0 +1,54 @@ +Values from family size distribution with all datasets + +Family size,Test_data,sum +FS=1,63,63 +FS=2,5,5 +FS=3,8,8 +FS=4,10,10 +FS=5,3,3 +FS=6,5,5 +FS=7,3,3 +FS=8,3,3 +FS=9,2,2 +FS=10,3,3 +FS=11,1,1 +FS=12,6,6 +sum,112,112 + +Dataset:,Test_data +max. family size:,13 +absolute frequency:,3 +relative frequency:,0.027 + +,singletons:,,family size > 20: +,absolute nr.,rel. freq,absolute nr.,rel. freq,total length +Test_data,63,0.562,6,0.054,112 + +The unique frequencies were calculated from the dataset where the tags occured only once (=ab without DCS, ba without DCS) +Whereas the total frequencies were calculated from the whole dataset (=including the DCS). + +FS >= 1,,unique:,total: +nr./rel. freq of ab=,47,0.431,0.420 +nr./rel. freq of ba=,59,0.488,0.476 +nr./rel. freq of DCS (total)=,3 (6),0.028,0.027 (0.054) +length of dataset=,109,109,112 +FS >= 3,,unique:,total: +nr./rel. freq of ab=,14,0.341,0.318 +nr./rel. freq of ba=,26,0.491,0.464 +nr./rel. freq of DCS (total)=,1 (2),0.037,0.023 (0.045) +length of dataset=,41,41,44 + +Values from family size distribution +,duplex,ab,ba,sum +FS=1,2,30,31,63 +FS=2,0,3,2,5 +FS=3,0,3,5,8 +FS=4,2,3,5,10 +FS=5,0,2,1,3 +FS=6,0,1,4,5 +FS=7,0,1,2,3 +FS=8,0,1,2,3 +FS=9,0,0,2,2 +FS=10,1,1,1,3 +FS=11,0,0,1,1 +FS=12,1,2,3,6