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1 <tool id="chip_seq_ratio_1" name="ChIP-Seq Mapper" version="0.1.1">
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2 <stdio>
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3 <exit_code range="1:" level="fatal" description="Error"/>
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4 </stdio>
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5 <description></description>
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6 <requirements>
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7 <requirement type="package">r-base64enc</requirement>
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8 <requirement type="package">r-r2html</requirement>
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9 <requirement type="package">blast</requirement>
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10 <!-- <requirement type="package">chip_seq_ration</requirement> -->
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11 </requirements>
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12 <command interpreter="python3">
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13 ChipSeqRatioAnalysis.py
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14 --ChipSeq=${ChipFile}
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15 --InputSeq=${InputFile}
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16 --Contigs=${ContigFile}
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17 --output=${OutputFile}
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18 --html=${ReportFile}
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19 --max_cl=${MaxCl}
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20 --bitscore=$bitscore
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21 --nproc=16
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22 </command>
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23
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24 <inputs>
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25 <param name="ChipFile" label="Chip reads" type="data" format="fasta" help="Reads in FASTA format"/>
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26 <param name="InputFile" label="Input reads" type="data" format="fasta" help="Reads in FASTA format"/>
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27 <param name="ContigFile" label="Reference - contig sequences" type="data" format="fasta"
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28 help="Contigs from RepeatExplorer clustering (the file "contigs.fasta")"/>
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29 <param name="MaxCl" label="Number of top clusters to be shown in graph" type="integer" value="200"/>
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30 <param name="bitscore" label="Bit score threshold" type="integer" value="50" help="Similarity hits with lower bit score will not be used for ChIP/Input ratio calculation"/>
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31 </inputs>
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32 <outputs>
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33 <data name="OutputFile" format="tabular"
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34 label="csv table from ChIP-Seq-Mapper on datasets ${InputFile.hid} (Input) ${ChipFile.hid} (ChIP) and ${ContigFile.hid} (reference)"/>
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35
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36 <data name="ReportFile" format="html"
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37 label="HTML report from ChIP-Seq-Mapper on datasets ${InputFile.hid} (Input) ${ChipFile.hid} (ChIP) and ${ContigFile.hid} (reference)"/>
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38 </outputs>
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39
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40 <help>
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41 **What it does**
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42
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43 The ChIP-seq Mapper evaluates the enrichment of repetitive sequences in sequencing data from chromatin
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44 immunoprecipitation experiments, using repeats identified by RepeatExplorer as the reference. The tool
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45 performs BLASTN similarity search of the read sequences to the reference,
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46 and the reads producing hits that passed the user-specified similarity threshold are assigned to the
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47 repeat clusters. The assignment is made to the cluster that produced the best similarity hit, and every
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48 read is assigned to only a single cluster. Following read mapping, the numbers of reads from the
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49 INPUT and ChIP samples are evaluated, and ChIP/INPUT ratios of the normalized read counts are reported
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50 for individual clusters.
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51 ChIP and INPUT reads should be of uniform lengths of at least 40 nt. The bit score threshold value should be
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52 adjusted based on the length of the analyzed reads (the value equal to the read length is recommended for a start).
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53 This method was first used in (`Neumann et al. 2012`__) for
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54 identification of repetitive sequences associated with centromeres:
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55
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56
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57 `PLoS Genet. Epub 2012 Jun 21. Stretching the rules: monocentric chromosomes with multiple centromere domains. Neumann P, Navrátilová A, Schroeder-Reiter E, Koblížková A, Steinbauerová V, Chocholová E, Novák P, Wanner G, Macas J.`__.
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58
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59 .. __: http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1002777
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60 .. __: http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1002777
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61
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62 </help>
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63
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64 </tool>
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