Mercurial > repos > shians > shrnaseq
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author | shian_su <registertonysu@gmail.com> |
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date | Fri, 21 Feb 2014 12:52:56 +1100 |
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<tool id="shRNAseq" name="shRNAseq Tool" version="1.0.5"> <description> Analyse hairpin differential representation using edgeR </description> <requirements> <requirement type="R-module">edgeR</requirement> <requirement type="R-module">limma</requirement> </requirements> <stdio> <exit_code range="1:" level="fatal" description="Tool exception" /> </stdio> <command interpreter="Rscript"> hairpinTool.R $inputOpt.type #if $inputOpt.type=="fastq": #for $i, $fas in enumerate($inputOpt.fastq): fastq::$fas.file #end for $inputOpt.hairpin $inputOpt.samples #if $inputOpt.positions.option=="yes": $inputOpt.positions.barstart $inputOpt.positions.barend $inputOpt.positions.hpstart $inputOpt.positions.hpend #else: 1 5 37 57 #end if #else: $inputOpt.counts $inputOpt.anno "$inputOpt.factors" 0 0 0 #end if #if $filterCPM.option=="yes": $filterCPM.cpmReq $filterCPM.sampleReq #else: -Inf -Inf #end if $fdr $lfc $workMode.mode $outFile $outFile.files_path #if $workMode.mode=="classic": "$workMode.pair1" "$workMode.pair2" #else: "$workMode.contrast" $workMode.roast.option #if $workMode.roast.option=="yes": $workMode.roast.hairpinReq $workMode.roast.select.option "$workMode.roast.select.selection" #else: 0 0 0 #end if #end if </command> <inputs> <conditional name="inputOpt"> <param name="type" type="select" label="Input File Type"> <option value="fastq">FastQ File</option> <option value="counts">Table of Counts</option> </param> <when value="fastq"> <param name="hairpin" type="data" format="tabular" label="Hairpin Annotation"/> <param name="samples" type="data" format="tabular" label="Sample Annotation"/> <repeat name="fastq" title="FastQ Files"> <param name="file" type="data" format="fastq"/> </repeat> <conditional name="positions"> <param name="option" type="select" label="Specify Barcode and Hairpin Locations?" help="Default Positions: Barcode: 1 to 5, Hairpin: 37 to 57."> <option value="no" selected="True">No</option> <option value="yes">Yes</option> </param> <when value="yes"> <param name="barstart" type="integer" value="1" label="Barcode Starting Position"/> <param name="barend" type="integer" value="5" label="Barcode Ending Position"/> <param name="hpstart" type="integer" value="37" label="Hairpin Starting Position"/> <param name="hpend" type="integer" value="57" label="Hairpin Ending Position"/> </when> <when value="no"/> </conditional> </when> <when value="counts"> <param name="counts" type="data" format="tabular" label="Counts Table"/> <param name="anno" type="data" format="tabular" label="Hairpin Annotation"/> <param name="factors" type="data" format="tabular" label="Sample Annotation"/> </when> </conditional> <conditional name="filterCPM"> <param name="option" type="select" label="Filter Low CPM?" help="Ignore hairpins with very low representation when performing analysis."> <option value="yes">Yes</option> <option value="no">No</option> </param> <when value="yes"> <param name="cpmReq" type="float" value="0.5" min="0" max="1" label="Minimum CPM"/> <param name="sampleReq" type="integer" value="1" min="0" label="Minimum Samples" help="Filter out all the genes that do not meet the minimum CPM in at least this many samples."/> </when> <when value="no"/> </conditional> <conditional name="workMode"> <param name="mode" type="select" label="Analysis Type" help="Classic Exact Tests are useful for simple comparisons across two sampling groups. Generalised linear models allow for more complex contrasts and gene level analysis to be made."> <option value="classic">Classic Exact Test</option> <option value="glm">Generalised Linear Model</option> </param> <when value="classic"> <param name="pair1" type="text" label="Compare" size="40"/> <param name="pair2" type="text" label="To" size="40" help="The analysis will subtract values of this group from those in the group above to establish the difference."/> </when> <when value="glm"> <param name="contrast" type="text" size="60" label="Contrasts of interest" help="Specify equations defining contrasts to be made. Eg. KD-Control will result in positive fold change if KD has greater expression and negative if Control has greater expression."/> <conditional name="roast"> <param name="option" type="select" label="Perform Gene Level Analysis?" help="Analyse LogFC tendencies for hairpins belonging to the same gene."> <option value="no">No</option> <option value="yes">Yes</option> </param> <when value="yes"> <param name="hairpinReq" type="integer" value="2" min="2" label="Minimum Hairpins" help="Only genes with at least this many hairpins will be analysed."/> <conditional name="select"> <param name="option" type="select" label="Gene Selection Method"> <option value="rank">By p-value Rank</option> <option value="geneID">By Gene Identifier</option> </param> <when value="rank"> <param name="selection" type="text" size="40" value="1:5" label="Ranks of Top Genes to Plot" help="Genes are ranked in ascending p-value for differential representation, individual ranks can be entered seperated by comma or a range seperated by colon."/> </when> <when value="geneID"> <param name="selection" type="text" size="80" value="" label="Symbols of Genes to Plot" help="Select genes based on their identifier in the 'Gene' column of the sample information file. Please ensure exact match with the values in input file and separate selections with commas."/> </when> </conditional> </when> <when value="no"/> </conditional> </when> </conditional> <param name="fdr" type="float" value="0.05" min="0" max="1" label="FDR Threshold" help="All observations below this threshold will be highlighted in the smear plot."/> <param name="lfc" type="float" value="0" min="0" label="Absolute LogFC Threshold" help="In additional to meeting the FDR requirement, the absolute value of the log-fold-change of the observation must be above this threshold to be highlighted."/> </inputs> <outputs> <data format="html" name="outFile" label="shRNAseq Analysis"/> </outputs> <help> .. class:: infomark **What it does** Given tables containing information about the hairpins and their associated barcodes, information about the samples and fastq file containing the hairpin reads. This tool will generate plots and tables for the analysis of differential representation. ----- .. class:: infomark **INPUTS** **Input File Type:** This tool is able to either generate counts from a raw FastQ file given the information regarding the samples and hairpins. Alternatively if a table of counts has already been generated it can also be used. **Counts Table (Counts Input):** A tab delimited text table of information regarding the counts of hairpins. Should have a column 'ID' to denote the hairpins that counts correspond to. Each additional column should have titles corresponding to the label for the sample. Example:: ID Sample1 Sample2 Sample3 Control1 49802 48014 40148 Control2 12441 16352 14232 Control3 9842 9148 9111 Hairpin1 3300 3418 2914 Hairpin2 91418 95812 93174 Hairpin3 32985 31975 35104 Hairpin4 12082 14081 14981 Hairpin5 2491 2769 2691 Hairpin6 1294 1486 1642 Hairpin7 49501 49076 47611 ... **Hairpin Annotation:** A tab delimited text table of information regarding the hairpins. Should have columns 'ID', 'Sequences' and 'Gene' to uniquely identify the hairpin, align it with the reads to produce counts and identify which gene the hairpin acts on. NOTE: the column names are case sensitive and should be input exactly as they are shown here. Example:: ID Sequences Gene Control1 TCTCGCTTGGGCGAGAGTAAG 2 Control2 CCGCCTGAAGTCTCTGATTAA 2 Control3 AGGAATTATAATGCTTATCTA 2 Hairpin1 AAGGCAGAGACTGACCACCTA 4 Hairpin2 GAGCGACCTGGTGTTACTCTA 4 Hairpin3 ATGGTGTAAATAGAGCTGTTA 4 Hairpin4 CAGCTCATCTTCTGTGAAGAA 4 Hairpin5 CAGCTCTGTGGGTCAGAAGAA 4 Hairpin6 CCAGGCACAGATCTCAAGATA 4 Hairpin7 ATGACAAGAAAGACATCTCAA 7 ... **Sample Annotation (FastQ Input):** A tab delimited text table of information regarding the samples. Should have columns 'ID', 'Sequences' and 'group' to uniquely identify each sample, identify the sample in the reads by its barcode sequence and correctly group replicates for analysis. Additional columns may inserted for annotation purposes and will not interfere with analysis as long as the necessary columns are present. NOTE: the column names are case sensitive and should be input exactly as they are shown here. Example:: ID Sequences group Replicate 3 GAAAG Day 2 1 6 GAACC Day 10 1 9 GAAGA Day 5 GFP neg 1 16 GAATT Day 5 GFP pos 1 18 GACAC Day 2 2 21 GACCA Day 10 2 28 GACGT Day 5 GFP neg 2 31 GACTG Day 5 GFP pos 2 33 GAGAA Day 2 3 40 GAGCT Day 10 3 ... **Specify Barcode and Hairpin Locations (FastQ Input):** It is assumed that in the sequencing reads that the first 5 bases are the barcodes and that bases 37-57 are the hairpins. If this is not the case then the values of the positions can be changed, however it still requires the barcodes and hairpins to be in a consistent location an in a continuous sequence. **Filter Low CPM?:** Often in a large screen there may members with very low counts which are of no interest in the experiment, these may be filtered out to speed up computations. Filtering will be based on counts per million in a required number of samples. **Analysis Type:** * **Classic Exact Test:** This allows two experimental groups to be compared and p-values for differential representation derivec for each hairpin. Simple and fast for straightforward comparisons. In this option you will have the option of "*Compare* x *To* y" which implicitly subtracts the data from y from that of x to produce the comparison. * **Generalised Linear Model:** This allow for complex contrasts to be specified and also gene level analysis to be performed. If this option is chosen then contrasts must be explicitly stated in equations and multiple contrasts can be made. In addition there will be the option to analyse hairpins on a per-gene basis to see if hairpins belonging to a particular gene have any overall tendencies for the direction of their log-fold-change. **FDR Threshold:** The smear plot in the output will have hairpins highlighted to signify significant differential representation. The significance is determined by contorlling the false discovery rate, only those with a FDR lower than the threshold will be highlighted in the plot. ----- **Citations:** .. class:: infomark limma Please cite the paper below for the limma software itself. Please also try to cite the appropriate methodology articles that describe the statistical methods implemented in limma, depending on which limma functions you are using. The methodology articles are listed in Section 2.1 of the limma User's Guide. * Smyth, GK (2005). Limma: linear models for microarray data. In: 'Bioinformatics and Computational Biology Solutions using R and Bioconductor'. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds), Springer, New York, pages 397-420. .. class:: infomark edgeR Please cite the first paper for the software itself and the other papers for the various original statistical methods implemented in edgeR. See Section 1.2 in the User's Guide for more detail. * Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140 * Robinson MD and Smyth GK (2007). Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881-2887 * Robinson MD and Smyth GK (2008). Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics, 9, 321-332 * McCarthy DJ, Chen Y and Smyth GK (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research 40, 4288-4297 .. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html .. _limma: http://www.bioconductor.org/packages/release/bioc/html/limma.html </help> </tool>