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comparison edger-repenrich.xml @ 1:51b4590a972d draft

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/repenrich commit 98f4b00d71cbc2dd15fc633a6cc3246235308e46
author artbio
date Mon, 18 Sep 2017 17:22:07 -0400
parents f6f0f1e5e940
children 15e3e29f310e
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0:f6f0f1e5e940 1:51b4590a972d
1 <tool id="edger-repenrich" name="edgeR-repenrich" version="1.4.0"> 1 <tool id="edger-repenrich" name="edgeR-repenrich" version="1.4.1">
2 <description>Determines differentially expressed features from RepEnrich counts</description> 2 <description>Determines differentially expressed features from RepEnrich counts</description>
3 <requirements> 3 <requirements>
4 <requirement type="package" version="3.16.5">bioconductor-edger</requirement> 4 <requirement type="package" version="3.16.5">bioconductor-edger</requirement>
5 <requirement type="package" version="3.30.13">bioconductor-limma</requirement> 5 <requirement type="package" version="3.30.13">bioconductor-limma</requirement>
6 <requirement type="package" version="1.20.0">r-getopt</requirement> 6 <requirement type="package" version="1.20.0">r-getopt</requirement>
128 <![CDATA[ 128 <![CDATA[
129 .. class:: infomark 129 .. class:: infomark
130 130
131 **What it does** 131 **What it does**
132 132
133 Estimate Distance between samples (MDS) and Biological Coefficient Variation (BCV) in count data from high-throughput sequencing assays and test for differential expression using edgeR_. 133 Estimate Distance between samples (MDS) and Biological Coefficient Variation (BCV) in count
134 data from high-throughput sequencing assays and test for differential expression using edgeR_.
134 135
135 **Inputs** 136 **Inputs**
136 137
137 edger-repenrich takes count tables generated by repenrich as input. Count tables must be generated for each sample individually. Here, edgeR_ is handling a single factor (genotype, age, treatment, etc) that effect your experiment. This factor has two levels/states (for instance, "wild-type" and "mutant". 138 edger-repenrich takes count tables generated by repenrich as inputs. A repenrich count table looks
139 like:
140
141 ============== ========== ========== ==========
142 LSU-rRNA_Dme rRNA rRNA 3659329
143 -------------- ---------- ---------- ----------
144 FW3_DM LINE Jockey 831
145 -------------- ---------- ---------- ----------
146 DMTOM1_LTR LTR Gypsy 1004
147 -------------- ---------- ---------- ----------
148 R1_DM LINE R1 7343
149 -------------- ---------- ---------- ----------
150 TAHRE LINE Jockey 4560
151 -------------- ---------- ---------- ----------
152 G4_DM LINE Jockey 3668
153 -------------- ---------- ---------- ----------
154 BS LINE Jockey 7296
155 -------------- ---------- ---------- ----------
156 Stalker2_I-int LTR Gypsy 12252
157 -------------- ---------- ---------- ----------
158 Stalker3_LTR LTR Gypsy 593
159 -------------- ---------- ---------- ----------
160 TABOR_I-int LTR Gypsy 3947
161 -------------- ---------- ---------- ----------
162 G7_DM LINE Jockey 162
163 -------------- ---------- ---------- ----------
164 BEL_I-int LTR Pao 23757
165 -------------- ---------- ---------- ----------
166 Gypsy6_I-int LTR Gypsy 7489
167 ============== ========== ========== ==========
168
169 Count tables must be
170 generated for each sample individually. Here, edgeR_ is handling a single factor
171 (genotype, age, treatment, etc) that effect your experiment. This factor has two
172 levels/states (for instance, "wild-type" and "mutant".
138 You need to select appropriate count table from your history for each factor level. 173 You need to select appropriate count table from your history for each factor level.
139 174
140 The following table gives some examples of factors and their levels: 175 The following table gives some examples of factors and their levels:
141 176
142 ========= ============== =============== 177 ========= ============== ===============
149 TimePoint Day4 Day1 184 TimePoint Day4 Day1
150 --------- -------------- --------------- 185 --------- -------------- ---------------
151 Gender Female Male 186 Gender Female Male
152 ========= ============== =============== 187 ========= ============== ===============
153 188
154 *Note*: Output log2 fold changes are based on primary factor level 1 vs. factor level2. Here the order of factor levels is important. For example, for the factor 'Treatment' given in above table, DESeq2 computes fold changes of 'Treated' samples against 'Untreated', i.e. the values correspond to up or down regulations of genes in Treated samples. 189 *Note*: Output log2 fold changes are based on primary factor level 1 vs. factor level2.
190 Here the order of factor levels is important. For example, for the factor 'Treatment' given
191 in above table, edgeR computes fold changes of 'Treated' samples against 'Untreated',
192 i.e. the values correspond to up or down regulations of genes in Treated samples.
193
194 *Number of aligned reads*:
195
196 A file containing the number of reads aligned to transposons by repenrich must me provided
197 to edger-repenrich. This file is a single-column tabular file containing a single value.
155 198
156 **Output** 199 **Output**
157 200
158 edgeR_ generates a tabular file containing the different columns and results visualized in a PDF: 201 edgeR_ generates a tabular file containing the different columns and results visualized in a PDF:
159 202