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1 <tool id="shRNAseq" name="shRNAseq Tool" version="1.0.5">
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2 <description>
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3 Analyse hairpin differential representation using edgeR
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4 </description>
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5
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6 <requirements>
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7 <requirement type="R-module">edgeR</requirement>
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8 <requirement type="R-module">limma</requirement>
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9 </requirements>
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10
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11 <stdio>
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12 <exit_code range="1:" level="fatal" description="Tool exception" />
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13 </stdio>
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14
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15 <command interpreter="Rscript">
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16 hairpinTool.R $inputOpt.type
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17 #if $inputOpt.type=="fastq":
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18 #for $i, $fas in enumerate($inputOpt.fastq):
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19 fastq::$fas.file
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20 #end for
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21
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22 $inputOpt.hairpin
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23 $inputOpt.samples
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24
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25 #if $inputOpt.positions.option=="yes":
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26 $inputOpt.positions.barstart
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27 $inputOpt.positions.barend
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28 $inputOpt.positions.hpstart
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29 $inputOpt.positions.hpend
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30 #else:
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31 1
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32 5
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33 37
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34 57
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35 #end if
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36 #else:
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37 $inputOpt.counts
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38 $inputOpt.anno
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39 "$inputOpt.factors"
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40 0 0 0
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41 #end if
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42
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43 #if $filterCPM.option=="yes":
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44 $filterCPM.cpmReq
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45 $filterCPM.sampleReq
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46 #else:
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47 -Inf
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48 -Inf
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49 #end if
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50
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51 $fdr
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52 $lfc
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53 $workMode.mode
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54 $outFile
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55 $outFile.files_path
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56
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57 #if $workMode.mode=="classic":
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58 "$workMode.pair1"
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59 "$workMode.pair2"
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60 #else:
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61 "$workMode.contrast"
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62 $workMode.roast.option
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63 #if $workMode.roast.option=="yes":
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64 $workMode.roast.hairpinReq
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65 $workMode.roast.select.option
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66 "$workMode.roast.select.selection"
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67 #else:
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68 0
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69 0
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70 0
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71 #end if
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72 #end if
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73 </command>
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74
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75 <inputs>
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76 <conditional name="inputOpt">
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77 <param name="type" type="select" label="Input File Type">
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78 <option value="fastq">FastQ File</option>
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79 <option value="counts">Table of Counts</option>
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80 </param>
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81
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82 <when value="fastq">
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83 <param name="hairpin" type="data" format="tabular"
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84 label="Hairpin Annotation"/>
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85
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86
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87 <param name="samples" type="data" format="tabular"
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88 label="Sample Annotation"/>
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89
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90 <repeat name="fastq" title="FastQ Files">
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91 <param name="file" type="data" format="fastq"/>
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92 </repeat>
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93
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94 <conditional name="positions">
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95 <param name="option" type="select"
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96 label="Specify Barcode and Hairpin Locations?"
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97 help="Default Positions: Barcode: 1 to 5, Hairpin: 37 to 57.">
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98 <option value="no" selected="True">No</option>
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99 <option value="yes">Yes</option>
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100 </param>
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101
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102 <when value="yes">
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103 <param name="barstart" type="integer" value="1"
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104 label="Barcode Starting Position"/>
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105 <param name="barend" type="integer" value="5"
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106 label="Barcode Ending Position"/>
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107
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108 <param name="hpstart" type="integer" value="37"
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109 label="Hairpin Starting Position"/>
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110
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111 <param name="hpend" type="integer" value="57"
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112 label="Hairpin Ending Position"/>
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113 </when>
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114
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115 <when value="no"/>
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116 </conditional>
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117 </when>
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118
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119 <when value="counts">
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120 <param name="counts" type="data" format="tabular" label="Counts Table"/>
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121 <param name="anno" type="data" format="tabular"
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122 label="Hairpin Annotation"/>
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123 <param name="factors" type="data" format="tabular"
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124 label="Sample Annotation"/>
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125 </when>
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126 </conditional>
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127
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128 <conditional name="filterCPM">
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129 <param name="option" type="select" label="Filter Low CPM?"
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130 help="Ignore hairpins with very low representation when performing
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131 analysis.">
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132 <option value="yes">Yes</option>
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133 <option value="no">No</option>
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134 </param>
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135
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136 <when value="yes">
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137 <param name="cpmReq" type="float" value="0.5" min="0" max="1"
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138 label="Minimum CPM"/>
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139
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140 <param name="sampleReq" type="integer" value="1" min="0"
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141 label="Minimum Samples"
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142 help="Filter out all the genes that do not meet the minimum
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143 CPM in at least this many samples."/>
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144 </when>
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145
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146 <when value="no"/>
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147
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148 </conditional>
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149
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150 <conditional name="workMode">
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151 <param name="mode" type="select" label="Analysis Type"
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152 help="Classic Exact Tests are useful for simple comparisons across
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153 two sampling groups. Generalised linear models allow for more
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154 complex contrasts and gene level analysis to be made.">
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155 <option value="classic">Classic Exact Test</option>
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156 <option value="glm">Generalised Linear Model</option>
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157 </param>
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158
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159 <when value="classic">
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160 <param name="pair1" type="text" label="Compare" size="40"/>
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161 <param name="pair2" type="text" label="To" size="40"
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162 help="The analysis will subtract values of this group from those
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163 in the group above to establish the difference."/>
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164 </when>
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165
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166 <when value="glm">
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167 <param name="contrast" type="text" size="60"
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168 label="Contrasts of interest"
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169 help="Specify equations defining contrasts to be made. Eg.
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170 KD-Control will result in positive fold change if KD has
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171 greater expression and negative if Control has greater
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172 expression."/>
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173
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174 <conditional name="roast">
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175 <param name="option" type="select"
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176 label="Perform Gene Level Analysis?"
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177 help="Analyse LogFC tendencies for hairpins belonging
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178 to the same gene.">
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179 <option value="no">No</option>
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180 <option value="yes">Yes</option>
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181 </param>
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182
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183 <when value="yes">
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184 <param name="hairpinReq" type="integer" value="2" min="2"
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185 label="Minimum Hairpins"
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186 help="Only genes with at least this many hairpins will
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187 be analysed."/>
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188
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189 <conditional name="select">
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190 <param name="option" type="select"
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191 label="Gene Selection Method">
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192 <option value="rank">By p-value Rank</option>
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193 <option value="geneID">By Gene Identifier</option>
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194 </param>
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195 <when value="rank">
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196 <param name="selection" type="text" size="40" value="1:5"
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197 label="Ranks of Top Genes to Plot"
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198 help="Genes are ranked in ascending p-value for
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199 differential representation, individual ranks can
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200 be entered seperated by comma or a range seperated
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201 by colon."/>
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202 </when>
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203 <when value="geneID">
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204 <param name="selection" type="text" size="80" value=""
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205 label="Symbols of Genes to Plot"
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206 help="Select genes based on their identifier in the
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207 'Gene' column of the sample information file.
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208 Please ensure exact match with the values in input
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209 file and separate selections with commas."/>
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210 </when>
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211 </conditional>
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212
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213
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214 </when>
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215
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216 <when value="no"/>
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217 </conditional>
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218 </when>
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219 </conditional>
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220
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221 <param name="fdr" type="float" value="0.05" min="0" max="1"
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222 label="FDR Threshold"
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223 help="All observations below this threshold will be highlighted
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224 in the smear plot."/>
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225 <param name="lfc" type="float" value="0" min="0"
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226 label="Absolute LogFC Threshold"
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227 help="In additional to meeting the FDR requirement, the absolute
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228 value of the log-fold-change of the observation must be above
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229 this threshold to be highlighted."/>
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230 </inputs>
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231
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232 <outputs>
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233 <data format="html" name="outFile" label="shRNAseq Analysis"/>
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234 </outputs>
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235
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236 <help>
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237 .. class:: infomark
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238
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239 **What it does**
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240
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241 Given tables containing information about the hairpins and their associated
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242 barcodes, information about the samples and fastq file containing the hairpin
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243 reads. This tool will generate plots and tables for the analysis of differential
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244 representation.
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245
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246 -----
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247
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248 .. class:: infomark
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249
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250 **INPUTS**
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251
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252 **Input File Type:**
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253
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254 This tool is able to either generate counts from a raw FastQ file given the
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255 information regarding the samples and hairpins. Alternatively if a table of
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256 counts has already been generated it can also be used.
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257
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258 **Counts Table (Counts Input):**
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259
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260 A tab delimited text table of information regarding the counts of hairpins.
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261 Should have a column 'ID' to denote the hairpins that counts correspond to. Each
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262 additional column should have titles corresponding to the label for the sample.
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263
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264 Example::
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265
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266 ID Sample1 Sample2 Sample3
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267 Control1 49802 48014 40148
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268 Control2 12441 16352 14232
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269 Control3 9842 9148 9111
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270 Hairpin1 3300 3418 2914
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271 Hairpin2 91418 95812 93174
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272 Hairpin3 32985 31975 35104
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273 Hairpin4 12082 14081 14981
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274 Hairpin5 2491 2769 2691
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275 Hairpin6 1294 1486 1642
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276 Hairpin7 49501 49076 47611
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277 ...
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278
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279 **Hairpin Annotation:**
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280
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281 A tab delimited text table of information regarding the hairpins. Should have
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282 columns 'ID', 'Sequences' and 'Gene' to uniquely identify the hairpin, align it
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283 with the reads to produce counts and identify which gene the hairpin acts on.
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284
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285 NOTE: the column names are case sensitive and should be input exactly as they
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286 are shown here.
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287
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288 Example::
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289
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290 ID Sequences Gene
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291 Control1 TCTCGCTTGGGCGAGAGTAAG 2
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292 Control2 CCGCCTGAAGTCTCTGATTAA 2
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293 Control3 AGGAATTATAATGCTTATCTA 2
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294 Hairpin1 AAGGCAGAGACTGACCACCTA 4
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295 Hairpin2 GAGCGACCTGGTGTTACTCTA 4
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296 Hairpin3 ATGGTGTAAATAGAGCTGTTA 4
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297 Hairpin4 CAGCTCATCTTCTGTGAAGAA 4
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298 Hairpin5 CAGCTCTGTGGGTCAGAAGAA 4
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299 Hairpin6 CCAGGCACAGATCTCAAGATA 4
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300 Hairpin7 ATGACAAGAAAGACATCTCAA 7
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301 ...
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302
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303 **Sample Annotation (FastQ Input):**
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304
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305 A tab delimited text table of information regarding the samples. Should have
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306 columns 'ID', 'Sequences' and 'group' to uniquely identify each sample, identify
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307 the sample in the reads by its barcode sequence and correctly group replicates
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308 for analysis. Additional columns may inserted for annotation purposes and will
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309 not interfere with analysis as long as the necessary columns are present.
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310
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311 NOTE: the column names are case sensitive and should be input exactly as they
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312 are shown here.
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313
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314 Example::
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315
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316 ID Sequences group Replicate
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317 3 GAAAG Day 2 1
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318 6 GAACC Day 10 1
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319 9 GAAGA Day 5 GFP neg 1
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320 16 GAATT Day 5 GFP pos 1
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321 18 GACAC Day 2 2
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322 21 GACCA Day 10 2
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323 28 GACGT Day 5 GFP neg 2
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324 31 GACTG Day 5 GFP pos 2
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325 33 GAGAA Day 2 3
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326 40 GAGCT Day 10 3
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327 ...
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328
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329 **Specify Barcode and Hairpin Locations (FastQ Input):**
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330
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331 It is assumed that in the sequencing reads that the first 5 bases are the
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332 barcodes and that bases 37-57 are the hairpins. If this is not the case then the
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333 values of the positions can be changed, however it still requires the barcodes
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334 and hairpins to be in a consistent location an in a continuous sequence.
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335
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336 **Filter Low CPM?:**
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337
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338 Often in a large screen there may members with very low counts which are of no
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339 interest in the experiment, these may be filtered out to speed up computations.
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340 Filtering will be based on counts per million in a required number of samples.
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341
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342 **Analysis Type:**
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343
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344 * **Classic Exact Test:** This allows two experimental groups to be compared and
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345 p-values for differential representation derivec for each hairpin. Simple and
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346 fast for straightforward comparisons. In this option you will have the option of
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347 "*Compare* x *To* y" which implicitly subtracts the data from y from that of x
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348 to produce the comparison.
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349
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350 * **Generalised Linear Model:** This allow for complex contrasts to be specified
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351 and also gene level analysis to be performed. If this option is chosen then
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352 contrasts must be explicitly stated in equations and multiple contrasts can be
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353 made. In addition there will be the option to analyse hairpins on a per-gene
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354 basis to see if hairpins belonging to a particular gene have any overall
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355 tendencies for the direction of their log-fold-change.
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356
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357 **FDR Threshold:**
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358 The smear plot in the output will have hairpins highlighted to signify
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359 significant differential representation. The significance is determined by
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360 contorlling the false discovery rate, only those with a FDR lower than the
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361 threshold will be highlighted in the plot.
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362
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363 -----
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364
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365 **Citations:**
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366
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367 .. class:: infomark
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368
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369 limma
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370
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371 Please cite the paper below for the limma software itself. Please also try
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372 to cite the appropriate methodology articles that describe the statistical
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373 methods implemented in limma, depending on which limma functions you are
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374 using. The methodology articles are listed in Section 2.1 of the limma
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375 User's Guide.
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376
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377 * Smyth, GK (2005). Limma: linear models for microarray data. In:
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378 'Bioinformatics and Computational Biology Solutions using R and
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379 Bioconductor'. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry,
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380 W. Huber (eds), Springer, New York, pages 397-420.
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381
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382 .. class:: infomark
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383
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384 edgeR
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385
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386 Please cite the first paper for the software itself and the other papers for
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387 the various original statistical methods implemented in edgeR. See
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388 Section 1.2 in the User's Guide for more detail.
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389
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390 * Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor
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391 package for differential expression analysis of digital gene expression
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392 data. Bioinformatics 26, 139-140
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393
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394 * Robinson MD and Smyth GK (2007). Moderated statistical tests for assessing
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395 differences in tag abundance. Bioinformatics 23, 2881-2887
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396
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397 * Robinson MD and Smyth GK (2008). Small-sample estimation of negative
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398 binomial dispersion, with applications to SAGE data.
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399 Biostatistics, 9, 321-332
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400
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401 * McCarthy DJ, Chen Y and Smyth GK (2012). Differential expression analysis
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402 of multifactor RNA-Seq experiments with respect to biological variation.
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403 Nucleic Acids Research 40, 4288-4297
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404
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405 .. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html
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406 .. _limma: http://www.bioconductor.org/packages/release/bioc/html/limma.html
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407 </help>
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408 </tool>
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409
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