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1 <tool id="sartools_deseq2" name="SARTools DESeq2" version="@TOOL_VERSION@+galaxy0">
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3 <description>Compare two or more biological conditions in a RNA-Seq framework with DESeq2</description>
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5 <macros>
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6 <import>macros.xml</import>
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7 </macros>
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8
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9 <expand macro="requirements"/>
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10 <expand macro="stdio"/>
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11
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12 <command><![CDATA[
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13
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14 python '$__tool_directory__/abims_sartools_deseq2_wrapper.py'
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15 ## parameters
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16 @COMMAND_BASIC_PARAMETERS@
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17 @COMMAND_BATCH_PARAM@
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18 --fitType '$advanced_parameters.fitType'
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19 --cooksCutoff '$advanced_parameters.cooksCutoff'
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20 --independentFiltering '$advanced_parameters.independentFiltering'
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21 --alpha '$advanced_parameters.alpha'
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22 --pAdjustMethod '$advanced_parameters.pAdjustMethod'
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23 --typeTrans '$advanced_parameters.typeTrans'
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24 --locfunc '$advanced_parameters.locfunc'
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25 --colors "'$advanced_parameters.colors'"
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26 --forceCairoGraph '$advanced_parameters.forceCairoGraph'
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27 ## ouputs
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28 @COMMAND_OUTPUTS@
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29
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30 ]]></command>
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31
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32 <inputs>
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33
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34 <expand macro="basic_parameters" />
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35
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36 <section name="advanced_parameters" title="Advanced Parameters" expanded="false">
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37 <expand macro="batch_param" />
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38 <param type="select" label="Mean-variance relationship" argument="--fitType" help="Type of model for the mean-dispersion relationship. Parametric by default." >
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39 <option value="parametric" selected="true">parametric</option>
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40 <option value="local">local</option>
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41 <option value="mean">mean</option>
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42 </param>
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43 <param type="boolean" checked="true" truevalue="TRUE" falsevalue="FALSE" label="Perform the outliers detection" argument="--cooksCutoff" help="Checked by default."/>
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44 <param type="boolean" checked="true" truevalue="TRUE" falsevalue="FALSE" label="Perform independent filtering" argument="--independentFiltering" help="Checked by default."/>
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45 <expand macro="alpha_param" />
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46 <expand macro="padjustmethod_param" />
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47 <param type="select" label="Transformation for PCA/clustering" argument="--typeTrans" help="Method of transformation of the counts for the clustering and the PCA: 'VST' (default) for Variance Stabilizing Transformation, or 'rlog' for Regularized Log Transformation." >
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48 <option value="VST" selected="true">VST</option>
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49 <option value="rlog">rlog</option>
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50 </param>
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51 <param type="select" label="Estimation of the size factors" argument="--locfunc" help="'median' (default) or 'shorth' from the genefilter package." >
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52 <option value="median" selected="true">median</option>
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53 <option value="shorth">shorth</option>
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54 </param>
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55 <expand macro="colors_param" />
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56 <expand macro="forceCairoGraph_param" />
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57 </section>
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58
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59 </inputs>
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60
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61 <outputs>
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62
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63 <expand macro="outputs" />
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64
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65 </outputs>
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66
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67 <tests>
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68 <test>
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69 <!-- Test with 2 conditions, 2 replicates, 8217 features -->
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70 <param name="targetFile" dbkey="?" value="target.txt" />
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71 <param name="rawDir" value="raw.zip" dbkey="?" ftype="zip"/>
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72 <output name="log">
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73 <assert_contents>
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74 <has_text text="KO vs WT 0.1 171" />
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75 <has_text text="KO vs WT 2583 2663 5246" />
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76 <has_text text="HTML report created" />
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77 </assert_contents>
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78 </output>
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79 </test>
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80 <!-- <test>
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81 --> <!-- NOT WORKING YET: Test with 3 conditions, 3 replicates, 10160 features, with batch effect -->
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82 <!-- <param name="targetFile" dbkey="?" value="targetT048.txt" />
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83 <param name="rawDir" value="rawT048.zip" dbkey="?" ftype="no_unzip.zip"/>
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84 <param name="condRef" value="T0"/>
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85 <param name="condition" value="true"/>
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86 <output name="tables_html" file="SARTools_DESeq2_targetT048_tables.html" lines_diff="14">
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87 <extra_files type="file" name="T4vsT0.complete.txt" value="SARTools_DESeq2_T4vsT0.complete.txt"/>
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88 <extra_files type="file" name="T8vsT0.complete.txt" value="SARTools_DESeq2_T8vsT0.complete.txt"/>
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89 <extra_files type="file" name="T8vsT4.complete.txt" value="SARTools_DESeq2_T8vsT4.complete.txt"/>
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90 </output>
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91 </test>
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92 --> </tests>
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93
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94 <help><![CDATA[
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95
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96 @HELP_AUTHORS@
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97
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98 ===============
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99 SARTools DESeq2
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100 ===============
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101
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102 -----------
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103 Description
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104 -----------
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105
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106 @HELP_DESCRIPTION@
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107
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108
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109 -----------
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110 Input files
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111 -----------
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112
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113 @HELP_INPUT_FILES@
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114
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115
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116 ----------
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117 Parameters
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118 ----------
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119
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120 @HELP_BASIC_PARAMETERS@
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121 * **batch:** adjustment variable to use as a batch effect, must be a column of the target file (NULL if no batch effect needs to be taken into account);
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122 * **alpha:** significance threshold applied to the adjusted p-values to select the differentially expressed features (default is 0.05);
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123 * **fitType:** type of model for the mean-dispersion relationship ("parametric" by default, or "local");
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124 * **cooksCutoff:** TRUE (default) of FALSE to execute or not the detection of the outliers [4];
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125 * **independentFiltering:** TRUE (default) of FALSE to execute or not the independent filtering [5];
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126 * **pAdjustMethod:** p-value adjustment method for multiple testing [6, 7] ("BH" by default, "BY" or any value of p.adjust.methods);
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127 * **typeTrans:** method of transformation of the counts for the clustering and the PCA (default is "VST" for Variance Stabilizing Transformation, or "rlog" for Regularized Log Transformation);
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128 * **locfunc:** function used for the estimation of the size factors (default is "median", or "shorth" from the genefilter package);
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129 * **colors:** colors used for the figures (one per biological condition), 8 are given by default.
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130 * **forceCairoGraph:** TRUE or FALSE (default) to force the use of cairo with options(bitmapType="cairo").
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131
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132
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133 ------------
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134 Output files
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135 ------------
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136
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137 @HELP_OUTPUT_FILES@
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138
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139
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140 ---------------------------------------------------
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141
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142 [1] G.-K. Smyth. Limma: linear models for microarray data. In R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, and W. Huber, editors, Bioinformatics and Computational Biology Solutions Using R and Bioconductor, pages 397–420. Springer, New York, 2005.
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143
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144 [2] S. Anders. HTSeq: Analysing high-throughput sequencing data with Python. http://www-huber.embl.de/users/anders/HTSeq/, 2011.
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145
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146 [3] S. Anders, P.-T. Pyl, and W. Huber. HTSeq - A Python framework to work with high-throughput sequencing data. bioRxiv preprint, 2014. URL: http://dx.doi.org/10.1101/002824.
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147
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148 [4] R.-D. Cook. Detection of Influential Observation in Linear Regression. Technometrics, February 1977.
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149
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150 [5] R. Bourgon, R. Gentleman, and W. Huber. Independent filtering increases detection power for high-throughput experiments. PNAS, 107(21):9546–9551, 2010. URL: http://www.pnas.org/content/107/21/9546.long.
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151
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152 [6] Y. Benjamini and Y. Hochberg. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society B, 57:289–300, 1995.
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153
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154 [7] Y. Benjamini and D. Yekutieli. The control of the false discovery rate in multiple testing under dependency. Ann. Statist., 29(4):1165–1188, 2001.
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155
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156
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157 ]]></help>
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158
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159 <citations>
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160 <expand macro="common_citations" />
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161 <citation type="bibtex">@ARTICLE{Cook77,
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162 author = {R.-D. Cook},
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163 title = {Detection of Influential Observation in Linear Regression},
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164 journal = {Technometrics},
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165 year = {1977},
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166 month = {February}
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167 }</citation>
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168 <citation type="bibtex">@ARTICLE{Bourgon10,
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169 author = {R. Bourgon, R. Gentleman, and W. Huber},
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170 title = {Independent filtering increases detection power for high-throughput experiments},
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171 journal = {PNAS},
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172 year = {2010},
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173 volume = {107},
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174 number = {21},
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175 pages = {9546–9551},
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176 note = {URL: http://www.pnas.org/content/107/21/9546.long}
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177 }</citation>
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178 </citations>
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179
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180 </tool>
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