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1 <tool id="plot_spades_stats" name="SPAdes stats" version="0.1">
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2 <description>coverage vs. length plot</description>
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3 <requirements>
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4 <requirement type="package">R</requirement>
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5 </requirements>
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6 <command interpreter="bash">r_wrapper.sh $script_file</command>
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7
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8 <inputs>
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9 <param name="input_scaffolds" type="data" format="tabular" label="Scaffold stats"/>
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10 <param name="input_contigs" type="data" format="tabular" label="Contig stats"/>
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11 <param name="length_co" type="integer" value="1000" min="0" label="Length cut-off" help="Contigs with length under that value are shown in red"/>
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12 <param name="coverage_co" type="integer" value="10" min="0" label="Coverage cut-off" help="Contigs with length under that value are shown in red"/>
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13 </inputs>
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14 <configfiles>
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15 <configfile name="script_file">
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16 ## Setup R error handling to go to stderr
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17 options( show.error.messages=F,
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18 error = function () {
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19 cat( geterrmessage(), file=stderr() ); q( "no", 1, F )
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20 } )
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21 files = c("${input_contigs}", "${input_scaffolds}")
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22 types = c("Contigs", "Scaffolds")
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23
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24 ## Start plotting device
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25 png("${out_file}", w=500, h=1000)
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26 par(mfrow=c(2,1))
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27
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28 ## Loop over the two files
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29 for (i in 1:length(types)){
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30 seqs = read.table(files[i], header=FALSE, comment.char="#")
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31 colnames = c("name", "length", "coverage")
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32 names(seqs) = colnames
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33
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34 ## Stats over all sequences
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35 sl_all = sort(seqs\$length, decreasing=TRUE)
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36 cs_all = cumsum(sl_all)
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37 s_all = sum(seqs\$length)
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38 n50_idx_all = which.min(sl_all[cs_all < 0.5*s_all])
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39 n90_idx_all = which.min(sl_all[cs_all < 0.9*s_all])
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40 n50_all = sl_all[n50_idx_all]
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41 n90_all = sl_all[n90_idx_all]
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42
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43 ## Filter short seqs, redo stats
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44 seqs_filt = seqs[seqs\$length >= ${length_co} & seqs\$coverage >= ${coverage_co},]
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45 if (nrow(seqs_filt) > 0){
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46 sl_filt = sort(seqs_filt\$length, decreasing=TRUE)
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47 cs_filt = cumsum(sl_filt)
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48 s_filt = sum(seqs_filt\$length)
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49 n50_idx_filt = which.min(sl_filt[cs_filt < 0.5*s_filt])
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50 n90_idx_filt = which.min(sl_filt[cs_filt < 0.9*s_filt])
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51 n50_filt = sl_filt[n50_idx_filt]
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52 n90_filt = sl_filt[n90_idx_filt]
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53 }
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54 seqs_bad = seqs[seqs\$length < ${length_co} | seqs\$coverage < ${coverage_co},]
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55
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56 ## Length vs coverage
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57 plot(length~coverage, data=seqs, log="xy", type="n", main=paste(types[i], ": coverage vs. length", sep=""), xlab="Coverage", ylab="Length")
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58 if (nrow(seqs_bad) > 0){
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59 points(length~coverage, data=seqs_bad, cex=0.5, col="red")
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60 }
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61 if (nrow(seqs_filt) > 0){
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62 points(length~coverage, data=seqs_filt, cex=0.5, col="black")
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63 }
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64 abline(v=${coverage_co}, h=${length_co}, lty=2, col=grey(0.3))
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65 legend(x="topleft", legend=c("Before/after filtering", paste(c("N50: ", "N90: ", "Median cov.: "), c(n50_all, n90_all, round(median(seqs\$coverage))), rep("/", 3), c(n50_filt, n90_filt, round(median(seqs_filt\$coverage))), sep="")), cex=0.8)
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66 }
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67 dev.off()
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68 </configfile>
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69 </configfiles>
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70 <outputs>
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71 <data format="png" name="out_file" />
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72 </outputs>
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73 <help>
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74 **What it does**
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75
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76 Using the output of SPAdes (a pair of fasta file and stat file for each of the contigs and scaffolds), it produces a coverage vs. contig plot. Each dot represent a contig/scaffold. Given a coverage and a length cutoff, sequences that do not meet those criteria are shown in red. Some statistics are also given (N50, N90, median contig/scaffold length) both before and after filtering.
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77
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78 Use the "filter SPAdes output" tool to actually filter sequences.
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79 </help>
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80 </tool> |
