1. Taverna and SoapLab Experience @ Elda Rossi – CINECA (Italy)

2. What is CINECA Cineca is a consortium of Italian Universities and CNR Funded in 1969, now under the control of Research and University Ministry

3. Resources The most important national infrastructure in Italy for the computational support to scientific research Mission: promoting the use of the most advanced computing systems to support public and private scientific and technological research

4. R & Bioconductor Bioconductor is an open source and open development software project for the analysis and comprehension of genomic data. It is based on R, a language and environment for statistical computing and graphics.

5. R & Bioconductor BioConductor is a collection of “packages” Two main types: 1. provides basic infrastructure support. 2. Provides innovative methodology We chose a function in the affy package (type 2. )

6. The affy package Package: affy Description: The package contains functions for exploratory oligonucleotide array analysis. The dependance to tkWidgets only concerns few convenience functions. 'affy' is fully functional without it. Version: 1.5.8-1 Author: Rafael A. Irizarry, Laurent Gautier, Benjamin Milo Bolstad, and Crispin Miller with contributions from … Maintainer: Rafael A. Irizarry Dependencies: R (>= 1.9.0), Biobase (>= 1.4.22), reposTools Suggests: tkWidgets (>= 1.2.2), affydata SystemRequirements: None License: LGPL version 2 or newer URL: None available Function: Expresso. From raw probe intensities to expression values

7. The expresso function Expression measures The most common operation is certainly to convert probe level data to expression values. 1. reading in probe level data 2. background correction 4 methods 3. Normalization 7 methods 4. probe specific background correction, e.g. subtracting MM 3 methods 5. summarizing the probe set values into one expression measure and, in some cases, a standard error for this summary 5 methods

8. How to run expresso $ R > library(affy) > data<-ReadAffy() > data.mas<-expresso(data,bgcorrect.method="mas", pmcorrect.method="mas", normalize.method="constant", summary.method="medianpolish") > write.exprs(data.mas,file=“Data.out") Data.CEL Data.out $ R CMD BATCH script library(affy) data<-ReadAffy() data.mas<-expresso(data,bgcorrect.method="mas", pmcorrect.method="mas", normalize.method="constant", summary.method="medianpolish") write.exprs(data.mas,file=“Data.out") Report script

9. The files [CEL] Version=3 [HEADER] Cols=126 Rows=126 TotalX=126 TotalY=126 Baseline=Not normalized DatHeader=ctrl150:CLS=1167 … [INTENSITY] NumberCells=15876 CellHeader=X Y MEAN 0 0 551.0 1 0 10651.0 2 0 642.0 3 0 10855.0 4 0 278.0 5 0 452.0 6 0 11139.0 Sample001.cel Sample002.cel Sample003.cel 100084_at 2.68016528652511 2.75619854567269 3.82550383255225 101482_at 2.41830136307405 2.19230548692681 3.4173900695363 31962_at 12.3667390890414 12.4534076075796 12.8658623516881 32466_at 12.4078453130306 12.5262787728982 13.2129784659009 35201_at 6.73875347104673 6.36824635919863 7.53465018481639 36189_at 6.91195864883172 6.77835938949316 7.94585515997792 36678_at 10.0269997503136 9.76893096184106 11.1443619988943 37001_at 8.7690698709579 8.57322443505215 9.80956768540462 37029_at 7.58176898579828 7.24297853600119 8.67002397585278 37046_at 4.7250160934765 4.7250160934765 5.68254863921313 37189_at 7.08125646141077 7.0999566997911 7.92512679504857 37719_at 5.33679629782696 5.33679629782696 6.39140386282694 37725_at 7.634367429284 7.41050271151406 8.85664197069339 38437_at 7.54693596951725 7.16216316289552 8.3816810916508 38730_at 7.61959398527742 7.65907193898742 9.00657184492387 39425_at 6.07663839694708 6.03298499862286 7.14769809957403 40276_at 6.33983152588017 6.21300599988174 6.85968858773872 CEL file OUT file

10. Setting up SoapLab A linux based server was chosen Tomcat was installed Java was upgraded Axis was installed SoapLab was installed Vega.cineca.it Tomcat 5.0.28 Java 1.4 Axis 1.1 SoapLab precompiled for Suse Linux Up to here: No Problems !!!

11. Defining the Application 1. Write the application wrapper 2. Write the ACD file for the application 3. Convert ACD to XML 4. Start up the SoapLab server 5. Deploy the new service

12. 1. Write the application wrapper #!/usr/bin/perl use Getopt::Long; # command arguments (with default) GetOptions("bgcorrect=s"=>\$bgcorrect, "normalize=s"=>\$normalize); $bgcorrect="mas" if $bgcorrect eq ""; $normalize="constant" if $normalize eq ""; # location of R executable $rexe="/biotools/R/R-2.1.0/bin/R"; # data directory $datadir=“/biotools/services/data"; # R code to run analysis open(AFFY,">$datadir/affy"); print AFFY <<EOF ; library(affy) data<-ReadAffy() data.mas<-expresso(data, bgcorrect.method="$bgcorrect", pmcorrect.method="mas", normalize.method="$normalize", summary.method="medianpolish") write.exprs(data.mas,file="data.txt") EOF close(AFFY); # now run program system "cd $datadir; $rexe CMD BATCH affy"; # print output open(OUT,"$datadir/data.txt"); while ( ) {print $_;} close(OUT); /biotools/services/affy-expresso.pl

13. 2. Write the ACD file appl: bioconductor [ documentation: "affy/expresso function of BioConductor" version: "1.0" groups: "Microarrays" nonemboss: "Y" executable: affy-expresso.pl ] string: bgcorrect [ additional: "Y" parameter: "Y" default: "mas" ] string: normalize [ additional:"Y" parameter: "Y" default: "constant" ] outfile: output [ additional: "Y" default:“stdout" /biotools/soapbin/analysis-interfaces/metadata/affy.acd The path is defined in the shell Input1: Background correction Input1: Normalization method Output: standard output

14. 3, 4, 5: Final steps 3. Convert ACD to XML 4. Start up the SoapLab server 5. Deploy the new service /biotools/soapbin/analysis-interfaces/generator/acd2xml From:../metadata/affy.acd To:../metadata/microarrays/affy-al.xml /biotools/soapbin/analysis-interfaces/run-AppLab-serverHow to shut down the server? /biotools/soapbin/analysis-interfaces/ws/deploy-web-services

15. Using the service from Taverna From the Available service window select Add new SoapLab scavenger and enter our server address http://vega.cineca.it:8082/axis/services

16. Using the service … (2) The new processor appears in the microarrays folder you can find the affy service After connecting input & output ports, the service can be launched

17. Problems encountered Documentation is not so clear and complete How can we transfer (large) files from the personal WS to the server machine We need a permanent and private data area for storing data We would like to monitor the service while it is running (asynchronous services?) How can we return data in addition to stdOut and stdErr …..

18. A possible (future) workflow Upload one or more CEL files on the server Analyse the data and get expression levels Verify the output data download the output data and clear the personal space WS-plot WS-expresso OK ? NO YES WS-upload