Input: * a list of Ensembl gene identifiers. It is also possible to give any external gene identifiers Ensembl recognizes (e.g. RefSeq ids) * a namespace. Output: * a list of gene sub-clusters * foreach cluster, a TF alignment map that defines a consensus transcriptional regulatory pattern. See, http://genome.imim.es/webservices/workflows.html page for more details. content org.biomoby.client.taverna.plugin.ExtractMobyData org.embl.ebi.escience.scuflworkers.biomoby.ExtractMobyData ArrayList list = new ArrayList(); list.add(strings); strings list import java.io.*; import java.util.ArrayList; import javax.xml.parsers.*; import org.w3c.dom.*; import org.xml.sax.*; import org.apache.xml.serialize.XMLSerializer; String OutObj=""; String skel="<?xml version='1.0' encoding='ISO-8859-1'?><moby:MOBY xmlns:moby='http://www.biomoby.org/moby'><moby:mobyContent><moby:mobyData queryID='one'><moby:Collection moby:articleName=\"matscan_predictions\"></moby:Collection></moby:mobyData></moby:mobyContent></moby:MOBY>"; // clusters list processing String[] cluster_identifiers_temp = clusters.split("\n"); ArrayList clusters_identifiers = new ArrayList (); int i = 0; int maxi = cluster_identifiers_temp.length; while (i < maxi) { String cluster = cluster_identifiers_temp[i]; clusters_identifiers.add (cluster); i++; } // MatScan moby collection processing DocumentBuilderFactory dbf=DocumentBuilderFactory.newInstance(); DocumentBuilder db = dbf.newDocumentBuilder(); // input initialisation InputSource Source1 = new InputSource(new StringReader(MatScan)); Document doc1=db.parse(Source1); // output initialisation InputSource SourceSkel = new InputSource(new StringReader(skel)); Document docsalida=db.parse(SourceSkel); NodeList nodelistfinal = docsalida.getElementsByTagName("moby:Collection"); Node comienzo = nodelistfinal.item(0); // serializer initialisation XMLSerializer serializer = new XMLSerializer(); StringWriter sw=new StringWriter(); serializer.setOutputCharStream(sw); NodeList nodelist1bn = doc1.getElementsByTagName("moby:GFF"); // System.err.println ("NodeList global1bn size, " + nodelist1bn.getLength()); NodeList nodelist1b = doc1.getElementsByTagName("GFF"); // System.err.println ("NodeList global1b size, " + nodelist1b.getLength()); NodeList[] globalC={nodelist1bn,nodelist1b}; // Common index variables for all this work int gi; int maxgi; int si; int maxsi; for(gi=0,maxgi=globalC.length;gi<maxgi;gi++) { for (si=0, maxsi=globalC[gi].getLength();si<maxsi;si++) { Node found = globalC[gi].item(si); Node parent = found.getParentNode(); // System.err.println ("found node, " + found.getNodeName() + ", type, " + found.getNodeType()); NamedNodeMap nMap = found.getAttributes (); Node n = nMap.getNamedItem("id"); if (n == null) { n = nMap.getNamedItem ("moby:id"); } String id = ""; if (n == null) { // System.err.println ("error, id is null"); } else { id = n.getNodeValue(); // System.err.println ("id, " + id); } if (clusters_identifiers.contains (id)) { // System.err.println ("clusters_identifiers contains id, " + id); Node changed=docsalida.importNode(parent,true); comienzo.appendChild(changed); } } } // Last step!!! serializer.serialize(docsalida); OutObj = sw.toString(); // Convert it as a List, to be consistent with the current simple to collection Taverna Moby plugin behaviour ArrayList filtered_MatScan = new ArrayList(); filtered_MatScan.add (OutObj); clusters MatScan filtered_MatScan org.embl.ebi.escience.scuflworkers.biomoby.ExtractMobyData org.embl.ebi.escience.scuflworkers.java.DecodeBase64 org.biomoby.client.taverna.plugin.ExtractMobyData org.embl.ebi.escience.scuflworkers.biomoby.ExtractMobyData org.biomoby.client.taverna.plugin.ExtractMobyData org.biomoby.client.taverna.plugin.ExtractMobyData org.biomoby.client.taverna.plugin.ExtractMobyData org.biomoby.client.taverna.plugin.ExtractMobyData Runs SOTA algorithm to partition the gene space into subclusters. The input is a gene score matrix represented as a MicroArrayData_Text object. http://moby.ucalgary.ca/moby/MOBY-Central.pl runSOTAClustering genome.imim.es 35 euclidean Converts a collection of generic sequences into FASTA sequences http://moby.ucalgary.ca/moby/MOBY-Central.pl fromGenericSequenceCollectionToFASTA genome.imim.es Sequence retrieval tool from Ensembl database. It returns the upstream sequence of a given set of Ensembl gene identifiers. These identifiers could be external ones, such as Refseq Ids or Affymetrix ids. In case you select the orthologous mode, it will returns the upstream sequence of all orthologous genes of a given input gene (only one input gene identifier in that case) http://moby.ucalgary.ca/moby/MOBY-Central.pl getUpstreamSeqFromEnsembl genome.imim.es 500 False Homo sapiens Parses a collection of meta-alignment outputs to produce a text-formatted sequence similarity score matrix http://moby.ucalgary.ca/moby/MOBY-Central.pl fromMetaAlignmentsToTextScoreMatrix genome.imim.es Runs Multiple-Meta-Alignment software to perform multiple non-collinear transcription factor map alignments of promoter regions. It returns the multiple-meta-alignment output in GFF format. http://moby.ucalgary.ca/moby/MOBY-Central.pl runMultiMetaAlignmentGFF genome.imim.es 0.1 0.1 0.5 100 -10 runMultiPairwiseMetaAlignment runs Meta-alignment software on a multiple running mode, receiving a collection of maps, making pairs of them and, foreach pair, it produces alignments of sequences of TF binding sites. It returns the predictions in 'Meta-alignment' format. You can use runMatScanGFF to produce the input GFF files http://moby.ucalgary.ca/moby/MOBY-Central.pl runMultiPairwiseMetaAlignment genome.imim.es 0.1 0.5 0.1 Runs Meta-alignment software on a multiple running mode, receiving a collection of maps, making pairs of them and, foreach pair, producing, in GFF format, alignments of sequences of TF binding sites http://moby.ucalgary.ca/moby/MOBY-Central.pl runMultiPairwiseMetaAlignmentGFF genome.imim.es 0.1 0.1 0.5 Reports putative predicted motifs on a given collection of DNA sequences. The motifs collections currently available are Transcription Factor binding site collections. The predicted set of motifs are reported in GFF format. If you want to give MatScan output to Meta-alignment program, you MUST use the 'log-likelihood matrix' mode. http://moby.ucalgary.ca/moby/MOBY-Central.pl runMatScanGFFCollection genome.imim.es log-likelihood Both 0.85 Jaspar Runs Multiple-Meta-Alignment software to perform multiple non-collinear transcription factor map alignments of promoter regions. http://moby.ucalgary.ca/moby/MOBY-Central.pl runMultiMetaAlignment genome.imim.es 0.5 0.1 100 -10 0.1 Processor to parse the datatype b64_encoded_jpeg http://moby.ucalgary.ca/moby/MOBY-Central.pl b64_encoded_jpeg image Processor to parse the datatype b64_encoded_jpeg a string http://moby.ucalgary.ca/moby/MOBY-Central.pl String Runs gff2ps software to vizualize a set of GFF annotations maps. It returns an encoded image in JPEG format. http://moby.ucalgary.ca/moby/MOBY-Central.pl runGFF2JPEG genome.imim.es Fractional solvation report http://moby.ucalgary.ca/moby/MOBY-Central.pl List_Text text/xml text/xml image/*