Nucleic Acids Research, 2007, Vol. 35, Web Server issue W449 Searching DSSP (Definition of Secondary Structure of the Protein) BLAST vs PDB Extract structures from Local ALignments Filter structures in the same region Helix turn Helix Coil Pepcoil FINAL XML Signal peptides sigcleave Peptide statistics pepstat Antigenicity antigenic Hydrophilicity Surface probability pepinfo protchargee Restriction Sites digest INPUT Protein Sequence && Organism type Quality checks Check for Illigal characters No Check for stop codons Yes Yes STOP Use only first Peptide STOP No Check the length > 2500 aa Yes No Good Peptide Transmembrane Regions THMM DAS TMPRED Psiblast vs nrpep MSF Generation MSF generator MSF for 2D prediction NOT successful MSF generator minidentity=30% maxidentity=90% NOT successful MSF generator without restrictions Successful MSF NOT successful query Prot as input PSSMs JNET Prof TMAP DSC Localization if eukariote Psort Structures Features Secondary Structure Prediction Figure 4. 2DSweep ﬂowchart. specifying dependency rules between the used applica- tions in the meta-data, which allows the design of high complexity bioinformatics tasks, and stores the results of the diﬀerent applications together with the new results computed during the process. The ﬁnal output of the task is an XML ﬁle which contains all relevant information generated. The XML information is transformed by means of W2H’s (28) post-processing mechanism into an HTML page for the task report using the Extensible Style-sheet Language Transformations (XSLT; http://www.w3.org/TR/xslt for facilitating a ﬁnal visual inspection of the results. Furthermore, the XML output can be also required and used for further analysis (i.e. direct integration in user’s databases, additional pipeline analysis). All public data- bases used by these servers are installed under the Sequence Retrieval System (SRS) at the DKFZ (29). The DKFZ SRS server contains more than 500 databases that are automatically updated whenever new releases become available; this means that the webservers will be using the very last version of each database. The use of this integrated approach provides great ﬂexibility and extensibility of the process. Therefore, as new and improved algorithms and methodologies are developed, they are incorporated into the protein analysis process without having to redesign the entire task. It is also possible to incorporate speciﬁc sets of databases as they become available, and to implement arbitrary conﬁguration parameters. OUTLINE The development of the three pipelines presented here, has been user-driven from the beginning. Their functionalities are continually being updated and extended in response to requests and suggestions emerging from our core users like LIFEDB (30,31), where these servers are actively used in their protein analysis and annotation. We are currently developing checks especially through the application of ﬁltering strategies and algorithms that will take into account the relationships between domain structure and homology searches. At the moment we are starting to develop a ﬁltering system for the homology searches results taking into account the diﬀerent quality of annotation in diﬀerent protein databases with the idea to assign conﬁdence levels and cross-checking results between tasks. We are additionally working on the implementation of directed text mining using the keywords of the proteins description.