W448 Nucleic Acids Research, 2007, Vol. 35, Web Server issue Input Protein Sequence File Different search methods to scan a number of protein/domain family databases Prosite Blocks Prints Motifs + Pfscan Blocks- HMM- searcher scan PfamA HMM- scan Smart Tigrfams Prodom Scop HMM- scan HMM- scan BlastP2 HscMaMn- Domain hits Data interpretation and valuation steps Selection of true positive domain hits according to predefined rules: (Hits, which fulfill database specific criterias of cutoff values or evalue thresholds) Hits are compared with domains / families described in: INTERPRO Hits, which belong to an identical Interpro entry are grouped Identification of significant and putative hits according to predefined rules Summary of relevant results FINAL XML Figure 3. DomainSweep overall processing ﬂowchart. the sequence with the domain corresponding to the match, the hit ID, description, begin, end and Gene Ontology (GO) annotation. The user has immediate access to all complete application outputs and database entries (via hyperlinks) by clicking on the corresponding part of the picture. At the bottom of each graph there is a link to the task explanatory legend. The table output contains all hits, IDs, descriptions and links to the original output. The XML output containing all the generated information is available via hyperlink at the bottom of the task output. 2DSweep identiﬁes the structural domains in the protein and therefore aids in ﬁnding structural elements. It reports on predictions for alpha-helix, beta-strand, coiled-coil and helix-turn-helix motifs, transmembrane regions, signal sequences, hydrophobicity, antigenicity, protease cleavage sites and more. When predicting the secondary structure of a protein, it is useful to exploit the features of several available prediction algorithms rather than to rely on a single program. Unfortunately, combining prediction methods on a large scale is complicated by the fact that prediction programs have very diﬀerent input requirements and output formats. Some of them perform much better when they have a multiple sequence alignment covering diﬀerent degrees of similarity as input instead of a single sequence. We have developed MSFGenerator, a program, which creates a multiple sequence alignment for a single protein sequence according to user, deﬁned rules (Supplementary Data MSF). It performs a BLAST search against a non-redundant protein database following diﬀerent strategies that will generate diﬀerent kind of alignments (Supplementary Data MSF, Figure 4). The output of MSFGenerator is an alignment in MSF format (multiple sequence ﬁle). The generated MSF will be used as input for four diﬀerent structure prediction programs: PsiPred (18), Jnet (19), Prof (20), and DSC (21). Each derives its prediction using a diﬀerent heuristic. PsiPred is a two-stage neural network that bases its prediction on position speciﬁc scoring matrices, Jnet is a neural network method that works by utilizing an alignment as input, alongside Psiblast (22) and HMM proﬁles. Prof is a classiﬁer that combines linear discriminations and neural networks. DSC is based on decomposing secondary structure prediction into basic concepts and then uses simple and linear statistical methods to combine them. Since DSC is known to perform worse than the other prediction methods employed in 2Dsweep, the usage of DSC is optional. As a second concept, 2DSweep searches for DSSP (Deﬁnition of Secondary Structure of the Protein, (23) annotation for the input protein. 2DSweep runs a Blast against the PDB database. For all local alignments found it extracts secondary structure elements (if any) from the structure deﬁnition of the DSSP database. If there is more than one element covering the same sequence region, 2DSweep uses a simple majority vote to determine the structure at each position. The result of this procedure is shown together with the prediction of the diﬀerent secondary structure prediction tools. Additionally, 2DSweep shows several other common measures of secondary structure. First, the distribution of small, charged and hydrophilic amino acids are shown and probable antigenic regions are indicated. Furthermore, the task searches for transmembrane helices and intervening loop regions using four diﬀerent methods: TmHmm (24), DAS (25), TMap (26) and TmPred. In eukaryotic protein sequences, it additionally searches for signal peptides. Finally, information is given about molecular weight, isoelectric point, the distribution of protease cleavage-sites, and the possible sub-cellular localization of the protein. The web output of 2DSweep (Supplementary Figure 3) is divided in ﬁve sections: (i) General Information, (ii) Secondary structure, (iii) Features and (iv) Cleavage sites. The information provided in each of these sections is shown in Figure 4 and Supplementary Table 3. The complete results can be viewed by clicking on the corresponding part of the picture. At the bottom of each graph there is a link to the corresponding explanatory legend. As in the other tasks the XML output containing all the generated information is available via a hyperlink at the bottom of the task output. IMPLEMENTATION These servers have been implemented using the W3H task framework (27), which allows the execution of compound jobs using work and data ﬂow descriptions in a heterogeneous bioinformatics environment using meta- data information. The system regulates the dataﬂow by