|Use Case 1||Annotation of 100,000 invertebrate ESTs|
A researcher needs to annotate 100,000 sequences obtained from an invertebrate species and also needs to provide|
the result as a public database.
Annotate sequences by similarity and complement these annotations for sequences showing no similarity by integrated|
analysis tools. Then, store the results into BioMart or TogoDB to make the database publicly available.
Needed to identify which tool was most suitable for each step. Some tools turned out to require very long time for|
execution. The resulting annotations needed to be archived in a database and made accessible on the Web.
Firstly, use relatively fast tools like Blast2GO and KAAS then use ANNOTATOR for limted number of sequences.|
BioMart is suitable for integration of remote BioMart resources like Ensembl,
while TogoDB can be used to host databases without installation.
Both database systems are accessible through the Web service interface for workflow tools like jORCA and Taverna.
|Tools||Blast2GO, KAAS, ANNOTATOR, BioMart, TogoDB, TogoWS, jORCA, Taverna|
|Databases||Ensembl, BioMart, KEGG|
|Use Case 2||TFBS enrichment within differential microarray gene expression data|
|Task||Identify SNPs in transcription factor binding sites and visualize the result as a genome browser.|
|Strategy||Retrieve SNP and TSS datasets through the DAS protocol, then compute enrichment and export results for a DAS viewer.|
|Problem||Needed to integrate information from multiple databases and needed to customize the visualization.|
Developed a custom-made prediction system for the data obtained from DAS sources, then customize the Ajax|
DAS viewer to show the result in a genomic view.
|Tools||BioDAS, Ajax DAS viewer|
|Databases||FESD II, DBTSS|
|Use Case 3||Protein interactions among enzymes in a KEGG metabolic pathway|
|Task||Predict interacting pairs of proteins in a given metabolic pathway.|
Retrieve enzymes from a specified pathway and search pairs of homologous proteins forming complexes in a|
Found version incompatilibity of the server and client implementations of SOAP protocol. Non-standard BLAST output|
format was returned by PDBj Web service. There were no Web services to calculate phylogenetic profile.
Switch programming languages according to the service in use. Programs are written to parse BLAST results and to|
generate a phylogenetic profile.
|Tools||Java, OCaml, Perl, Ruby, BLAST, DDBJ WABI, PDBj Mine, KEGG API|
|Databases||DDBJ, KEGG, PDBj, UniProt|
|Use Case 4||Analyzing glyco-gene-related diseases|
|Task||Find human diseases which are potentially related to SNPs and glycans.|
Retrieve disease genes and search for homologs in other organisms to which glyco-gene interactions are recoreded,|
then search for epitopes to identify glycans and retrieve their structures.
No Web service existed to query GlycoEpitopeDB and to convert a glycan structure in IUPAC format into KCF format.|
The output of OMIM search was in XML including entries which did not contain SNPs.
|Solution||Implemented and registered BioMoby compliant Web services. Wrote custom BeanShell script for a Taverna workflow.|
|Tools||Taverna, BioMoby, KEGG API|
|Databases||OMIM, H-InvDB, GlycoEpitopeDB, RINGS, Consortium for Functional Glycomics, GlycomeDB, GlycoGene DataBase, KEGG|