Section: New Results
Large-Scale Annotation of Protein Domains and Sequences
Many protein chains in the Protein Data Bank (PDB) are cross-referenced with Pfam domains and Gene Ontology (GO) terms. However, these annotations do not explicitly indicate any relation between EC numbers and Pfam domains, and many others lack GO annotations. In order to address this limitation, as part of the PhD thesis project of Seyed Alborzi, we developed the CODAC approach for mining multiple protein data sources (i.e. SwissProt, TremBL, and SIFTS) in order to associate GO molecular function terms with Pfam domains, for example. We named the software implementation “GO-DomainMiner”. This work was first presented at IWBBIO 2017 . A full paper has recently been accepted for a special issue of BMC Bioinformatics .
In collaboration with Maria Martin's team at the European Bioinformatics Institute (EBI), we combined the CODAC approach with a novel combinatorial association rule based approach called “CARDM” for annotating protein sequences. When applied to the large UniProt/TrEMBL sequence database of 63 million protein entries, CARDM predicted over 24 million Enzyme Commission (EC) numbers and 188 million GO terms for those entries. A journal paper in collaboration with the EBI on comparing the quality of these predicted annotations with other state of the art annotation methods is in preparation, and a poster was presented at ISMB-ECCB-2017 . As part of the PhD thesis of Bishnu Sarker, we also developed GrAPFI, a graph-based protein function annotation approach. GrAPFI applies a label propagation algorithm to a complex network representation of protein sequence data. A full paper on this work has recently been accepted by the International Conference on Complex Networks and their Applications .