HMMerCTTer: Tailor-made Decision Making for the Semi-automatic Clustering of large Protein Superfamilies

HERNAN BONDINO; INTI ANABELA PAGNUCO; VICTORIA REVUELTA; MARCEL BRUN; ARJEN TEN HAVE

Oro Verde

Congreso; 3er Congreso Argentino de Bioinformática y Biología Computacional; 2012

Asociación Argentina de Bioinformática y Biología Computacional

The sheer amount of protein sequences derived from public genome sequences provide many opportunities but also challenges to biologists. Many protein superfamilies appear to consists of many, sometimes unknown, subfamilies that are often difficult to be discriminated. Computational analyses play an important role in what is referred to as function assignation but typically require both specific biological knowledge, insight in the available biocomputational tools and heavy computation of large phylogenies. We set out to develop a tool for the bioinformatics layman that, based on a training set of high quality expert annotation, automatically clusters superfamily protein sequences into subfamilies. We developed an automatic but user-supervised procedure that results in a high quality clustering, cluster specific HMMer profiles and corresponding cut-off threshold values for reliable sequence identification and clustering. Hence, we refer to this new tool as HMMer Cut-off Threshold Tool or HMMerCTTer