SigniSite: Identification of residue-level genotype-phenotype correlations in protein multiple sequence alignments

• Published in: Nucleic acids research 2013-07, Vol.41 (Web Server issue), p.W286-W291
• Main Authors: Jessen, Leon Eyrich, Hoof, Ilka, Lund, Ole, Nielsen, Morten
• Format: Article
• Language: English
• Subjects: Drug Resistance, Viral - genetics; Genetic Association Studies - methods; Genotype; HIV Protease - genetics; HIV Protease Inhibitors - pharmacology; HIV-1 - drug effects; HIV-1 - genetics; Internet; Mutation; Phenotype; Position-Specific Scoring Matrices; Sequence Alignment; Sequence Analysis, Protein; Software
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkt497

Identifying which mutation(s) within a given genotype is responsible for an observable phenotype is important in many aspects of molecular biology. Here, we present SigniSite, an online application for subgroup-free residue-level genotype-phenotype correlation. In contrast to similar methods, SigniSite does not require any pre-definition of subgroups or binary classification. Input is a set of protein sequences where each sequence has an associated real number, quantifying a given phenotype. SigniSite will then identify which amino acid residues are significantly associated with the data set phenotype. As output, SigniSite displays a sequence logo, depicting the strength of the phenotype association of each residue and a heat-map identifying 'hot' or 'cold' regions. SigniSite was benchmarked against SPEER, a state-of-the-art method for the prediction of specificity determining positions (SDP) using a set of human immunodeficiency virus protease-inhibitor genotype-phenotype data and corresponding resistance mutation scores from the Stanford University HIV Drug Resistance Database, and a data set of protein families with experimentally annotated SDPs. For both data sets, SigniSite was found to outperform SPEER. SigniSite is available at: http://www.cbs.dtu.dk/services/SigniSite/.