ArticleNucleic acids research2013
SigniSite: Identification of residue-level genotype-phenotype correlations in protein multiple sequence alignments.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Multiplex measurement of protein-peptide dissociation constants using dialysis and mass spectrometry.Protein science : a publication of the Protein Society · 2023Article
- The Changes in the p53 Protein across the Animal Kingdom Point to Its Involvement in Longevity.International journal of molecular sciences · 2021Article
- Cross-recognition of a pit viper (Crotalinae) polyspecific antivenom explored through high-density peptide microarray epitope mapping.PLoS neglected tropical diseases · 2017Article
- Characterization and functional analysis of hypoxia-inducible factor HIF1α and its inhibitor HIF1αn in tilapia.PloS one · 2017Article
- LEON-BIS: multiple alignment evaluation of sequence neighbours using a Bayesian inference system.BMC bioinformatics · 2016Article
- Insights from 20 years of bacterial genome sequencing.Functional & integrative genomics · 2015Review
- Identification of a Major Dimorphic Region in the Functionally Critical N-Terminal ID1 Domain of VAR2CSA.PloS one · 2015Article
- IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform.PLoS computational biology · 2014Article
- NEP: web server for epitope prediction based on antibody neutralization of viral strains with diverse sequences.Nucleic acids research · 2014Article
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Authors and funding
4 authors.
Funding
Abstract
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/.
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Registered trials
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