MAY 10, 2017 12:00 PM PDT

Not all variants are created equal: what went wrong in the prediction of functional effects of exomic variation

C.E. Credits: CEU
Speaker
  • Associate Professor, Department of Biochemistry and Microbiology, Rutgers University
    Biography
      Dr. Yana Bromberg is an associate professor at the Department of Biochemistry and Microbiology, Rutgers University. She holds an adjunct position at the Department of Genetics at Rutgers and is the Chief Scientific Officer at BioSof - a company for bioinformatics tool development. She is also a fellow at the Institute of Advanced Studies in the Technical University of Munich.

      Dr. Bromberg received her Bachelor degrees in Biology and Computer Sciences from the State University of New York at Stony Brook and a Ph.D. in Biomedical Informatics from Columbia University, New York. She is known for her seminal work on a method for screening for non-acceptable polymorphisms, or SNAP for short, which evaluates the effects of single amino acid substitutions on protein function. Currently, research in the Bromberg lab is focused on the molecular functional annotation of microbiomes, aiming to identify emergent functionality specific to individual environmental niches. The lab also analyses human variomes for disease predisposition and the studies evolution of life's electron transfer reactions. Dr. Bromberg is a member of the Board of Directors of the International Society for Computational Biology and actively participates in organizing the ISMB/ECCB conferences (ISMB stands for Intelligent Systems for Molecular Biology, and ECCB is it's European equivalent). She chairs conference proceedings, conducts workshops, and organizes a special interest group aimed at the study of genomic variation - VarI-SIG.

      Dr. Bromberg's work has been recognized by several awards, including the recent NSF CAREER award, the TSS young investigator award from the American Society for Microbiology, the Rutgers Board of Trustees Research fellowship for Scholarly Excellence, the PhRMA foundation young investigator research starter award and the Hans-Fischer award for outstanding early career scientists from the Institute of Advanced Studies in Technical University of Munich. Dr. Bromberg also serves as an editor and a reviewer of several top bioinformatics journals, including BMC Genomics and PLoS Computational Biology. To date, she has authored or co-authored over 40 peer reviewed scientific articles and has been invited to give over 80 talks.

    Abstract

    Many computational approaches exist for predicting the effects of amino acid substitutions from protein sequence. These are often (incorrectly) used for judging disease predisposition from individual exomic variation. Notably, all available prediction methods “top out” at about the same performance for a set of experimentally determined variant effects – regardless of the complexity of underlying algorithms or the number of protein features considered in making the prediction. We note that prediction errors likely stem from the fact that different methods are trained to recognize different patterns. Some consider protein molecular functional changes, others focus on selection pressure differences, but most aim to differentiate variation across orthologs from known severely damaging variants, e.g. those that cause monogenic disease. Regardless of the training set, however, the vast majority of these methods values evolutionary information as key contributor to the final decision.

    We considered whether the protein sequence position class – rheostat or toggle – affects these predictions. The classes are defined as follows: experimentally evaluated effects of amino acid substitutions at toggle positions are binary, while rheostat positions show progressive changes. In our testing, all evaluated methods failed two key expectations: toggle neutrals were incorrectly predicted as more non-neutral than rheostat non-neutrals, while toggle and rheostat neutrals were incorrectly predicted to be different. Since many toggle positions are conserved, and most rheostats are not, predictors appear to annotate position conservation better than mutational effect. This finding can explain why predictors assign disproportionate weight to evolutionary information as an input features, as well as the field’s inability to improve predictor performance.

    We thus propose that distinguishing between rheostat and toggle positions is necessary prior to attempting variant effect prediction.


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