title: Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST-Indel).
abstract: Insertion/deletion variants (indels) alter protein sequence and length, yet are highly prevalent in healthy populations, presenting a challenge to bioinformatics classifiers. Commonly used features--DNA and protein sequence conservation, indel length, and occurrence in repeat regions--are useful for inference of protein damage. However, these features can cause false positives when predicting the impact of indels on disease. Existing methods for indel classification suffer from low specificities, severely limiting clinical utility. Here, we further develop our variant effect scoring tool (VEST) to include the classification of in-frame and frameshift indels (VEST-indel) as pathogenic or benign. We apply 24 features, including a new "PubMed" feature, to estimate a gene's importance in human disease. When compared with four existing indel classifiers, our method achieves a drastically reduced false-positive rate, improving specificity by as much as 90%. This approach of estimating gene importance might be generally applicable to missense and other bioinformatics pathogenicity predictors, which often fail to achieve high specificity. Finally, we tested all possible meta-predictors that can be obtained from combining the four different indel classifiers using Boolean conjunctions and disjunctions, and derived a meta-predictor with improved performance over any individual method.
authors.affiliation: Department of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland.
authors.forename: Christopher
authors.initials: C
authors.lastname: Douville
authors.affiliation: Department of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland.
authors.forename: David L
authors.initials: DL
authors.lastname: Masica
authors.affiliation: Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, UK.
authors.forename: Peter D
authors.initials: PD
authors.lastname: Stenson
authors.affiliation: Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, UK.
authors.forename: David N
authors.initials: DN
authors.lastname: Cooper
authors.affiliation: In Silico Solutions, Fairfax, Virginia.
authors.forename: Derek M
authors.initials: DM
authors.lastname: Gygax
authors.affiliation: In Silico Solutions, Fairfax, Virginia.
authors.forename: Rick
authors.initials: R
authors.lastname: Kim
authors.affiliation: In Silico Solutions, Fairfax, Virginia.
authors.forename: Michael
authors.initials: M
authors.lastname: Ryan
authors.affiliation: Department of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland.|Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
authors.forename: Rachel
authors.initials: R
authors.lastname: Karchin
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D066264:Datasets as Topic; D006801:Humans; D054643:INDEL Mutation; D008957:Models, Genetic; D020125:Mutation, Missense; D015203:Reproducibility of Results; D012984:Software; D064878:Web Browser
keywords: bioinformatics pathogenicity predictor; in-frame frameshift; indel; insertion deletion variant; meta-predictor