DDIG-in: Discriminating between diseaseassociated and neutral non-frameshifting micro-indels

Huiying Zhao, Yuedong Yang, Hai Lin, Xinjun Zhang, Matthew Mort, David N. Cooper, Yunlong Liu, Yaoqi Zhou

Research output: Contribution to journalArticle

38 Scopus citations

Abstract

Micro-indels -insertions or deletions shorter than 21 bps- constitute the second most frequent class of human genemutation after single nucleotide variants. Despite the relative abundance of non-frameshifting indels, their damagingeffect on protein structure and function has gone largely unstudied. We have developed a support vector machine-basedmethod named DDIG-in (Detecting disease-causing genetic variations due to indels) to prioritize non-frameshifting indelsby comparing disease-associated mutations with putatively neutral mutations from the 1000 Genomes Project. The finalmodel gives good discrimination for indels and is robust against annotation errors. A webserver implementing DDIG-in isavailable at http://sparks.informatics.iupui.edu/ddig.

Original languageEnglish (US)
Article numberR23
JournalGenome biology
Volume14
Issue number3
DOIs
StatePublished - Mar 13 2013

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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