DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indels

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.



via BioMed Central - Latest Articles http://genomebiology.com/2013/14/3/R23/abstract