DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indels
Genome Biology 2013, 14:R23 doi:10.1186/gb-2013-14-3-r23Published: 13 March 2013
Micro-indels -insertions or deletions shorter than 21 bps- constitute the second most frequent class of human gene mutation after single nucleotide variants. Despite the relative abundance of non-frameshifting indels, their damaging effect on protein structure and function has gone largely unstudied. We have developed a support vector machine-based method named DDIG-in (Detecting disease-causing genetic variations due to indels) to prioritize non-frameshifting indels by comparing disease-associated mutations with putatively neutral mutations from the 1000 Genomes Project. The final model gives good discrimination for indels and is robust against annotation errors. A webserver implementing DDIG-in is available at http://sparks.informatics.iupui.edu/ddig.