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

Zhao, Huiying, Yang, Yuedong, Lin, Hai, Zhang, Xinjun, Mort, Matthew, Cooper, David Neil, Liu, Yunlong and Zhou, Yaoqi 2013. DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indels. Genome Biology 14 (3) , R23. 10.1186/gb-2013-14-3-r23.

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Abstract

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 1,000 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-lab.org/ddig.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Subjects: R Medicine > RZ Other systems of medicine
Publisher: BioMed Central
ISSN: 1465-6906
Last Modified: 23 Nov 2018 19:53
URI: http://orca.cf.ac.uk/id/eprint/84048

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