PT  - JOURNAL ARTICLE
AU  - Sharad Vikram
AU  - Matthew D. Rasmussen
AU  - Eric A. Evans
AU  - Imran S. Haque
TI  - SSCM: A method to analyze and predict the pathogenicity of sequence variants
AID  - 10.1101/021527
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 021527
4099  - http://biorxiv.org/content/early/2015/06/26/021527.short
4100  - http://biorxiv.org/content/early/2015/06/26/021527.full
AB  - The advent of cost-effective DNA sequencing has provided clinics with high-resolution information about patient’s genetic variants, which has resulted in the need for efficient interpretation of this genomic data. Traditionally, variant interpretation has been dominated by many manual, time-consuming processes due to the disparate forms of relevant information in clinical databases and literature. Computational techniques promise to automate much of this, and while they currently play only a supporting role, their continued improvement for variant interpretation is necessary to tackle the problem of scaling genetic sequencing to ever larger populations. Here, we present SSCM-Pathogenic, a genome-wide, allele-specific score for predicting variant pathogenicity. The score, generated by a semi-supervised clustering algorithm, shows predictive power on clinically relevant mutations, while also displaying predictive ability in noncoding regions of the genome.