SSCM: A method to analyze and predict the pathogenicity of sequence variants

SSCM: A method to analyze and predict the pathogenicity of sequence variants

Sharad Vikram, Matthew D Rasmussen, Eric A Evans, Imran S Haque
doi: http://dx.doi.org/10.1101/021527

The advent of cost-effective DNA sequencing has provided clinics with high-resolution information about patients’ 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.

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