Matthew D Rasmussen | bioRxiv Science

bioRxiv2015

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

AbstractThe 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.

Bioinformatics↗

bioRxiv2014

Phylogenetic Identification and Functional Characterization of Orthologs and Paralogs across Human, Mouse, Fly, and Worm

Model organisms can serve the biological and medical community by enabling the study of conserved gene families and pathways in experimentally-tractable systems. Their use, however, hinges on the ability to reliably identify evolutionary orthologs and paralogs with high accuracy, which can be a great challenge at both small and large evolutionary distances. Here, we present a phylogenomics-based approach for the identification of orthologous and paralogous genes in human, mouse, fly, and worm, which forms the foundation of the comparative analyses of the modENCODE and mouse ENCODE projects. We study a median of 16,101 genes across 2 mammalian genomes (human, mouse), 12 Drosophila genomes, 5 Caenorhabditis genomes, and an outgroup yeast genome, and demonstrate that accurate inference of evolutionary relationships and events across these species must account for frequent gene-tree topology errors due to both incomplete lineage sorting and insufficient phylogenetic signal. Furthermore, we show that integration of two separate phylogenomic pipelines yields increased accuracy, suggesting that their sources of error are independent, and finally, we leverage the resulting annotation of homologous genes to study the functional impact of gene duplication and loss in the context of rich gene expression and functional genomic datasets of the modENCODE, mouse ENCODE, and human ENCODE projects.

Genomics↗