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Chunk #32 — Results — Facilitating the High-Throughput Analysis of Large Genomic Datasets

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Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models.
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Anticipating a massive increase in the number of available whole-genome and whole-exome datasets, the need for accurate computational prediction methods capable of processing these datasets in a timely fashion is increasingly apparent. As a result, the majority of computational prediction algorithms now offer some form of precomputed facility allowing for near-instant predictions to be returned (see Supp. Table S3). However, only SIFT [Ng and Henikoff, 2001] and PolyPhen-2 [Adzhubei et al., 2010] allow for batch submissions (with restrictions) to be made. To facilitate the high-throughput analysis of large-scale genomic datasets, our public Web-server provides up-to-date (precomputed) domain assignments for several large sequence collections, including SwissProt/TrEMBL [Apweiler et al., 2004]; thereby enabling (unrestricted) near-instant predictions to be made for AASs falling within conserved protein domains. Furthermore, our precomputed database is available as an optional download enabling near-instant predictions to be made while running our software locally.