High-throughput sequencing data have been produced at unprecedented rates for diverse genomes. There is a strong need for novel informatics and analytical strategies, including methods for sequencing reads alignment, variant identification, genotype calling and association tests, in order to take advantage of the massive amounts of sequencing data. There have been dozens of short read alignment software available now with different functionalities (1), as well as several single nucleotide variants (SNV) and copy number variant (CNV) calling algorithms (2). However, there is a paucity of methods that can simultaneously handle a large number of called variants (typically >3 million variants for a given human genome) and annotate their functional impacts, despite the fact that this is an important task in many sequencing applications. Even when sequencing only exonic regions for Mendelian diseases such as Freeman–Sheldon syndrome, each subject still carries a total of ∼20 000 variants, but only two variants in trans are the true disease causal mutations (3). Therefore, identifying a small subset of functionally important variants from large amounts of sequencing data is important to pinpoint potential disease causal genes and causal mutations.
