Abstract
We present CAGe, a statistical algorithm which exploits high sequence identity between sampled genomes and a reference assembly to streamline the variant calling process. Using a combination of changepoint detection, classification, and online variant detection, CAGe is able to call simple variants quickly and accurately on the 90-95% of a sampled genome which differs little from the reference, while correctly learning the remaining 5-10% that must be processed using more computationally expensive methods. CAGe runs on a deeply sequenced human whole genome sample in approximately 20 minutes, potentially reducing the burden of variant calling by an order of magnitude after one memory-efficient pass over the data.
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Bloniarz, A. et al. (2014). Changepoint Analysis for Efficient Variant Calling. In: Sharan, R. (eds) Research in Computational Molecular Biology. RECOMB 2014. Lecture Notes in Computer Science(), vol 8394. Springer, Cham. https://doi.org/10.1007/978-3-319-05269-4_3
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DOI: https://doi.org/10.1007/978-3-319-05269-4_3
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