Abstract
Next-generation sequencing technologies have been widely used to query genetic variants in normal individuals as well as in those with diseases. Large-scale structural variations are a common source of genetic diversity in human population, and some of them have significant contributions to the etiology of diseases. However, the detection of large-scale structural variations from sequencing data remains challenging. Here, we describe Meerkat—an algorithm which can reliably detect structural variations from Illumina short-read sequencing data at basepair resolution. A unique feature of Meerkat is that it can infer the variant forming mechanisms based on the DNA content and features at the breakpoints.
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Yang, L. (2022). Meerkat: An Algorithm to Reliably Identify Structural Variations and Predict Their Forming Mechanisms. In: Ng, C., Piscuoglio, S. (eds) Variant Calling. Methods in Molecular Biology, vol 2493. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2293-3_8
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DOI: https://doi.org/10.1007/978-1-0716-2293-3_8
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