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Chunk #1 — Background

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Statistical modeling for sensitive detection of low-frequency single nucleotide variants.
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In cancer genetics research, low frequency tumor somatic SNV identification is crucial due to the inevitable normal tissue contamination [6, 7] and the highly heterogeneous, constantly evolving nature of tumors [8]. Accurate and sensitive identification of low frequency SNVs also carries clinical significance, since it enables the early diagnosis, cancer progression monitor and relapse identification. The recent discovery of circulating tumor DNA (ctDNA) also gained much attention. Contrast to traditional tumor biopsies, which is invasive and can only offer a snapshot of the tumor genetics landscape at certain checkpoints, ctDNA based ‘liquid biopsy’ [9] is non-invasive and can be done repeatedly for close monitoring of early sign of relapse or metastasis. However, ctDNA only takes a small percentage of all blood sample DNA, a previous research [10] reported for some advanced cancers, ctDNA is about 1 ~ 10 % of blood DNA.