KNNCNV: A K-Nearest Neighbor Based Method for Detection of Copy Number Variations Using NGS Data.

ABSTRACT: Copy number variation (CNV) is a well-known type of genomic mutation that is associated with the development of human cancer diseases. Detection of CNVs from the human genome is a crucial step for the pipeline of starting from mutation analysis to cancer disease diagnosis and treatment. Next-generation sequencing (NGS) data provides an unprecedented opportunity for CNVs detection at the base-level resolution, and currently, many methods have been developed for CNVs detection using NGS data. However, due to the intrinsic complexity of CNVs structures and NGS data itself, accurate detection of CNVs still faces many challenges. In this paper, we present an alternative method, called KNNCNV (K-Nearest Neighbor based CNV detection), for the detection of CNVs using NGS data. Compared to current

SUBMITTER: Xie K 

PROVIDER: S-EPMC8728060 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature