CNVcaller: highly efficient and widely applicable software for detecting copy number variations in large populations | Zendy

Xihong Wang | Zendy; Zhuqing Zheng | Zendy; Yudong Cai | Zendy; Ting Chen | Zendy; Chao Li | Zendy; Weiwei Fu | Zendy; Yu Jiang | Zendy

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CNVcaller: highly efficient and widely applicable software for detecting copy number variations in large populations

Author(s) -

Xihong Wang,

Zhuqing Zheng,

Yudong Cai,

Ting Chen,

Chao Li,

Weiwei Fu,

Yu Jiang

Publication year - 2017

Publication title -

gigascience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.947

H-Index - 54

ISSN - 2047-217X

DOI - 10.1093/gigascience/gix115

Subject(s) - copy number variation , genome , robustness (evolution) , biology , population , computer science , structural variation , human genome , computational biology , genetics , gene , demography , sociology

The increasing amount of sequencing data available for a wide variety of species can be theoretically used for detecting copy number variations (CNVs) at the population level. However, the growing sample sizes and the divergent complexity of nonhuman genomes challenge the efficiency and robustness of current human-oriented CNV detection methods.

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