A robust model for read count data in exome sequencing experiments and implications for copy number variant calling | Zendy

Vincent Plagnol | Zendy; James Curtis | Zendy; Michael P. Epstein | Zendy; Kin Y. Mok | Zendy; Emma Stebbings | Zendy; Sofia Grigoriadou | Zendy; Nicholas Wood | Zendy; Sophie Hambleton | Zendy; Siobhan O. Burns | Zendy; Adrian J. Thrasher | Zendy; Dinakantha Kumararatne | Zendy; Rainer Döffinger | Zendy; Sergey Nejentsev | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A robust model for read count data in exome sequencing experiments and implications for copy number variant calling

Author(s) -

Vincent Plagnol,

James Curtis,

Michael P. Epstein,

Kin Y. Mok,

Emma Stebbings,

Sofia Grigoriadou,

Nicholas Wood,

Sophie Hambleton,

Siobhan O. Burns,

Adrian J. Thrasher,

Dinakantha Kumararatne,

Rainer Döffinger,

Sergey Nejentsev

Publication year - 2012

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bts526

Subject(s) - exome sequencing , exome , copy number variation , computer science , spurious relationship , computational biology , data mining , biology , genetics , mutation , machine learning , gene , genome

Exome sequencing has proven to be an effective tool to discover the genetic basis of Mendelian disorders. It is well established that copy number variants (CNVs) contribute to the etiology of these disorders. However, calling CNVs from exome sequence data is challenging. A typical read depth strategy consists of using another sample (or a combination of samples) as a reference to control for the variability at the capture and sequencing steps. However, technical variability between samples complicates the analysis and can create spurious CNV calls.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research