ExomeAI: detection of recurrent allelic imbalance in tumors using whole-exome sequencing data | Zendy

Javad Nadaf | Zendy; Jacek Majewski | Zendy; Somayyeh Fahiminiya | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

ExomeAI: detection of recurrent allelic imbalance in tumors using whole-exome sequencing data

Author(s) -

Javad Nadaf,

Jacek Majewski,

Somayyeh Fahiminiya

Publication year - 2014

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/btu665

Subject(s) - exome sequencing , allele , exome , genetics , computational biology , computer science , biology , gene , mutation

Whole-exome sequencing (WES) has extensively been used in cancer genome studies; however, the use of WES data in the study of loss of heterozygosity or more generally allelic imbalance (AI) has so far been very limited, which highlights the need for user-friendly and flexible software that can handle low-quality datasets. We have developed a statistical approach, ExomeAI, for the detection of recurrent AI events using WES datasets, specifically where matched normal samples are not available.

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