ExomeAI: detection of recurrent allelic imbalance in tumors using whole-exome sequencing data.
Ontology highlight
ABSTRACT: Summary
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.Availability
ExomeAI is a web-based application, publicly available at: http://genomequebec.mcgill.ca/exomeai.Contact
JavadNadaf@gmail.com or somayyeh.fahiminiya@mcgill.caSupplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Nadaf J
PROVIDER: S-EPMC4308664 | biostudies-literature | 2015 Feb
REPOSITORIES: biostudies-literature
Publications
ExomeAI: detection of recurrent allelic imbalance in tumors using whole-exome sequencing data.
Nadaf Javad J Majewski Jacek J Fahiminiya Somayyeh S
Bioinformatics (Oxford, England) 20141008 3
<h4>Summary</h4>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 no ...[more]
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