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Needlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing dataстатья
Информация о цитировании статьи получена из
Web of Science,
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 4 марта 2022 г.
- Авторы: Delhomme T.M., Avogbe P.H., Gabriel A.A.G, Alcala N., Leblay N., Voegele C., Vallée M., Chopard P., Chabrier A., Abedi-Ardekani B., Gaborieau V., Holcatova I., Janout V., Foretová L., Milosavljevic S., Zaridze D., Mukeriya A., Brambilla E., Brennan P., Scelo G., Fernandez-Cuesta L., Byrnes G., Calvez-Kelm F.L., McKay J.D., Foll M.
- Журнал: NAR Genomics and Bioinformatics
- Том: 2
- Номер: 2
- Год издания: 2020
- Издательство: Oxford University Press
- Местоположение издательства: United Kingdom
- Первая страница: 1
- Последняя страница: 10
- Аннотация: The emergence of next-generation sequencing (NGS) has revolutionized the way of reaching a genome sequence, with the promise of potentially providing a comprehensive characterization of DNA variations. Nevertheless, detecting somatic mutations is still a difficult problem, in particular when trying to identify low abundance mutations, such as subclonal mutations, tumour-derived alterations in body fluids or somatic mutations from histological normal tissue. The main challenge is to precisely distinguish between sequencing artefacts and true mutations, particularly when the latter are so rare they reach similar abundance levels as artefacts. Here, we present needlestack, a highly sensitive variant caller, which directly learns from the data the level of systematic sequencing errors to accurately call mutations. Needlestack is based on the idea that the sequencing error rate can be dynamically estimated from analysing multiple samples together. We show that the sequencing error rate varies across alterations, illustrating the need to precisely estimate it. We evaluate the performance of needlestack for various types of variations, and we show that needlestack is robust among positions and outperforms existing state-of-the-art method for low abundance mutations. Needlestack, along with its source code is freely available on the GitHub platform: https://github.com/IARCbioinfo/needlestack.
- Добавил в систему: Заридзе Давид Георгиевич