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Validation of genetic variants from NGS data using deep convolutional neural networks.

ABSTRACT: Accurate somatic variant calling from next-generation sequencing data is one most important tasks in personalised cancer therapy. The sophistication of the available technologies is ever-increasing, yet, manual candidate refinement is still a necessary step in state-of-the-art processing pipelines. This limits reproducibility and introduces a bottleneck with respect to scalability. We demonstrate that the validation of genetic variants can be improved using a machine learning approach resting on a Convolutional Neural Network, trained using existing human annotation. In contrast to existing approaches, we introduce a way in which contextual data from sequencing tracks can be included into the automated assessment. A rigorous evaluation shows that the resulting model is robust and performs on par with trained researchers following published standard operating procedure.

SUBMITTER: Vaisband M

PROVIDER: S-EPMC10116675 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Validation of genetic variants from NGS data using deep convolutional neural networks.

Vaisband Marc M Schubert Maria M Gassner Franz Josef FJ Geisberger Roland R Greil Richard R Zaborsky Nadja N Hasenauer Jan J

BMC bioinformatics 20230420 1

Accurate somatic variant calling from next-generation sequencing data is one most important tasks in personalised cancer therapy. The sophistication of the available technologies is ever-increasing, yet, manual candidate refinement is still a necessary step in state-of-the-art processing pipelines. This limits reproducibility and introduces a bottleneck with respect to scalability. We demonstrate that the validation of genetic variants can be improved using a machine learning approach resting on ...[more]

PMID: 37081386

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