A deep learning model for predicting next-generation sequencing depth from DNA sequence

A deep learning model for predicting next-generation sequencing depth from DNA sequence

DNA probes used in next generation sequencing (NGS) have variable hybridisation kinetics, resulting in non-uniform coverage. Here, the authors develop a deep learning model to predict NGS depth using DNA probe sequences and apply to human and non-human sequencing panels.

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Detalles Bibliográficos
Autores principales: Jinny X. Zhang, Boyan Yordanov, Alexander Gaunt, Michael X. Wang, Peng Dai, Yuan-Jyue Chen, Kerou Zhang, John Z. Fang, Neil Dalchau, Jiaming Li, Andrew Phillips, David Yu Zhang
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Science
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Acceso en línea:https://doaj.org/article/38cfd7ab6401440b9ae00deafd4781f0
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Sumario:DNA probes used in next generation sequencing (NGS) have variable hybridisation kinetics, resulting in non-uniform coverage. Here, the authors develop a deep learning model to predict NGS depth using DNA probe sequences and apply to human and non-human sequencing panels.

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