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.
复制标题
深度学习模型，用于从DNA序列预测下一代测序深度。
DOI：
10.1038/s41467-021-24497-8
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发表时间：
2021-07-19
影响因子：
16.6
通讯作者：
Zhang DY
中科院分区：
文献类型：
--
作者：
Zhang JX;Yordanov B;Gaunt A;Wang MX;Dai P;Chen YJ;Zhang K;Fang JZ;Dalchau N;Li J;Phillips A;Zhang DY
Targeted high-throughput DNA sequencing is a primary approach for genomics and molecular diagnostics, and more recently as a readout for DNA information storage. Oligonucleotide probes used to enrich gene loci of interest have different hybridization kinetics, resulting in non-uniform coverage that increases sequencing costs and decreases sequencing sensitivities. Here, we present a deep learning model (DLM) for predicting Next-Generation Sequencing (NGS) depth from DNA probe sequences. Our DLM includes a bidirectional recurrent neural network that takes as input both DNA nucleotide identities as well as the calculated probability of the nucleotide being unpaired. We apply our DLM to three different NGS panels: a 39,145-plex panel for human single nucleotide polymorphisms (SNP), a 2000-plex panel for human long non-coding RNA (lncRNA), and a 7373-plex panel targeting non-human sequences for DNA information storage. In cross-validation, our DLM predicts sequencing depth to within a factor of 3 with 93% accuracy for the SNP panel, and 99% accuracy for the non-human panel. In independent testing, the DLM predicts the lncRNA panel with 89% accuracy when trained on the SNP panel. The same model is also effective at predicting the measured single-plex kinetic rate constants of DNA hybridization and strand displacement. 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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影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
影响因子：
21.8
作者：
Das, Jagotamoy;Ivanov, Ivaylo;Kelley, Shana O.
通讯作者：
Kelley, Shana O.
影响因子：
46.9
作者：
Gnirke A;Melnikov A;Maguire J;Rogov P;LeProust EM;Brockman W;Fennell T;Giannoukos G;Fisher S;Russ C;Gabriel S;Jaffe DB;Lander ES;Nusbaum C
通讯作者：
Nusbaum C
影响因子：
64.5
作者：
Presnyak V;Alhusaini N;Chen YH;Martin S;Morris N;Kline N;Olson S;Weinberg D;Baker KE;Graveley BR;Coller J
通讯作者：
Coller J
影响因子：
--
作者：
Burnett JC;Rossi JJ
通讯作者：
Rossi JJ