ArticleNature communications2021
A deep learning model for predicting next-generation sequencing depth from DNA sequence.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed.
- Anchored random reverse primer sequencing for quantitative detection of novel gene fusions.Nature biomedical engineering · 2026Article
- Machine learning-directed massively parallel programmable nucleic acid amplification.Science advances · 2026Article
- Empowering low-crosstalk, dynamic-decision random access of DNA storage via 384-multiplexed nanopore signatures.Nature communications · 2025Article
- From Panels to Pathogen Networks: The Expanding Role of Targeted Sequencing in Veterinary Medicine.Biology · 2025Review
- Environmental adaptations in metagenomes revealed by deep learning.BMC biology · 2025Article
- Investigating Single-Molecule Molecular Inversion Probes for Medium-Scale Targeted DNA Methylation Analysis.Epigenomes · 2025Article
- Machine learning-optimized targeted detection of alternative splicing.Nucleic acids research · 2025Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- En masse evaluation of RNA guides (EMERGe) for ADARs.Methods in enzymology · 2025Article
- Deep learning segmentation model for quantification of infarct size in pigs with myocardial ischemia/reperfusion.Basic research in cardiology · 2024Article
- Deep learning-based temporal deconvolution for photon time-of-flight distribution retrieval.Optics letters · 2024Article
- Integrated multiomics analysis identified comprehensive crosstalk between diverse programmed cell death patterns and novel molecular subtypes in Hepatocellular Carcinoma.Scientific reports · 2024Article
- Predicting DNA Reactions with a Quantum Chemistry-Based Deep Learning Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Machine learning-optimized targeted detection of alternative splicing.bioRxiv : the preprint server for biology · 2024Article
- Understanding the relationship between sequences and kinetics of DNA strand displacements.Nucleic acids research · 2024Article
- Explorer: efficient DNA coding by De Bruijn graph toward arbitrary local and global biochemical constraints.Briefings in bioinformatics · 2024Article
- ctGAN: combined transformation of gene expression and survival data with generative adversarial network.Briefings in bioinformatics · 2024Article
- Whole exome sequencing and proteomics-based investigation of the pathogenesis of coronary artery disease with diffuse long lesion.Journal of cardiothoracic surgery · 2024Article
- Predicting DNA toehold-mediated strand displacement rate constants using a DNA-BERT transformer deep learning model.Heliyon · 2024Article
- APIPred: An XGBoost-Based Method for Predicting Aptamer-Protein Interactions.Journal of chemical information and modeling · 2024Article
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12 authors.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.