ArticleBioinformatics (Oxford, England)2020
EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- GraphyloVar: predicting the impact of non-coding variants using a multi-species sequence model.Bioinformatics (Oxford, England) · 2026Article
- Review
- Nested birth-death processes are competitive with parameter-heavy neural networks as time-dependent models of protein evolution.bioRxiv : the preprint server for biology · 2026Article
- Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications.Molecular biology and evolution · 2024Review
- Context-Dependent Substitution Dynamics in Plastid DNA Across a Wide Range of Taxonomic Groups.Journal of molecular evolution · 2022Article
- Attention-Based Deep Multiple-Instance Learning for Classifying Circular RNA and Other Long Non-Coding RNA.Genes · 2021Article
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Authors and funding
2 authors.
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Abstract
motivationAccurate probabilistic models of sequence evolution are essential for a wide variety of bioinformatics tasks, including sequence alignment and phylogenetic inference. The ability to realistically simulate sequence evolution is also at the core of many benchmarking strategies. Yet, mutational processes have complex context dependencies that remain poorly modeled and understood.
resultsWe introduce EvoLSTM, a recurrent neural network-based evolution simulator that captures mutational context dependencies. EvoLSTM uses a sequence-to-sequence long short-term memory model trained to predict mutation probabilities at each position of a given sequence, taking into consideration the 14 flanking nucleotides. EvoLSTM can realistically simulate mammalian and plant DNA sequence evolution and reveals unexpectedly strong long-range context dependencies in mutation probabilities. EvoLSTM brings modern machine-learning approaches to bear on sequence evolution. It will serve as a useful tool to study and simulate complex mutational processes. AVAILABILITY AND IMPLEMENTATION: Code and dataset are available at https://github.com/DongjoonLim/EvoLSTM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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