EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
01 07 2020
01 07 2020
Historique:
entrez:
14
7
2020
pubmed:
14
7
2020
medline:
9
3
2021
Statut:
ppublish
Résumé
Accurate 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. We 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. Code and dataset are available at https://github.com/DongjoonLim/EvoLSTM. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 32657367
pii: 5870475
doi: 10.1093/bioinformatics/btaa447
pmc: PMC7355264
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
i353-i361Informations de copyright
© The Author(s) 2020. Published by Oxford University Press.
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