End-to-end learning of multiple sequence alignments with differentiable Smith–Waterman

MOTIVATION: Multiple sequence alignments (MSAs) of homologous sequences contain information on structural and functional constraints and their evolutionary histories. Despite their importance for many downstream tasks, such as structure prediction, MSA generation is often treated as a separate pre-p...

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Detalles Bibliográficos
Autores principales: Petti, Samantha, Bhattacharya, Nicholas, Rao, Roshan, Dauparas, Justas, Thomas, Neil, Zhou, Juannan, Rush, Alexander M, Koo, Peter, Ovchinnikov, Sergey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9805565/
https://www.ncbi.nlm.nih.gov/pubmed/36355460
http://dx.doi.org/10.1093/bioinformatics/btac724

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9805565/
https://www.ncbi.nlm.nih.gov/pubmed/36355460
http://dx.doi.org/10.1093/bioinformatics/btac724

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