The DNA dialect: a comprehensive guide to pretrained genomic language models

Following their success in natural language processing and protein biology, pretrained large language models have started appearing in genomics in large numbers. These genomic language models (gLMs), trained on diverse DNA and RNA sequences, promise improved performance on a variety of downstream pr...

Descripción completa

Detalles Bibliográficos
Autores: Veiner, Marcell, Supek, Fran
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/226202
Acceso en línea:https://hdl.handle.net/2445/226202
Access Level:acceso abierto
Palabra clave:Argot
Portades
Neuritis
Slang
Title pages
id ES_ab682f44aeb17dca96c36b91f3bb566f
oai_identifier_str oai:recercat.cat:2445/226202
network_acronym_str ES
network_name_str España
repository_id_str
spelling The DNA dialect: a comprehensive guide to pretrained genomic language modelsVeiner, MarcellSupek, FranArgotPortadesNeuritisSlangTitle pagesNeuritisFollowing their success in natural language processing and protein biology, pretrained large language models have started appearing in genomics in large numbers. These genomic language models (gLMs), trained on diverse DNA and RNA sequences, promise improved performance on a variety of downstream prediction and understanding tasks. In this review, we trace the rapid evolution of gLMs, analyze current trends, and offer an overview of their application in genomic research. We investigate each gLM component in detail, from training data curation to the architecture, and highlight the present trends of increasing model complexity. We review major benchmarking efforts, suggesting that no single model dominates, and that task-specific design and pretraining data often outweigh general model scale or architecture. In addition, we discuss requirements for making gLMs practically useful for genomic research. While several applications, ranging from genome annotation to DNA sequence generation, showcase the potential of gLMs, their use highlights gaps and pitfalls that remain unresolved. This guide aims to equip researchers with a grounded understanding of gLM capabilities, limitations, and best practices for their effective use in genomics.EMBO Press en asociación con Springer Nature2026202620262026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion24 p.application/pdfhttps://hdl.handle.net/2445/226202Articles publicats en revistes (Institut de Recerca Biomèdica (IRB Barcelona))reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1038/s44320-025-00184-4Molecular Systems Biology, 2026https://doi.org/10.1038/s44320-025-00184-4cc-by (c) Veiner, Marcell et al., 2026https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2262022026-05-29T05:05:01Z
dc.title.none.fl_str_mv The DNA dialect: a comprehensive guide to pretrained genomic language models
title The DNA dialect: a comprehensive guide to pretrained genomic language models
spellingShingle The DNA dialect: a comprehensive guide to pretrained genomic language models
Veiner, Marcell
Argot
Portades
Neuritis
Slang
Title pages
Neuritis
title_short The DNA dialect: a comprehensive guide to pretrained genomic language models
title_full The DNA dialect: a comprehensive guide to pretrained genomic language models
title_fullStr The DNA dialect: a comprehensive guide to pretrained genomic language models
title_full_unstemmed The DNA dialect: a comprehensive guide to pretrained genomic language models
title_sort The DNA dialect: a comprehensive guide to pretrained genomic language models
dc.creator.none.fl_str_mv Veiner, Marcell
Supek, Fran
author Veiner, Marcell
author_facet Veiner, Marcell
Supek, Fran
author_role author
author2 Supek, Fran
author2_role author
dc.subject.none.fl_str_mv Argot
Portades
Neuritis
Slang
Title pages
Neuritis
topic Argot
Portades
Neuritis
Slang
Title pages
Neuritis
description Following their success in natural language processing and protein biology, pretrained large language models have started appearing in genomics in large numbers. These genomic language models (gLMs), trained on diverse DNA and RNA sequences, promise improved performance on a variety of downstream prediction and understanding tasks. In this review, we trace the rapid evolution of gLMs, analyze current trends, and offer an overview of their application in genomic research. We investigate each gLM component in detail, from training data curation to the architecture, and highlight the present trends of increasing model complexity. We review major benchmarking efforts, suggesting that no single model dominates, and that task-specific design and pretraining data often outweigh general model scale or architecture. In addition, we discuss requirements for making gLMs practically useful for genomic research. While several applications, ranging from genome annotation to DNA sequence generation, showcase the potential of gLMs, their use highlights gaps and pitfalls that remain unresolved. This guide aims to equip researchers with a grounded understanding of gLM capabilities, limitations, and best practices for their effective use in genomics.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/226202
url https://hdl.handle.net/2445/226202
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1038/s44320-025-00184-4
Molecular Systems Biology, 2026
https://doi.org/10.1038/s44320-025-00184-4
dc.rights.none.fl_str_mv cc-by (c) Veiner, Marcell et al., 2026
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Veiner, Marcell et al., 2026
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 24 p.
application/pdf
dc.publisher.none.fl_str_mv EMBO Press en asociación con Springer Nature
publisher.none.fl_str_mv EMBO Press en asociación con Springer Nature
dc.source.none.fl_str_mv Articles publicats en revistes (Institut de Recerca Biomèdica (IRB Barcelona))
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869416266335256576
score 15.811543

Similares en LA Referencia