Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

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• dc.contributor.author Grapotte, Mathys
• dc.contributor.author FANTOM consortium
• dc.contributor.author Lecellier, Charles-Henri
• dc.date.accessioned 2021-08-03T06:18:57Z
• dc.date.available 2021-08-03T06:18:57Z
• dc.date.issued 2021
• dc.description.abstract Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism.
• dc.format.mimetype application/pdf
• dc.identifier.citation Grapotte M, Saraswat M, Bessière C, Menichelli C, Ramilowski JA, Severin J et al. Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network. Nat Commun. 2021;12(1):3297. DOI: 10.1038/s41467-021-23143-7
• dc.identifier.doi http://dx.doi.org/10.1038/s41467-021-23143-7
• dc.identifier.issn 2041-1723
• dc.identifier.uri http://hdl.handle.net/10230/48307
• dc.language.iso eng
• dc.publisher Nature Research
• dc.relation.ispartof Nat Commun. 2021;12(1):3297
• dc.rights © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Genomics
• dc.subject.keyword Machine learning
• dc.subject.keyword Transcriptomics
• dc.title Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion