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Pré-Publication, Document De Travail (Preprint/Prepublication) Année : 2020

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

Yoshihide Hayashizaki
  • Fonction : Auteur
Masayoshi Itoh
  • Fonction : Auteur
Michihira Tagami
  • Fonction : Auteur
Mitsuyoshi Murata
  • Fonction : Auteur
Miki Kojima-Ishiyama
  • Fonction : Auteur
Shohei Noma
  • Fonction : Auteur
Wyeth W. Wasserman
  • Fonction : Auteur
  • PersonId : 952234

Résumé

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 probed these unassigned TSSs and showed 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 developed Cap Trap RNA-seq, a technology which combines cap trapping and long reads MinION sequencing. We trained sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveiled the importance of STR surrounding sequences not only to distinguish STR classes, as defined by the repeated DNA motif, one from each other, but also to predict their transcription. Excitingly, our models predicted that genetic variants linked to human diseases affect STR-associated transcription and correspond precisely to the key positions identified by our models to predict transcription. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism.
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Dates et versions

hal-03024896 , version 1 (15-12-2020)

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Paternité - Pas de modifications

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Mathys Grapotte, Manu Saraswat, Chloé Bessière, Christophe Menichelli, Jordan A. Ramilowski, et al.. Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network. 2020. ⟨hal-03024896⟩
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