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

Journal: Nature communications
PMID: 34078885

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.

Authors

  • Mathys Grapotte
    Institut de Biologie Computationnelle, Montpellier, France.
  • Manu Saraswat
    Institut de Biologie Computationnelle, Montpellier, France.
  • Chloé Bessière
    Institut de Biologie Computationnelle, Montpellier, France.
  • Christophe Menichelli
    Institut de Biologie Computationnelle, Montpellier, France.
  • Jordan A Ramilowski
    RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
  • Jessica Severin
    RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
  • Yoshihide Hayashizaki
    RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama, Japan.
  • Masayoshi Itoh
    RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama, Japan.
  • Michihira Tagami
    RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
  • Mitsuyoshi Murata
    RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
  • Miki Kojima-Ishiyama
    RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
  • Shohei Noma
    RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
  • Shuhei Noguchi
    RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
  • Takeya Kasukawa
    RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
  • Akira Hasegawa
    School of Health Sciences, Faculty of Medicine, Niigata University.
  • Harukazu Suzuki
    RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
  • Hiromi Nishiyori-Sueki
    RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
  • Martin C Frith
    Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.
  • Clément Chatelain
    Normandie Univ, UNIROUEN, UNIHAVRE, INSA Rouen, LITIS, 76000, Rouen, France.
  • Piero Carninci
    RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
  • Michiel J L de Hoon
    RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
  • Wyeth W Wasserman
    Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia Vancouver, British Columbia V5Z 4H4, Canada. Electronic address: wyeth@cmmt.ubc.ca.
  • Laurent Bréhélin
    LIRMM, Univ Montpellier, CNRS, Montpellier, France.
  • Charles-Henri Lecellier
    Institut de Biologie Computationnelle, Montpellier, France. charles.lecellier@igmm.cnrs.fr.

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