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

  • FANTOM consortium
  • Institut de Biologie Computationnelle
  • Institute of Molecular Genetics of Montpellier
  • Sanofi-Aventis
  • RIKEN
  • Artificial Intelligence Research Center
  • National Institute of Advanced Industrial Science and Technology
  • Graduate School of Frontier Sciences
  • The University of Tokyo
  • Waseda University
  • Division of Genomic Technologies
  • University of Edinburgh
  • European Bioinformatics Institute
  • European Molecular Biology Laboratory
  • Wellcome Trust Sanger Institute
  • Computational Bioscience Research Centre
  • King Abdullah University of Science and Technology
  • McGill University
  • University of New South Wales
  • Harry Perkins Institute of Medical Research
  • University of Western Australia
  • Department of Systems Biology
  • Columbia University
  • The Bioinformatics Center
  • University of Copenhagen
  • Department of Transfusion Medicine and Stem Cell Regulation
  • Juntendo University
  • Department of Statistics
  • University of California at Berkeley
  • Department of Medicine
  • Karolinska Institutet
  • Biotechnology Research Institute for Drug Discovery
  • Department of Dermatology and Allergy
  • Charité – Universitätsmedizin Berlin
  • Jackson Laboratory
  • Agency for Science, Technology and Research, Singapore
  • Stanford University
  • University of Queensland
  • Department of Medical and Biological Sciences
  • University of Udine

Research output: Contribution to journal › Article › peer-review

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.

Original languageEnglish
Article number3297
JournalNature Communications
Volume12
Issue number1
DOIs
Publication statusPublished - 1 Dec 2021
Externally publishedYes

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