4.8 Article

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

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NATURE COMMUNICATIONS
卷 12, 期 1, 页码 -

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NATURE RESEARCH
DOI: 10.1038/s41467-021-23143-7

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资金

  1. CNRS (International Associated Laboratory miREGEN)
  2. INSERM-ITMO Cancer project LIONS [BIO2015-04]
  3. Plan d'Investissement d'Avenir [ANR-11-BINF-0002]
  4. GEM Flagship project from Labex NUMEV [ANR-10-LABX-0020]
  5. Conventions Industrielles de Formation par la Recherche (CIFRE) PhD fellowship from SANOFI RD
  6. Research Grant for RIKEN Omics Science Center from MEXT
  7. Innovative Cell Biology by Innovative Technology (Cell Innovation Program) from the MEXT
  8. MEXT
  9. Agence Nationale de la Recherche (ANR) [ANR-11-BINF-0002] Funding Source: Agence Nationale de la Recherche (ANR)

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The FANTOM5 consortium used CAGE technology to map transcription start sites, finding that a large portion of non-coding transcription initiates at microsatellites. They developed Cap Trap RNA-seq to confirm this transcription, and trained deep learning models to predict CAGE signal at STRs with high accuracy. These models revealed the importance of STR surrounding sequences for distinguishing STR classes and predicting transcription initiation levels.
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, similar to 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.

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