Leveraging omic features with F3UTER enables identification of unannotated 3’UTRs for synaptic genes

Siddharth Sethi; David Zhang; Sebastian Guelfi; Zhongbo Chen; Sonia Garcia-Ruiz; Emmanuel O. Olagbaju; Mina Ryten; Harpreet Saini; Juan A. Botia

doi:10.1038/s41467-022-30017-z

Nature Communications (Apr 2022)

Leveraging omic features with F3UTER enables identification of unannotated 3’UTRs for synaptic genes

Siddharth Sethi,
David Zhang,
Sebastian Guelfi,
Zhongbo Chen,
Sonia Garcia-Ruiz,
Emmanuel O. Olagbaju,
Mina Ryten,
Harpreet Saini,
Juan A. Botia

Affiliations

Siddharth Sethi: Astex Pharmaceuticals
David Zhang: Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London
Sebastian Guelfi: Department of Neurodegenerative Disease, Institute of Neurology, University College London
Zhongbo Chen: Department of Neurodegenerative Disease, Institute of Neurology, University College London
Sonia Garcia-Ruiz: Department of Neurodegenerative Disease, Institute of Neurology, University College London
Emmanuel O. Olagbaju: Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London
Mina Ryten: Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London
Harpreet Saini: Astex Pharmaceuticals
Juan A. Botia: Department of Neurodegenerative Disease, Institute of Neurology, University College London

DOI: https://doi.org/10.1038/s41467-022-30017-z
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 15

Abstract

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3’ untranslated regions (3’UTRs) play a crucial role in regulating gene expression, but our 3’UTR catalogue is incomplete. Here, the authors develop a machine learning-based framework to predict previously unannotated 3’UTRs in 39 human tissues.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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