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uORF-seqr: A Machine Learning-Based Approach to the Identification of Upstream Open Reading Frames in Yeast

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Ribosome Profiling

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2252))

Abstract

The identification of upstream open reading frames (uORFs) using ribosome profiling data is complicated by several factors such as the noise inherent to the procedure, the substantial increase in potential translation initiation sites (and false positives) when one includes non-canonical start codons, and the paucity of molecularly validated uORFs. Here we present uORF-seqr, a novel machine learning algorithm that uses ribosome profiling data, in conjunction with RNA-seq data, as well as transcript aware genome annotation files to identify statistically significant AUG and near-cognate codon uORFs.

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Authors and Affiliations

  1. Center for Genomics and Systems Biology, New York University, New York, NY, USA

    Pieter Spealman

  2. Scientific & Digital Innovation, Sanofi Pasteur, Cambridge, MA, USA

    Armaghan Naik

  3. Carnegie Mellon University, Pittsburgh, PA, USA

    Joel McManus

Authors
  1. Pieter Spealman
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  2. Armaghan Naik
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  3. Joel McManus
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Correspondence to Pieter Spealman .

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Editors and Affiliations

  1. Department of Dermatology, Boston University School of Medicine, Boston, MA, USA

    Vyacheslav M. Labunskyy

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Spealman, P., Naik, A., McManus, J. (2021). uORF-seqr: A Machine Learning-Based Approach to the Identification of Upstream Open Reading Frames in Yeast. In: Labunskyy, V.M. (eds) Ribosome Profiling. Methods in Molecular Biology, vol 2252. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1150-0_15

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  • DOI: https://doi.org/10.1007/978-1-0716-1150-0_15

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1149-4

  • Online ISBN: 978-1-0716-1150-0

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