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Cited by 125RNA Research and SplicingRNA and protein synthesis mechanismsGenetic Associations and Epidemiology

Massively parallel functional annotation of 3′ untranslated regions

Open access
Zhao, Wenxue, Pollack, Joshua L, Blagev, Denitza P, Zaitlen, Noah, McManus, Michael T, Erle, David J·2014·Nature Biotechnology
DOI 10.1038/nbt.2851PMC PMC3981918
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Abstract

Functional characterization of noncoding sequences is crucial for understanding the human genome and learning how genetic variation contributes to disease. 3' untranslated regions (UTRs) are an important class of noncoding sequences, but their functions remain largely uncharacterized. We developed a method for massively parallel functional annotation of sequences from 3' UTRs (fast-UTR) and used this approach to measure the effects of a total of >450 kilobases of 3' UTR sequences from >2,000 human genes on steady-state mRNA abundance, mRNA stability and protein production. We found widespread regulatory effects on mRNA that were coupled to effects on mRNA stability and protein production. Furthermore, we discovered 87 novel cis-regulatory elements and measured the effects of genetic variation within known and novel 3' UTR motifs. This work shows how massively parallel approaches can improve the functional annotation of noncoding sequences, advance our understanding of cis-regulatory mechanisms and quantify the effects of human genetic variation.

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  • Massively parallel reporter assays coupled with machine learning have proven useful in addressing similar voids by producing quantitative biological insight that would be difficult to achieve through traditional approaches.

    “Massively parallel reporter assays (MPRAs) – methods that assess thousands to millions of sequence variants in a single experiment – coupled with machine learning have proven useful in addressing similar voids by producing quantitative biological insight that would be difficult to achieve through traditional approaches3–9.”

    From Human 5′ UTR design and variant effect prediction from a massively parallel translation assay (2019) · cites this paper as [9]

    The main text (page unavailable) describes fast-UTR, a massively parallel reporter approach that quantified effects of 3′ UTR sequences and genetic variants. It does not report coupling the assay with machine learning; it says the resulting data could be useful for refining computational prediction methods. Thus the paper supports the assay’s quantitative value, but not the machine-learning part of the claim. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • The paper did not find sequence elements related to mRNA half-life in Borzoi's sequence attributions.

    “Furthermore, we did not find sequence elements related to mRNA half-life in Borzoi’s sequence attributions23,82.”

    From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [82]

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Cites

Flagged first, then by citations

Research works printed but not linked: 31

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Cited by

Sorted by citations

Authors
Zhao, W., Pollack, J. L., Blagev, D. P., Zaitlen, N., McManus, M. T., & Erle, D. J.
Affiliations
University of California, San Francisco (US)
Corresponding author
Erle, David J
ORCID
Venue
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Published
2014-03-16
Volume
32
Issue
4
Pages
387-391
ISSN
  • 0733-222X (ISSN-L)
  • 1087-0156
  • 1546-1696
DOI
10.1038/nbt.2851
PMC
PMC3981918
OpenAlex
W2061942838
Cited by
125
References
9
Citations by year
2026: 8 · 2025: 15 · 2024: 15 · 2023: 11 · 2022: 7 · 2021: 10 · 2020: 4 · 2019: 10 · 2018: 10 · 2017: 9 · 2016: 14 · 2015: 9 · 2014: 3
FWCI
4.79
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature Biotechnology (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • National Heart, Lung, and Blood Institute (R21HL108596)
  • National Human Genome Research Institute (R21HG004665)
  • National Institute of Allergy and Infectious Diseases (U19AI077439)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords
3' untranslated region, non-coding sequences, cis-regulatory elements, mRNA stability, mRNA levels, protein production, genetic variation, human genome
MeSH terms
Humans, RNA, Messenger, Genome, Human, Sequence Analysis, RNA, 3' Untranslated Regions, Genomics, MicroRNAs, Molecular Sequence Annotation

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