Evidence map›Paper›PMID 37968271›Full record

ArticleNature communications2023

Deep learning of human polyadenylation sites at nucleotide resolution reveals molecular determinants of site usage and relevance in disease.

Emily Kunce Stroup, Zhe Ji

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
6.5field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 42 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Emily Kunce StroupDepartment of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.
Zhe JiDepartment of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA. zhe.ji@northwestern.edu.ORCID 0000-0002-1809-8099
Northwestern University · US

Funding

Characterizing functional translation in putative 'noncoding' regions of a genome: Admin SupplR35GM138192 · NIGMS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI JI, ZHE · 2020 to 2024
$2.1M
An ultra-low-input RNase footprinting assay to quantify cytosolic and mitochondrial translation simultaneouslyR01HL161389 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI JI, ZHE · 2022 to 2025
$1.9M
Predoctoral Training Program in Biomedical Data Driven Discovery (BD3)T32LM012203 · NLM · NORTHWESTERN UNIVERSITY AT CHICAGO · PI KLABJAN, DIEGO, STARREN, JUSTIN B. · 2015 to 2019
$1.1M
NHLBI NIH HHS R01 HL161389NIGMS NIH HHS R35 GM138192NLM NIH HHS T32 LM012203
6 · The paper itself

Abstract

The genomic distribution of cleavage and polyadenylation (polyA) sites should be co-evolutionally optimized with the local gene structure. Otherwise, spurious polyadenylation can cause premature transcription termination and generate aberrant proteins. To obtain mechanistic insights into polyA site optimization across the human genome, we develop deep/machine learning models to identify genome-wide putative polyA sites at unprecedented nucleotide-level resolution and calculate their strength and usage in the genomic context. Our models quantitatively measure position-specific motif importance and their crosstalk in polyA site formation and cleavage heterogeneity. The intronic site expression is governed by the surrounding splicing landscape. The usage of alternative polyA sites in terminal exons is modulated by their relative locations and distance to downstream genes. Finally, we apply our models to reveal thousands of disease- and trait-associated genetic variants altering polyadenylation activity. Altogether, our models represent a valuable resource to dissect molecular mechanisms mediating genome-wide polyA site expression and characterize their functional roles in human diseases.

Indexed as

Deep LearningPolyadenylationGenome, HumanHumansNucleotidesTranscription, GeneticNucleotides

Identifiers

PMID37968271
PMCPMC10651852
OpenAlexW4388699655

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.