Evidence map›Paper›PMID 24025092›Full record

ArticleBMC genomics2013

Distinct polyadenylation landscapes of diverse human tissues revealed by a modified PA-seq strategy.

Ting Ni, Yanqin Yang, Dina Hafez, Wenjing Yang, Kurtis Kiesewetter, Yoshi Wakabayashi, Uwe Ohler, Weiqun Peng, Jun Zhu

Abstract read
In one paragraph

Article in BMC genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
–field-weighted citation impact
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

31 citing papers in PubMed.

  1. Trial
  2. Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Alternative Polyadenylation: Methods, Findings, and Impacts.Genomics, proteomics & bioinformatics · 2017
    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

9 authors.

Ting NiNational Heart Lung Blood Institute, National Institutes of Health, Genetics and Development Biology Center, 9000 Rockville Pike, Bethesda, MD, 20892, USA. tingni@fudan.edu.cn.
Yanqin Yang
Dina Hafez
Wenjing Yang
Kurtis Kiesewetter
Yoshi Wakabayashi
Uwe Ohler
Weiqun Peng
Jun Zhu

Funding

Intramural NIH HHS
6 · The paper itself

Abstract

backgroundPolyadenylation is a key regulatory step in eukaryotic gene expression and one of the major contributors of transcriptome diversity. Aberrant polyadenylation often associates with expression defects and leads to human diseases.

resultsTo better understand global polyadenylation regulation, we have developed a polyadenylation sequencing (PA-seq) approach. By profiling polyadenylation events in 13 human tissues, we found that alternative cleavage and polyadenylation (APA) is prevalent in both protein-coding and noncoding genes. In addition, APA usage, similar to gene expression profiling, exhibits tissue-specific signatures and is sufficient for determining tissue origin. A 3' untranslated region shortening index (USI) was further developed for genes with tandem APA sites. Strikingly, the results showed that different tissues exhibit distinct patterns of shortening and/or lengthening of 3' untranslated regions, suggesting the intimate involvement of APA in establishing tissue or cell identity.

conclusionsThis study provides a comprehensive resource to uncover regulated polyadenylation events in human tissues and to characterize the underlying regulatory mechanism.

Indexed as

Polyadenylation3' Untranslated RegionsChromosome MappingCluster AnalysisGene LibraryHumansOrgan SpecificitySequence Analysis, RNA3' Untranslated Regions

Identifiers

PMID24025092
PMCPMC3848854

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.