Evidence map›Paper›PMID 20445623›Full record

ArticleNature2010

Deciphering the splicing code.

Yoseph Barash, John A Calarco, Weijun Gao, Qun Pan, Xinchen Wang, Ofer Shai, Benjamin J Blencowe, Brendan J Frey

Abstract read
PubMed Publisher
In one paragraph

Article in Nature, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 515 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
515citing papers in PubMed, 1 pooled it
–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

515 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. Review
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  15. Genetic therapies for neurological diseases.Pharmacological reviews · 2026
    Review
  16. Article
  17. BlendSplice: A Frequency-Blended Generative Framework forComputational and structural biotechnology journal · 2026
    Article
  18. Review
  19. Article
  20. Article

455 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Yoseph BarashBiomedical Engineering, Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto M5S 3G4, Canada.
John A Calarco
Weijun Gao
Qun Pan
Xinchen Wang
Ofer Shai
Benjamin J Blencowe
Brendan J Frey

Funding

Canadian Institutes of Health Research
6 · The paper itself

Abstract

Alternative splicing has a crucial role in the generation of biological complexity, and its misregulation is often involved in human disease. Here we describe the assembly of a 'splicing code', which uses combinations of hundreds of RNA features to predict tissue-dependent changes in alternative splicing for thousands of exons. The code determines new classes of splicing patterns, identifies distinct regulatory programs in different tissues, and identifies mutation-verified regulatory sequences. Widespread regulatory strategies are revealed, including the use of unexpectedly large combinations of features, the establishment of low exon inclusion levels that are overcome by features in specific tissues, the appearance of features deeper into introns than previously appreciated, and the modulation of splice variant levels by transcript structure characteristics. The code detected a class of exons whose inclusion silences expression in adult tissues by activating nonsense-mediated messenger RNA decay, but whose exclusion promotes expression during embryogenesis. The code facilitates the discovery and detailed characterization of regulated alternative splicing events on a genome-wide scale.

Indexed as

Gene Expression RegulationModels, GeneticAlternative SplicingAnimalsGene SilencingGenetic CodeHumansMiceReproducibility of ResultsRNA, MessengerRNA, Messenger

Identifiers

PMID20445623

What OpenQuestion holds

Textmetadata
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.