Evidence map›Paper›PMID 24321485›Full record

ArticleMethods (San Diego, Calif.)2014

In silico to in vivo splicing analysis using splicing code models.

Matthew R Gazzara, Jorge Vaquero-Garcia, Kristen W Lynch, Yoseph Barash

Abstract read
In one paragraph

Article in Methods (San Diego, Calif.), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.6field-weighted citation impact, top 31% 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

8 citing papers in PubMed, 15 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Integrative deep models for alternative splicing.Bioinformatics (Oxford, England) · 2017
    Article
  7. Article
  8. Article
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

4 authors at 1 institution in 1 country.

Matthew R GazzaraDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Jorge Vaquero-GarciaDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
Kristen W LynchDepartment of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Yoseph BarashDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: yosephb@mail.med.upenn.edu.
University of Pennsylvania · US

Funding

Mechanisms and consequences of CELF2 regulation in T cell developmentR01GM103383 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI LYNCH, KRISTEN W · 2012 to 2015
$1.2M
NIGMS NIH HHS R01 GM103383
6 · The paper itself

Abstract

With the growing appreciation of RNA splicing's role in gene regulation, development, and disease, researchers from diverse fields find themselves investigating exons of interest. Commonly, researchers are interested in knowing if an exon is alternatively spliced, if it is differentially included in specific tissues or in developmental stages, and what regulatory elements control its inclusion. An important step towards the ability to perform such analysis in silico was made with the development of computational splicing code models. Aimed as a practical how-to guide, we demonstrate how researchers can now use these code models to analyze a gene of interest, focusing on Bin1 as a case study. Bridging integrator 1 (BIN1) is a nucleocytoplasmic adaptor protein known to be functionally regulated through alternative splicing in a tissue-specific manner. Specific Bin1 isoforms have been associated with muscular diseases and cancers, making the study of its splicing regulation of wide interest. Using AVISPA, a recently released web tool based on splicing code models, we show that many Bin1 tissue-dependent isoforms are correctly predicted, along with many of its known regulators. We review the best practices and constraints of using the tool, demonstrate how AVISPA is used to generate high confidence novel regulatory hypotheses, and experimentally validate predicted regulators of Bin1 alternative splicing.

Indexed as

Models, GeneticRNA SplicingSoftwareAdaptor Proteins, Signal TransducingAnimalsBase SequenceCell LineComputer SimulationHumansNuclear ProteinsOrgan SpecificityPolypyrimidine Tract-Binding ProteinReverse Transcriptase Polymerase Chain ReactionRNA-Binding ProteinsRNA, MessengerTumor Suppressor ProteinsAdaptor Proteins, Signal TransducingBIN1 protein, humanNuclear ProteinsPolypyrimidine Tract-Binding ProteinQKI protein, humanRNA-Binding ProteinsRNA, MessengerTumor Suppressor ProteinsAlternative splicingAVISPABIN1Centronuclear myopathy (CNM)Myotonic dystrophy (DM)Splicing code

Identifiers

PMID24321485
PMCPMC4321995
OpenAlexW2041615592

What OpenQuestion holds

Textmetadata
LicenceTDM
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.