Evidence map›Paper›PMID 30224349›Full record

ArticleRNA (New York, N.Y.)2018

A sequence-based, deep learning model accurately predicts RNA splicing branchpoints.

Joseph M Paggi, Gill Bejerano

Open access · bronzeAbstract read
In one paragraph

Article in RNA (New York, N.Y.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed
2.6field-weighted citation impact, top 9% 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

47 citing papers in PubMed, 81 citations in OpenAlex.

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  20. The role of noncoding genetic variants in cardiomyopathy.Frontiers in cardiovascular medicine · 2023
    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.

Joseph M PaggiDepartment of Computer Science, Stanford University, Stanford, California 94305, USA.
Gill BejeranoDepartment of Computer Science, Stanford University, Stanford, California 94305, USA.
Stanford University · US

Funding

Combining genetics, genomics, and anatomy to classify cell types across mammalsU01MH105949 · NIMH · BRANDEIS UNIVERSITY · PI BEJERANO, GILL, LOIS, CARLOS · 2014 to 2016
$3.4M
NIH HHS U01MH105949NIMH NIH HHS U01 MH105949
6 · The paper itself

Abstract

Experimental detection of RNA splicing branchpoints is difficult. To date, high-confidence experimental annotations exist for 18% of 3' splice sites in the human genome. We develop a deep-learning-based branchpoint predictor, LaBranchoR, which predicts a correct branchpoint for at least 75% of 3' splice sites genome-wide. Detailed analysis of cases in which our predicted branchpoint deviates from experimental data suggests a correct branchpoint is predicted in over 90% of cases. We use our predicted branchpoints to identify a novel sequence element upstream of branchpoints consistent with extended U2 snRNA base-pairing, show an association between weak branchpoints and alternative splicing, and explore the effects of genetic variants on branchpoints. We provide genome-wide branchpoint annotations and in silico mutagenesis scores at http://bejerano.stanford.edu/labranchor.

Indexed as

Alternative SplicingComputer SimulationDeep LearningExonsGenome, HumanHumansIntronsMolecular Sequence AnnotationMutagenesisRNA, Small NuclearRNA Splice SitesRNA SplicingRNA, Small NuclearRNA Splice SitesU2 small nuclear RNAalternative splicingdeep learningRNA splicingRNA splicing branchpoints

Identifiers

PMID30224349
PMCPMC6239175
OpenAlexW2951364430

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.