Evidence map›Paper›PMID 29065858›Full record

ArticleBMC bioinformatics2017

A heuristic model for computational prediction of human branch point sequence.

Jia Wen, Jue Wang, Qing Zhang, Dianjing Guo

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 26% 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

3 citing papers in PubMed, 10 citations in OpenAlex.

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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 2 countries.

Jia WenSchool of Life Science, State Key Laboratory of Agrobiotechnology and ShenZhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China.ORCID http://orcid.org/0000-0003-4816-5528
Jue WangSchool of Life Science, State Key Laboratory of Agrobiotechnology and ShenZhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China.
Qing ZhangSchool of Life Science, State Key Laboratory of Agrobiotechnology and ShenZhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China.
Dianjing GuoSchool of Life Science, State Key Laboratory of Agrobiotechnology and ShenZhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China. djguo@cuhk.edu.hk.
Chinese University of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPre-mRNA splicing is the removal of introns from precursor mRNAs (pre-mRNAs) and the concurrent ligation of the flanking exons to generate mature mRNA. This process is catalyzed by the spliceosome, where the splicing factor 1 (SF1) specifically recognizes the seven-nucleotide branch point sequence (BPS) and the U2 snRNP later displaces the SF1 and binds to the BPS. In mammals, the degeneracy of BPS motifs together with the lack of a large set of experimentally verified BPSs complicates the task of BPS prediction in silico.

resultsIn this paper, we develop a simple and yet efficient heuristic model for human BPS prediction based on a novel scoring scheme, which quantifies the splicing strength of putative BPSs. The candidate BPS is restricted exclusively within a defined BPS search region to avoid the influences of other elements in the intron and therefore the prediction accuracy is improved. Moreover, using two types of relative frequencies for human BPS prediction, we demonstrate our model outperformed other current implementations on experimentally verified human introns.

conclusionWe propose that the binding energy contributes to the molecular recognition involved in human pre-mRNA splicing. In addition, a genome-wide human BPS prediction is carried out. The characteristics of predicted BPSs are in accordance with experimentally verified human BPSs, and branch site positions relative to the 3'ss and the 5'end of the shortened AGEZ are consistent with the results of published papers. Meanwhile, a webserver for BPS predictor is freely available at http://biocomputer.bio.cuhk.edu.hk/BPS .

Indexed as

Models, MolecularExonsHumansIntronsProtein BindingRibonucleoprotein, U2 Small NuclearRNA, MessengerRNA PrecursorsRNA SplicingThermodynamicsRibonucleoprotein, U2 Small NuclearRNA, MessengerRNA PrecursorsBinding energyBpsGenome-wide predictionHeuristic modelPre-mRNA splicing

Identifiers

PMID29065858
PMCPMC5655975
OpenAlexW2766093145

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.