Evidence map›Paper›PMID 34152934›Full record

ArticleRNA biology2021

SUVA: splicing site usage variation analysis from RNA-seq data reveals highly conserved complex splicing biomarkers in liver cancer.

Chao Cheng, Lei Liu, Yongli Bao, Jingwen Yi, Weili Quan, Yaqiang Xue, Luguo Sun, Yi Zhang

Abstract read
In one paragraph

Article in RNA biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

29 citing papers in PubMed.

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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

8 authors.

Chao ChengABLife BioBigData Institute, Wuhan, Hubei China.ORCID 0000-0003-4330-9342
Lei LiuNational Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun China.
Yongli BaoNational Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun China.
Jingwen YiNational Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun China.
Weili QuanABLife BioBigData Institute, Wuhan, Hubei China.
Yaqiang XueABLife BioBigData Institute, Wuhan, Hubei China.
Luguo SunNational Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun China.
Yi ZhangABLife BioBigData Institute, Wuhan, Hubei China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most of the current alternative splicing (AS) analysis tools are powerless to analyse complex splicing. To address this, we developed SUVA (Splice sites Usage Variation Analysis) that decomposes complex splicing events into five types of splice junction pairs. By analysing real and simulated data, SUVA showed higher sensitivity and accuracy in detecting AS events than the compared methods. Notably, SUVA detected extensive complex AS events and screened out 69 highly conserved and dominant AS events associated with cancer. The cancer-associated complex AS events in FN1 and the co-regulated RNA-binding proteins were significantly correlated with patient survival.

Indexed as

Alternative SplicingGene Expression Regulation, NeoplasticSoftwareBiomarkers, TumorComputational BiologyHumansLiver NeoplasmsPrognosisRNA-Binding ProteinsRNA-SeqSequence Analysis, RNASurvival RateBiomarkers, TumorRNA-Binding ProteinsAlternative splicingascomplex splicingfn1liver cancersplicing biomarkersplicing site usage

Identifiers

PMID34152934
PMCPMC8682974

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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.