Evidence map›Paper›PMID 29688305›Full record

ArticleBioinformatics (Oxford, England)2018

PathwaySplice: an R package for unbiased pathway analysis of alternative splicing in RNA-Seq data.

Aimin Yan, Yuguang Ban, Zhen Gao, Xi Chen, Lily Wang

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Aimin YanSylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
Yuguang BanSylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
Zhen GaoSylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
Xi ChenSylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
Lily WangSylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.

Funding

iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTACU24CA210954 · NCI · BAYLOR COLLEGE OF MEDICINE · PI ZHANG, BING · 2016 to 2020
$4.9M
Integrative statistical models for TNBC biomarker discoveryR01CA200987 · NCI · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI CHEN, XI STEVEN · 2016 to 2020
$1.8M
NCI NIH HHS R01 CA200987NCI NIH HHS U24 CA210954
6 · The paper itself

Abstract

Summary: Pathway analysis of alternative splicing would be biased without accounting for the different number of exons or junctions associated with each gene, because genes with higher number of exons or junctions are more likely to be included in the 'significant' gene list in alternative splicing. We present PathwaySplice, an R package that (i) Performs pathway analysis that explicitly adjusts for the number of exons or junctions associated with each gene; (ii) visualizes selection bias due to different number of exons or junctions for each gene and formally tests for presence of bias using logistic regression; (iii) supports gene sets based on the Gene Ontology terms, as well as more broadly defined gene sets (e.g. MSigDB) or user defined gene sets; (iv) identifies the significant genes driving pathway significance and (v) organizes significant pathways with an enrichment map, where pathways with large number of overlapping genes are grouped together in a network graph. Availability and implementation: https://bioconductor.org/packages/release/bioc/html/PathwaySplice.html. Supplementary information: Supplementary data are available at Bioinformatics online.

Indexed as

Alternative SplicingGene OntologySoftwareSequence Analysis, RNA

Identifiers

PMID29688305
PMCPMC6137985

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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.