spliceR: an R package for classification of alternative splicing and prediction of coding potential from RNA-seq data

被引:76
|
作者
Vitting-Seerup, Kristoffer [1 ,2 ]
Porse, Bo Torben [2 ,3 ,4 ]
Sandelin, Albin [1 ,2 ]
Waage, Johannes [1 ,2 ,3 ,4 ]
机构
[1] Univ Copenhagen, Bioinformat Ctr, Dept Biol, DK-2200 Copenhagen, Denmark
[2] Univ Copenhagen, Biotech Res & Innovat Ctr, DK-2200 Copenhagen, Denmark
[3] Univ Copenhagen, Rigshosp, Fac Hlth Sci, Finsen Lab, DK-2200 Copenhagen, Denmark
[4] Univ Copenhagen, Fac Hlth Sci, Danish Stem Cell Ctr DanStem, DK-2200 Copenhagen, Denmark
来源
BMC BIOINFORMATICS | 2014年 / 15卷
关键词
spliceR; RNA-Seq; Alternative splicing; Nonsense mediated decay (NMD); Isoform switch; DIFFERENTIAL EXPRESSION; EVENTS; VISUALIZATION; BIOCONDUCTOR; TOOL;
D O I
10.1186/1471-2105-15-81
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: RNA-seq data is currently underutilized, in part because it is difficult to predict the functional impact of alternate transcription events.Recent software improvements in full-length transcript deconvolution prompted us to develop spliceR, an R package for classification of alternative splicing and prediction of coding potential. Results: spliceR uses the full-length transcript output from RNA-seq assemblers to detect single or multiple exon skipping, alternative donor and acceptor sites, intron retention, alternative first or last exon usage, and mutually exclusive exon events.For each of these events spliceR also annotates the genomic coordinates of the differentially spliced elements, facilitating downstream sequence analysis.For each transcript isoform fraction values are calculated to identify transcript switching between conditions.Lastly, spliceR predicts the coding potential, as well as the potential nonsense mediated decay (NMD) sensitivity of each transcript. Conclusions: spliceR is an easy-to-use tool that extends the usability of RNA-seq and assembly technologies by allowing greater depth of annotation of RNA-seq data.spliceR is implemented as an R package and is freely available from the Bioconductor repository (http://www.bioconductor.org/ packages/ 2.13/ bioc/ html/ spliceR.html).
引用
收藏
页数:7
相关论文
共 50 条
  • [31] pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components
    Federico Marini
    Harald Binder
    BMC Bioinformatics, 20
  • [32] dupRadar: a Bioconductor package for the assessment of PCR artifacts in RNA-Seq data
    Sergi Sayols
    Denise Scherzinger
    Holger Klein
    BMC Bioinformatics, 17
  • [33] CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data
    Liu, Haibo
    Hu, Kai
    O'Connor, Kevin
    Kelliher, Michelle A.
    Zhu, Lihua Julie
    BIOTECH, 2024, 13 (03):
  • [34] Integrative analysis of many RNA-seq datasets to study alternative splicing
    Li, Wenyuan
    Dai, Chao
    Kang, Shuli
    Zhou, Xianghong Jasmine
    METHODS, 2014, 67 (03) : 313 - 324
  • [35] RNA-Seq of Arabidopsis Pollen Uncovers Novel Transcription and Alternative Splicing
    Loraine, Ann E.
    McCormick, Sheila
    Estrada, April
    Patel, Ketan
    Qin, Peng
    PLANT PHYSIOLOGY, 2013, 162 (02) : 1092 - 1109
  • [36] 3D RNA-seq: a powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists
    Guo, Wenbin
    Tzioutziou, Nikoleta A.
    Stephen, Gordon
    Milne, Iain
    Calixto, Cristiane P. G.
    Waugh, Robbie
    Brown, John W. S.
    Zhang, Runxuan
    RNA BIOLOGY, 2021, 18 (11) : 1574 - 1587
  • [37] Transcriptional Landscapes of Long Non-coding RNAs and Alternative Splicing in Pyricularia oryzae Revealed by RNA-Seq
    Li, Zhigang
    Yang, Jun
    Peng, Junbo
    Cheng, Zhihua
    Liu, Xinsen
    Zhang, Ziding
    Bhadauria, Vijai
    Zhao, Wensheng
    Peng, You-Liang
    FRONTIERS IN PLANT SCIENCE, 2021, 12
  • [38] SUVA: splicing site usage variation analysis from RNA-seq data reveals highly conserved complex splicing biomarkers in liver cancer
    Cheng, Chao
    Liu, Lei
    Bao, Yongli
    Yi, Jingwen
    Quan, Weili
    Xue, Yaqiang
    Sun, Luguo
    Zhang, Yi
    RNA BIOLOGY, 2021, 18 : 157 - 171
  • [39] ToPASeq: an R package for topology-based pathway analysis of microarray and RNA-Seq data
    Ivana Ihnatova
    Eva Budinska
    BMC Bioinformatics, 16
  • [40] lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models
    Vestal, Brian E.
    Wynn, Elizabeth
    Moore, Camille M.
    BMC BIOINFORMATICS, 2022, 23 (01)