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
  • [41] lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models
    Brian E. Vestal
    Elizabeth Wynn
    Camille M. Moore
    BMC Bioinformatics, 23
  • [42] ASTool: An Easy-to-Use Tool to Accurately Identify Alternative Splicing Events from Plant RNA-Seq Data
    Qi, Huan
    Guo, Xiaokun
    Wang, Tianpeng
    Zhang, Ziding
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2022, 23 (08)
  • [43] Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification
    Al-Aamri, Amira
    Kudlicki, Andrzej S. S.
    Maalouf, Maher
    Taha, Kamal
    Homouz, Dirar
    BIOLOGY-BASEL, 2023, 12 (04):
  • [44] consensusDE: an R package for assessing consensus of multiple RNA-seq algorithms with RUV correction
    Waardenberg, Ashley J.
    Field, Matt A.
    PEERJ, 2019, 7
  • [45] Classification of RNA-Seq data via Gaussian copulas
    Zhang, Qingyang
    STAT, 2017, 6 (01): : 171 - 183
  • [46] Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems
    Liu, Ruolin
    Loraine, Ann E.
    Dickerson, Julie A.
    BMC BIOINFORMATICS, 2014, 15
  • [47] Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems
    Ruolin Liu
    Ann E Loraine
    Julie A Dickerson
    BMC Bioinformatics, 15
  • [48] RNA-seq analysis reveals alternative splicing under salt stress in cotton, Gossypium davidsonii
    Guozhong Zhu
    Weixi Li
    Feng Zhang
    Wangzhen Guo
    BMC Genomics, 19
  • [49] RNA-seq analysis reveals alternative splicing under salt stress in cotton, Gossypium davidsonii
    Zhu, Guozhong
    Li, Weixi
    Zhang, Feng
    Guo, Wangzhen
    BMC GENOMICS, 2018, 19
  • [50] Analysis of Genomic Alternative Splicing Patterns in Rat under Heat Stress Based on RNA-Seq Data
    Huang, Shangzhen
    Dou, Jinhuan
    Li, Zhongshu
    Hu, Lirong
    Yu, Ying
    Wang, Yachun
    GENES, 2022, 13 (02)