Identification and visualization of differential isoform expression in RNA-seq time series

Motivation As sequencing technologies improve their capacity to detect distinct transcripts of the same gene and to address complex experimental designs such as longitudinal studies, there is a need to develop statistical methods for the analysis of isoform expression changes in time series data. Results Iso‐maSigPro is a new functionality of the R package maSigPro for transcriptomics time series data analysis. Iso‐maSigPro identifies genes with a differential isoform usage across time. The package also includes new clustering and visualization functions that allow grouping of genes with similar expression patterns at the isoform level, as well as those genes with a shift in major expressed isoform. Availability and implementation The package is freely available under the LGPL license from the Bioconductor web site.

[1]  Wing Hung Wong,et al.  Characterization of the human ESC transcriptome by hybrid sequencing , 2013, Proceedings of the National Academy of Sciences.

[2]  Antti Honkela,et al.  Analysis of differential splicing suggests different modes of short-term splicing regulation , 2016, Bioinform..

[3]  Ana Conesa,et al.  Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series , 2014, Bioinform..

[4]  L. Pachter,et al.  Streaming fragment assignment for real-time analysis of sequencing experiments , 2012, Nature Methods.

[5]  W. Tang,et al.  MetaDiff: differential isoform expression analysis using random-effects meta-regression , 2015, BMC Bioinformatics.

[6]  A. Youkilis,et al.  Mxi1-0, an alternatively transcribed Mxi1 isoform, is overexpressed in glioblastomas. , 2004, Neoplasia.

[7]  M. Mandal,et al.  Ikaros and Aiolos Inhibit Pre-B-Cell Proliferation by Directly Suppressing c-Myc Expression , 2010, Molecular and Cellular Biology.

[8]  W. Huber,et al.  Detecting differential usage of exons from RNA-seq data , 2012, Genome research.

[9]  K. Mariappan,et al.  Herboxidiene triggers splicing repression and abiotic stress responses in plants , 2017, BMC Genomics.

[10]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[11]  Eric R. Ziegel,et al.  Generalized Linear Models , 2002, Technometrics.

[12]  Ana Conesa,et al.  Gene expression maSigPro : a method to identify significantly differential expression profiles in time-course microarray experiments , 2006 .

[13]  D. Black,et al.  The neurogenetics of alternative splicing , 2016, Nature Reviews Neuroscience.

[14]  J. Harrow,et al.  Assessment of transcript reconstruction methods for RNA-seq , 2013, Nature Methods.

[15]  Guido Sanguinetti,et al.  Statistical modeling of isoform splicing dynamics from RNA-seq time series data , 2016, Bioinform..