TY - GEN
T1 - Identification of differential alternative splicing events with an adjusted beta-distribution model
AU - Liu, Kan
AU - Du, Qian
AU - Ren, Guodong
AU - Yu, Bin
AU - Zhang, Chi
N1 - Funding Information:
ACKNOWLEDGMENT We thank Dr. Shangang Jia and Weilong Yang for technical assistance and method discussion. This study is supported by NE Soybean Board funds.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/27
Y1 - 2017/9/27
N2 - High-throughput next generation sequencing of cDNA, i.e. RNA-Seq, presents an unprecedented resource for characterizing the alternative splicing (AS) in complex eukaryotic transcriptomes. Accumulating evidences indicate that AS is developmentally regulated, but the precise responses of AS event to development is not well understood. Here, we describe a new method, based on an adjusted beta-distribution model, for detection of differential AS patterns from RNA-Seq data comparisons. Applying our method to two datasets of RNA-Seq for zika infection in human cells and pollen tissue in Arabidopsis thaliana, we identified 1,871 differentially AS events for 1,394 protein-coding genes in human and 496 differentially AS events for 358 protein-coding genes in Arabidopsis, respectively. The results included known AS events reported before as well as novel events, which demonstrate that the biological replicates are important in the effective identification using ß-distribution. With a high accurate rate, our new method in differential AS identification will facilitate future investigation on transcriptomic annotation.
AB - High-throughput next generation sequencing of cDNA, i.e. RNA-Seq, presents an unprecedented resource for characterizing the alternative splicing (AS) in complex eukaryotic transcriptomes. Accumulating evidences indicate that AS is developmentally regulated, but the precise responses of AS event to development is not well understood. Here, we describe a new method, based on an adjusted beta-distribution model, for detection of differential AS patterns from RNA-Seq data comparisons. Applying our method to two datasets of RNA-Seq for zika infection in human cells and pollen tissue in Arabidopsis thaliana, we identified 1,871 differentially AS events for 1,394 protein-coding genes in human and 496 differentially AS events for 358 protein-coding genes in Arabidopsis, respectively. The results included known AS events reported before as well as novel events, which demonstrate that the biological replicates are important in the effective identification using ß-distribution. With a high accurate rate, our new method in differential AS identification will facilitate future investigation on transcriptomic annotation.
KW - RNA-seq
KW - alternative splicing (AS)
KW - next generation sequencing
UR - http://www.scopus.com/inward/record.url?scp=85033692073&partnerID=8YFLogxK
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U2 - 10.1109/EIT.2017.8053369
DO - 10.1109/EIT.2017.8053369
M3 - Conference contribution
AN - SCOPUS:85033692073
T3 - IEEE International Conference on Electro Information Technology
SP - 276
EP - 279
BT - 2017 IEEE International Conference on Electro Information Technology, EIT 2017
PB - IEEE Computer Society
T2 - 2017 IEEE International Conference on Electro Information Technology, EIT 2017
Y2 - 14 May 2017 through 17 May 2017
ER -