Information transduction capacity reduces the uncertainties in annotation-free isoform discovery and quantification. Issue 15 (7th July 2017)

• Record Type: Journal Article
• Authors: Deng, Yue; Bao, Feng; Yang, Yang; Ji, Xiangyang; Du, Mulong; Zhang, Zhengdong; Wang, Meilin; Dai, Qionghai
• Is Part Of: Nucleic acids research. Volume 45:Issue 15(2017)
• Page Start: e143
• Page End: e143
• Publication Date: 2017-07-07
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkx585
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: The automated transcript discovery and quantification of high-throughput RNA sequencing (RNA-seq) data are important tasks of next-generation sequencing (NGS) research. However, these tasks are challenging due to the uncertainties that arise in the inference of complete splicing isoform variants from partially observed short reads. Here, we address this problem by explicitly reducing the inherent uncertainties in a biological system caused by missing information. In our approach, the RNA-seq procedure for transforming transcripts into short reads is considered an information transmission process. Consequently, the data uncertainties are substantially reduced by exploiting the information transduction capacity of information theory. The experimental results obtained from the analyses of simulated datasets and RNA-seq datasets from cell lines and tissues demonstrate the advantages of our method over state-of-the-art competitors. Our algorithm is an open-source implementation of MaxInfo.