Inverse analysis of transcription elongation process using Bayesian Inference

2016 Fiscal Year Final Research Report

• Project/Area Number: 15K12145
• Research Category: Grant-in-Aid for Challenging Exploratory Research
• Allocation Type: Multi-year Fund
• Research Field: Life / Health / Medical informatics
• Research Institution: The Institute of Statistical Mathematics
• Principal Investigator: Yoshida Ryo 統計数理研究所, モデリング研究系, 准教授 (70401263)
• Co-Investigator(Renkei-kenkyūsha): KAWAOKA SHINPEI 株式会社国際電気通信基礎技術研究所, 主任研究員 (70740009) ; SHINSUKE KOYAMA 統計数理研究所, モデリング研究系, 准教授 (20589999)
• Project Period (FY): 2015-04-01 – 2017-03-31
• Keywords: ベイズ統計 / 転写伸長 / 新生転写産物 / Total RNA-seq / 逆問題

Outline of Final Research Achievements

Combining data science technologies and a widely used RNA sequencing technique called Total RNA-seq, we aimed to inversely predict a highly complex process of transcription elongation through which RNA polymerase II traverses the DNA template strand. Recently several experimental technologies, for instance, GRO-seq and NET-seq, have been developed for the genome-wide measurement of transcription elongation rates. However, such methods have not widely spread so far because of their experimental difficulties. Our study has shown that using the well-established RNA sequencing method coupled with a simple data science algorithm enables us to reconstruct the transcription elongation process by solving the inverse problem. This has opened a new possibility for transcription elongation studies.

Free Research Field

統計科学