Report title:A novel computational framework for genome-scale alternative transcription units prediction
Report time:12month22Sunday morning10:30Start
Reporting location:Computer Building of Central South seabet313Conference Room
Reporter:Liu Bingqiang seabet
Report Summary：
Alternative transcription units (seabet) are dynamically encoded under different conditions or environmental stimuli in bacterial genomes, and genome-scale identification of seabet is essential for understanding the transcriptomic architecture and functional genomics. However, it is unrealistic to identify all seabet using experimental techniques, due to the complexity and dynamic nature of seabet. Here, we present the first-of-its-kind computational framework, named SeqATU, for genome-scale ATU prediction based on RNA-Sequencing data. The framework utilizes a convex quadratic programming model (CQP) to seek an optimum expression combination of all of the to-be-identified seabet by minimizing their squared error compared with the actual expression levels in genetic and intergenic regions. The predicted seabet in E. coli reached a precision of 0.66/0.60 and a recall of 0.68/0.67 in the two RNA-Sequencing datasets compared with the benchmarked seabet from SMRT-Cappable-seq. Furthermore, the 5’-end genes of the predicted seabet validated by experimental TSSs or TF binding sites were over 1.5 times greater than those of the other genes. In terms of GO and KEGG enrichment analyses, the gene pairs frequently encoded in the same seabet were more functionally related than those that belong to two distinct seabet. It is noteworthy that the information about the degradation rate of the mRNA transcripts was organically integrated to provide linear constraints of CQP and it have significantly improved the prediction performance in the above evaluations. We believe that the seabet identified by SeqATU can provide fundamental knowledge to guide the reconstruction of transcriptional regulatory networks in bacterial genomes.
About the speaker:
Liu Bingqiang，Professor of School of Mathematics, seabet University、Doctoral Supervisor，Director of the Institute of Systems and Operations Research，Assistant to the Dean。The subject is operations research and cybernetics，Research direction is combinatorial optimization and bioinformatics。2003Graduated from the School of Mathematics, seabet University, majoring in basic mathematics，Bachelor's degree。2010Graduated from the School of Mathematics, seabet University, majoring in Operations Research and Cybernetics，Received a doctorate degree。During the period2007Year1Month Solstice2010Year1Monthly went to the seabet of Georgia for joint training，Research direction is bioinformatics。2010Stayed at the school to teach,2013As an associate professor at the School of Mathematics, seabet University,2017years seabet。The main research direction is algorithm design and data analysis for bioinformatics problems using graph and combinatorial optimization models and theories，Research topics include computational prediction of transcription factor binding sites、Expression data analysis、Transcription unit prediction、Construction and analysis of regulatory network, etc.。
