Computational prediction of alternative transcription units in prokaryotic genomes

發布者:文明辦作者:發布時間:2019-06-27瀏覽次數:786


主講人:劉丙強 山東大學教授 博士生導師


時間:2019年6月29日14:20


地點:三號樓332會議廳


舉辦單位:數理學院


主講人介紹:山東大學數學學院教授、博士生導師。所在學科為運籌學與控制論,研究方向為組合最優化與生物信息學。2003年畢業于山東大學數學學院基礎數學專業,獲學士學位。2010年畢業于山東大學數學學院運籌學與控制論專業,獲博士學位。其間于2007年1月至2010年1月赴美國喬治亞大學聯合培養,研究方向為生物信息學。2010年留校任教,2013年任山東大學數學學院副教授,2017年任教授。主要研究方向為利用圖與組合優化的模型與理論針對生物信息學問題進行算法設計與數據分析,研究課題包括轉錄因子結合位點計算預測、表達數據分析、調控網絡構建等等。


內容介紹:Identification of transcription units (TUs) encoded in prokaryotes is essential  to predict the function of unknown genes, annotate the prokaryotic genome and  construct the transcriptional and translation regulatory networks at the gene  level. The alternative transcription units (ATUs) are the dynamic TUs from a  cluster of genes. The identification of ATUs is recognized as a more challenging  computational problem due to their condition-dependent nature, and the next  generation sequencing technique provided a good opportunity. We are trying to  develop a method to predict ATUs in prokaryotes based on RNA-seq data. The  problem was described as a mathematical programming model, along with the  integrating of other factors including RNA degradation effect, cross-gene reads.  We tested the methods with two RNA-seq data on E.coli genome and compared the  predicted ATUs with experimentally validated ATUs from previous studies. The  comparison results show that our algorithm can recover the majority of  previously known ATUs with average precision of 0.70/0.66 and recall of  0.77/0.79 on two datasets. As the first de novo computational ATU prediction  pipeline, the new method will facilitate the research on complex mechanism of  transcriptional regulation, and bring more attention to the function of  alternative transcription units in prokaryotic genomes.

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