Computational prediction of alternative transcription units in prokaryotic genomes


主讲人:刘丙强 山东大学教授 博士生导师





内容先容: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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