A novel computational framework for genome-scale alternative transcription units prediction
A novel computational framework for genome-scale alternative transcription units prediction
复制标题
用于基因组规模替代转录单元预测的新型计算框架
DOI：
10.1093/bib/bbab162
复制
发表时间：
2021-05-06
影响因子：
9.5
通讯作者：
Liu，Bingqiang
中科院分区：
文献类型：
--
作者：
Wang，Qi;Liu，Zhaoqian;Liu，Bingqiang
交替转录单位（ATU）在不同条件下动态编码，并在细菌基因组中的特定条件下显示重叠模式（共享一个或多个基因）。ATU的基因组规模鉴定对于研究由细菌生物体引起的人类疾病的出现至关重要。然而，由于ATU的复杂性和动态性，使用实验技术来识别所有ATU是不现实的。在这里，我们提出了第一个名为SeqATU的计算框架，用于基于下一代RNA-Seq数据的基因组规模ATU预测。该框架利用凸二次规划模型寻求所有待识别ATU的最优表达式组合。与来自第三代RNA-Seq数据的基准ATU相比，在两个RNA-Sequencing数据集中预测的大肠杆菌ATU达到了0.77/0.74的精度和0.75/0.76的召回率。此外，预测的ATU的5 '-或3'-末端基因的比例，有记录的转录因子结合位点和转录终止位点，是三倍以上的没有5 '-或3'-末端基因。我们进一步通过基因本体论和京都基因百科全书和基因组功能富集分析来评估预测的ATU。结果表明，在相同的ATU中经常编码的基因对比那些可能属于两个不同的ATU的基因对在功能上更相关。总的来说，这些结果证明了预测ATU的高可靠性。我们希望SeqATU的新见解不仅能提高对细菌转录机制的理解，还能指导基因组规模的转录调控网络的重建。
Alternative transcription units (ATUs) are dynamically encoded under different conditions and display overlapping patterns (sharing one or more genes) under a specific condition in bacterial genomes. Genome-scale identification of ATUs is essential for studying the emergence of human diseases caused by bacterial organisms. However, it is unrealistic to identify all ATUs using experimental techniques because of the complexity and dynamic nature of ATUs. Here, we present the first-of-its-kind computational framework, named SeqATU, for genome-scale ATU prediction based on next-generation RNA-Seq data. The framework utilizes a convex quadratic programming model to seek an optimum expression combination of all of the to-be-identified ATUs. The predicted ATUs in Escherichia coli reached a precision of 0.77/0.74 and a recall of 0.75/0.76 in the two RNA-Sequencing datasets compared with the benchmarked ATUs from third-generation RNA-Seq data. In addition, the proportion of 5'- or 3'-end genes of the predicted ATUs, having documented transcription factor binding sites and transcription termination sites, was three times greater than that of no 5'- or 3'-end genes. We further evaluated the predicted ATUs by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses. The results suggested that gene pairs frequently encoded in the same ATUs are more functionally related than those that can belong to two distinct ATUs. Overall, these results demonstrated the high reliability of predicted ATUs. We expect that the new insights derived by SeqATU will not only improve the understanding of the transcription mechanism of bacteria but also guide the reconstruction of a genome-scale transcriptional regulatory network.