rSeqTU—A Machine-Learning Based R Package for Prediction of Bacterial Transcription Units

Frontiers in Genetics

Qin Ma¹  Sheng-Yong Niu²  Wen-Chi Chou³  Binqiang Liu⁴ 

[1] Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States;Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, United States;Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, United States;School of Mathematics, Shandong University, Jinan, China;

关键词: machine learning;    bacteria;    transcription unit;    R package;    transcriptome;   

DOI  :  10.3389/fgene.2019.00374

来源: DOAJ

【 摘 要 】

A transcription unit (TU) is composed of one or multiple adjacent genes on the same strand that are co-transcribed in mostly prokaryotes. Accurate identification of TUs is a crucial first step to delineate the transcriptional regulatory networks and elucidate the dynamic regulatory mechanisms encoded in various prokaryotic genomes. Many genomic features, for example, gene intergenic distance, and transcriptomic features including continuous and stable RNA-seq reads count signals, have been collected from a large amount of experimental data and integrated into classification techniques to computationally predict genome-wide TUs. Although some tools and web servers are able to predict TUs based on bacterial RNA-seq data and genome sequences, there is a need to have an improved machine learning prediction approach and a better comprehensive pipeline handling QC, TU prediction, and TU visualization. To enable users to efficiently perform TU identification on their local computers or high-performance clusters and provide a more accurate prediction, we develop an R package, named rSeqTU. rSeqTU uses a random forest algorithm to select essential features describing TUs and then uses support vector machine (SVM) to build TU prediction models. rSeqTU (available at https://s18692001.github.io/rSeqTU/) has six computational functionalities including read quality control, read mapping, training set generation, random forest-based feature selection, TU prediction, and TU visualization.

【 授权许可】

Unknown