bioRxiv preprint doi: https://doi.org/10.1101/261453; this version posted March 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 RECTA: Regulon Identification Based on Comparative 2 Genomics and Transcriptomics Analysis 3 Xin Chen1,*, Anjun Ma5,6,*, Adam McDermaid5,6, Hanyuan Zhang2, Chao Liu3, 4 Huansheng Cao4, Qin Ma,5,6,§ 5 1Center for Applied Mathematics, Tianjin University, Tianjin, 300072, China. 6 2College of computer science and engineering, University of Nebraska Lincoln, NE 7 68588, USA. 8 3Shandong Provincial Hospital affiliated to Shandong University, Jinan, 250021, 9 China 10 4Center for Fundamental and Applied Microbiomics, Biodesign Institute, Arizona 11 State University, Tempe, Arizona 85287, USA. 12 5Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, 13 Horticulture and Plant Science, South Dakota State University, Brookings, SD 57006, 14 USA. 15 6Department of Mathematics and Statistics, South Dakota State University, 16 Brookings, SD 57006, USA. 17 *The authors wish it to be known that, in their opinion, the first two authors should be 18 regarded as Joint First Authors 19 §To whom correspondence should be addressed. Email:
[email protected] (QM). 20 21 ABSTRACT 22 Regulons, which serve as co-regulated gene groups contributing to the transcriptional 23 regulation of microbial genomes, have the potential to aid in understanding of 24 underlying regulatory mechanisms. In this study, we designed a novel computational 25 pipeline, RECTA, for regulon prediction related to the gene regulatory network under 26 certain conditions.