# RECTA: Region Identification Based on Comparative Genomics and Transcriptomics Analysis
###### Abstract  
Regulons, which serve as co-regulated gene groups contributing to the transcriptional regulation of microbial genomes, have the potential to aid in understanding of underlying regulatory mechanisms. In this study, we designed a novel computational pipeline, regulon identification based on comparative genomics and transcriptomics analysis (RECTA), for regulon prediction related to the gene regulatory network under certain conditions. To demonstrate the effectiveness of this tool, we implemented RECTA on _Lactococcus lactis_ MG1363 data to elucidate acid-response regulons. A total of 51 regulons were identified, 14 of which have computational-verified significance. Among these 14 regulons, five of them were computationally predicted to be connected with acid stress response. Validated by literature, 33 genes in _Lactococcus lactis_ MG1363 were found to have orthologous genes which were associated with six regulons. An acid response related regulatory network was constructed, involving two trans-membrane proteins, eight regulons (_llrA, llrC, hllA, ccpA_, NHP6A, _rcfB_, regulons #8 and #39), nine functional modules, and 33 genes with orthologous genes known to be associated with acid stress. The predicted response pathways could serve as promising candidates for better acid tolerance engineering in _Lactococcus lactis_. Our RECTA pipeline provides an effective way to construct a reliable gene regulatory network through regulon elucidation, and has strong application power and can be effectively applied to other bacterial genomes where the elucidation of the transcriptional regulation network is needed. RECTA; _Lactococcus lactis_ MG1363; acid stress response; differentially expressed gene; gene co-expression; cis-regulatory motif finding; regulon; gene regulatory network +
Footnote †: journal: Medical  
same promoter and are co-regulated by one or a set of transcriptional factors (TFs) [8]. The elucidation of regulons can improve the identification of transcriptional genes, and thus, reliably predict the gene transcription regulation networks [9]. There are three ways for regulon prediction: (i) predicting new operons for a known regulon [10; 11]. This method combines motif profiling with a comparative genomic strategy to search for related regulon members and carries out systematical gene regulation study; (ii) Integrating cis-regulatory motif (motif for short) comparison and clustering to find significantly enriched motif candidates [12; 13]. The candidate motifs are then assembled into regulons; (iii) Performing ab initio novel regulon inference using the de novo motif finding strategy [14]. This approach uses a phylogenetic footprinting technique which mostly relies on reference verification [15; 16; 17] and can perform a horizontal sequential comparison to predict regulons in target organisms by searching known functionally-related regulons or TFs from other relevant species. One algorithm for phylogenetic footprinting analysis called Motif Prediction by Phylogenetic footprinting (MP3) has been used for regulon prediction in _Escherichia coli_[17]. Phylogenetic footprinting was then integrated into the DMINDA webserver along with other algorithms, such as the Database of Prokaryotic Operons 2.0 (DOOR2) [7; 18], Bottleneck Broken (BoBro) [19], and BoBro-based motif comparison (BBC) [13], to construct a complete pipeline for regulon prediction. In the latest research, a newly developed pipeline called Single-cell Regulatory Network Inference and Clustering (SCENIC) combines motif finding from co-expression gene modules (CEMs) with regulon prediction for single-cell clustering and analysis [20]. Such a method builds up a way of regulon application in single-cell and metagenomic research. Nevertheless, without a suitable regulon database, researchers need to build up the library first through operon identification, CEM analysis, motif prediction and comparison [21].