RhoTermPredict: an algorithm for predicting Rho-dependent transcription terminators based on Escherichia coli, Bacillus subtilis and Salmonella enterica databases

Abstract

Background In bacterial genomes, there are two mechanisms to terminate the DNA transcription: the “intrinsic” or Rho-independent termination and the Rho-dependent termination. Intrinsic terminators are characterized by a RNA hairpin followed by a run of 6–8 U residues relatively easy to identify using one of the numerous available prediction programs. In contrast, Rho-dependent termination is mediated by the Rho protein factor that, firstly, binds to ribosome-free mRNA in a site characterized by a C > G content and then reaches the RNA polymerase to induce its release. Conversely on intrinsic terminators, the computational prediction of Rho-dependent terminators in prokaryotes is a very difficult problem because the sequence features required for the function of Rho are complex and poorly defined. This is the reason why it still does not exist an exhaustive Rho-dependent terminators prediction program. Results In this study we introduce RhoTermPredict, the first published algorithm for an exhaustive Rho-dependent terminators prediction in bacterial genomes. RhoTermPredict identifies these elements based on a previously proposed consensus motif common to all Rho-dependent transcription terminators. It essentially searches for a 78 nt long RUT site characterized by a C > G content and with regularly spaced C residues, followed by a putative pause site for the RNA polymerase. We tested RhoTermPredict performances by using available genomic and transcriptomic data of the microorganism Escherichia coli K-12, both in limited-length sequences and in the whole-genome, and available genomic sequences from Bacillus subtilis 168 and Salmonella enterica LT2 genomes. We also estimated the overlap between the predictions of RhoTermPredict and those obtained by the predictor of intrinsic terminators ARNold webtool. Our results demonstrated that RhoTermPredict is a very performing algorithm both for limited-length sequences (F 1 -score obtained about 0.7) and for a genome-wide analysis. Furthermore the degree of overlap with ARNold predictions was very low. Conclusions Our analysis shows that RhoTermPredict is a powerful tool for Rho-dependent terminators search in the three analyzed genomes and could fill this gap in computational genomics. We conclude that RhoTermPredict could be used in combination with an intrinsic terminators predictor in order to predict all the transcription terminators in bacterial genomes. Electronic supplementary material The online version of this article (10.1186/s12859-019-2704-x) contains supplementary material, which is available to authorized users.

A high-resolution map of bacteriophage øX174 transcription

“…Rho-dependent terminators have been observed before in vitro (Axelrod, 1976a; Brendel, 1985; Smith and Sinsheimer, 1976a, b, c) but failed to be substantiated when probed in vivo (Hayashi et al, 1981). To identify locations of potential Rho utilisation (RUT) sites, which precede the Rho-terminator region by 10-100 nt (Koslover et al, 2012; Ray-Soni et al, 2016), we used the RhoTermPredict algorithm (Di Salvo et al, 2019). This analysis identified 13 RUT sites across the øX174 genome (Supplementary Fig.…”

“…Potential Rho-dependent terminators were identified by analysing the whole ϕX174 genome (Genbank No. NC_001422.1) with the RhoTermPredict python script (Di Salvo et al, 2019). All predicted sequences are collected in Supplementary File S2.…”

Rho factor mediates flagellum and toxin phase variation and impacts virulence in Clostridioides difficile

“…More work is needed to distinguish between these two models. Using the RhoTermPredict algorithm [79], we were unable to identify any predicted rut sites within the flgB leader sequence of either flg ON or OFF sequences. RhoTermPredict is based on E. coli, B. subtilis, and S. enterica databases and searches for rut sites with regularly spaced C residues and C>G content followed by a…”

“…putative RNA polymerase pause site [79]. However, the C. difficile genome has low G+C content (<30%) [80], and the flgB leader sequence has 23% G+C content.…”

iterb-PPse: Identification of transcriptional terminators in bacterial by incorporating nucleotide properties into PseKNC

“…The specificity and sensitivity of the final results were 95.3% and 87.8%, respectively. In 2019, Macro Di Simore et al utilized the secondary structure of the sequence as a feature [4], the classification accuracy of the Rho-independent terminators was 67.5%. Not like the above experiments Lin Hao et al studied the prediction of two kinds of terminators in bacterial [5],they developed a prediction tool for terminators with an accuracy of 95% in 2018.…”