Benchmarking Bacterial Promoter Prediction Tools: Potentialities and Limitations

Abstract

The promoter region is a key element required for the production of RNA in bacteria. While new high-throughput technology allows massively parallel mapping of promoter elements, we still mainly rely on bioinformatics tools to predict such elements in bacterial genomes. Additionally, despite many different prediction tools having become popular to identify bacterial promoters, no systematic comparison of such tools has been performed. Here, we performed a systematic comparison between several widely used promoter prediction tools (BPROM, bTSSfinder, BacPP, CNNProm, IBBP, Virtual Footprint, iPro70-FMWin, 70ProPred, iPromoter-2L, and MULTiPly) using well-defined sequence data sets and standardized metrics to determine how well those tools performed related to each other. For this, we used data sets of experimentally validated promoters from Escherichia coli and a control data set composed of randomly generated sequences with similar nucleotide distributions. We compared the performance of the tools using metrics such as specificity, sensitivity, accuracy, and Matthews correlation coefficient (MCC). We show that the widely used BPROM presented the worse performance among the compared tools, while four tools (CNNProm, iPro70-FMWin, 70ProPred, and iPromoter-2L) offered high predictive power. Of these tools, iPro70-FMWin exhibited the best results for most of the metrics used. We present here some potentials and limitations of available tools, and we hope that future work can build upon our effort to systematically characterize this useful class of bioinformatics tools. IMPORTANCE The correct mapping of promoter elements is a crucial step in microbial genomics. Also, when combining new DNA elements into synthetic sequences, predicting the potential generation of new promoter sequences is critical. Over the last years, many bioinformatics tools have been created to allow users to predict promoter elements in a sequence or genome of interest. Here, we assess the predictive power of some of the main prediction tools available using well-defined promoter data sets. Using Escherichia coli as a model organism, we demonstrated that while some tools are biased toward AT-rich sequences, others are very efficient in identifying real promoters with low false-negative rates. We hope the potentials and limitations presented here will help the microbiology community to choose promoter prediction tools among many available alternatives.

Early Evolution of Transcription Systems and Divergence of Archaea and Bacteria

“…The initiator element is thought to be recognized directly by RNAP. Bacteria also have an initiator sequence ( Cassiano and Silva-Rocha, 2020 ). Both Archaea and Bacteria utilize ribosome attachment sequences (i.e., AGGA) on some mRNAs with a corresponding interaction sequence near the 3′-end of 16S rRNA (i.e., UCCU).…”

MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors

“…EIIP and integer) and AAC, varying the ACC/BACC of 0.6450–0.9300 in all problems. For DNA/RNA molecules, the problems used are (i) SARS-CoV-2, hot topic in bioinformatics [ 10 , 11 ]; (ii) detection of sigma70 promoters to study the dynamics of gene expression [ 12 , 85 ]; and (iii) lncRNA sequences, that can play essential roles in biological processes, e.g. transcriptional regulation [ 68 , 86 ].…”

Regulation of Expression of the TIR-Containing Protein C Gene of the Uropathogenic Escherichia coli Strain CFT073

“…We also used the promoter prediction tool iPro70-FMWin, which predicts σ 70 promoters with comparably high accuracy [ 24 ]. We focused on σ 70 promoters since these are the most common promoters in E. coli [ 25 ].…”