ArticlemSystems2020
Benchmarking Bacterial Promoter Prediction Tools: Potentialities and Limitations.
Article in mSystems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
34 citing papers in PubMed, 52 citations in OpenAlex.
- High-resolution profiling of bacterial transcriptomes by SEnd-seq.The Biochemical journal · 2026Review
- Core elements play distinct roles in promoter birth and transcriptional regulation.Nature communications · 2026Article
- TheMicroorganisms · 2026Article
- PromoterAtlas: decoding regulatory sequences across Gammaproteobacteria using a transformer model.Nature communications · 2026Article
- Enhancement of Nitrogenase Activity in Recombinant Escherichia coli by Replacing Upstream Region of nif Operon from Paenibacillus Species.Chembiochem : a European journal of chemical biology · 2026Article
- Functional characterization of a type I-F1 CRISPR-cas system from the clinical isolate Shewanella xiamenensis Sh95 reveals constitutive activity and plasmid-curing capability.Scientific reports · 2026Article
- Synthetic Biology Strategies for Activating Cryptic BGCs inACS omega · 2026Review
- Deep Generative Model-Driven Design of Microbial Synthetic Promoters.Journal of microbiology and biotechnology · 2025Review
- Unraveling the start element and regulatory divergence of core promoters across the domain bacteria.Nucleic acids research · 2025Article
- Computational Pipeline for Targeted Integration and Variable Payload Expression in Bacteriophage Engineering.ACS synthetic biology · 2025Article
- Dam-dependent epigenetic memory regulates prophage reintegration in Salmonella.Nucleic acids research · 2025Article
- Negative dataset selection impacts machine learning-based predictors for multiple bacterial species promoters.Bioinformatics (Oxford, England) · 2025Article
- Genomic features and fitness cost of co-existence ofFrontiers in cellular and infection microbiology · 2025Article
- Predicting Promoters in Multiple Prokaryotes with Prompt.Interdisciplinary sciences, computational life sciences · 2024Article
- Multifaceted regulation of siderophore synthesis by multiple regulatory systems in Shewanella oneidensis.Communications biology · 2024Article
- Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering.ACS synthetic biology · 2023Article
- Construction of a bacteriophage-derived recombinase system in Bacillus licheniformis for gene deletion.AMB Express · 2023Article
- Classification of Promoter Sequences from Human Genome.International journal of molecular sciences · 2023Article
- AamA-mediated epigenetic control of genome-wide gene expression and phenotypic traits inMicrobial genomics · 2023Article
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.