ArticleBioinformatics (Oxford, England)2017
bTSSfinder: a novel tool for the prediction of promoters in cyanobacteria and Escherichia coli.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed, 92 citations in OpenAlex.
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- Bidirectional promoters in Escherichia coli: regulatory rules and implications for gene expression noise.Nucleic acids research · 2026Article
- Supervised clustering of bacterial promoter identifies two groups with different relevant positions at -10.Briefings in functional genomics · 2026Article
- Computational Pipeline for Targeted Integration and Variable Payload Expression in Bacteriophage Engineering.ACS synthetic biology · 2025Article
- Bioinformatics-driven discovery of skin microbiota bacteriocins as potential antibiotics and probiotics.The Journal of antibiotics · 2025Article
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- Predicting Promoters in Multiple Prokaryotes with Prompt.Interdisciplinary sciences, computational life sciences · 2024Article
- Article
- iPro2L-DG: Hybrid network based on improved densenet and global attention mechanism for identifying promoter sequences.Heliyon · 2024Article
- Recognition of cyanobacteria promoters via Siamese network-based contrastive learning under novel non-promoter generation.Briefings in bioinformatics · 2024Article
- Article
- Non-canonical transcriptional start sites in E. coli O157:H7 EDL933 are regulated and appear in surprisingly high numbers.BMC microbiology · 2023Article
- Deep learning and support vector machines for transcription start site identification.PeerJ. Computer science · 2023Article
- ProkBERT family: genomic language models for microbiome applications.Frontiers in microbiology · 2023Article
- Genomic Profiling of Non-O157 Shiga ToxigenicPHAGE (New Rochelle, N.Y.) · 2022Article
- Quantitative contribution of the spacer length in the supercoiling-sensitivity of bacterial promoters.Nucleic acids research · 2022Article
- Mapping the Complex Transcriptional Landscape of the Phytopathogenic Bacterium Dickeya dadantii.mBio · 2022Article
- Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction.Briefings in bioinformatics · 2022Article
- SAPPHIRE.CNN: Implementation of dRNA-seq-driven, species-specific promoter prediction using convolutional neural networks.Computational and structural biotechnology journal · 2022Article
- Promotech: a general tool for bacterial promoter recognition.Genome biology · 2021Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: The computational search for promoters in prokaryotes remains an attractive problem in bioinformatics. Despite the attention it has received for many years, the problem has not been addressed satisfactorily. In any bacterial genome, the transcription start site is chosen mostly by the sigma (σ) factor proteins, which control the gene activation. The majority of published bacterial promoter prediction tools target σ 70 promoters in Escherichia coli . Moreover, no σ-specific classification of promoters is available for prokaryotes other than for E. coli . Results: Here, we introduce bTSSfinder, a novel tool that predicts putative promoters for five classes of σ factors in Cyanobacteria (σ A , σ C , σ H , σ G and σ F ) and for five classes of sigma factors in E. coli (σ 70 , σ 38 , σ 32 , σ 28 and σ 24 ). Comparing to currently available tools, bTSSfinder achieves higher accuracy (MCC = 0.86, F 1 -score = 0.93) compared to the next best tool with MCC = 0.59, F 1 -score = 0.79) and covers multiple classes of promoters. Availability and Implementation: bTSSfinder is available standalone and online at http://www.cbrc.kaust.edu.sa/btssfinder . Contacts: ilham.shahmuradov@kaust.edu.sa or vladimir.bajic@kaust.edu.sa. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.