ArticleEnvironmental microbiome2022
PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning.
Article in Environmental microbiome, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Article
- Thermal modulation of transcriptional states, biofilm architecture and survival strategies inFrontiers in microbiology · 2026Article
- Unveiling the landscape of prokaryotic global regulators through deep protein language models.mSystems · 2025Article
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- Targeting bacterial transcription factors for infection control: opportunities and challenges.Transcription · 2025Review
- Microbial biosensors for diagnostics, surveillance and epidemiology: Today's achievements and tomorrow's prospects.Microbial biotechnology · 2024Review
- Fundamentals and Exceptions of the LysR-type Transcriptional Regulators.ACS synthetic biology · 2024Review
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
- Transcription factor-based biosensors for screening and dynamic regulation.Frontiers in bioengineering and biotechnology · 2023Review
- Identification and functional analysis of novel protein-encoding sequences related to stress-resistance.Frontiers in microbiology · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundTranscription factors (TFs) are proteins controlling the flow of genetic information by regulating cellular gene expression. A better understanding of TFs in a bacterial community context may open novel revenues for exploring gene regulation in ecosystems where bacteria play a key role. Here we describe PredicTF, a platform supporting the prediction and classification of novel bacterial TF in single species and complex microbial communities. PredicTF is based on a deep learning algorithm.
resultsTo train PredicTF, we created a TF database (BacTFDB) by manually curating a total of 11,961 TF distributed in 99 TF families. Five model organisms were used to test the performance and the accuracy of PredicTF. PredicTF was able to identify 24-62% of the known TFs with an average precision of 88% in our five model organisms. We demonstrated PredicTF using pure cultures and a complex microbial community. In these demonstrations, we used (meta)genomes for TF prediction and (meta)transcriptomes for determining the expression of putative TFs.
conclusionPredicTF demonstrated high accuracy in predicting transcription factors in model organisms. We prepared the pipeline to be easily implemented in studies profiling TFs using (meta)genomes and (meta)transcriptomes. PredicTF is an open-source software available at https://github.com/mdsufz/PredicTF .
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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.