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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

redicTF: prediction of bacterial transcription factors in complex microbial communities using deep learnin

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Monteiro, Lummy Maria Oliveira [1, 2, 3] ; Saraiva, Joao Pedro [1] ; Toscan, Rodolfo Brizola [1] ; Stadler, Peter F. [3] ; Silva-Rocha, Rafael [2] ; da Rocha, Ulisses Nunes [1]
Total Authors: 6
[1] UFZ Helmholtz Ctr Environm Res, Leipzig - Germany
[2] Univ Sao Paulo, Ribeirao Preto Med Sch FMRP, Ribeirao Preto - Brazil
[3] Univ Leipzig, Bioinformat Grp, Inst Comp Sci, Leipzig - Germany
Total Affiliations: 3
Document type: Journal article
Source: ENVIRONMENTAL MICROBIOME; v. 17, n. 1 FEB 8 2022.
Web of Science Citations: 0

Background: Transcription factors (TFs) are proteins controlling the flow of genetic information by regulating cellular gene expression. A better understanding ofTFs 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. Results: To 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. Conclusion: PredicTF 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 (AU)

FAPESP's process: 19/15675-0 - Unravelling the complexity of microbial gene regulatory networks
Grantee:Rafael Silva Rocha
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 18/21133-2 - Reconstruction of bacterial regulatory networks in environmental terrestrial samples
Grantee:Lummy Maria Oliveira Monteiro
Support Opportunities: Scholarships abroad - Research Internship - Doctorate
FAPESP's process: 16/19179-9 - Deciphering the architecture/function relationship in complex bacterial promoters through synthetic biology approaches
Grantee:Lummy Maria Oliveira Monteiro
Support Opportunities: Scholarships in Brazil - Doctorate