Details zur Publikation

Referenztyp Zeitschriften
DOI / URL Link
Creative Commons Lizenz creative commons licence
Titel (primär) PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning
Autor Oliveira Monteiro, L.M.; Saraiva, J.P.; Brizola Toscan, R.; Stadler, P.F.; Silva-Rocha, R.; Nunes da Rocha, U.
Journal / Serie Environmental Microbiome
Erscheinungsjahr 2022
Department UMB
Band/Volume 17
Seite von art. 7
Sprache englisch
Topic T7 Bioeconomy
Supplements https://ndownloader.figstatic.com/articles/19144645/versions/1
Keywords Gene regulation; Transcription factors; Deep learning; Transcription factor database; Microbial communities
Abstract

Background

Transcription 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.

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 https://github.com/mdsufz/PredicTF.

dauerhafte UFZ-Verlinkung https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=25457
Oliveira Monteiro, L.M., Saraiva, J.P., Brizola Toscan, R., Stadler, P.F., Silva-Rocha, R., Nunes da Rocha, U. (2022):
PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning
Environ. Microbiome 17 , art. 7