Dataset Information

Systematic prediction of genes functionally associated with bacterial retrons and classification of the encoded tripartite systems.

ABSTRACT: Bacterial retrons consist of a reverse transcriptase (RT) and a contiguous non-coding RNA (ncRNA) gene. One third of annotated retrons carry additional open reading frames (ORFs), the contribution and significance of which in retron biology remains to be determined. In this study we developed a computational pipeline for the systematic prediction of genes specifically associated with retron RTs based on a previously reported large dataset representative of the diversity of prokaryotic RTs. We found that retrons generally comprise a tripartite system composed of the ncRNA, the RT and an additional protein or RT-fused domain with diverse enzymatic functions. These retron systems are highly modular, and their components have coevolved to different extents. Based on the additional module, we classified retrons into 13 types, some of which include additional variants. Our findings provide a basis for future studies on the biological function of retrons and for expanding their biotechnological applications.

SUBMITTER: Mestre MR

PROVIDER: S-EPMC7736814 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Systematic prediction of genes functionally associated with bacterial retrons and classification of the encoded tripartite systems.

Mestre Mario Rodríguez MR González-Delgado Alejandro A Gutiérrez-Rus Luis I LI Martínez-Abarca Francisco F Toro Nicolás N

Nucleic acids research 20201201 22

Bacterial retrons consist of a reverse transcriptase (RT) and a contiguous non-coding RNA (ncRNA) gene. One third of annotated retrons carry additional open reading frames (ORFs), the contribution and significance of which in retron biology remains to be determined. In this study we developed a computational pipeline for the systematic prediction of genes specifically associated with retron RTs based on a previously reported large dataset representative of the diversity of prokaryotic RTs. We fo ...[more]

PMID: 33275130

Similar Datasets

Project description:Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However in silico identification of these proteins is limited by their high sequence diversity. In this study we developed supervised learning based classification models for plant viral RNA silencing suppressor proteins in plant viruses. We developed four classifiers based on supervised learning algorithms: J48, Random Forest, LibSVM and Naïve Bayes algorithms, with enriched model learning by correlation based feature selection. Structural and physicochemical features calculated for experimentally verified primary protein sequences were used to train the classifiers. The training features include amino acid composition; auto correlation coefficients; composition, transition, and distribution of various physicochemical properties; and pseudo amino acid composition. Performance analysis of predictive models based on 10 fold cross-validation and independent data testing revealed that the Random Forest based model was the best and achieved 86.11% overall accuracy and 86.22% balanced accuracy with a remarkably high area under the Receivers Operating Characteristic curve of 0.95 to predict viral RNA silencing suppressor proteins. The prediction models for plant viral RNA silencing suppressors can potentially aid identification of novel viral RNA silencing suppressors, which will provide valuable insights into the mechanism of RNA silencing and could be further explored as potential targets for designing novel antiviral therapeutics. Also, the key subset of identified optimal features may help in determining compositional patterns in the viral proteins which are important determinants for RNA silencing suppressor activities. The best prediction model developed in the study is available as a freely accessible web server pVsupPred at http://bioinfo.icgeb.res.in/pvsup/.

Dataset Information

Systematic prediction of genes functionally associated with bacterial retrons and classification of the encoded tripartite systems.

Publications

Systematic prediction of genes functionally associated with bacterial retrons and classification of the encoded tripartite systems.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets