Dataset Information

Predicting transfer RNA gene activity from sequence and genome context.

ABSTRACT: Transfer RNA (tRNA) genes are among the most highly transcribed genes in the genome owing to their central role in protein synthesis. However, there is evidence for a broad range of gene expression across tRNA loci. This complexity, combined with difficulty in measuring transcript abundance and high sequence identity across transcripts, has severely limited our collective understanding of tRNA gene expression regulation and evolution. We establish sequence-based correlates to tRNA gene expression and develop a tRNA gene classification method that does not require, but benefits from, comparative genomic information and achieves accuracy comparable to molecular assays. We observe that guanine + cytosine (G + C) content and CpG density surrounding tRNA loci is exceptionally well correlated with tRNA gene activity, supporting a prominent regulatory role of the local genomic context in combination with internal sequence features. We use our tRNA gene activity predictions in conjunction with a comprehensive tRNA gene ortholog set spanning 29 placental mammals to estimate the evolutionary rate of functional changes among orthologs. Our method adds a new dimension to large-scale tRNA functional prediction and will help prioritize characterization of functional tRNA variants. Its simplicity and robustness should enable development of similar approaches for other clades, as well as exploration of functional diversification of members of large gene families.

SUBMITTER: Thornlow BP

PROVIDER: S-EPMC6961574 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Predicting transfer RNA gene activity from sequence and genome context.

Thornlow Bryan P BP Armstrong Joel J Holmes Andrew D AD Howard Jonathan M JM Corbett-Detig Russell B RB Lowe Todd M TM

Genome research 20191219 1

Transfer RNA (tRNA) genes are among the most highly transcribed genes in the genome owing to their central role in protein synthesis. However, there is evidence for a broad range of gene expression across tRNA loci. This complexity, combined with difficulty in measuring transcript abundance and high sequence identity across transcripts, has severely limited our collective understanding of tRNA gene expression regulation and evolution. We establish sequence-based correlates to tRNA gene expressio ...[more]

PMID: 31857444

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Project description:BackgroundThe protozoan pathogens Leishmania major, Trypanosoma brucei and Trypanosoma cruzi (the Tritryps) are parasites that produce devastating human diseases. These organisms show very unusual mechanisms of gene expression, such as polycistronic transcription. We are interested in the study of tRNA genes, which are transcribed by RNA polymerase III (Pol III). To analyze the sequences and genomic organization of tRNA genes and other Pol III-transcribed genes, we have performed an in silico analysis of the Tritryps genome sequences.ResultsOur analysis indicated the presence of 83, 66 and 120 genes in L. major, T. brucei and T. cruzi, respectively. These numbers include several previously unannotated selenocysteine (Sec) tRNA genes. Most tRNA genes are organized into clusters of 2 to 10 genes that may contain other Pol III-transcribed genes. The distribution of genes in the L. major genome does not seem to be totally random, like in most organisms. While the majority of the tRNA clusters do not show synteny (conservation of gene order) between the Tritryps, a cluster of 13 Pol III genes that is highly syntenic was identified. We have determined consensus sequences for the putative promoter regions (Boxes A and B) of the Tritryps tRNA genes, and specific changes were found in tRNA-Sec genes. Analysis of transcription termination signals of the tRNAs (clusters of Ts) showed differences between T. cruzi and the other two species. We have also identified several tRNA isodecoder genes (having the same anticodon, but different sequences elsewhere in the tRNA body) in the Tritryps.ConclusionA low number of tRNA genes is present in Tritryps. The overall weak synteny that they show indicates a reduced importance of genome location of Pol III genes compared to protein-coding genes. The fact that some of the differences between isodecoder genes occur in the internal promoter elements suggests that differential control of the expression of some isoacceptor tRNA genes in Tritryps is possible. The special characteristics found in Boxes A and B from tRNA-Sec genes from Tritryps indicate that the mechanisms that regulate their transcription might be different from those of other tRNA genes.

Project description:BackgroundRNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions.ResultsWe propose RPISeq, a family of classifiers for predicting RNA-protein interactions using only sequence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a Support Vector Machine (SVM) classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens.ConclusionsOur experiments with RPISeq demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. RPISeq offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. RPISeq is freely available as a web-based server at http://pridb.gdcb.iastate.edu/RPISeq/.

Dataset Information

Predicting transfer RNA gene activity from sequence and genome context.

Publications

Predicting transfer RNA gene activity from sequence and genome context.

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