Dataset Information

Bioinformatic tools for tRNA gene analyses in mitochondrial DNA sequence data.

ABSTRACT: The data presented here are related to the research article entitled "Hidden cases of tRNA genes duplication and remolding in mitochondrial genomes of amphipods" (Romanova et al., 2020) [1]. Correct tRNA gene sequence annotation in mitochondrial (mt) and nuclear genomes sometimes can be a challenging task because of the differential performances of tRNA annotation/prediction programmes. These programmes may cause false positive or false negative predictions. Moreover, additional difficulties with annotation may be caused by the presence of duplicated tRNA genes and those coding tRNAs with altered identities occurring as due to a mutation in their anticodon sequence (tRNA gene remolding/recruitment). We developed an R script automating the diagnosis of ancestor tRNA gene coding specificity regardless of anticodon sequence based on genetic distance comparison. Some of the predicted tRNA genes from the mt genomes of amphipods are presented. We also developed an R script for estimation of the best mode of sequence alignment, which was applied to determine the best alignment of tRNA genes in [1], but is also suitable for testing of any nucleotide alignment sets used in phylogenetic inferences.

SUBMITTER: Romanova EV

PROVIDER: S-EPMC7049592 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Bioinformatic tools for tRNA gene analyses in mitochondrial DNA sequence data.

Romanova Elena V EV Bukin Yurij S YS Sherbakov Dmitry Yu DY

Data in brief 20200222

The data presented here are related to the research article entitled "Hidden cases of tRNA genes duplication and remolding in mitochondrial genomes of amphipods" (Romanova et al., 2020) [1]. Correct tRNA gene sequence annotation in mitochondrial (mt) and nuclear genomes sometimes can be a challenging task because of the differential performances of tRNA annotation/prediction programmes. These programmes may cause false positive or false negative predictions. Moreover, additional difficulties wit ...[more]

PMID: 32140506

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Project description:BackgroundBioinformatic tools for the enrichment of 'omics' datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined.ResultsAn exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard's distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites.ConclusionsWe have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome.

Dataset Information

Bioinformatic tools for tRNA gene analyses in mitochondrial DNA sequence data.

Publications

Bioinformatic tools for tRNA gene analyses in mitochondrial DNA sequence data.

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