MINTbase: a framework for the interactive exploration of mitochondrial and nuclear tRNA fragments

Abstract

Motivation: It has been known that mature transfer RNAs (tRNAs) that are encoded in the nuclear genome give rise to short molecules, collectively known as tRNA fragments or tRFs. Recently, we reported that, in healthy individuals and in patients, tRFs are constitutive, arise from mitochondrial as well as from nuclear tRNAs, and have composition and abundances that depend on a person’s sex, population origin and race as well as on tissue, disease and disease subtype. Our findings as well as similar work by other groups highlight the importance of tRFs and presage an increase in the community’s interest in elucidating the roles of tRFs in health and disease.Results: We created MINTbase, a web-based framework that serves the dual-purpose of being a content repository for tRFs and a tool for the interactive exploration of these newly discovered molecules. A key feature of MINTbase is that it deterministically and exhaustively enumerates all possible genomic locations where a sequence fragment can be found and indicates which fragments are exclusive to tRNA space, and thus can be considered as tRFs: this is a very important consideration given that the genomes of higher organisms are riddled with partial tRNA sequences and with tRNA-lookalikes whose aberrant transcripts can be mistaken for tRFs. MINTbase is extremely flexible and integrates and presents tRF information from multiple yet interconnected vantage points (‘vistas’). Vistas permit the user to interactively personalize the information that is returned and the manner in which it is displayed. MINTbase can report comparative information on how a tRF is distributed across all anticodon/amino acid combinations, provides alignments between a tRNA and multiple tRFs with which the user can interact, provides details on published studies that reported a tRF as expressed, etc. Importantly, we designed MINTbase to contain all possible tRFs that could ever be produced by mature tRNAs: this allows us to report on their genomic distributions, anticodon/amino acid properties, alignments, etc. while giving users the ability to at-will investigate candidate tRF molecules before embarking on focused experimental explorations. Lastly, we also introduce a new labeling scheme that is tRF-sequence-based and allows users to associate a tRF with a universally unique label (‘tRF-license plate’) that is independent of a genome assembly and does not require any brokering mechanism.Availability and Implementation: MINTbase is freely accessible at http://cm.jefferson.edu/MINTbase/. Dataset submissions to MINTbase can be initiated at http://cm.jefferson.edu/MINTsubmit/.Contact: isidore.rigoutsos@jefferson.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API

“…The proper identification of isomiRs between studies is still an open problem, since different tools and research groups utilize different nomenclatures and representations. To this end, we adopted a unique identifier (UID), or 'IsomiR license plate', inspired by the MINTmap approach for tRNA fragments [52,53] and acting as a sequence-dependent unique ID that is independent of genome assembly and doesn't require a brokering naming mechanism. Any isomiR sequence can be mapped to a UID and any UID can be converted back to the isomiR sequence it represents ( Figure 1B).…”

Mitochondrial Genomic Backgrounds Affect Nuclear DNA Methylation and Gene Expression

“…Sixth, the identity(ies) of the signal(s) from mitochondria responsible for changes in DNA methylation and gene expression have not yet been determined. In addition to metabolite differences discussed above, ncRNA, tRNA and tRF, which were once disregarded as useless fragments of RNA, are now increasingly recognized as important (59–61). tRNA represent about 4%–10% of the total cellular DNA; yet, are the predominate transcripts encoded by mtDNA.…”

“…tRNA represent about 4%–10% of the total cellular DNA; yet, are the predominate transcripts encoded by mtDNA. It has been shown that tRF can affect gene silencing (59–61). …”

tRNA-Derived Fragments as Novel Predictive Biomarkers for Trastuzumab-Resistant Breast Cancer

“…The lookup table is then used to process a (quality-filtered and adaptertrimmed) short RNA-seq dataset to generate a tRF expression profile table. For each tRF, MINTmap identified whether it is exclusive to tRNA space [37,38]. Because tRF-1s are derived from the precursor tRNAs, they require a different type of analytical work than fragments that overlap with the mature tRNA, and as such, tRF-1s were not included in our data.…”