MINTbase v2.0: a comprehensive database for tRNA-derived fragments that includes nuclear and mitochondrial fragments from all The Cancer Genome Atlas projects

Abstract

MINTbase is a repository that comprises nuclear and mitochondrial tRNA-derived fragments (‘tRFs’) found in multiple human tissues. The original version of MINTbase comprised tRFs obtained from 768 transcriptomic datasets. We used our deterministic and exhaustive tRF mining pipeline to process all of The Cancer Genome Atlas datasets (TCGA). We identified 23 413 tRFs with abundance of ≥ 1.0 reads-per-million (RPM). To facilitate further studies of tRFs by the community, we just released version 2.0 of MINTbase that contains information about 26 531 distinct human tRFs from 11 719 human datasets as of October 2017. Key new elements include: the ability to filter tRFs on-the-fly by minimum abundance thresholding; the ability to filter tRFs by tissue keywords; easy access to information about a tRF’s maximum abundance and the datasets that contain it; the ability to generate relative abundance plots for tRFs across cancer types and convert them into embeddable figures; MODOMICS information about modifications of the parental tRNA, etc. Version 2.0 of MINTbase contains 15x more datasets and nearly 4x more distinct tRFs than the original version, yet continues to offer fast, interactive access to its contents. Version 2.0 is available freely at http://cm.jefferson.edu/MINTbase/.

Identification of a novel tRNA‐derived RNA fragment exhibiting high prognostic potential in chronic lymphocytic leukemia

“…The i‐tRF examined in this original research, namely, i‐tRF‐GlyGCC, derives from tRNA molecules bearing the glycine anticodon “GCC.” The respective tRNA genes are located to chromosomes 1, 2, 6, 16, 17, and 21. This particular i‐tRF does not include the anticodon, but part of the T‐loop . Since tRNA fragments have only recently emerged, little is known about their physiological role(s); moreover, there is not sufficient information about their nomenclature and target prediction, based on bioinformatic tools.…”

“…Since tRNA fragments have only recently emerged, little is known about their physiological role(s); moreover, there is not sufficient information about their nomenclature and target prediction, based on bioinformatic tools. Two bioinformatic bases are currently available and provide information about such fragments: tRFdb and MINTbase v.2 . The sequence of this i‐tRF is GAGGCCCGGGUUCGAUUC; this sequence is registered in MINTbase v.2 with MINTbase Unique ID tRF‐18‐5J3KYU05, but its biological functions have not been studied before.…”

“…This particular i-tRF does not include the anticodon, but part of the T-loop. 31 Since tRNA fragments have only recently emerged, little is known about their physiological role(s); moreover, there is not sufficient information about their nomenclature and target prediction, based on bioinformatic tools. Two bioinformatic bases are currently available and provide information about such fragments: tRFdb 32 and MINTbase v.2.…”

“…Two bioinformatic bases are currently available and provide information about such fragments: tRFdb 32 and MINTbase v.2. 31 The sequence of this i-tRF is GAGGCCCGGGUUCGAUUC; this sequence is registered in MINTbase v.2 with MINTbase Unique ID tRF-18-5J3KYU05, but its biological functions have not been studied before. Our study group confirmed experimentally the existence of this i-tRF and submitted its sequence to GenBank (accession number: MK642309).…”

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.…”