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tRNA-derived RNA annotation software tools | Small RNA sequencing data analysis

Small RNA-sequencing has revealed the diversity and high abundance of small RNAs derived from tRNAs, referred to as tRNA-derived RNAs. Source text: Selitsky and Sethupathy, 2015.

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Facilitates the identification of tRNA-derived small RNA fragments (tRFs) to study their expression in cancers from deep-sequencing data with user-friendly interfaces and time-efficient algorithms. tRF2Cancer provides three useful tools for researchers to investigate tRFs. ‘tRFfinder’ is developed to identify genuine tRF signals from random degradation RNA fragments. One statistical method, the binomial test, is introduced to evaluate the significance of the abundance of sequenced sRNAs distributed on each tRNA. A classification method is subsequently used to annotate the types of tRFs based on their position of origin in pre-tRNA or mature tRNA; the four types of tRFs are tRF-5, tRF-3, tRF-1 and tRF-novel. ‘tRFinCancer’ enables users to inspect the expression of any tRFs in different types of cancers. ‘tRFBrowser’ presents both the sites of origin and the distribution of modification sites of tRFs, including m5C, 2′-O-Me, Ψ and m6A., on their corresponding source tRNA. In addition to cancer samples, tRFfinder can be applied to many samples from different kind of tissue/disease context.
MINTmap / MItochondrial and Nuclear TRF mapping
Identifies the subset of discovered transfer RNA fragments (tRFs) that could be originating outside of tRNA space and flags them as candidate false positives. MINTmap is a method and a software package that was developed specifically for the quick, deterministic and exhaustive identification of tRFs in short RNAseq datasets. In addition to identifying them, MINTmap is able to unambiguously calculate and report both raw and normalized abundances for the discovered tRFs.
SPORTS / small RNA annotation pipeline optimized for rRNA- and tRNA- derived small RNAs
Estimates potential RNA modification sites basing on nucleotide mismatches within sRNAs. SPORTS1.0 cleans reads by removing sequence adapters and discarding sequences with length beyond the defined range and those with bases other than ATUCG. It studies sequence mismatch information if mismatches are allowed during alignment process. This tool is useful in biomedical and evolution research that relate to sRNAs.
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