SPORTS1.0: A Tool for Annotating and Profiling Non-coding RNAs Optimized for rRNA- and tRNA-derived Small RNAs

Junchao Shi,Eun-A Ko,Kenton M Sanders,Qi Chen,Tong Zhou

doi:10.1016/j.gpb.2018.04.004

Junchao Shi, Eun-A Ko + Show 3 more

Open Access

https://doi.org/10.1016/j.gpb.2018.04.004

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Abstract

High-throughput RNA-seq has revolutionized the process of small RNA (sRNA) discovery, leading to a rapid expansion of sRNA categories. In addition to the previously well-characterized sRNAs such as microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and small nucleolar RNA (snoRNAs), recent emerging studies have spotlighted on tRNA-derived sRNAs (tsRNAs) and rRNA-derived sRNAs (rsRNAs) as new categories of sRNAs that bear versatile functions. Since existing software and pipelines for sRNA annotation are mostly focused on analyzing miRNAs or piRNAs, here we developed the sRNA annotation pipelineoptimized for rRNA- and tRNA-derived sRNAs (SPORTS1.0). SPORTS1.0 is optimized for analyzing tsRNAs and rsRNAs from sRNA-seq data, in addition to its capacity to annotate canonical sRNAs such as miRNAs and piRNAs. Moreover, SPORTS1.0 can predict potential RNA modification sites based on nucleotide mismatches within sRNAs. SPORTS1.0 is precompiled to annotate sRNAs for a wide range of 68 species across bacteria, yeast, plant, and animal kingdoms, while additional species for analyses could be readily expanded upon end users’ input. For demonstration, by analyzing sRNA datasets using SPORTS1.0, we reveal that distinct signatures are present in tsRNAs and rsRNAs from different mouse cell types. We also find that compared to other sRNA species, tsRNAs bear the highest mismatch rate, which is consistent with their highly modified nature. SPORTS1.0 is an open-source software and can be publically accessed at https://github.com/junchaoshi/sports1.0.

Highlights

Expanding classes of small RNAs have emerged as key regulators of gene expression, genome stability, and epigenetic regulation [1,2]
SPORTS1.0 can help predict potential RNA modification sites based on nucleotide mismatches within small RNA (sRNA)
TsRNAs are dominant in sperm, and rRNA-derived sRNAs (rsRNAs) are highest in bone marrow cells, whereas intestinal epithelial cells contain an appreciable amount of both tRNA-derived sRNAs (tsRNAs) and rsRNAs in addition to a miRNA peak

Summary

Introduction

Expanding classes of small RNAs (sRNAs) have emerged as key regulators of gene expression, genome stability, and epigenetic regulation [1,2]. In addition to the previously wellcharacterized sRNA classes such as microRNAs (miRNAs), Piwi-interacting RNA (piRNAs), and small nucleolar RNA (snoRNAs), recent analysis of sRNA-seq data has led to the identification of expanding novel sRNA families These include tRNA-derived sRNAs (tsRNAs; known as tRNA-derived fragments, tRFs) and rRNA-derived sRNAs (rsRNAs) [3]. SPORTS1.0 can help predict potential RNA modification sites based on nucleotide mismatches within sRNAs. The workflow of SPORTS1.0 consists of four main steps, i.e., pre-processing, mapping, annotation output, and annotation summary (Figure 1). A second method is included, in which read number of sequences from multiple matching loci are uniformly distributed (based on the assumption that each of these multiple sites expresses RNAs) and generates an adjusted nmut. User guideline is provided online (https://github.com/ junchaoshi/sports1.0)

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