SPORTS1.0: A Tool for Annotating and Profiling Non-coding RNAs Optimized for rRNA- and tRNA-derived Small RNAs.
10.1016/j.gpb.2018.04.004
- Author:
Junchao SHI
1
;
Eun-A KO
2
;
Kenton M SANDERS
2
;
Qi CHEN
3
;
Tong ZHOU
4
Author Information
1. Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA. Electronic address: junchaoshi@nevada.unr.edu.
2. Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA.
3. Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA. Electronic address: cqi@med.unr.edu.
4. Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA. Electronic address: tongz@med.unr.edu.
- Publication Type:Journal Article
- Keywords:
Annotation pipeline;
RNA-seq data analysis;
Small RNA;
rsRNA;
tsRNA
- MeSH:
Animals;
Gene Expression Profiling;
High-Throughput Nucleotide Sequencing;
Mice;
MicroRNAs;
chemistry;
metabolism;
Molecular Sequence Annotation;
RNA, Ribosomal;
chemistry;
metabolism;
RNA, Small Interfering;
chemistry;
metabolism;
RNA, Small Untranslated;
chemistry;
metabolism;
RNA, Transfer;
chemistry;
metabolism;
Sequence Analysis, RNA;
methods;
Software
- From:
Genomics, Proteomics & Bioinformatics
2018;16(2):144-151
- CountryChina
- Language:English
-
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.
