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SPORTS1.0: a tool for annotating and profiling non-coding RNAs optimized for rRNA- and tRNA-derived small RNAs

View ORCID ProfileJunchao Shi, View ORCID ProfileEun-A Ko, View ORCID ProfileKenton M. Sanders, View ORCID ProfileQi Chen, View ORCID ProfileTong Zhou
doi: https://doi.org/10.1101/296970
Junchao Shi
1Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, NV 89512 USA.
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  • For correspondence: junchaoshi@nevada.unr.edu cqi@med.unr.edu tongz@med.unr.edu
Eun-A Ko
1Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, NV 89512 USA.
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Kenton M. Sanders
1Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, NV 89512 USA.
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Qi Chen
1Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, NV 89512 USA.
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  • For correspondence: junchaoshi@nevada.unr.edu cqi@med.unr.edu tongz@med.unr.edu
Tong Zhou
1Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, NV 89512 USA.
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  • For correspondence: junchaoshi@nevada.unr.edu cqi@med.unr.edu tongz@med.unr.edu
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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 previously well-characterized sRNAs such as miRNAs, piRNAs and snoRNAs, recent emerging studies have spotlighted on tsRNAs (tRNA-derived small RNAs) and rsRNAs (rRNA-derived small RNAs) as new categories of sRNAs that bear versatile functions. Since existing software and pipelines for sRNA annotation are mostly focusing on analyzing miRNAs or piRNAs, here we developed SPORTS1.0 (small RNA annotation pipeline optimized for rRNA- and tRNA-derived small RNAs), which is optimized for analyzing tsRNAs and rsRNAs from sRNA-seq data, also with the capacity to annotate canonical sRNAs such as miRNAs and piRNAs. In addition, SPORTS1.0 can predict potential RNA modification sites basing 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 additional species for analyses could be readily expanded upon end users’ input. As an example, SPORTS1.0 revealed distinct tsRNA and rsRNA signatures from different mice tissues/cells; and discovered that tsRNAs bear the highest mismatch rate compared with other sRNA species, which is consistent with their highly modified nature. SPORTS1.0 is an open-source software deposited at https://github.com/junchaoshi/sports1.0.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 07, 2018.
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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
bioRxiv 296970; doi: https://doi.org/10.1101/296970
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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
bioRxiv 296970; doi: https://doi.org/10.1101/296970

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