High-throughput RNAoseq has revolutionized the process of small RNA （sRNA） discovery, leading to a rapid expansion of sRNA categories. In addition to the previously wellcharacterized sRNAs such as microRNAs （miRNAs...High-throughput RNAoseq has revolutionized the process of small RNA （sRNA） discovery, leading to a rapid expansion of sRNA categories. In addition to the previously wellcharacterized 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 pipeline _optimized for rRNA- and tRNA-derived s_RNAs （SPORTS 1 .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 opensource software and can be publically accessed at https：//github.com/junchaoshi/sports1.0.展开更多
基金supported by Start-up funds for Zhou and Chen labs from Reno School of Medicine,University of Nevada and from the National Institutes of Health,United States(Grant Nos.R01DK091336 and P01DK041315 to KMSGrant Nos.R01HD092431 and P30GM110767-03 to QC)
文摘High-throughput RNAoseq has revolutionized the process of small RNA （sRNA） discovery, leading to a rapid expansion of sRNA categories. In addition to the previously wellcharacterized 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 pipeline _optimized for rRNA- and tRNA-derived s_RNAs （SPORTS 1 .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 opensource software and can be publically accessed at https：//github.com/junchaoshi/sports1.0.
