High-throughput RNA-seq has revolutionized the process of small RNA（sRNA） discovery, leading to a rapid expansion of s RNA categories. In addition to the previously wellcharacterized sRNAs such as micro RNAs（mi RNAs）, piwi-interacting RNAs（piRNAs）, and small nucleolar RNA（sno RNAs）, recent emerging studies have spotlighted on t RNA-derived s RNAs（tsRNAs） and rRNA-derived s RNAs（rs RNAs） as new categories of s RNAs that bear versatile functions. Since existing software and pipelines for sRNA annotation are mostly focused on analyzing mi RNAs or pi RNAs, here we developed the sRNA annotation pipeline optimized for rRNA-and tRNA-derived sRNAs（SPORTS1.0）. SPORTS1.0 is optimized for analyzing ts RNAs and rsRNAs from sRNA-seq data, in addition to its capacity to annotate canonical sRNAs such as mi RNAs and pi RNAs. Moreover, SPORTS1.0 can predict potential RNA modification sites based on nucleotide mismatches within sRNAs. SPORTS1.0 is precompiled to annotate s RNAs 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 s RNA 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▼
