unitas: the universal tool for annotation of small RNAs

Abstract

BackgroundNext generation sequencing is a key technique in small RNA biology research that has led to the discovery of functionally different classes of small non-coding RNAs in the past years. However, reliable annotation of the extensive amounts of small non-coding RNA data produced by high-throughput sequencing is time-consuming and requires robust bioinformatics expertise. Moreover, existing tools have a number of shortcomings including a lack of sensitivity under certain conditions, limited number of supported species or detectable sub-classes of small RNAs.ResultsHere we introduce unitas, an out-of-the-box ready software for complete annotation of small RNA sequence datasets, supporting the wide range of species for which non-coding RNA reference sequences are available in the Ensembl databases (currently more than 800). unitas combines high quality annotation and numerous analysis features in a user-friendly manner. A complete annotation can be started with one simple shell command, making unitas particularly useful for researchers not having access to a bioinformatics facility. Noteworthy, the algorithms implemented in unitas are on par or even outperform comparable existing tools for small RNA annotation that map to publicly available ncRNA databases.Conclusionsunitas brings together annotation and analysis features that hitherto required the installation of numerous different bioinformatics tools which can pose a challenge for the non-expert user. With this, unitas overcomes the problem of read normalization. Moreover, the high quality of sequence annotation and analysis, paired with the ease of use, make unitas a valuable tool for researchers in all fields connected to small RNA biology.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-4031-9) contains supplementary material, which is available to authorized users.

PIWI genes and piRNAs are ubiquitously expressed in mollusks and show patterns of lineage-specific adaptation

“…2a ). Annotation of sRNA sequences with unitas 50 revealed a similar proportion of different sRNA classes in each tissue type, with miRNAs accounting for 47% and 53% of reads in the reproductive tract and muscle, respectively (Fig. 2b , Supplementary Table 1 ).…”

5′ tRNA halves are highly expressed in the primate hippocampus and might sequence-specifically regulate gene expression

“…Adapter and quality trimming was performed using BBDuk (version 36.77; ktrim=r overwrite=true k=20 mink=9 ziplevel=2 hdist=1 qtrim=rl trimq=10 minlen=15 maxlen=34; for Encode data additionally: forcetrimleft=6) before the reads were examined via FastQC again and mapped to the human genome (version GCA_000001405.27_GRCh38.p12) using Bowtie 2 (version 2.3.0). Based on these map-files small RNA annotation was performed with unitas (version 1.7.3; Gebert et al 2017).…”

“…The remaining sequences were mapped to the respective genome (versions GCA_000001405.27_GRCh38.p12, GCA_000001515.5_Pan_tro_3.0, GCA_000772875.3_Mmul_8.0.1, GCA_000003025.6_Sscrofa11.1, GCA_000001895.4_Rnor_6.0 and GCA_000001635.8_GRCm38.p6) using the Perl script sRNAmapper (version 1.0.5; -a best) that employs SeqMap (Jiang and Wong 2008) as mapping tool. The map-files were used for small RNA annotation with unitas (version 1.6.1; Gebert et al 2017).…”

5’ tRNA halves are highly expressed in the primate hippocampus and sequence-specifically regulate gene expression

“…Adapter and quality trimming was performed using BBDuk (version 36.77; ktrim=r overwrite=true k=20 mink=9 ziplevel=2 hdist=1 qtrim=rl trimq=10 minlen=15 maxlen=34; for Encode data additionally: forcetrimleft=6) before the reads were FastQC checked again and mapped to the human genome (version GCA_000001405.27_GRCh38.p12) using Bowtie 2 (version 2.3.0). Based on these map-files small RNA annotation was performed with unitas (version 1.7.3; Gebert et al 2017).…”