sRNAbench and sRNAtoolbox 2019: intuitive fast small RNA profiling and differential expression

Abstract

Since the original publication of sRNAtoolbox in 2015, small RNA research experienced notable advances in different directions. New protocols for small RNA sequencing have become available to address important issues such as adapter ligation bias, PCR amplification artefacts or to include internal controls such as spike-in sequences. New microRNA reference databases were developed with different foci, either prioritizing accuracy (low number of false positives) or completeness (low number of false negatives). Additionally, other small RNA molecules as well as microRNA sequence and length variants (isomiRs) have continued to gain importance. Finally, the number of microRNA sequencing studies deposited in GEO nearly triplicated from 2014 (280) to 2018 (764). These developments imply that fast and easy-to-use tools for expression profiling and subsequent downstream analysis of miRNA-seq data are essential to many researchers. Key features in this sRNAtoolbox release include addition of all major RNA library preparation protocols to sRNAbench and improvements in sRNAde, a tool that summarizes several aspects of small RNA sequencing studies including the detection of consensus differential expression. A special emphasis was put on the user-friendliness of the tools, for instance sRNAbench now supports parallel launching of several jobs to improve reproducibility and user time efficiency.

Deciphering miRNAs’ Action through miRNA Editing

“…[112]) and related genes and pathway enrichment, and protein-protein interactions (e.g., CPSS 2.0 [113], miRGator v3.0 [114], miRTools 2.0 [115] and DeAnniso [116]). Finally, whether available algorithms are user-friendly or not, free or paid and allow parameters changes or not, often depends of whether these tools are available online (e.g., CPSS2.0 [113]; miRGator v3.0 [114]; miRTools 2.0 [115]; MIRPIPE [108]; sRNAbench [117]; DeAnnIso [116]), or require a local installation using the terminal, or are provided as independent packages for use with Python/R (e.g., SeqBuster [110], miRSeqNovel [105], iMir [111], IsomiRage [107], miRge 2.0 [102], mirPRo [109] and Prost! [112]).…”

“…[112]). The more recent online algorithms are usually faster, but do not always allow one to analyze data in batch mode [108,117]. This variability of protocols and pipelines dedicated to the identification of miRNA editing sites from sequencing data can of course strengthen the reliability of data obtained through different bioinformatic approaches, but also can lead to differences and inconsistencies between studies.…”

MirGeneDB 2.0: The metazoan microRNA complement

“…For the expansion from version 1.0 to 2.0, we analyzed more than 400 publicly available smallRNA sequencing datasets with at least one representative dataset for each organism, that were automatically downloaded and processed using sRNAbench (52) and miRTrace (53), respectively. This allowed for a consistent and uniform annotation of miRNAomes for each species using MirMiner (11) (see Supplementary Table, "file_info" for files and see…”

Antiviral effects of miRNAs in extracellular vesicles against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and mutations in SARS-CoV-2 RNA virus

“…Expression of each miRNA was transformed to CPM. Read quality control was performed using qrqc in R package and the distribution of small RNAs was calculated using sRNAtoolbox [50].…”