Small RNA research is a rapidly growing field. Apart from microRNAs, which are important regulators of gene expression, other types of functional small RNA molecules have been reported in animals and plants. MicroRNAs are important in host-microbe interactions and parasite microRNAs might modulate the innate immunity of the host. Furthermore, small RNAs can be detected in bodily fluids making them attractive non-invasive biomarker candidates. Given the general broad interest in small RNAs, and in particular microRNAs, a large number of bioinformatics aided analysis types are needed by the scientific community. To facilitate integrated sRNA research, we developed sRNAtoolbox, a set of independent but interconnected tools for expression profiling from high-throughput sequencing data, consensus differential expression, target gene prediction, visual exploration in a genome context as a function of read length, gene list analysis and blast search of unmapped reads. All tools can be used independently or for the exploration and downstream analysis of sRNAbench results. Workflows like the prediction of consensus target genes of parasite microRNAs in the host followed by the detection of enriched pathways can be easily established. The web-interface interconnecting all these tools is available at http://bioinfo5.ugr.es/srnatoolbox

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.

The 2017 update of NGSmethDB stores whole genome methylomes generated from short-read data sets obtained by bisulfite sequencing (WGBS) technology. To generate high-quality methylomes, stringent quality controls were integrated with third-part software, adding also a two-step mapping process to exploit the advantages of the new genome assembly models. The samples were all profiled under constant parameter settings, thus enabling comparative downstream analyses. Besides a significant increase in the number of samples, NGSmethDB now includes two additional data-types, which are a valuable resource for the discovery of methylation epigenetic biomarkers: (i) differentially methylated single-cytosines; and (ii) methylation segments (i.e. genome regions of homogeneous methylation). The NGSmethDB back-end is now based on MongoDB, a NoSQL hierarchical database using JSON-formatted documents and dynamic schemas, thus accelerating sample comparative analyses. Besides conventional database dumps, track hubs were implemented, which improved database access, visualization in genome browsers and comparative analyses to third-part annotations. In addition, the database can be also accessed through a RESTful API. Lastly, a Python client and a multiplatform virtual machine allow for program-driven access from user desktop. This way, private methylation data can be compared to NGSmethDB without the need to upload them to public servers. Database website: http://bioinfo2.ugr.es/NGSmethDB.

Tomato (Solanum lycopersicum L.) is a major horticultural crop and a model species among eudicots, especially for traits related to reproductive development. Although considerable progress has been made since the tomato genome sequence project was completed, most of the genes identified remain predictions with an unknown or hypothetical function. This lack of functional characterization hampers the use of the huge amount of genomic information available to improve the quality and productivity of this crop. Reverse genetics strategies such as artificial mutagenesis and next-generation sequencing approaches build the perfect tandem for increasing knowledge on functional annotation of tomato genes. This work reports the phenotypic characterization of a tomato mutant collection generated from an EMS chemical mutagenesis program aimed to identify interesting agronomic mutants and novel gene functions. Tomato mutants were grouped into fourteen phenotypic classes, including vegetative and reproductive development traits, and the inheritance pattern of the identified mutations was studied. In addition, causal mutation of a selected mutant line was isolated through a mapping-by-sequencing approach as a proof of concept of this strategy’s successful implementation. Results support tomato mutagenesis as an essential tool for functional genomics in this fleshy-fruited model species and a highly valuable resource for future breeding programs of this crop species aimed at the development of more productive and resilient new varieties under challenging climatic and production scenarios.