Current microRNA (miRNA) prediction methods are generally based on annotation criteria that tend to miss potential functional miRNAs. Recently, new miRNA annotation criteria have been proposed that could lead to improvements in miRNA prediction methods in plants. Here, we investigate the effect of the new criteria on miRNA prediction in Arabidopsis thaliana and present a new degradome assisted functional miRNA prediction approach. We investigated the effect by applying the new criteria, and a more permissive criteria on miRNA prediction using existing miRNA prediction tools. We also developed an approach to miRNA prediction that is assisted by the functional information extracted from the analysis of degradome sequencing. We demonstrate the improved performance of degradome assisted miRNA prediction compared to unassisted prediction and evaluate the approach using miRNA differential expression analysis. We observe how the miRNA predictions fit under the different criteria and show a potential novel miRNA that has been missed within Arabidopsis thaliana. Additionally, we introduce a freely available software 'PAREfirst' that employs the degradome assisted approach. The study shows that some miRNAs could be missed due to the stringency of the former annotation criteria, and combining a degradome assisted approach with more permissive miRNA criteria can expand confident miRNA predictions.

Motivation: RNA interference, a highly conserved regulatory mechanism, is mediated via small RNAs (sRNA). Recent technical advances enabled the analysis of larger, complex datasets and the investigation of microRNAs and the less known small interfering RNAs. However, the size and intricacy of current data requires a comprehensive set of tools, able to discriminate the patterns from the low-level, noise-like, variation; numerous and varied suggestions from the community represent an invaluable source of ideas for future tools, the ability of the community to contribute to this software is essential. Results: We present a new version of the UEA sRNA Workbench, reconfigured to allow an easy insertion of new tools/workflows. In its released form, it comprises of a suite of tools in a user-friendly environment, with enhanced capabilities for a comprehensive processing of sRNA-seq data e.g. tools for an accurate prediction of sRNA loci (CoLIde) and miRNA loci (miRCat2), as well as workflows to guide the users through common steps such as quality checking of the input data, normalization of abundances or detection of differential expression represent the first step in sRNA-seq analyses. Availability and implementation: The UEA sRNA Workbench is available at: http://srna-workbench.cmp.uea.ac.uk. The source code is available at: https://github.com/sRNAworkbenchuea/UEA_sRNA_Workbench. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: MicroRNAs are a class of ∼21-22 nt small RNAs which are excised from a stable hairpin-like secondary structure. They have important gene regulatory functions and are involved in many pathways including developmental timing, organogenesis and development in eukaryotes. There are several computational tools for miRNA detection from next-generation sequencing datasets. However, many of these tools suffer from high false positive and false negative rates. Here we present a novel miRNA prediction algorithm, miRCat2. miRCat2 incorporates a new entropy-based approach to detect miRNA loci, which is designed to cope with the high sequencing depth of current next-generation sequencing datasets. It has a user-friendly interface and produces graphical representations of the hairpin structure and plots depicting the alignment of sequences on the secondary structure. RESULTS: We test miRCat2 on a number of animal and plant datasets and present a comparative analysis with miRCat, miRDeep2, miRPlant and miReap. We also use mutants in the miRNA biogenesis pathway to evaluate the predictions of these tools. Results indicate that miRCat2 has an improved accuracy compared with other methods tested. Moreover, miRCat2 predicts several new miRNAs that are differentially expressed in wild-type versus mutants in the miRNA biogenesis pathway. AVAILABILITY AND IMPLEMENTATION: miRCat2 is part of the UEA small RNA Workbench and is freely available from http://srna-workbench.cmp.uea.ac.uk/. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: RNA silencing is a complex, highly conserved mechanism mediated by small RNAs (sRNAs), such as microRNAs (miRNAs), that is known to be involved in a diverse set of biological functions including development, pathogen control, genome maintenance and response to environmental change. Advances in next generation sequencing technologies are producing increasingly large numbers of sRNA reads per sample at a fraction of the cost of previous methods. However, many bioinformatics tools do not scale accordingly, are cumbersome, or require extensive support from bioinformatics experts. Therefore, researchers need user-friendly, robust tools, capable of not only processing large sRNA datasets in a reasonable time frame but also presenting the results in an intuitive fashion and visualizing sRNA genomic features. Herein, we present the UEA sRNA workbench, a suite of tools that is a successor to the web-based UEA sRNA Toolkit, but in downloadable format and with several enhanced and additional features. AVAILABILITY: The program and help pages are available at http://srna-workbench.cmp.uea.ac.uk. CONTACT: [email protected].

UNLABELLED: Recent developments in high-throughput sequencing technologies have generated considerable demand for tools to analyse large datasets of small RNA sequences. Here, we describe a suite of web-based tools for processing plant small RNA datasets. Our tools can be used to identify micro RNAs and their targets, compare expression levels in sRNA loci, and find putative trans-acting siRNA loci. AVAILABILITY: The tools are freely available for use at http://srna-tools.cmp.uea.ac.uk.