Serves for the prediction of micro-RNA (miRNA) from small RNAseq data. miRDeep is a software for determining miRNAs, displaying RNAseq reads and the number of reads relative to the predicted pre-miRNA. It can provide the target prediction for both known and novel miRNAs expression levels, and display them in an interface showing each RNAseq read relative to the pre-miRNA hairpin.
Processes small-RNA data from next-generation sequencing (NGS) platforms such as Illumina and SOLID. sRNAbench can analyze an unlimited number of genomes simultaneously without the need to pool all sequences into a single index. Users can perform adapter trimming and extensive profiling of all microRNA sequences and length variants. This tool is available as a web application and as a standalone program with full parameter space and more customized analysis steps.
Predicts miRNA on both plant and animal data. miRCat is a part of the UEA small RNA Workbench and implements a new approach to differentiate miRNA candidates from background sequences, then applies novel filters on the candidate sequence alignments and secondary structure. The algorithm is performing well on animal datasets and also allows the detection of complex structures and even multiple miRNA loci within a single precursor in plants.
An easy to use perl-based analysis pipeline to handle sequencing data in several automated steps including data format conversion, 3' adapter removal, genome alignment and annotation to non-coding RNA transcripts. It reports the most commonly used results to the user in a comprehensive expression table and data visualization track files for the UCSC genome browser. Although successfully tested on chicken data, the pipeline can also be used to analyze miRNA data from any species. There are two versions of the E-miR pipeline, either implementing the Bowtie or Eland aligner.
Enables high-throughput, kingdom-wide prediction and functional annotation of bacterial sRNA-encoding genes. SIPHT utilizes automatic workflow and distributive computing to enable conducting rapid kingdom-wide searches for sRNAs. It identifies candidate intergenic loci based on the co-localization of intergenic conservation and Rho-independent terminators. It then annotates each of these loci for numerous features designed to provide information regarding the strength of its prediction and/ or its potential biological functions.
Uses coordinate-based algorithms to integrate the respective positions of individual predictive features of sRNAs and rapidly identify putative intergenic sRNAs. sRNAPredict can be used to efficiently identify novel sRNAs even in bacteria for which promoter consensus sequences are not available. It completes a genome-wide search for putative sRNAs in a matter of seconds, allowing searches using different parameters to be efficiently conducted until the desired stringency is achieved.