Classification of the large-scale short reads into known categories, such as known miRNAs, non-coding RNA, genomic repeats or coding sequences.
Providing a detailed annotation information of known miRNAs, such as miRNA/miRNA*, absolute/relative reads count and the most abundant tag.
Discovery of the novel miRNAs from the high-throughput sequencing technology.
Identification of the differentially expressed miRNAs according to read tag counts (the number of reads for each tag reflects relative express level).
The miRDeep package was developed to discover active known or novel miRNAs from deep sequencing data (Solexa/Illumina, 454, …). The package consists of everything you need to analyze your own deep sequencing data after removal of ligation adapters: a number of scripts to preprocess the mapped data, and the core miRDeep algorithm that will analyze and score these data.
a novel database, developed to facilitate the comprehensive annotation and discovery of small RNAs from transcriptomic data. The current release of deepBase contains deep sequencing data from 185 small RNA libraries from diverse tissues and cell lines of seven organisms: human, mouse, chicken, Ciona intestinalis, Drosophila melanogaster, Caenhorhabditis elegans and Arabidopsis thaliana. For the purpose of comparative analysis, deepBase provides an integrative, interactive and versatile display. A convenient search option, related publications and other useful information are also provided for further investigation.
A systematic method, for extracting miRNA expression profiles from sequencing reads generated by second-generation sequencing. miRExpress contains miRNA information from miRBase and efficiently reveals miRNA expression profiles by aligning sequencing reads against the sequences of known miRNAs. miRExpress can be used to find novel miRNA candidates by aligning reads with sequences of known miRNAs of various species.
The web servertool requires a simple input file containing a list of uniquereads and its copy numbers (expression levels). Using thesedata, miRanalyzer (i) detects all known microRNA sequences annotatedin miRBase, (ii) finds all perfect matches against other librariesof transcribed sequences and (iii) predicts new microRNAs. Theprediction of new microRNAs is an especially important pointas there are many species with very few known microRNAs.
A web server for identification of miRNA precursors in a given DNA sequence, utilizing secondary structure-based filtering systems and an algorithm based on stochastic context free grammar trained on human miRNAs. CID-miRNA analyses a given sequence using a web interface, for presence of putative miRNA precursors and the generated output lists all the potential regions that can form miRNA-like structures. It can also scan large genomic sequences for the presence of potential miRNA precursors in its stand-alone form.
miRCat is a tool to identify miRNAs in high-throughput small RNA sequence data. miRCat takes a FASTA file of small RNA reads as input and will map them to a reference genome. The tool then looks at genomic hit distribution patterns and secondary structure of genomic regions corresponding to sRNA hits and will predict miRNAs and their precursor structures. miRCat has been tested on published datasets.