1 - 24 of 24 results

NASTIseq / Natural AntiSense Transcripts Identification using RNA-seq

Identifies cis-natural antisense transcripts (cis-NATs) pairs using strand-specific RNA sequencing (ssRNA-seq) data. NASTIseq provides an R package based on model comparison for characterizing the mechanism by which cis-NAT pairs regulate gene expression. This method first calculates a score based on Bayesian information criterion (BIC), and then identifies candidate cis-NAT pairs that have small intergenic distances and are on opposite strands in the genome.


Utilizes locally mapped reads, derived from self-priming and ligation, to precisely determine the termini of ncRNAs and provide support for predicated terminal stem-loops. The Vicinal package contains three python script files: samsoftfilter.py, Vicinal_1.0.py and Vicinal_2.0.py, and one shell script readnum.sh. The samsoftfilter.py script filters Bowtie2 mapped reads in SAM files, and select partially mapped reads. The Vicinal scripts then maps the unmapped fragments to the vicinity of the mapped fragments.

MiRME / miRNA Mutation and Editing sites

An effective and efficient computational pipeline for detecting and visualizing editing sites and SNPs in miRNAs. The unique idea is the three-round alignment strategy with a strict control of false positive predictions. MiRME is different from the existing approaches at several aspects. First, MiRME has three progressive rounds of sequence alignment steps to reach a high sensitivity without loosing speed. Second, reads mapped to multiple loci in the genome are normalized using the cross-mapping correction method to reduce the number of false positive predictions. Third, MiRME can identify and visualize all types of editing and mutation sites at one system. Compared with six existing studies or methods, MiRME has shown much superior performance for the identification and visualization of the mutation and editing (M/E) sites of miRNAs from the ever-increasing sRNA high-throughput sequencing profiles.


Determines thresholds in deep-sequencing datasets of short RNA transcripts. Threshold-seq addresses the critical question of how many reads need to support a short RNA molecule in a given dataset before it can be considered different from “background. It can work with individual datasets; i.e. it does not require the availability of technical or of biological replicates. The tool achieves a good balance between sensitivity and specificity by resampling the distinct sequences of the dataset at hand.


Quantifies individual immune response based on a recombination landscape of genes encoding B and T cell receptors (BCR and TCR). ImReP is a computational method for rapid and accurate profiling of the adaptive immune repertoire from regular RNA-Seq data. It is able to efficiently extract TCR- and BCR- derived reads from the RNA-Seq data and accurately assemble clonotypes (defined as clones with identical CDR3 amino acid sequences) and detect corresponding V(D)J recombinations. Using CAST clustering technique, ImReP is able to correct assembled clonotypes for PCR and sequencing errors.

ProFED / Profile Filtering of Expression Data

Facilitates descriptive data investigation including quality control and determination of screen hits. ProFED employs pooled shRNA libraries and Ion Proton next generation sequencing (NGS) -based deconvolution method to proceed. It is useful to find tumor cell-specific targets and synthetic lethal dependencies. This tool offers a function to study read count data in box plots, correlation plots, cluster plots, and principal component analyses.

miTRATA / microRNA Truncation and Tailing Analysis

A web-based tool for the analysis of 3' modifications of microRNAs including the loss or gain of nucleotides relative to the canonical sequence. miTRATA employs parallel processing modules to enhance its scalability when analyzing multiple small RNA (sRNA) sequencing datasets. It utilizes miRBase, currently version 21, as a source of known microRNAs for analysis. miTRATA notifies user(s) via email to download as well as visualize the results online. miTRATA's strengths lies in 1) its biologist-focused web interface, 2) improved scalability via parallel processing, and 3) its uniqueness as a webtool to perform microRNA truncation and tailing analysis.


Enables integrative analysis of in silico target prediction. MAGIA is a web application that comprises three main sections: (i) data upload, (ii) data analysis and methods setup and (iii) results visualization, browsing and linking to external knowledgebase and tools. The software tries to dissect regulatory complexity reconstructing mixed regulatory circuits involving either human microRNA (miRNA) or transcription factor (TF) as regulators. Both data and analyses results are stored in a user-specific environment keeping the data private.

TIGER / Tools for Integrative Genome analysis of Extracellular sRNAs

Allows to automatically perform analysis of extracellular RNA (exRNAs). TIGER is a data analysis pipeline designed for the study of lipoprotein small RNA sequencing sRNAs; but it has great applicability to all exRNA studies. The software integrates host and non-host sRNA analysis through both genome and database alignments. It allowed for extensive comparisons between lipoproteins, biofluids, and liver samples across many different levels and features.


An online web server enabling users perform A-to-Z functional analyses, starting from next-generation sequencing expression data to the identification of important regulators with crucial roles in the investigated libraries. Users can analyze their own experiments or utilize the extensive mirExTra NGS expression library, in order to assess the role of microRNAs and transcription factors in various states, diseases and conditions. DIANA-mirExTra v2.0 permits complete substitution of in silico predictions with experimentally supported interactions and TSS positions for human and mouse. Importantly, the new web server performs sophisticated methodologies and advanced visualizations from a user-friendly interface. The multifaceted modular structure of this web application permits numerous different use-case scenarios and enables researchers to utilize DIANA-mirExTra v2.0 as a one stop shop for differential expression, functional or investigative analyses.


A highly interactive application that visualizes the sequence alignment, secondary structure and normalized read counts in synchronous multi-panel windows. This helps users to easily examine the relationships between the structure of precursor and the sequences and abundance of final products and thereby will facilitate the studies on miRNA biogenesis and regulation. The project manager handles multiple samples of multiple groups. The read alignment is imported in BAM file format. Implemented features comprise sorting, zooming, highlighting, editing, filtering, saving, exporting, etc. Currently, miRseqViewer supports 84 organisms whose annotation is available at miRBase.


Predicts novel miRNA candidates. mirDBA is based on alignment of read profiles generated from short RNA-seq data. It aligns ncRNA read profiles from ENCODE against the miRBase read profiles (cleaned for self-matches) and are able to separate ENCODE miRNAs from the other ncRNAs by a Matthews Correlation Coefficient (MCC) of 0.8 and obtain an area under the curve of 0.93. The method can predict 523 novel miRNA candidates. Known human and mouse miRNA read profiles were analyzed and the method found two distinct classes; the first containing two blocks and the second containing >2 blocks of reads.

PreMIR detector

Identifies miRNA precursors (MIRs) or tasiRNA precursor (TASs) of input small RNA (sRNA). PreMIR detector is part of SoMART, the Server for plant miRNA/tasiRNA Analysis Resources and Tools. PreMIR detector identifies precursor sequences of input sRNAs and checks whether the precursor sequence can fold into the pre-miRNA structure. As input, it uses a FASTA format sRNA sequence and a selection of DNA database(s). The background program uses BLASTN to query the input sRNA sequence(s) against the selected DNA databases to find perfect matching DNA sequences, retrieves up to 200 nt flanking sequences, and reports the DNA sequence as sRNA precursor. Next it uses the UNAFold program to fold the precursor and predict the coding arms for the miRNA, and coordinates for the miRNA. If a perfect matching sequence is found that does not fold into a pre-miRNA structure, no miRNA coding arm or miRNA coordinates are predicted. Multiple sRNA FASTA sequences and multiple DNA databases can be used concurrently.

Slicer Detector

Detects small RNAs that can potentially target user’s gene of interest from sRNA-seq libraries in Arabidopsis, potato, tomato, and tobacco. Slicer Detector is part of SoMART, the Server for plant miRNA/tasiRNA Analysis Resources and Tools. Slicer Detector is linked to the fRNAdb database, which contains sRNA sequences in FASTA format. As input, it uses a FASTA format sequence and an fRNAdb selected from the pull-down menu. The background script uses BLASTN to query the input sequence against the selected fRNAdb with very low stringency and retrieves all matching sRNAs. For each alignment, sequences of approximately 30 nt covering the aligned input region are retrieved and aligned to the matching sRNAs using a modified Smith-Waterman algorithm. Finally, the new alignment is examined for cleavability and cleavable slicer-target pairs are reported in two files. One shows the FASTA format sequences of potential slicers for the input. The other shows the alignments between the input and each slicer, with the predicted cleavage site on the target.

CoLIde / Co-expression based sRNA Loci Identification

Determines sRNA loci. CoLIde integrates dynamic sRNA expression levels and size class with genomic location to assist in finding distinct loci. It was applied to a total of four plant data sets on Arabidopsis thaliana, Solanum lycopersicum, and the Drosophila melanogaster, animal data set. This tool can preserve patterns from the sRNA level to locus level. It tends to predict compact loci for which the probability of hitting two distinct annotations is low.