A method that can identify RNA editing sites using one RNA-seq data set without requiring genome sequence data. GIREMI calculates the mutual information (MI) of the mismatch pairs identified in the RNA-seq reads to distinguish RNA editing sites and SNPs. It also trains a generalized linear model (GLM) to achieve enhanced predictive power, which makes use of sequence bias information and the difference between the mismatch ratio of the unknown single nucleotide variants (SNVs) and the estimated allelic ratio of the gene.

Performs investigation of RNA editing from next-generation sequencing (NGS) data. REDItools contains three main scripts to study RNA editing using both RNA-Seq and DNA-Seq data from the same sample/individual or RNA-Seq data alone: (1) REDItoolDnaRNA.py for detecting RNA editing candidates, (2) REDItoolKnown.py for exploring the RNA editing potential of RNA-Seq experiments, and (3) REDItoolDenovo.py for performing de novo detection of RNA editing candidates. It also includes some accessory scripts and allows to annotate all candidate positions using relevant databases.

A web application for assessing RNA editing in human at known or user-specified sites supported by transcript data obtained by RNA-Seq experiments. The most relevant step of the ExpEdit workflow is the accurate read-to-genome mapping, because erroneous alignments can seriously affect editing detection and quantification.

Identifies RNA editing sites (RESs) by clustering single nucleotide variant (SNV) duplets, bypassing the need of single nucleotide polymorphism (SNP) annotations. SPRINT is applicable to any RNA-seq data that have reference genome sequences available. It can distinguish RESs from SNPs, but also effectively removes the one-read-count SNVs that are likely system errors, allowing the utilization of all SNVs that significantly increases the number of called RESs.

Detects and annotates RNA-editing sites using matching RNA-seq and DNA-seq data. RES-Scanner can recognize potential false positives resulting from genetic variants. It is based on sophisticated statistical models to infer the reliability of homozygous genotypes called from DNA-seq data. This tool provides statistical tests to distinguish genuine RNA-editing sites from sequencing errors. It is able to remove false positives resulting from mapping errors.

AnalyzeS RNA editing events from RNA sequencing data. RNAEditor maps the reads to the genome, calculates sequence variations, filters for “non-editing sites” and applies a cluster algorithm to detect highly edited sites (“editing islands”), which indicates potential ADAR binding sites, gives higher confidence that the contained editing sites are ‘true’ editing sites and higher likelihood of biological importance. RNAEditor is valuable to detect RNA editing events from RNA-seq data without for additional experimental techniques.

A useful tool for investigating condition-specific RNA-editing with RNA-seq. RDDpred is a Random Forest RDD classifier. It reports potentially true RNA-editing events from RNA-seq data. RDDpred was tested with two publicly available RNA-editing datasets and successfully reproduced RDDs reported in the two studies (90 %, 95 %) while rejecting false-discoveries (NPV: 75 %, 84 %).

Assists users in RNA-Seq comparison and annotation for RNA editing. RCARE was developed to (i) determine condition-dependent RNA editing sites and (ii) provide rich systematic annotations and the evidence level of each RNA editing site. It identifies novel RNA editing sites in the same/individual DNA sequence and RNA-Seq variant call format (VCF) data. It also implements a script to preprocess raw RNA-Seq data and convert FASTQ and BAM files into RNA VCF files.

Identifies and visualizes RNA editing sites form next-generation sequencing data (NGS). RED includes multiple filters and stringent statistical assessment. It provides sets of RNA editing candidate sites according to the user’s requirements. This software offers several rules and statistical filters to avoid false RNA-editing sites and to display the candidate RNA-editing sites.

Predicts single-nucleotide variant (SNV) positions from head-to-head comparisons of read stacks/pileups from Illumina sequencing. JACUSA is a versatile one-stop solution to detect SNV positions from comparing RNA-DNA and/or RNA-RNA sequencing samples. The performance of JACUSA has been carefully evaluated and compared to other variant callers in an in silico benchmark. JACUSA outperforms other algorithms in terms of the F measure, which combines precision and recall, in all benchmark scenarios. This performance margin is highest for the RNA-RNA comparison scenario.

Identifies RNA editing sites based on RNA-seq data in organelles based on variant call results. REDO can pinpoint RNA editing events in multiple samples simultaneously and detect differential proportion of RNA editing events in different samples. This software offers detailed annotations (such as gene name, nucleotide and amino acid position in gene, codon phase etc.), figures and statistics for RNA editing sites.

Predicts RNA-DNA differences in RNA-seq alignment data. rddChecker examines short read alignments to identify variable sites. This software exploits several filters to remove potential sequencing, alignment artifacts and single nucleotide polymorphisms (SNPs). The utilization of this tool is appropriate when the RNA-DNA differences might be caused by post-translation editing of RNA.