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GIREMI / Genome-independent Identification of RNA Editing by Mutual Information
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.
REDItools
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.
SPRINT / SNP-free RNA editing IdeNtification Toolkit
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.
RNAEditor
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.
RCARE / RNA-sequence Comparison and Annotation for RNA Editing
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.
JACUSA / JAVA framework for accurate SNV assessment
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.
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