Detects RNA editing by learning and summarizing essential features from the surrounding primitive sequence of candidate single nucleotide variants (SNVs). DeepRed is a program that can serve for discovering the developmental pattern of RNA editing changes during human early embryogenesis. It also can be used for exploring the evolutionary pattern of RNA editing in the primate lineage and Drosophila species.
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.
Assists users to predict adenosine-to-inosine editing from a single RNA-Seq data set. GIREMI can separate the RNA editing sites from genomic variations (such as single nucleotide polymorphisms (SNPs)) based on single RNA-Seq datasets. This program processes by determining the RNA-editing sites from a list of credible single nucleotide variants (SNVs) with known SNPs and the corresponding bam file.
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 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.
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.
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.