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.
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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.
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.
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.
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.
New
Serves for studying differential RNA editing. DRETools is able to find differentially edited sites and islands. Users can calculate units that reduce sample-bias and find differentially edited sites and islands even when the global-editing-rate of groups being compared is different. This software includes several other features to explore RNA editing and investigate epitranscriptomics.
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.
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Whole transcriptome profiling of Late-Onset Alzheimer’s Disease patients provides insights into the molecular changes involved in the disease
Tools (3):
REDItools, Cufflinks, DESeq2
Topics (5):
RNA-seq analysis, Homo sapiens, Brain Diseases, Tauopathies, Brain Diseases
Editorial: Bioinformatics of Non-Coding RNAs with Applications to Biomedicine: Recent Advances and Open Challenges
Tools (3):
REDItools, Cytoscape, ncPred
Topics (1):
RNA-seq analysis
Uncovering RNA Editing Sites in Long Non-Coding RNAs
Tools (3):
GSNAP, BLAT, REDItools
Topics (8):
RNA-seq analysis, Homo sapiens, Ribonucleosides, Purine Nucleosides, Heterocyclic Compounds, 2-Ring, Ribonucleosides, Purine Nucleosides, Heterocyclic Compounds, 2-Ring
