Allows users to predict, analyze, annotate and display RNA editing sites for both directions of pyrimidine exchanges. PREPACT aims to (i) standardize RNA editing annotation and nomenclature, (ii) make editing information available with manually curated reference editomes, and (iii) analyze and predict RNA editing in organelle sequence data. It also includes information on pentatricopeptide repeat (PPR)-type editing factors.
Provides web servers dedicated to predict sites of RNA editing in plant organellar genes. PREP Suite identifies sites based on the principle that editing increases protein conservation among species. This server contains 3 differents predictive RNA editors (i) for plant mitochondrial genes (PREP-Mt), (ii) for chloroplast genes (PREP-Cp), and (iii) for alignments submitted by the user (PREPAln).
Performs genome-wide RED based on human RNA-seq data alone. RED-ML is a highly accurate, speedy and general-purpose tool for RNA editing detection using RNA-seq data. It can take advantage of matching DNA-seq data if available, and integrates well with other common RNA-seq data analysis steps. It can accurately detect novel RNA editing sites without relying on curated RNA editing databases. It can benefit the RNA editing research community and has profound impact to accelerate our understanding of this intriguing post-transcriptional modification process.
Integrates acquisition, storage, display and analysis of high-throughput RNA data. RNA Editing Plus allows for seamless integration of multiple, published or locally produced data sets via loading BAM. It annotates A-to-I RNA editing sites in all the functional genes, identifying whether these editing occurring in pri-mRNA splicing signals, miRNAs, or miRNA target regions from human high-throughput sequencing data.
Performs C-to-U RNA editing sites’ prediction in the chloroplasts of seed plants. CURE-Chloroplast provides a platform for processing an entire chloroplast genome sequence according a basic mode and an advanced mode. The application allows users to customize micro-analyze and blast parameter. Each job submitted is returned including the job summary, the sequence filtering report, the prediction details and the result downloading engine.
Identifies the adenosine to inosine editing sites in RNA sequences. iRNA-AI incorporates the chemical properties of nucleotides and their sliding occurrence density distribution along a RNA sequence into the general form of pseudo nucleotide composition. It is the first predictor ever established by using the computational approach and sequence information alone to identify the human adenosine to inosine editing sites.
Predicts adenosine to inosine editing sites in D. melanogaster. PAI uses pseudo nucleotide compositions. In the tool, RNA sequences are encoded by “pseudo dinucleotide composition” into which six RNA physiochemical properties were incorporated. It achieves promising performances in jackknife test and independent dataset test, indicating that it holds very high potential to become a useful tool for identifying A-to-I editing site.
Identifies Adenosine-to-Inosine (A-to-I) RNA editing sites into genomic sequences. Once you submit DNA sequences, it will analyze them and will highlight putative A-to-I RNA Editing sites. It is built on top of a knowledge-base integrating information of genes from UCSC, EST of NCBI, SNPs, DARNED, and next-generations sequencing data. The tool is equipped with a user-friendly interface allowing users to analyze genomic sequences in order to identify candidate A-to-I editing sites.
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