Dataset Information

Predicting sites of ADAR editing in double-stranded RNA.

ABSTRACT: ADAR (adenosine deaminase that acts on RNA) editing enzymes target coding and noncoding double-stranded RNA (dsRNA) and are essential for neuronal function. Early studies showed that ADARs preferentially target adenosines with certain 5' and 3' neighbours. Here we use current Sanger sequencing protocols to perform a more accurate and quantitative analysis. We quantified editing sites in an ?800-bp dsRNA after reaction with human ADAR1 or ADAR2, or their catalytic domains alone. These large data sets revealed that neighbour preferences are mostly dictated by the catalytic domain, but ADAR2's dsRNA-binding motifs contribute to 3' neighbour preferences. For all proteins, the 5' nearest neighbour was most influential, but adjacent bases also affected editing site choice. We developed algorithms to predict editing sites in dsRNA of any sequence, and provide a web-based application. The predictive power of the algorithm on fully base-paired dsRNA, compared with biological substrates containing mismatches, bulges and loops, elucidates structural contributions to editing specificity.

SUBMITTER: Eggington JM

PROVIDER: S-EPMC3113232 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Publications

Predicting sites of ADAR editing in double-stranded RNA.

Eggington Julie M JM Greene Tom T Bass Brenda L BL

Nature communications 20110101

ADAR (adenosine deaminase that acts on RNA) editing enzymes target coding and noncoding double-stranded RNA (dsRNA) and are essential for neuronal function. Early studies showed that ADARs preferentially target adenosines with certain 5' and 3' neighbours. Here we use current Sanger sequencing protocols to perform a more accurate and quantitative analysis. We quantified editing sites in an ∼800-bp dsRNA after reaction with human ADAR1 or ADAR2, or their catalytic domains alone. These large data ...[more]

PMID: 21587236

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Project description:BackgroundConversion or editing of adenosine (A) into inosine (I) catalyzed by specialized cellular enzymes represents one of the most common post-transcriptional RNA modifications with emerging connection to disease. A-to-I conversions can happen at specific sites and lead to increase in proteome diversity and changes in RNA stability, splicing, and regulation. Such sites can be detected as adenine-to-guanine sequence changes by next-generation RNA sequencing which resulted in millions reported sites from multiple genome-wide surveys. Nonetheless, the lack of extensive independent validation in such endeavors, which is critical considering the relatively high error rate of next-generation sequencing, leads to lingering questions about the validity of the current compendiums of the editing sites and conclusions based on them.ResultsStrikingly, we found that the current analytical methods suffer from very high false positive rates and that a significant fraction of sites in the public databases cannot be validated. In this work, we present potential solutions to these problems and provide a comprehensive and extensively validated list of A-to-I editing sites in a human cancer cell line. Our findings demonstrate that most of true A-to-I editing sites in a human cancer cell line are located in the non-coding transcripts, the so-called RNA 'dark matter'. On the other hand, many ADAR editing events occurring in exons of human protein-coding mRNAs, including those that can recode the transcriptome, represent false positives and need to be interpreted with caution. Nonetheless, yet undiscovered authentic ADAR sites that increase the diversity of human proteome exist and warrant further identification.ConclusionsAccurate identification of human ADAR sites remains a challenging problem, particularly for the sites in exons of protein-coding mRNAs. As a result, genome-wide surveys of ADAR editome must still be accompanied by extensive Sanger validation efforts. However, given the vast number of unknown human ADAR sites, there is a need for further developments of the analytical techniques, potentially those that are based on deep learning solutions, in order to provide a quick and reliable identification of the editome in any sample.

Dataset Information

Predicting sites of ADAR editing in double-stranded RNA.

Publications

Predicting sites of ADAR editing in double-stranded RNA.

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