Dataset Information

Systematic identification of edited microRNAs in the human brain.

ABSTRACT: Adenosine-to-inosine (A-to-I) editing modifies RNA transcripts from their genomic blueprint. A prerequisite for this process is a double-stranded RNA (dsRNA) structure. Such dsRNAs are formed as part of the microRNA (miRNA) maturation process, and it is therefore expected that miRNAs are affected by A-to-I editing. Editing of miRNAs has the potential to add another layer of complexity to gene regulation pathways, especially if editing occurs within the miRNA-mRNA recognition site. Thus, it is of interest to study the extent of this phenomenon. Current reports in the literature disagree on its extent; while some reports claim that it may be widespread, others deem the reported events as rare. Utilizing a next-generation sequencing (NGS) approach supplemented by an extensive bioinformatic analysis, we were able to systematically identify A-to-I editing events in mature miRNAs derived from human brain tissues. Our algorithm successfully identified many of the known editing sites in mature miRNAs and revealed 17 novel human sites, 12 of which are in the recognition sites of the miRNAs. We confirmed most of the editing events using in vitro ADAR overexpression assays. The editing efficiency of most sites identified is very low. Similar results are obtained for publicly available data sets of mouse brain-regions tissues. Thus, we find that A-to-I editing does alter several miRNAs, but it is not widespread.

SUBMITTER: Alon S

PROVIDER: S-EPMC3409266 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Systematic identification of edited microRNAs in the human brain.

Alon Shahar S Mor Eyal E Vigneault Francois F Church George M GM Locatelli Franco F Galeano Federica F Gallo Angela A Shomron Noam N Eisenberg Eli E

Genome research 20120412 8

Adenosine-to-inosine (A-to-I) editing modifies RNA transcripts from their genomic blueprint. A prerequisite for this process is a double-stranded RNA (dsRNA) structure. Such dsRNAs are formed as part of the microRNA (miRNA) maturation process, and it is therefore expected that miRNAs are affected by A-to-I editing. Editing of miRNAs has the potential to add another layer of complexity to gene regulation pathways, especially if editing occurs within the miRNA-mRNA recognition site. Thus, it is of ...[more]

PMID: 22499667

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Project description:BackgroundMicroRNAs (miRNAs) are short, non-coding RNA molecules that are directly involved in post-transcriptional regulation of gene expression. The mature miRNA sequence binds to more or less specific target sites on the mRNA. Both their small size and sequence specificity make the detection of completely new miRNAs a challenging task. This cannot be based on sequence information alone, but requires structure information about the miRNA precursor. Unlike comparative genomics approaches, ab initio approaches are able to discover species-specific miRNAs without known sequence homology.ResultsMiRPred is a novel method for ab initio prediction of miRNAs by genome scanning that only relies on (predicted) secondary structure to distinguish miRNA precursors from other similar-sized segments of the human genome. We apply a machine learning technique, called linear genetic programming, to develop special classifier programs which include multiple regular expressions (motifs) matched against the secondary structure sequence. Special attention is paid to scanning issues. The classifiers are trained on fixed-length sequences as these occur when shifting a window in regular steps over a genome region. Various statistical and empirical evidence is collected to validate the correctness of and increase confidence in the predicted structures. Among other things, we propose a new criterion to select miRNA candidates with a higher stability of folding that is based on the number of matching windows around their genome location. An ensemble of 16 motif-based classifiers achieves 99.9 percent specificity with sensitivity remaining on an acceptable high level when requiring all classifiers to agree on a positive decision. A low false positive rate is considered more important than a low false negative rate, when searching larger genome regions for unknown miRNAs. 117 new miRNAs have been predicted close to known miRNAs on human chromosome 19. All candidate structures match the free energy distribution of miRNA precursors which is significantly shifted towards lower free energies. We employed a human EST library and found that around 75 percent of the candidate sequences are likely to be transcribed, with around 35 percent located in introns.ConclusionOur motif finding method is at least competitive to state-of-the-art feature-based methods for ab initio miRNA discovery. In doing so, it requires less previous knowledge about miRNA precursor structures while programs and motifs allow a more straightforward interpretation and extraction of the acquired knowledge.

Dataset Information

Systematic identification of edited microRNAs in the human brain.

Publications

Systematic identification of edited microRNAs in the human brain.

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