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Redundancy analysis allows improved detection of methylation changes in large genomic regions.

ABSTRACT: DNA methylation is an epigenetic process that regulates gene expression. Methylation can be modified by environmental exposures and changes in the methylation patterns have been associated with diseases. Methylation microarrays measure methylation levels at more than 450,000 CpGs in a single experiment, and the most common analysis strategy is to perform a single probe analysis to find methylation probes associated with the outcome of interest. However, methylation changes usually occur at the regional level: for example, genomic structural variants can affect methylation patterns in regions up to several megabases in length. Existing DMR methods provide lists of Differentially Methylated Regions (DMRs) of up to only few kilobases in length, and cannot check if a target region is differentially methylated. Therefore, these methods are not suitable to evaluate methylation changes in large regions. To address these limitations, we developed a new DMR approach based on redundancy analysis (RDA) that assesses whether a target region is differentially methylated.Using simulated and real datasets, we compared our approach to three common DMR detection methods (Bumphunter, blockFinder, and DMRcate). We found that Bumphunter underestimated methylation changes and blockFinder showed poor performance. DMRcate showed poor power in the simulated datasets and low specificity in the real data analysis. Our method showed very high performance in all simulation settings, even with small sample sizes and subtle methylation changes, while controlling type I error. Other advantages of our method are: 1) it estimates the degree of association between the DMR and the outcome; 2) it can analyze a targeted or region of interest; and 3) it can evaluate the simultaneous effects of different variables. The proposed methodology is implemented in MEAL, a Bioconductor package designed to facilitate the analysis of methylation data.We propose a multivariate approach to decipher whether an outcome of interest alters the methylation pattern of a region of interest. The method is designed to analyze large target genomic regions and outperforms the three most popular methods for detecting DMRs. Our method can evaluate factors with more than two levels or the simultaneous effect of more than one continuous variable, which is not possible with the state-of-the-art methods.

SUBMITTER: Ruiz-Arenas C

PROVIDER: S-EPMC5729265 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Redundancy analysis allows improved detection of methylation changes in large genomic regions.

Ruiz-Arenas Carlos C González Juan R JR

BMC bioinformatics 20171214 1

<h4>Background</h4>DNA methylation is an epigenetic process that regulates gene expression. Methylation can be modified by environmental exposures and changes in the methylation patterns have been associated with diseases. Methylation microarrays measure methylation levels at more than 450,000 CpGs in a single experiment, and the most common analysis strategy is to perform a single probe analysis to find methylation probes associated with the outcome of interest. However, methylation changes usu ...[more]

PMID: 29237399

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Project description:BackgroundThe human gastrointestinal (GI) tract contains a diverse collection of bacteria, most of which are unculturable by conventional microbiological methods. Increasingly molecular profiling techniques are being employed to examine this complex microbial community. The purpose of this study was to develop a microarray technique based on 16S ribosomal gene sequences for rapidly monitoring the microbial population of the GI tract.ResultsWe have developed a culture-independent, semi-quantitative, rapid method for detection of gut bacterial populations based on 16S rDNA probes using a DNA microarray. We compared the performance of microarrays based on long (40- and 50-mer) and short (16-21-mer) oligonucleotides. Short oligonucleotides consistently gave higher specificity. Optimal DNA amplification and labelling, hybridisation and washing conditions were determined using a probe with an increasing number of nucleotide mismatches, identifying the minimum number of nucleotides needed to distinguish between perfect and mismatch probes. An independent PCR-based control was used to normalise different hybridisation results, and to make comparisons between different samples, greatly improving the detection of changes in the gut bacterial population. The sensitivity of the microarray was determined to be 8.8 x 104 bacterial cells g-1 faecal sample, which is more sensitive than a number of existing profiling methods. The short oligonucleotide microarray was used to compare the faecal flora from healthy individuals and a patient suffering from Ulcerative Colitis (UC) during the active and remission states. Differences were identified in the bacterial profiles between healthy individuals and a UC patient. These variations were verified by Denaturing Gradient Gel Electrophoresis (DGGE) and DNA sequencing.ConclusionIn this study we demonstrate the design, testing and application of a highly sensitive, short oligonucleotide community microarray. Our approach allows the rapid discrimination of bacteria inhabiting the human GI tract, at taxonomic levels ranging from species to the superkingdom bacteria. The optimised protocol is available at: http://www.ifr.ac.uk/safety/microarrays/#protocols. It offers a high throughput method for studying the dynamics of the bacterial population over time and between individuals.

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Redundancy analysis allows improved detection of methylation changes in large genomic regions.

Publications

Redundancy analysis allows improved detection of methylation changes in large genomic regions.

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