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

Overview of attention for article published in BMC Bioinformatics, December 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

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7 tweeters

Citations

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5 Dimensions

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11 Mendeley
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Title
Redundancy analysis allows improved detection of methylation changes in large genomic regions
Published in
BMC Bioinformatics, December 2017
DOI 10.1186/s12859-017-1986-0
Pubmed ID
Authors

Carlos Ruiz-Arenas, Juan R. González

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.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 27%
Student > Doctoral Student 2 18%
Professor > Associate Professor 2 18%
Student > Master 2 18%
Other 1 9%
Other 0 0%
Unknown 1 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 36%
Agricultural and Biological Sciences 3 27%
Environmental Science 1 9%
Neuroscience 1 9%
Unknown 2 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 December 2017.
All research outputs
#6,224,935
of 12,319,220 outputs
Outputs from BMC Bioinformatics
#2,121
of 4,488 outputs
Outputs of similar age
#124,604
of 346,442 outputs
Outputs of similar age from BMC Bioinformatics
#59
of 188 outputs
Altmetric has tracked 12,319,220 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,488 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 52% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 346,442 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 188 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.