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Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods

Overview of attention for article published in Genome Research, June 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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14 X users
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3 patents
wikipedia
2 Wikipedia pages

Citations

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132 Dimensions

Readers on

mendeley
294 Mendeley
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4 CiteULike
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Title
Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods
Published in
Genome Research, June 2013
DOI 10.1101/gr.152231.112
Pubmed ID
Authors

Michael Stevens, Jeffrey B. Cheng, Daofeng Li, Mingchao Xie, Chibo Hong, Cécile L. Maire, Keith L. Ligon, Martin Hirst, Marco A. Marra, Joseph F. Costello, Ting Wang

Abstract

Recent advancements in sequencing-based DNA methylation profiling methods provide an unprecedented opportunity to map complete DNA methylomes. These include whole-genome bisulfite sequencing (WGBS, MethylC-seq, or BS-seq), reduced-representation bisulfite sequencing (RRBS), and enrichment-based methods such as MeDIP-seq, MBD-seq, and MRE-seq. These methods yield largely comparable results but differ significantly in extent of genomic CpG coverage, resolution, quantitative accuracy, and cost, at least while using current algorithms to interrogate the data. None of these existing methods provides single-CpG resolution, comprehensive genome-wide coverage, and cost feasibility for a typical laboratory. We introduce methylCRF, a novel conditional random fields-based algorithm that integrates methylated DNA immunoprecipitation (MeDIP-seq) and methylation-sensitive restriction enzyme (MRE-seq) sequencing data to predict DNA methylation levels at single-CpG resolution. Our method is a combined computational and experimental strategy to produce DNA methylomes of all 28 million CpGs in the human genome for a fraction (<10%) of the cost of whole-genome bisulfite sequencing methods. methylCRF was benchmarked for accuracy against Infinium arrays, RRBS, WGBS sequencing, and locus-specific bisulfite sequencing performed on the same human embryonic stem cell line. methylCRF transformation of MeDIP-seq/MRE-seq was equivalent to a biological replicate of WGBS in quantification, coverage, and resolution. We used conventional bisulfite conversion, PCR, cloning, and sequencing to validate loci where our predictions do not agree with whole-genome bisulfite data, and in 11 out of 12 cases, methylCRF predictions of methylation level agree better with validated results than does whole-genome bisulfite sequencing. Therefore, methylCRF transformation of MeDIP-seq/MRE-seq data provides an accurate, inexpensive, and widely accessible strategy to create full DNA methylomes.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 294 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 3%
Germany 2 <1%
Canada 2 <1%
Belgium 2 <1%
United Kingdom 1 <1%
Mexico 1 <1%
China 1 <1%
Hungary 1 <1%
Luxembourg 1 <1%
Other 1 <1%
Unknown 272 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 80 27%
Researcher 64 22%
Student > Master 32 11%
Student > Doctoral Student 25 9%
Student > Bachelor 20 7%
Other 39 13%
Unknown 34 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 136 46%
Biochemistry, Genetics and Molecular Biology 68 23%
Medicine and Dentistry 15 5%
Computer Science 13 4%
Neuroscience 3 1%
Other 21 7%
Unknown 38 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 17 October 2023.
All research outputs
#1,980,597
of 25,711,518 outputs
Outputs from Genome Research
#941
of 4,445 outputs
Outputs of similar age
#16,418
of 209,634 outputs
Outputs of similar age from Genome Research
#17
of 51 outputs
Altmetric has tracked 25,711,518 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,445 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.3. This one has done well, scoring higher than 78% 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 209,634 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 51 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 66% of its contemporaries.