↓ Skip to main content

Development and application of an integrated allele-specific pipeline for methylomic and epigenomic analysis (MEA)

Overview of attention for article published in BMC Genomics, June 2018
Altmetric Badge

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 (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
1 blog
twitter
9 tweeters

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
49 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Development and application of an integrated allele-specific pipeline for methylomic and epigenomic analysis (MEA)
Published in
BMC Genomics, June 2018
DOI 10.1186/s12864-018-4835-2
Pubmed ID
Authors

Julien Richard Albert, Tasuku Koike, Hamid Younesy, Richard Thompson, Aaron B. Bogutz, Mohammad M. Karimi, Matthew C. Lorincz

Abstract

Allele-specific transcriptional regulation, including of imprinted genes, is essential for normal mammalian development. While the regulatory regions controlling imprinted genes are associated with DNA methylation (DNAme) and specific histone modifications, the interplay between transcription and these epigenetic marks at allelic resolution is typically not investigated genome-wide due to a lack of bioinformatic packages that can process and integrate multiple epigenomic datasets with allelic resolution. In addition, existing ad-hoc software only consider SNVs for allele-specific read discovery. This limitation omits potentially informative INDELs, which constitute about one fifth of the number of SNVs in mice, and introduces a systematic reference bias in allele-specific analyses. Here, we describe MEA, an INDEL-aware Methylomic and Epigenomic Allele-specific analysis pipeline which enables user-friendly data exploration, visualization and interpretation of allelic imbalance. Applying MEA to mouse embryonic datasets yields robust allele-specific DNAme maps and low reference bias. We validate allele-specific DNAme at known differentially methylated regions and show that automated integration of such methylation data with RNA- and ChIP-seq datasets yields an intuitive, multidimensional view of allelic gene regulation. MEA uncovers numerous novel dynamically methylated loci, highlighting the sensitivity of our pipeline. Furthermore, processing and visualization of epigenomic datasets from human brain reveals the expected allele-specific enrichment of H3K27ac and DNAme at imprinted as well as novel monoallelically expressed genes, highlighting MEA's utility for integrating human datasets of distinct provenance for genome-wide analysis of allelic phenomena. Our novel pipeline for standardized allele-specific processing and visualization of disparate epigenomic and methylomic datasets enables rapid analysis and navigation with allelic resolution. MEA is freely available as a Docker container at https://github.com/julienrichardalbert/MEA .

Twitter Demographics

The data shown below were collected from the profiles of 9 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 27%
Researcher 13 27%
Student > Master 7 14%
Student > Doctoral Student 3 6%
Student > Bachelor 2 4%
Other 3 6%
Unknown 8 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 35%
Biochemistry, Genetics and Molecular Biology 12 24%
Medicine and Dentistry 3 6%
Nursing and Health Professions 2 4%
Veterinary Science and Veterinary Medicine 1 2%
Other 2 4%
Unknown 12 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 10 July 2018.
All research outputs
#2,004,681
of 19,220,747 outputs
Outputs from BMC Genomics
#687
of 9,729 outputs
Outputs of similar age
#47,880
of 292,541 outputs
Outputs of similar age from BMC Genomics
#2
of 20 outputs
Altmetric has tracked 19,220,747 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,729 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 92% 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 292,541 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.