Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Jul 1;34(13):2283-2285.
doi: 10.1093/bioinformatics/bty069.

RaMWAS: fast methylome-wide association study pipeline for enrichment platforms

Andrey A Shabalin  1   2 , Mohammad W Hattab  1 , Shaunna L Clark  1 , Robin F Chan  1 , Gaurav Kumar  1 , Karolina A Aberg  1 , Edwin J C G van den Oord  1
Affiliations

RaMWAS: fast methylome-wide association study pipeline for enrichment platforms

Andrey A Shabalin et al. Bioinformatics. .

Abstract

Motivation: Enrichment-based technologies can provide measurements of DNA methylation at tens of millions of CpGs for thousands of samples. Existing tools for methylome-wide association studies cannot analyze datasets of this size and lack important features like principal component analysis, combined analysis with SNP data and outcome predictions that are based on all informative methylation sites.

Results: We present a Bioconductor R package called RaMWAS with a full set of tools for large-scale methylome-wide association studies. It is free, cross-platform, open source, memory efficient and fast.

Availability and implementation: Release version and vignettes with small case study at bioconductor.org/packages/ramwas Development version at github.com/andreyshabalin/ramwas.

Supplementary information: Supplementary data are available at Bioinformatics online.

PubMed Disclaimer

Figures

Fig. 1.
Fig. 1.
RaMWAS features. (a)––sex check via read count on chr. X and Y, (b)––enrichment diagnostic via average score by CpG density plot, (c)—fragment size distribution estimation, (d)—principal component analysis, (e)––QQ-plot for major depression disorder phenotype and (f)––methlylation risk score for age. The correlation between age and its MRS is 0.95. Details of the study are provided in the Supplementary Material
See this image and copyright information in PMC

References

    1. Aberg K.A. et al. (2017) A MBD-seq protocol for large-scale methylome-wide studies with (very) low amounts of DNA. Epigenetics, 12, 743–750. - PMC - PubMed
    1. Andersen A.M. et al. (2015) Current and future prospects for epigenetic biomarkers of substance use disorders. Genes, 6, 991–1022. - PMC - PubMed
    1. Chan R.F. et al. (2017) Enrichment methods provide a feasible approach to comprehensive and adequately powered investigations of the brain methylome. Nucleic Acids Res., 45, e97. - PMC - PubMed
    1. Horvath S. (2013) DNA methylation age of human tissues and cell types. Genome Biol., 14, R115.. - PMC - PubMed
    1. Lienhard M. et al. (2016) QSEA—modelling of genome-wide DNA methylation from sequencing enrichment experiments. Nucleic Acids Res., 45, e44. - PMC - PubMed

Publication types

LinkOut - more resources