Modeling, simulation and analysis of methylation profiles from reduced representation bisulfite sequencing experiments

Lacey Michelle R., Baribault Carl and Ehrlich Melanie
Additional contact information
Lacey Michelle R.: Department of Mathematics, Tulane University, New Orleans, LA, USA Tulane Cancer Center, Tulane Health Sciences Center, New Orleans, LA, USA
Baribault Carl: Tulane Cancer Center, Tulane Health Sciences Center, New Orleans, LA, USA
Ehrlich Melanie: Tulane Cancer Center, Tulane Health Sciences Center, New Orleans, LA, USA Program in Human Genetics, Tulane Health Sciences Center, New Orleans, LA, USA

Statistical Applications in Genetics and Molecular Biology, 2013, vol. 12, issue 6, 723-742

Abstract: The ENCODE project has funded the generation of a diverse collection of methylation profiles using reduced representation bisulfite sequencing (RRBS) technology, enabling the analysis of epigenetic variation on a genomic scale at single-site resolution. A standard application of RRBS experiments is in the location of differentially methylated regions (DMRs) between two sets of samples. Despite numerous publications reporting DMRs identified from RRBS datasets, there have been no formal analyses of the effects of experimental and biological factors on the performance of existing or newly developed analytical methods. These factors include variable read coverage, differing group sample sizes across genomic regions, uneven spacing between CpG dinucleotide sites, and correlation in methylation levels among sites in close proximity. To better understand the interplay among technical and biological variables in the analysis of RRBS methylation profiles, we have developed an algorithm for the generation of experimentally realistic RRBS datasets. Applying insights derived from our simulation studies, we present a novel procedure that can identify DMRs spanning as few as three CpG sites with both high sensitivity and specificity. Using RRBS data from muscle vs. non-muscle cell cultures as an example, we demonstrate that our method reveals many more DMRs that are likely to be of biological significance than previous methods.

Keywords: DMR detection; DNA methylation; RRBS; simulation; statistical methods (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2013-0027 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:12:y:2013:i:6:p:723-742:n:5

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.1515/sagmb-2013-0027

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().