Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify between-group differences in DNA methylation

Autor:	Isabel Castanho, Eilis Hannon, Aisha Dahir, Dorothea Seiler Vellame, Jonathan Mill
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Epigenomics Computer science Bisulfite sequencing Computational biology Biology QH426-470 Genome 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Genetics Sulfites 030304 developmental biology 0303 health sciences DNA methylation RRBS Sample size Research High-Throughput Nucleotide Sequencing Reproducibility of Results dNaM Sequence Analysis DNA Amplicon Missing data Read depth chemistry Sodium bisulfite Sample size determination Reduced representation bisulfite sequencing Power Epigenetics DNA microarray 030217 neurology & neurosurgery TP248.13-248.65 Biotechnology
Zdroj:	BMC Genomics, Vol 22, Iss 1, Pp 1-16 (2021) BMC Genomics
ISSN:	1471-2164
Popis:	Background The combination of sodium bisulfite treatment with highly-parallel sequencing is a common method for quantifying DNA methylation across the genome. The power to detect between-group differences in DNA methylation using bisulfite-sequencing approaches is influenced by both experimental (e.g. read depth, missing data and sample size) and biological (e.g. mean level of DNA methylation and difference between groups) parameters. There is, however, no consensus about the optimal thresholds for filtering bisulfite sequencing data with implications for the reproducibility of findings in epigenetic epidemiology. Results We used a large reduced representation bisulfite sequencing (RRBS) dataset to assess the distribution of read depth across DNA methylation sites and the extent of missing data. To investigate how various study variables influence power to identify DNA methylation differences between groups, we developed a framework for simulating bisulfite sequencing data. As expected, sequencing read depth, group size, and the magnitude of DNA methylation difference between groups all impacted upon statistical power. The influence on power was not dependent on one specific parameter, but reflected the combination of study-specific variables. As a resource to the community, we have developed a tool, POWEREDBiSeq, which utilizes our simulation framework to predict study-specific power for the identification of DNAm differences between groups, taking into account user-defined read depth filtering parameters and the minimum sample size per group. Conclusions Our data-driven approach highlights the importance of filtering bisulfite-sequencing data by minimum read depth and illustrates how the choice of threshold is influenced by the specific study design and the expected differences between groups being compared. The POWEREDBiSeq tool, which can be applied to different types of bisulfite sequencing data (e.g. RRBS, whole genome bisulfite sequencing (WGBS), targeted bisulfite sequencing and amplicon-based bisulfite sequencing), can help users identify the level of data filtering needed to optimize power and aims to improve the reproducibility of bisulfite sequencing studies.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6e9c8b6f7cc66ed75dd7593ae6b572d https://doaj.org/article/edc75c961ca64a479ac03dcf4a9936b1 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.