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Maximizing ecological and evolutionary insight from bisulfite sequencing data sets

Amanda J. Lea, Tauras P. Vilgalys, Paul A.P. Durst, Jenny Tung
doi: https://doi.org/10.1101/091488
Amanda J. Lea
1Department of Biology, Duke University, Box 90338, Durham, NC 27708, USA
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  • For correspondence: jt5@duke.edu amanda.lea@duke.edu
Tauras P. Vilgalys
2Department of Evolutionary Anthropology, Box 90383, Durham, NC 27708, USA
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Paul A.P. Durst
3Department of Biology, University of North Carolina at Chapel Hill, CB #3280, Coker Hall, Chapel Hill, NC 27599
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Jenny Tung
1Department of Biology, Duke University, Box 90338, Durham, NC 27708, USA
2Department of Evolutionary Anthropology, Box 90383, Durham, NC 27708, USA
4Institute of Primate Research, National Museums of Kenya, P. O. Box 24481, Karen 00502, Nairobi, Kenya
5Duke University Population Research Institute, Box 90420, Durham, NC 27708, USA
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  • For correspondence: jt5@duke.edu amanda.lea@duke.edu
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Abstract

The role of DNA methylation in development, divergence, and the response to environmental stimuli is of substantial interest in ecology and evolutionary biology. Measuring genome-wide DNA methylation is increasingly feasible using sodium bisulfite sequencing. Here, we analyze simulated and published data sets to demonstrate how effect size, kinship/population structure, taxonomic differences, and cell type heterogeneity influence the power to detect differential methylation in bisulfite sequencing data sets. Our results reveal that the effect sizes typical of evolutionary and ecological studies are modest, and will thus require data sets larger than those currently in common use. Additionally, our findings emphasize that statistical approaches that ignore the properties of bisulfite sequencing data (e.g., its count-based nature) or key sources of variance in natural populations (e.g., population structure or cell type heterogeneity) often produce false negatives or false positives, thus leading to incorrect biological conclusions. Finally, we provide recommendations for handling common issues that arise in bisulfite sequencing analyses and a freely available R Shiny application for simulating and performing power analyses on bisulfite sequencing data. This app, available at www.tung-lab.org/protocols-and-software.html, allows users to explore the effects of sequencing depth, sample size, population structure, and expected effect size, tailored to their own system.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted December 04, 2016.
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Maximizing ecological and evolutionary insight from bisulfite sequencing data sets
Amanda J. Lea, Tauras P. Vilgalys, Paul A.P. Durst, Jenny Tung
bioRxiv 091488; doi: https://doi.org/10.1101/091488
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Maximizing ecological and evolutionary insight from bisulfite sequencing data sets
Amanda J. Lea, Tauras P. Vilgalys, Paul A.P. Durst, Jenny Tung
bioRxiv 091488; doi: https://doi.org/10.1101/091488

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