Rapid Development and Distribution of Statistical Tools for High-Throughput Sequencing Data

Benchmarking Platforms and Methods for DNA Methylation Analysis

One of the main challenges, from a statistician’s perspective, is accessing appropriate datasets for developing and testing methodology improvements. Getting the right balance between detailed information for a small number of samples (high cost per sample) and less detail about a biological replicated cohort (low cost per sample) is still an open question. This part of our benchmarking work package addresses the lack of analyses comparing platforms in these terms, i.e., cost per information content.

The technical report on contrasting DNA methylation platforms is available here

 

References
Bock, C. (2012). Analysing and interpreting DNA methylation data. Nature Reviews Genetics, 13, 705-719

Laird, P. W. (2010). Principles and challenges of genome-wide DNA methylation  analysis. Nature Reviews Genetics, 11(3), 191–203.

Riebler, A., Menigatti, M., Song, J. Z., Statham, A. L., Stirzaker, C., Mahmud, N., Robinson, M. D. (2014). BayMeth: improved DNA methylation quantification for  affinity capture sequencing data using a flexible Bayesian approach. Genome Biology, 15(2), R35.

Robinson, M. D., Statham, A. L., Speed, T. P.,   Clark, S. J. (2010). Protocol matters:  which methylome are you actually studying? Epigenomics, 2(4), 587–598.

Ziller, M. J., Gu, H., Muller, F., Donaghey, J., Tsai, L. T., Kohlbacher, O., Meissner, A.  (2013). Charting a dynamic DNA methylation landscape of the human genome.  Nature, 500(7463), 477–481.