AIM: Validation of sequencing-based DNA methylation data is an important step for meaningful translation of findings. However, there has been limited assessment of different platforms to validate methylation data from next generation sequencing. METHODS: We performed a comparative methylation analysis between the genome-wide platform of reduced representation bisulfite sequencing with a targeted, Sequenom EpiTyper platform (four genes were analyzed in 15 cell lines covering 52 CpG sites). RESULTS: We show that the accuracy of validation substantially improves if results from multiple and adjacent CpG sites are combined rather than at single CpG sites. We demonstrate increased read number improves accuracy of reduced representation bisulfite sequencing results. Further, by using series of replicates, we document variation in samples analyzed by Sequenom EpiTyper, indicating the importance of including replicates to increase precision. CONCLUSION: The results reveal potential sources of bias and provide a guideline for refining study design for DNA methylation analysis.
AIM: Validation of sequencing-based DNA methylation data is an important step for meaningful translation of findings. However, there has been limited assessment of different platforms to validate methylation data from next generation sequencing. METHODS: We performed a comparative methylation analysis between the genome-wide platform of reduced representation bisulfite sequencing with a targeted, Sequenom EpiTyper platform (four genes were analyzed in 15 cell lines covering 52 CpG sites). RESULTS: We show that the accuracy of validation substantially improves if results from multiple and adjacent CpG sites are combined rather than at single CpG sites. We demonstrate increased read number improves accuracy of reduced representation bisulfite sequencing results. Further, by using series of replicates, we document variation in samples analyzed by Sequenom EpiTyper, indicating the importance of including replicates to increase precision. CONCLUSION: The results reveal potential sources of bias and provide a guideline for refining study design for DNA methylation analysis.
Authors: Euan J Rodger; Suzan N Almomani; Jackie L Ludgate; Peter A Stockwell; Bruce C Baguley; Michael R Eccles; Aniruddha Chatterjee Journal: Cancers (Basel) Date: 2021-04-28 Impact factor: 6.639
Authors: Amber M Helliwell; Peter A Stockwell; Christina D Edgar; Aniruddha Chatterjee; Warren P Tate Journal: Int J Mol Sci Date: 2022-10-06 Impact factor: 6.208
Authors: Jackie L Ludgate; James Wright; Peter A Stockwell; Ian M Morison; Michael R Eccles; Aniruddha Chatterjee Journal: BMC Med Genomics Date: 2017-08-31 Impact factor: 3.063
Authors: Aniruddha Chatterjee; Euan J Rodger; Antonio Ahn; Peter A Stockwell; Matthew Parry; Jyoti Motwani; Stuart J Gallagher; Elena Shklovskaya; Jessamy Tiffen; Michael R Eccles; Peter Hersey Journal: iScience Date: 2018-06-28