Methylation profiles were calculated as described previously6 and are available from the HEP database/browser at www.epigenome.org. Kruskall-Wallis tests were used to determine differential methylation between tissues (T-DMRs), measuring the proportion of uncorrected p-values that were smaller 0.001 for all CpGs. As this test is insensitive to samples that were only measured in a single sample such as sperm and placenta, the obtained number of T-DMRs is unlikely to be overstated due to putative aberrant methylation within these samples. Some T-DMRs were experimentally validated by sequencing independent DNA samples. Equality between two groups (age and sex) was performed using Wilcoxon tests. For the analysis of co-methylation, median methylation values were used over all technical replicates to minimize any skewing effect because of possible outliers. In addition, we excluded all CpGs where the methylation values derived from the forward and reverse reads of the same amplicon differed by more than 10%. Based on this criterion, 38% of CpGs were excluded from the analysis. As only one DNA strand was analysed following bisulfite conversion, no assessment of hemimethylation was possible in this case. Methylation changes were calculated based on the absolute methylation differences between CpG pairs of identical samples. To minimize a bias introduced by the amplicon selection, the analysis was performed using both, individual CpGs (window size 20,000bp) and CpGs of the same amplicons. Co-methylation of CpGs was described as a function of similar methylation levels over distance (in bp). For scatter plots, equal amounts of measurements were binned and ranked by numerical order of the X-axis values, representing means of X- and Y- data. For box plots and histograms, data were binned according to the intervals indicated on the X-axis containing different numbers of measurements.
Partial Protocol Preview
This section provides a glimpse into the protocol. The remaining content is hidden due to licensing restrictions, but the full text is available at the following link:
Access Free Full Text.
Eckhardt F., Lewin J., Cortese R., Rakyan V.K., Attwood J., Burger M., Burton J., Cox T.V., Davies R., Down T.A., Haefliger C., Horton R., Howe K., Jackson D.K., Kunde J., Koenig C., Liddle J., Niblett D., Otto T., Pettett R., Seemann S., Thompson C., West T., Rogers J., Olek A., Berlin K, & Beck S. (2006). DNA methylation profiling of human chromosomes 6, 20 and 22. Nature genetics, 38(12), 1378-1385.
Technical replicates (to minimize the effect of outliers)
Exclusion of CpGs where the methylation values derived from the forward and reverse reads differed by more than 10%
Amplicon selection (analysis performed using both individual CpGs and CpGs of the same amplicons to minimize bias)
positive controls
None specified
negative controls
None specified
Annotations
Based on most similar protocols
Etiam vel ipsum. Morbi facilisis vestibulum nisl. Praesent cursus laoreet felis. Integer adipiscing pretium orci. Nulla facilisi. Quisque posuere bibendum purus. Nulla quam mauris, cursus eget, convallis ac, molestie non, enim. Aliquam congue. Quisque sagittis nonummy sapien. Proin molestie sem vitae urna. Maecenas lorem.
As authors may omit details in methods from publication, our AI will look for missing critical information across the 5 most similar protocols.
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to
get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required