Liang et al. BMC Genomics 2011, 12:231 http://www.biomedcentral.com/1471-2164/12/231 Data analysis Raw data processing and methylation peak identification The first stage of raw data processing was performed by NimbleGen as part of its commercial service (Detailed description is available at http://www.nimblegen.com). In brief, it involves the following 4 steps. 1) Signal extraction: Signal intensity data is extracted from the scanned images of each array using NimbleScan. 2) Cal- culation of log2 ratio: For each feature on the array, a corresponding scaled log2-ratio representing the ratio of the input signals for the experimental and test samples that were co-hybridized to the array is computed and scaled to center the ratio data around zero. Scaling is performed by subtracting the bi-weight mean for the log2-ratio values for all features on the array from each log2-ratio value. 3) Calculation of P score: From the scaled log2-ratio data, a fixed-length window (750 bp) is placed around each consecutive probe and the one- sided Kolmogorov-Smirnov (KS) test is applied to deter- mine whether the probes are drawn from a significantly more positive distribution of intensity log-ratios than those in the rest of the array. The resulting score for each probe, called P score, is the -log10 p-value from the windowed KS test around that probe. 4) Identification of methylation peaks: This is performed by searching for regions containing at least 2 consecutive probes with a P score ≥ 2. Peaks within 500 bp of each other are merged. The processed data (P Scores) and raw data (log2 ratios) for all 24 involved samples have been deposited in GEO at NCBI under accession# GSE21415. Identification of methylation peaks shared by or unique to a given set of samples This part was mostly done using an in-house bioinfor- matics tool based on the following criteria and definition: 1) A methylation peak of a sample is the methylated region shared between the two biological replicates of the same sample. This requirement applies to all categories of methylation peaks; 2) A methylation peak unique to a sam- ple or a group of samples is a methylated region that only appeared in the sample or group. It was further examined to ensure that this region does not overlap with any raw peaks (those of individual replicates from NimbleScan) of other individual samples; 3) A peak shared by one or more but not all samples in a group; 4) A methylation peak com- mon to a group of samples. In identifying these methyla- tion peaks, it is often necessary to break the larger overlapping raw peaks into several sub-peaks such that one part of an original peak may be identified as a common peak and another part as a unique peak. Defining the location category of DMRs To define the location of a methylation peak in context of a gene, we used the annotation data of promoters and CpGi regions of mouse genome provided by Page 13 of 16 NimbleGen, which is in turn based on UCSC February 2006 (mm8) genome freeze for annotation of CpGi regions and mRNAs and their genome coordinates. Using an in-house PERL script, each peak is assigned into one of the following categories: 1) CpGi promoter, located within 500 bp distance to a CpGi and is within an annotated promoter (1 kb flanking each side of the transcription start site or TSS); 2) non-CpGi promoter, located within a promoter without CpGi or more than 500 bp away from any CpGi in the promoter; 3) Intra- genic CpGi, located within 500 bp of a CpGi that locates in a gene (excluding the promoter as defined above); 4) Intergenic CpGi, located within 500 bp from a CpGi that is more than 2 kb from any annotated genes. The assignment is done in the above order of the four cate- gories such that a given DMR belongs to only one cate- gory with the CpGi promoter having a higher priority over the rest of the categories and so on. For DMRs in tiling regions, two additional categories were added as non-CpGi intergenic and non-CpGi intragenic. DMRs in tiling regions were identified by collecting all DMRs located within the tiling regions, which include Chr6: 51,984,676-52,408,211, Chr7: 142,162,309-143,552,631 and Chr17: 12,525,776-12,614,350. Gene ontology analysis and other statistical analyses For DMRs associated with a gene (including CpGi pro- moter, non-CpGi promoter, and Intragenic CpGi), we examined whether a statistically significant enrichment of gene ontology - a given gene list can be observed using the DAVID tool [54]. A significant enrichment requires a Benjamini adjusted P value no more than 0.05. Scatter plots were generated using R http://www.r-project.org/ and Heat maps of P scores were generated using MeV http://www.tm4.org/mev/[55]. Assessment of the MeDIP data quality and validation of MeDIP methylation peaks To evaluate the overall quality of the MeDIP data, we plotted the log2 ratios (methylation signals between the IP DNA and the input DNA) and P score of all probes between biological replicates representing different sam- ples of the same tissues from different animals. The scatter plots of log 2 ratios as shown in Additional file 14 indi- cates that a good level of consistency was seen among bio- logical replicates for all examined tissues (Additional file 4), considering these represented both biological and tech- nical replicates (Pearson Coefficient R = 0.68 to 0.88 with over half being > 0.8). The Pearson Coefficient between replicates is improved in all cases, ranging from 0.75 to 0.94, when P score was used (Additional file 4), on which the peak call was based, indicating some sporadic noise was removed by considering the signal level of neighbor- ing probes in a larger window.