17 tissues: Additional data

Link to article: DNA methylome profiling of human tissues identifies global and tissue-specific DNA methylation patterns.

The following section has links to whole data, tracks to UCSC genome browser, results on individual analysis, and tDMRs can be accessed from this page,

1. Data of all samples

1.1. Data

The files correspond to normalised whole data and the tissue annotation file. The whole data file have 485,580 rows. First three rows have annotation to individual id's, tissue numbers (details are in tissue annotation file) and the sample id's for the corresponding columns. The first column contains the probe id's.

1.2. Heatmap visualization

The whole data can be browsed using ExpressView which is an interactive tool to cluster and visualize data. Clustering can be performed by K-means or Hierarchical methods. To browse the data use the login details (user:pass tymri:BIITtym#) in the ExpressView tool. An example page can be accessed from here (NOTE: This page will take few minutes to open)

1.3. Quality control reports of samples

Quality reports of the samples can be accessed from QCreport file

2. Tracks to UCSC genome browser

2.1. Tracks for tDMRs

The files for UCSC browser can be downloaded here, Adipose, Aortas, Arteries, Bladder, Bone (Joint cartilage), Bone marrow, Gall Bladder, Gastric mucosa, Ischiatic nerve, Lymph Node, Medulla Oblongata, and Tonsils.

The tracks for the UCSC browser can be accessed here, Adipose, Aortas, Arteries, Bladder, Bone (Joint cartilage), Bone marrow, Gall Bladder, Gastric mucosa, Ischiatic nerve, Lymph Node, Medulla Oblongata, and Tonsils. If you get any errors do check the version of database in the URL link it should be hg18 and NOT hg19.

3. Tissue differential methylated regions (tDMRs)

3.1. tDMRs analysis

Identified tDMRs (tissue differential methylation regions) can be accessed from tDMRs results

4. Individual analysis

4.1. Correlation of individuals

Heatmap showing correlation of individuals. Each individual comprising of a vector of all tissues were constructed and then correlations (Pearson correlation coefficients) were computed.

4.2. Hierarchical clustering

Hierarchical clustering analysis of tissues in each individual was performed using the hclust command with complete method in R . The tree shows hierarchical clustering of tissues in each individual.

Article accepted and published in Genome Biology, 2014