Resources for the MeDUSA (Methylated DNA Utility for Sequence Analysis) MeDIP-seq computational analysis pipeline for the identification of differentially methylated regions, and associated methylome data from 18 wild-type and mutant mouse ES, NP and MEF cells.

Here we present 18 genome-wide DNA methylation profiles of wild type and Thymine DNA glycosylase (Tdg) knockout cells, which serve as an excellent murine methylome resource. The 18 samples represent 6 biological cohorts: 6 samples were derived from mouse embryonic stem cells (3 Tdg+/-, 3 Tdg-/-), 6 samples were from mouse neural precursor cells (3 Tdg+/-, 3 Tdg-/-) and 6 samples were obtained from mouse embryonic fibroblasts (3 Tdg+/+, 3 Tdg-/-). Next generation sequencing was performed on the libraries using an Illumina GAIIx for each sample. Paired end alignment against the mouse genome (Build NCBIM37) was performed using BWA (v0.5.8), and filtering to remove those reads failing to map was performed using SAMtools (v0.1.9) and a custom perl script. The Bioconductor (v2.7) package MeDIPs (v1.0.0) was used to normalize for size of the sequence library, done by calculating reads per million in tiled windows across the genome. Fragment length normalization was performed using a custom perl script. Wig tracks representing library size normalized alignment were generated using a combination of MEDIPS and custom R scripts. In addition to the total alignment wig track, strand specific wig tracks were also generated, enabling the user to infer whether the MeDIP signal is derived by methylation on the forward and/or reverse strand. The MeDIP-seq data were processed using the analysis pipeline MeDUSA (Methylated DNA Utility for Sequence Analysis). MeDUSA brings together numerous software packages to perform a full analysis of MeDIP-seq data, including sequence alignment, quality control, and determination and annotation of differentially methylated regions.

Read the peer-reviewed publication(s):

• Wilson, G. A., Dhami, P., Feber, A., Cortázar, D., Suzuki, Y., Schulz, R., Schär, P., & Beck, S. (2012). Resources for methylome analysis suitable for gene knockout studies of potential epigenome modifiers. GigaScience, 1(1). https://doi.org/10.1186/2047-217x-1-3 (PubMed:23587164)

Additional information:

http://projects.ensembl.org/heroic/

http://www2.cancer.ucl.ac.uk/medicalgenomics/tdg_web/trackList.php

http://www2.cancer.ucl.ac.uk/medicalgenomics/medusa/

Accessions (data included in GigaDB):

GEO: GSE27468
SRA: SRP005934
BioProject: PRJNA138437

Projects: