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Chatterjee Laboratory tools and resources

Novel software and pipeline development

Differential Methylation Analysis Package (DMAP)

Differential Methylation Analysis Package (DMAP) is a suite of tools to facilitate large-scale genomic DNA methylation analysis. Some of the tools have been described before1; these have been updated and are part of the DMAP package along with further new tools to complete the workflow for DNA methylation analysis. DMAP components can filter and process aligned bisulphite sequenced data to generate comprehensive reference methylomes in different units for any genome. DMAP can process aligned SAM files of multiple samples to provide reliable and statistically significant differentially methylated regions, then relate them to proximal genes and CpG features with reasonable rapidity. The package provides output in an appropriate format for bench scientists to further analyse the results without requiring specialist bioinformatics expertise.

DMAP is distributed as scripts and source code in a compressed tar archive which can be unpacked to generate the complete sources and Makefile. DMAP has been developed and tested on MacOS X systems (10.6 and 10.7) using gcc v4.2.1 and on various Linux platforms (RedHat, Centos, Fedora, Ubuntu) and will compile and run on any appropriate C compiler and 64 bit environment. The test data download unpacks into a directory named test_MDS_data containing fastq sequence reads and sam files from bismark mapping. A PDF in the download describes these in more detail.

DMAP was written by Peter Stockwell with considerable input from Aniruddha Chatterjee and support from Professor Ian Morison. We aim to continue development of the tool and its documentation to further facilitate DNA methylation analysis.

  • DMAP (compressed TAR archive 300 KB) Differential Methylation Analysis Package
  • Test dataset 1 for DMAP (compressed TAR archive ~ 650 MB) Reads from chromosome 1 for 2 samples (1 control and 1 disease)
  • Test dataset 2 for DMAP (compressed TAR archive ~ 88 MB) Reads from first 10 MBases of chromosome 21 for 6 samples ( 3 control and 3 disease)

Cite Publication: Stockwell P A, Chatterjee A, Rodger E J, and Morison I M, DMAP: Differential Methylation Analysis Package for RRBS and WGBS data Bioinformatics (2014) DOI: 10.1093/bioinformatics/btu126

1: Chatterjee A, Stockwell P A, Rodger E J, and Morison I M, Comparison of alignment software for genome-wide bisulphite sequence data, Nucleic Acids Research Volume 40, Issue 10Pp. e79

Scan_tcga suite of tools (for cancer genomics and epigenomics)

The Cancer Genome Atlas contains multiple levels of genomic data (mutation, gene expression, DNA methylation, copy number variation) for 33 cancer types in almost 11,000 patients. However, a dearth of appropriate software tools can make it difficult for bench scientists to use these data effectively. We present a suite of flexible, fast, command line based scripts that will allow retrieval and analysis of DNA methylation, mRNA, and miRNA expression from Cancer Genome Atlas network level 3 data.

Cite publication: Chatterjee A #, Stockwell P A #, Rodger E J, Parry M F, and Eccles M R: scan_tcga tools for integrated epigenomic and transcriptomic analysis of tumour subgroups Epigenomics September 2016. # equal. DOI: 10.2217/epi-2016-0063

Experimental and wet lab protocols and resources

A streamlined method for analysing genome-wide DNA methylation patterns from low amounts of FFPE DNA

Formalin fixed paraffin embedded (FFPE) tumor samples are a major source of DNA from patients in cancer research. However, FFPE is a challenging material to work with due to macromolecular fragmentation and nucleic acid crosslinking. FFPE tissue possesses particular challenges for methylation analysis and preparing sequencing-based libraries relying on bisulphite conversion. Successful bisulphite conversion is a key requirement for sequencing-based methylation analysis.

We describe a complete and streamlined workflow for preparing next generation sequencing libraries for methylation analysis from FFPE tissues. This includes counting cells from FFPE blocks and extracting DNA from FFPE slides, testing bisulphite conversion efficiency with a polymerase chain reaction (PCR) based test, preparing reduced representation bisulphite sequencing libraries, and massively parallel sequencing.

The main features and advantages of our protocol are

  • An optimised method for extracting good quality DNA from FFPE tissues
  • An efficient bisulfite conversion and next generation sequencing library preparation protocol that uses 50 ng DNA from FFPE tissue
  • Incorporation of a PCR-based test to assess bisulfite conversion efficiency prior to sequencing

Cite publication: Ludgate J L, Wright J, Stockwell P A, Morison I M, Eccles M R, and Chatterjee A, A streamlined method for analysing genome-wide DNA methylation patterns from low amounts of FFPE DNA, BMC Medical Genomics, 54 (10), August 2017 DOI: 10.1186/s12920-017-0290-1

Download the detailed protocol

Interactive resources and production

In late 2016, a series of YouTube videos were produced to cover Chatterjee, Stockwell, and colleagues' work on DNA methylation

Principles of genome-wide DNA methylation analysis and RRBS

Approaches for analysing genome-wide DNA methylation data

Tips and considerations for genome-wide DNA methylation and RRBS

Dr Chatterjee on the use of ORCID Research I