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1.  Cancer epigenetics: a perspective on the role of DNA methylation in acquired endocrine resistance 
Chinese journal of cancer  2011;30(11):749-756.
Epigenetic mechanisms, including DNA methylation, are responsible for determining and maintaining cell fate, stably differentiating the various tissues in our bodies. Increasing evidence shows that DNA methylation plays a significant role in cancer, from the silencing of tumor suppressors to the activation of oncogenes and the promotion of metastasis. Recent studies also suggest a role for DNA methylation in drug resistance. This perspective article discusses how DNA methylation may contribute to the development of acquired endocrine resistance, with a focus on breast cancer. In addition, we discuss DNA methylome profiling and how recent developments in this field are shedding new light on the role of epigenetics in endocrine resistance. Hormone ablation is the therapy of choice for hormone-sensitive breast tumors, yet as many as 40% of patients inevitably relapse, and these hormone refractory tumors often have a poor prognosis. Epigenetic studies could provide DNA methylation biomarkers to predict and diagnose acquired resistance in response to treatment. Elucidation of epigenetic mechanisms may also lead to the development of new treatments that specifically target epigenetic abnormalities or vulnerabilities in cancer cells. Expectations must be tempered by the fact that epigenetic mechanisms of endocrine resistance remain poorly understood, and further study is required to better understand how altering epigenetic pathways with therapeutics can promote or inhibit endocrine resistance in different contexts. Going forward, DNA methylome profiling will become increasingly central to epigenetic research, heralding a network-based approach to epigenetics that promises to advance our understanding of the etiology of cancer in ways not previously possible.
doi:10.5732/cjc.011.10128
PMCID: PMC3890241  PMID: 22035855
Epigenetics; breast cancer; endocrine resistance; methylation; methylome profiling
2.  Enrichment-based DNA methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes 
BMC Genomics  2012;13(Suppl 8):S6.
Background
DNA methylation is an important epigenetic mark and dysregulation of DNA methylation is associated with many diseases including cancer. Advances in next-generation sequencing now allow unbiased methylome profiling of entire patient cohorts, greatly facilitating biomarker discovery and presenting new opportunities to understand the biological mechanisms by which changes in methylation contribute to disease. Enrichment-based sequencing assays such as MethylCap-seq are a cost effective solution for genome-wide determination of methylation status, but the technical reliability of methylation reconstruction from raw sequencing data has not been well characterized.
Methods
We analyze three MethylCap-seq data sets and perform two different analyses to assess data quality. First, we investigate how data quality is affected by excluding samples that do not meet quality control cutoff requirements. Second, we consider the effect of additional reads on enrichment score, saturation, and coverage. Lastly, we verify a method for the determination of the global amount of methylation from MethylCap-seq data by comparing to a spiked-in control DNA of known methylation status.
Results
We show that rejection of samples based on our quality control parameters leads to a significant improvement of methylation calling. Additional reads beyond ~13 million unique aligned reads improved coverage, modestly improved saturation, and did not impact enrichment score. Lastly, we find that a global methylation indicator calculated from MethylCap-seq data correlates well with the global methylation level of a sample as obtained from a spike-in DNA of known methylation level.
Conclusions
We show that with appropriate quality control MethylCap-seq is a reliable tool, suitable for cohorts of hundreds of patients, that provides reproducible methylation information on a feature by feature basis as well as information about the global level of methylation.
doi:10.1186/1471-2164-13-S8-S6
PMCID: PMC3535705  PMID: 23281662
3.  Methods for high-throughput MethylCap-Seq data analysis 
BMC Genomics  2012;13(Suppl 6):S14.
Background
Advances in whole genome profiling have revolutionized the cancer research field, but at the same time have raised new bioinformatics challenges. For next generation sequencing (NGS), these include data storage, computational costs, sequence processing and alignment, delineating appropriate statistical measures, and data visualization. Currently there is a lack of workflows for efficient analysis of large, MethylCap-seq datasets containing multiple sample groups.
Methods
The NGS application MethylCap-seq involves the in vitro capture of methylated DNA and subsequent analysis of enriched fragments by massively parallel sequencing. The workflow we describe performs MethylCap-seq experimental Quality Control (QC), sequence file processing and alignment, differential methylation analysis of multiple biological groups, hierarchical clustering, assessment of genome-wide methylation patterns, and preparation of files for data visualization.
Results
Here, we present a scalable, flexible workflow for MethylCap-seq QC, secondary data analysis, tertiary analysis of multiple experimental groups, and data visualization. We demonstrate the experimental QC procedure with results from a large ovarian cancer study dataset and propose parameters which can identify problematic experiments. Promoter methylation profiling and hierarchical clustering analyses are demonstrated for four groups of acute myeloid leukemia (AML) patients. We propose a Global Methylation Indicator (GMI) function to assess genome-wide changes in methylation patterns between experimental groups. We also show how the workflow facilitates data visualization in a web browser with the application Anno-J.
Conclusions
This workflow and its suite of features will assist biologists in conducting methylation profiling projects and facilitate meaningful biological interpretation.
doi:10.1186/1471-2164-13-S6-S14
PMCID: PMC3481483  PMID: 23134780
4.  Contactin 4 as an Autism Susceptibility Locus 
Scientific Abstract
Structural and sequence variation have been described in several members of the contactin (CNTN) and contactin associated protein (CNTNAP) gene families in association with neurodevelopmental disorders, including autism. Using array comparative genome hybridization (CGH), we identified a maternally inherited ~535 kb deletion at 3p26.3 encompassing the 5′ end of the contactin 4 gene (CNTN4) in a patient with autism. Based on this finding and previous reports implicating genomic rearrangements of CNTN4 in autism spectrum disorders (ASDs) and 3p− microdeletion syndrome, we undertook sequencing of the coding regions of the gene in a local ASD cohort in comparison with a set of controls. Unique missense variants were identified in 4/75 unrelated individuals with an ASD, as well as in 1/107 controls. All of the amino acid substitutions were nonsynonomous, occurred at evolutionarily conserved positions, and were, thus, felt likely to be deleterious. However, these data did not reach statistical significance, nor did the variants segregate with disease within all of the ASD families. Finally, there was no detectable difference in binding of two of the variants to the interacting protein PTPRG in vitro. Thusadditional, larger studies will be necessary to determine whether CNTN4 functions as an autism susceptibility locus in combination with other genetic and/or environmental factors.
doi:10.1002/aur.184
PMCID: PMC3209658  PMID: 21308999
contactin 4; autism; autism spectrum disorder; 3p26 deletion; contactins; susceptibility locus
5.  A Scalable, Flexible Workflow for MethylCap-Seq Data Analysis 
Advances in whole genome profiling have revolutionized the cancer research field, but at the same time have raised new bioinformatics challenges. For next generation sequencing (NGS), these include data storage, computational costs, sequence processing and alignment, delineating appropriate statistical measures, and data visualization. The NGS application MethylCap-seq involves the in vitro capture of methylated DNA and subsequent analysis of enriched fragments by massively parallel sequencing. Here, we present a scalable, flexible workflow for MethylCap-seq Quality Control, secondary data analysis, tertiary analysis of multiple experimental groups, and data visualization. This workflow and its suite of features will assist biologists in conducting methylation profiling projects and facilitate meaningful biological interpretation.
doi:10.1109/GENSiPS.2011.6169426
PMCID: PMC3320741  PMID: 22484542
next generation sequencing; DNA methylation; epigenetics; cancer; data analysis; data visualization

Results 1-5 (5)