Summary: Manual processing of DNA methylation data from bisulfite sequencing is a tedious and error-prone task. Here we present an interactive software tool that provides start-to-end support for this process. In an easy-to-use manner, the tool helps the user to import the sequence files from the sequencer, to align them, to exclude or correct critical sequences, to document the experiment, to perform basic statistics and to produce publication-quality diagrams. Emphasis is put on quality control: The program automatically assesses data quality and provides warnings and suggestions for dealing with critical sequences. The BiQ Analyzer program is implemented in the Java programming language and runs on any platform for which a recent Java virtual machine is available. Availability: The program is available without charge for non-commercial users and can be downloaded from http://biq-analyzer.bioinf.mpi-inf.mpg.de/ Contact:cbock@mpi-inf.mpg.de

Biomarkers are naturally-occurring characteristics by which a particular pathological process or disease can be identified or monitored. They can reflect past environmental exposures, predict disease onset or course, or determine a patient’s response to therapy. Epigenetic changes are such characteristics, with most epigenetic biomarkers discovered to date based on the epigenetic mark of DNA methylation. Many tissue types are suitable for the discovery of DNA methylation biomarkers including cell-based samples such as blood and tumor material and cell-free DNA samples such as plasma. DNA methylation biomarkers with diagnostic, prognostic and predictive power are already in clinical trials or in a clinical setting for cancer. Outside cancer, strong evidence that complex disease originates in early life is opening up exciting new avenues for the detection of DNA methylation biomarkers for adverse early life environment and for estimation of future disease risk. However, there are a number of limitations to overcome before such biomarkers reach the clinic. Nevertheless, DNA methylation biomarkers have great potential to contribute to personalized medicine throughout life. We review the current state of play for DNA methylation biomarkers, discuss the barriers that must be crossed on the way to implementation in a clinical setting, and predict their future use for human disease.

CpG island methylation plays an important role in epigenetic gene control during mammalian development and is frequently altered in disease situations such as cancer. The majority of CpG islands is normally unmethylated, but a sizeable fraction is prone to become methylated in various cell types and pathological situations. The goal of this study is to show that a computational epigenetics approach can discriminate between CpG islands that are prone to methylation from those that remain unmethylated. We develop a bioinformatics scoring and prediction method on the basis of a set of 1,184 DNA attributes, which refer to sequence, repeats, predicted structure, CpG islands, genes, predicted binding sites, conservation, and single nucleotide polymorphisms. These attributes are scored on 132 CpG islands across the entire human Chromosome 21, whose methylation status was previously established for normal human lymphocytes. Our results show that three groups of DNA attributes, namely certain sequence patterns, specific DNA repeats, and a particular DNA structure, are each highly correlated with CpG island methylation (correlation coefficients of 0.64, 0.66, and 0.49, respectively). We predicted, and subsequently experimentally examined 12 CpG islands from human Chromosome 21 with unknown methylation patterns and found more than 90% of our predictions to be correct. In addition, we applied our prediction method to analyzing Human Epigenome Project methylation data on human Chromosome 6 and again observed high prediction accuracy. In summary, our results suggest that DNA composition of CpG islands (sequence, repeats, and structure) plays a significant role in predisposing CpG islands for DNA methylation. This finding may have a strong impact on our understanding of changes in CpG island methylation in development and disease. DNA methylation is the only epigenetic mechanism in eukaryotes that is known to directly modify the DNA. It plays an important role for gene control during development and cell differentiation, and it is a promising therapeutic target in cancer research. While a genome-wide picture of DNA methylation patterns is currently emerging, we have only fragmentary knowledge about the linkage between DNA methylation and other genomic attributes such as DNA sequence and structure, repetitive elements, or sequence conservation. The authors fill this gap by reporting on a comprehensive bioinformatical analysis of DNA methylation on human Chromosome 21—and in part, extending to other regions of the human genome. They report new associations that will help elucidate the functions of DNA methylation along the human genome. Furthermore, the authors show that their findings can be applied to predicting DNA methylation patterns from genome sequence. Such predictions have the potential of speeding up genome-wide epigenetic profiling: It may be possible to focus experimental resources on a few selected areas while bioinformatics procedures are applied to the bulk of the genome.

DNA methylation changes that are recurrent in cancer have generated great interest as potential biomarkers for the early detection and monitoring of cancer. In such situations, essential information is missed if the methylation detection is purely qualitative. We describe a new probe-free quantitative methylation-specific PCR (MSP) assay that incorporates evaluation of the amplicon by high-resolution melting (HRM) analysis. Depending on amplicon design, different types of information can be obtained from the HRM analysis. Much of this information cannot be obtained by electrophoretic analysis. In particular, identification of false positives due to incomplete bisulphite conversion or false priming is possible. Heterogeneous methylation can also be distinguished from homogeneous methylation. As proof of principle, we have developed assays for the promoter regions of the CDH1 , DAPK1 , CDKN2A ( p16 ^INK4a ) and RARB genes. We show that highly accurate quantification is possible in the range from 100% to 0.1% methylated template when 25 ng of bisulphite-modified DNA is used as a template for PCR. We have named this new approach to quantitative methylation detection, Sensitive Melting Analysis after Real Time (SMART)-MSP.