Motivation: Chromatin accessibility plays a key role in epigenetic regulation of gene activation and silencing. Open chromatin regions allow regulatory elements such as transcription factors and polymerases to bind for gene expression while closed chromatin regions prevent the activity of transcriptional machinery. Recently, Methyltransferase Accessibility Protocol for individual templates-Bisulfite Genome Sequencing (MAPit-BGS) and nucleosome occupancy and methylome sequencing (NOMe-seq) have been developed for simultaneously profiling chromatin accessibility and DNA methylation on single molecules. Therefore, there is a great demand in developing computational methods to identify chromatin accessibility from MAPit-BGS and NOMe-seq. Results: In this article, we present CAME (Chromatin Accessibility and Methylation), a seed-extension based approach that identifies chromatin accessibility from NOMe-seq. The efficiency and effectiveness of CAME were demonstrated through comparisons with other existing techniques on both simulated and real data, and the results show that our method not only can precisely identify chromatin accessibility but also outperforms other methods. Availability and Implementation: CAME is implemented in java and the program is freely available online at http://sourceforge.net/projects/came/. Contacts: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

It is well known that cancer cells are diverse and heterogeneous among patients, and that this heterogeneity makes cancer diagnosis and cure difficult. Intra-tumoral heterogeneity has very recently become important because a small proportion of drug-resistant or tumor-initiating cells can ultimately determine a patient's outcome. In this study, we propose new approaches to use variance tests, including the Lepage test, to identify inter-patient cancer heterogeneity, as well as multinomial logistic regression to identify intra-patient cancer heterogeneity, i.e., different epiallele composition. We conduct experiments to show the performance of the proposed approaches.

MOTIVATION: DNA methylation plays important roles in biological processes and human diseases, especially cancers. High-throughput bisulfite genomic sequencing based on new generation of sequencers, such as the 454-sequencing system provides an efficient method for analyzing DNA methylation patterns. The successful implementation of this approach depends on the use of primer design software capable of performing genome-wide scan for optimal primers from in silico bisulfite-treated genome sequences. We have developed a method, which fulfills this requirement and conduct primer design for sequences including regions of given promoter CpG islands. RESULTS: The developed method has been implemented using the C and JAVA programming languages. The primer design results were tested in the PCR experiments of 96 selected human DNA sequences containing CpG islands in the promoter regions. The results indicate that this method is efficient and reliable for designing sequence-specific primers. AVAILABILITY: The sequence-specific primer design for DNA meth-ylated sequences including CpG islands has been integrated into the second version of PRIMEGENS as one of the primer design features. The software is freely available for academic use at http://digbio.missouri.edu/primegens/.

Heterogeneous genetic and epigenetic alterations are commonly found in human non-Hodgkin's lymphomas (NHL). One such epigenetic alteration is aberrant methylation of gene promoter-related CpG islands, where hypermethylation frequently results in transcriptional inactivation of target genes, while a decrease or loss of promoter methylation (hypomethylation) is frequently associated with transcriptional activation. Discovering genes with these relationships in NHL or other types of cancers could lead to a better understanding of the pathobiology of these diseases. The simultaneous analysis of promoter methylation using Differential Methylation Hybridization (DMH) and its associated gene expression using Expressed CpG Island Sequence Tag (ECIST) microarrays generates a large volume of methylation-expression relational data. To analyze this data, we propose a set of algorithms based on fuzzy sets theory, in particular Possibilistic c-Means (PCM) and cluster fuzzy density. For each gene, these algorithms calculate measures of confidence of various methylation-expression relationships in each NHL subclass. Thus, these tools can be used as a means of high volume data exploration to better guide biological confirmation using independent molecular biology methods.