Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
The metabolism of tissues often involves interactions between several types of cell. Lewis et al. model metabolism within and between neurons in the human brain, gaining insight into energy metabolism and Alzheimer's disease.
Methods for profiling DNA methylation differ in the physical principles used to detect modified cytosines. Harris et al. compare the performances of four sequencing-based technologies for genome-wide analysis of DNA methylation and combine two methods to enable detection of allelic differences in epigenetic marks.
Comparison of the methylation patterns of cells in different developmental or disease states can help to elucidate both normal and pathological regulatory mechanisms. Bock et al. evaluate the ability of three sequencing-based methods and one microarray-based technology to detect differentially methylated regions on a genome-wide scale.
Label-free methods for assaying GPCR signaling promise to illuminate the effects of drugs in therapeutically relevant primary cells. Kostenis and colleagues demonstrate the utility of one such method, dynamic mass redistribution, in comparison with traditional second messenger–based assays.
Which of the possible combinations of epigenetic marks have biological significance is a major question in epigenetics. Analyzing data from human T-cells, Ernst and Kellis discover 51 distinct, recurring combinations of histone modifications that can be correlated with the functional annotations of the underlying DNA sequences.
Mining information from genomes often begins by aligning the sequences to identify evolutionarily conserved regions. Chen et al. assess the performance of four commonly used multiple sequence alignment tools.
ChIP-Seq data are usually analyzed with approaches developed for microarrays, which only consider binding events within a few kilobases of a gene. McLean et al. present an algorithm that takes into account more distant events, thereby improving functional annotation of regulatory regions.