Research articles

The physical properties that determine the propensity of a protein to form a well-ordered crystal suitable for structure determination are poorly understood. An analysis of large-scale crystallization results generated by a structural genomics consortium highlights the importance of low-entropy surface features capable of mediating protein-protein interactions.

Critical considerations in the design and analysis of ChIP-seq experiments include how to align sequenced tags to the genome, how to detect binding sites and how to estimate the number of tags needed to confidently determine where a protein binds DNA. Using data set for three transcription factors, Kharchenko et al. address these considerations by comparing three novel algorithms with published computational methods.

Chechik et al. define activity motifs, which extend the concept of a network motif from the static to the dynamic realm. Mapping functional data onto network structure enables them to reveal new systems-level principles describing how yeast cells integrate exogenous signals and use transcriptional regulation to optimize metabolic responses to environmental perturbations.

Metabolic network modeling in multicellular organisms is confounded by the existence of multiple tissues with distinct metabolic functions. By integrating a genome-scale metabolic network with tissue-specific gene- and protein-expression data, Shlomi et al. adapt constraint-based approaches used for microorganisms to predicting metabolism in ten human tissues. Their computational approach should facilitate interpretation of expression data in the context of metabolic disorders.

An inability to estimate absolute DNA methylation levels has slowed progress in understanding the role of this epigenetic modification in health and disease. Down et al. describe an algorithm for analyzing methylated DNA immunoprecipitation profiles generated using either high-throughput sequencing or oligonucleotide arrays.