Histones are frequently decorated with covalent modifications. These histone modifications are thought to be involved in various chromatin-dependent processes including transcription. To elucidate the relationship between histone modifications and transcription, we derived quantitative models to predict the expression level of genes from histone modification levels. We found that histone modification levels and gene expression are very well correlated. Moreover, we show that only a small number of histone modifications are necessary to accurately predict gene expression. We show that different sets of histone modifications are necessary to predict gene expression driven by high CpG content promoters (HCPs) or low CpG content promoters (LCPs). Quantitative models involving H3K4me3 and H3K79me1 are the most predictive of the expression levels in LCPs, whereas HCPs require H3K27ac and H4K20me1. Finally, we show that the connections between histone modifications and gene expression seem to be general, as we were able to predict gene expression levels of one cell type using a model trained on another one.

An analysis of cell-type-specific epigenomic features reveals a relationship between epigenomic and mutational profiles; chromatin characteristics can explain a large proportion of mutational variance in cancer genomes and the mutational distribution can identify the probable cell type from which a given cancer originated from. Genomic studies have shown that different cancer types vary substantially in the local density and types of somatic mutations. This has been explained not only by differences in DNA sequence but also by other features including epigenetic organization. Shamil Sunyaev and colleague now compare mutation densities to detailed epigenetic profiles of different cell types and tissues. They demonstrate that epigenomic features of a given cell type or tissue in which a cancer arises are much stronger determinants of mutational profiles than other properties. Conversely, the findings make it possible to deduce information on the possible tissue-of-origin of a tumour based on its mutational landscape. Cancer is a disease potentiated by mutations in somatic cells. Cancer mutations are not distributed uniformly along the human genome. Instead, different human genomic regions vary by up to fivefold in the local density of cancer somatic mutations1, posing a fundamental problem for statistical methods used in cancer genomics. Epigenomic organization has been proposed as a major determinant of the cancer mutational landscape1,2,3,4,5. However, both somatic mutagenesis and epigenomic features are highly cell-type-specific6,7. We investigated the distribution of mutations in multiple independent samples of diverse cancer types and compared them to cell-type-specific epigenomic features. Here we show that chromatin accessibility and modification, together with replication timing, explain up to 86% of the variance in mutation rates along cancer genomes. The best predictors of local somatic mutation density are epigenomic features derived from the most likely cell type of origin of the corresponding malignancy. Moreover, we find that cell-of-origin chromatin features are much stronger determinants of cancer mutation profiles than chromatin features of matched cancer cell lines. Furthermore, we show that the cell type of origin of a cancer can be accurately determined based on the distribution of mutations along its genome. Thus, the DNA sequence of a cancer genome encompasses a wealth of information about the identity and epigenomic features of its cell of origin.

Summary: Biological insights can be obtained through computational integration of genomics data sets consisting of diverse types of information. The integration is often hampered by a large variety of existing file formats, often containing similar information, and the necessity to use complicated tools to achieve the desired results. We have built an R package, genomation, to expedite the extraction of biological information from high throughput data. The package works with a variety of genomic interval file types and enables easy summarization and annotation of high throughput data sets with given genomic annotations. Availability and implementation: The software is currently distributed under MIT artistic license and freely available at http://bioinformatics.mdc-berlin.de/genomation, and through the Bioconductor framework. Contact:dirk.schubeler@fmi.ch, chm2042@med.cornell.edu, altuna.akalin@fmi.ch, or aakalin@gmail.com

Background: PSAIA (Protein Structure and Interaction Analyzer) was developed to compute geometric parameters for large sets of protein structures in order to predict and investigate protein-protein interaction sites. Results: In addition to most relevant established algorithms, PSAIA offers a new method PIADA (Protein Interaction Atom Distance Algorithm) for the determination of residue interaction pairs. We found that PIADA produced more satisfactory results than comparable algorithms implemented in PSAIA. Particular advantages of PSAIA include its capacity to combine different methods to detect the locations and types of interactions between residues and its ability, without any further automation steps, to handle large numbers of protein structures and complexes. Generally, the integration of a variety of methods enables PSAIA to offer easier automation of analysis and greater reliability of results. PSAIA can be used either via a graphical user interface or from the command-line. Results are generated in either tabular or XML format. Conclusion: In a straightforward fashion and for large sets of protein structures, PSAIA enables the calculation of protein geometric parameters and the determination of location and type for protein-protein interaction sites. XML formatted output enables easy conversion of results to various formats suitable for statistic analysis. Results from smaller data sets demonstrated the influence of geometry on protein interaction sites. Comprehensive analysis of properties of large data sets lead to new information useful in the prediction of protein-protein interaction sites.