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.high CpG content promoter | low CpG content promoter | regression analysis | transcription T he DNA of eukaryotic organisms is packaged into chromatin, whose basic repeating unit is the nucleosome. A nucleosome is formed by wrapping 147 base pairs of DNA around an octamer of four core histones, H2A, H2B, H3, and H4 (1-5) which are subject to a number of posttranslational covalent modifications [(6); for review, see ref. 7]. These modifications can alter the chromatin structure and function by changing the charge of the nucleosome particle, and/or by recruiting protein complexes either individually or in combination (8). Hence, histone modifications are thought to constitute a "Histone Code," which is read out by proteins to bring about specific downstream effects (9, 10).Histone modifications have been linked to a number of chromatin-dependent processes, including replication, DNA-repair, and transcription. The link between histone modifications and transcription has been particularly intensively studied. It has been found that individual modifications can be associated with transcriptional activation or repression. Acetylation and phosphorylation generally accompany transcription; sumoylation, deimination, and proline isomerization are usually found in transcriptionally silent regions; methylation and ubiquitination are implicated in both activation and repression of transcription (8). Furthermore, the establishment of some modifications is dependent on the presence of other modifications, e.g., the catalysis of H3K4me3 requires the presence of H2BK120ub1 (the so-called trans-tail pathway) and the phosphorylation on serine 5 on the C-terminal domain of RNA polymerase II (pol II) (for review, see ref. 11, which also reviews other examples for the combinatorial action of histone modifications).Transcription proceeds in a series of steps, also referred to as transcription cycle, starting with preinitiation complex form...

No abstract

The regulation of intragenic miRNAs by their own intronic promoters is one of the open problems of miRNA biogenesis. Here, we describe PROmiRNA, a new approach for miRNA promoter annotation based on a semi-supervised statistical model trained on deepCAGE data and sequence features. We validate our results with existing annotation, PolII occupancy data and read coverage from RNA-seq data. Compared to previous methods PROmiRNA increases the detection rate of intronic promoters by 30%, allowing us to perform a large-scale analysis of their genomic features, as well as elucidate their contribution to tissue-specific regulation. PROmiRNA can be downloaded from http://promirna.molgen.mpg.de.

Histone modifications are known to play an important role in the regulation of transcription. While individual modifications have received much attention in genome-wide analyses, little is known about their relationships. Some authors have built Bayesian networks of modifications, however most often they have used discretized data, and relied on unrealistic assumptions such as the absence of feedback mechanisms or hidden confounding factors. Here, we propose to infer undirected networks based on partial correlations between histone modifications. Within the partial correlation framework, correlations among two variables are controlled for associations induced by the other variables. Partial correlation networks thus focus on direct associations of histone modifications. We apply this methodology to data in CD4+ cells. The resulting network is well supported by common knowledge. When pairs of modifications show a large difference between their correlation and their partial correlation, a potential confounding factor is identified and provided as explanation. Data from different cell types (IMR90, H1) is also exploited in the analysis to assess the stability of the networks. The results are remarkably similar across cell types. Based on this observation, the networks from the three cell types are integrated into a consensus network to increase robustness. The data and the results discussed in the manuscript can be found, together with code, on http://spcn.molgen.mpg.de/index.html.