The ChIP-seq signal of histone modifications at promoters is a good predictor of gene expression in different cellular contexts, but whether this is also true at enhancers is not clear. To address this issue, we develop quantitative models to characterize the relationship of gene expression with histone modifications at enhancers or promoters. We use embryonic stem cells (ESCs), which contain a full spectrum of active and repressed (poised) enhancers, to train predictive models. As many poised enhancers in ESCs switch towards an active state during differentiation, predictive models can also be trained on poised enhancers throughout differentiation and in development. Remarkably, we determine that histone modifications at enhancers, as well as promoters, are predictive of gene expression in ESCs and throughout differentiation and development. Importantly, we demonstrate that their contribution to the predictive models varies depending on their location in enhancers or promoters. Moreover, we use a local regression (LOESS) to normalize sequencing data from different sources, which allows us to apply predictive models trained in a specific cellular context to a different one. We conclude that the relationship between gene expression and histone modifications at enhancers is universal and different from promoters. Our study provides new insight into how histone modifications relate to gene expression based on their location in enhancers or promoters.

MOTIVATION: High-throughput sequencing (HTS) has revolutionized gene regulation studies and is now fundamental for the detection of protein-DNA and protein-RNA binding, as well as for measuring RNA expression. With increasing variety and sequencing depth of HTS datasets, the need for more flexible and memory-efficient tools to analyse them is growing. RESULTS: We describe Pyicos, a powerful toolkit for the analysis of mapped reads from diverse HTS experiments: ChIP-Seq, either punctuated or broad signals, CLIP-Seq and RNA-Seq. We prove the effectiveness of Pyicos to select for significant signals and show that its accuracy is comparable and sometimes superior to that of methods specifically designed for each particular type of experiment. Pyicos facilitates the analysis of a variety of HTS datatypes through its flexibility and memory efficiency, providing a useful framework for data integration into models of regulatory genomics. AVAILABILITY: Open-source software, with tutorials and protocol files, is available at http://regulatorygenomics.upf.edu/pyicos or as a Galaxy server at http://regulatorygenomics.upf.edu/galaxy CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.