chromstaR: Tracking combinatorial chromatin state dynamics in space and time

Background: Post-translational modifications of histone residue tails are an important component of genome regulation. It is becoming increasingly clear that the combinatorial presence and absence of various modifications define discrete chromatin states which determine the functional properties of a locus. An emerging experimental goal is to track changes in chromatin state maps across different conditions, such as experimental treatments, cell-types or developmental time points. Results: Here we present chromstaR, an algorithm for the computational inference of combinatorial chromatin state dynamics across an arbitrary number of conditions. ChromstaR uses a multivariate Hidden Markov Model to determine the number of discrete combinatorial chromatin states using multiple ChIP-seq experiments as input and assigns every genomic region to a state based on the presence/absence of each modification in every condition. We demonstrate the advantages of chromstaR in the context of three common experimental data scenarios. First, we study how different histone modifications combine to form combinatorial chromatin states in a single tissue. Second, we infer genome-wide patterns of combinatorial state differences between two cell types or conditions. Finally, we study the dynamics of combinatorial chromatin states during tissue differentiation involving up to six differentiation points. Our findings reveal a striking sparcity in the combinatorial organization and temporal dynamics of chromatin state maps. Conclusions: chromstaR is a versatile computational tool that facilitates a deeper biological understanding of chromatin organization and dynamics. The algorithm is implemented as an R-package and freely available from http://bioconductor.org/packages/chromstaR/.