CMINT’s outputs were useful to identify both areas that were cell-type specific and additionally identify important transition points in the hierarchy

CMINT’s outputs were useful to identify both areas that were cell-type specific and additionally identify important transition points in the hierarchy. reliably detect chromatin transitions between cell types. We applied CMINT to PAPA1 gain novel insights in two complex processes: reprogramming to induced pluripotent stem cells (iPSCs) and hematopoiesis. In reprogramming, chromatin changes could happen without large gene manifestation changes, different mixtures of activating marks were associated with specific reprogramming factors, there was an order of acquisition of chromatin marks at pluripotency loci, and multivalent claims (comprising previously undetermined mixtures of activating and repressive histone modifications) were enriched for CTCF. In the hematopoietic system, we defined essential decision points in the lineage tree, recognized regulatory elements that were enriched in cell-typeCspecific areas, and found that the underlying chromatin state was achieved by specific erasure of preexisting chromatin marks in the precursor cell or by de novo assembly. Our method provides a systematic approach to model the dynamics of chromatin state to provide novel insights into the human relationships among cell types in varied cell-fate specification processes. Regulatory networks that control cell-typeCspecific gene manifestation patterns are founded through a complex interplay between epigenetic modifications and transcription element binding at regulatory regions of a gene. Transcription factors alone are adequate to convert differentiated somatic cells to induced pluripotent stem cells (iPSCs) (Takahashi and Yamanaka 2006) albeit at low effectiveness. Chemical or genetic modifiers that reduce repressive chromatin levels enhance reprogramming effectiveness implicating epigenetic contribution (Onder et al. 2012; Apostolou and Hochedlinger 2013; Papp and Plath 2013; Sridharan et al. 2013). Reciprocally, during development, the chromatin Skepinone-L state at specific loci has to become permissive concomitant with appropriate transcription factor levels for cell-typeCspecific manifestation to commence. Given the multitude of histone modifications and their mixtures, parsing which ones are necessary or adequate to enable a permissive environment for gene manifestation is definitely a challenge. Therefore, systematic approaches to study the dynamics of chromatin are essential to understand Skepinone-L the underlying regulatory networks that travel transitions during cell fate change. Several computational approaches, ChromHMM (Ernst and Kellis 2010), jMosaics (Zeng et al. 2013), EpiCSeg (Mammana and Chung 2015), Segway (Hoffman et al. 2012), and GATE (Yu et al. 2013), have been formulated to examine multiple chromatin marks in one or more cell types. With the exception of GATE, these methods focus more on instantly segmenting the genome to identify regulatory elements and less on analyzing dynamics of chromatin state. Most computational analyses of chromatin marks across multiple cell types have either focused on identifying differential areas between pairs of cell types or time points (Liang and Keles 2012; Shao et al. 2012), solitary clustering of loci using marks across all cell types (Suv et al. 2014), or clustering entire epigenomes one mark at a time (Roadmap Epigenomics Consortium et al. 2015). Importantly, existing methods for multiple cell-type chromatin data do not account for the hierarchical human relationships between the cell types. To enable systematic characterization of chromatin state dynamics across multiple related cell types, we developed Chromatin Module INference on Trees (CMINT). We define a chromatin module to be a set of genomic loci with the same combination of chromatin modifications that likely symbolize coordinately controlled genes exhibiting related regulatory claims analogous to gene manifestation modules (Tanay et al. 2004). A novel aspect of our approach is that we model the relationship of different cell types. We applied CMINT to eight chromatin marks to study chromatin state transitions during reprogramming to iPSCs. Seven of these marks correspond to histone post-translational modifications (PTMs) that we previously identified to be significantly changed during reprogramming using an unbiased mass spectrometry approach (Sridharan et al. 2013). These marks are associated with active transcription (H3K4me3, H3K9ac, H3K14ac, and H3K18ac), repression (H3K9me3 and H3K9me2), and transcription elongation (H3K79me2). We profiled these modifications in the promoters of somatic cells, partial and completely reprogrammed iPSCs, and combined it with published data measuring H3K4me3 and H3K27me3 (Maherali et al. 2007; Sridharan et al. 2009). We also applied CMINT to the hematopoietic lineage with Skepinone-L 15 different cell types in which four chromatin marks (H3K27ac, H3K4me1, H3K4me2, and H3K4me3) were measured (Lara-Astiaso et al. 2014). Results CMINT:.