BACKGROUND: Epigenetic modification of chromatin plays a pivotal role in regulating gene expression during cell differentiation. The scale and complexity of epigenetic data pose significant challenges for biologists to identify the regulatory events controlling cell differentiation. RESULTS: To reduce the complexity, we developed a package, called Snapshot, for clustering and visualizing candidate cis-regulatory elements (cCREs) based on their epigenetic signals during cell differentiation. This package first introduces a binarized indexing strategy for clustering the cCREs. It then provides a series of easily interpretable figures for visualizing the signal and epigenetic state patterns of the cCREs clusters during the cell differentiation. It can also use different hierarchies of cell types to highlight the epigenetic history specific to any particular cell lineage. We demonstrate the utility of Snapshot using data from a consortium project for ValIdated Systematic IntegratiON (VISION) of epigenomic data in hematopoiesis. CONCLUSION: The package Snapshot can identify all distinct clusters of genomic locations with unique epigenetic signal patterns during cell differentiation. It outperforms other methods in terms of interpreting and reproducing the identified cCREs clusters. The package of Snapshot is available at GitHub: https://github.com/guanjue/Snapshot .

SUMMARY: Epigenetic modifications reflect key aspects of transcriptional regulation, and many epigenomic datasets have been generated under different biological contexts to provide insights into regulatory processes. However, the technical noise in epigenomic datasets and the many dimensions (features) examined make it challenging to effectively extract biologically meaningful inferences from these datasets. We developed a package that reduces noise while normalizing the epigenomic data by a novel normalization method, followed by integrative dimensional reduction by learning and assigning epigenetic states. This package, called S3V2-IDEAS, can be used to identify epigenetic states for multiple features, or identify discretized signal intensity levels and a master peak list across different cell types for a single feature. We illustrate the outputs and performance of S3V2-IDEAS using 137 epigenomics datasets from the VISION project that provides ValIdated Systematic IntegratiON of epigenomic data in hematopoiesis. AVAILABILITY AND IMPLEMENTATION: S3V2-IDEAS pipeline is freely available as open source software released under an MIT license at: https://github.com/guanjue/S3V2_IDEAS_ESMP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Systems biology integrates expression, methylation, transcription factor binding and histone modification profiles with other physiological characteristics of a specific organ. Repositories that provide the required data, like ENCODE, generally work on a high level and do not take the heterogeneity of cell types within an organ into consideration. The hematopoietic system allows the characterization and study of each cell type involved in the generation of blood cells from bone marrow stem cells and thus provides a good foundation for systems biology studies. Here we compare RNA expression, DNA methylation, chromatin accessibility, DNA binding proteins and histone modification profiles in seven different hematopoietic populations using a Bayesian non-parametric hierarchical latent-class mixed-effect model known as IDEAS to characterize epigenetic changes associated with hematopoietic differentiation. Unlike other existing approaches IDEAS considers various cell types of a biological systems in concert instead of disjointly. Results: Using the VISION database and the IDEAS toolkit we provide insights into the transcriptional, epigenetic and regulatory programs governing the hematopoietic differentiation process. The characterization of the different hematopoietic components and their interactions provide the foundations for a systems biology model of hematopoiesis. Previous hematopoietic epigenome segmentation studies have focused on histone modifications, chromatin accessibility and DNA binding protein profiles. DNA methylation has been shown to vary markedly in hematopoietic populations. Inclusion of DNA methylation in these segmentation studies increased the original 36-state model of regulatory interactions to 41 states. These new DNA methylation-related states were associated with repressive marks, active RNA transcription, and a novel state regulated by DNA methylation alone. Imputing epigenetic models on inputs systematically perturbed for hematopoietic populations resulted in models of varying degrees of overlap, which were quantified and set in context with underlying biological processes. Conclusion: Our data show that methylation has a strong impact on functional genomic modeling and can be used to discern cell type specific epigenetic regulatory behavior by leveraging imputation for missing cell type data.

SUMMARY: Hidden Markov models (HMMs) are flexible and widely used in scientific studies. Particularly in genomics and genetics, there are multiple distinct regimes in the genome within each of which the relationships among multivariate features are distinct. Examples include differential gene regulation depending on gene functions and experimental conditions, and varying combinatorial patterns of multiple transcription factors. We developed a software package called MRHMMs (Multivariate Regression Hidden Markov Models and the variantS) that accommodates a variety of HMMs that can be flexibly applied to many biological studies and beyond. MRHMMs supplements existing HMM software packages in two aspects. First, MRHMMs provides a diverse set of emission probability structures, including mixture of multivariate normal distributions and (logistic) regression models. Second, MRHMMs is computationally efficient for analyzing large data-sets generated in current genome-wide studies. Especially, the software is written in C for the speed advantage and further amenable to implement alternative models to meet users' own purposes. AVAILABILITY AND IMPLEMENTATION: http://sourceforge.net/projects/mrhmms/

Within a single alignment of two DNA sequences or two protein sequences, some regions may be much better conserved than others. Such strong conservation may reveal a region that possesses an important function. When alignments are so long that it is infeasible, or at least undesirable, to inspect them in complete detail, it is helpful to have an automatic process that computes information about the varying degree of conservation along the alignment and displays the information in a graphical representation that is readily assimilated. This paper presents methods for computing several such 'robustness measures' at each position of a given alignment. These methods are all very space-efficient; they use only space proportional to the sum of the two sequence lengths. To illustrate their effectiveness, one of the methods is used to locate particularly well-conserved regions in the beta-globin gene locus control region and in the 5' flank of the gamma-globin gene.

The local similarity problem is to determine the similar regions within two given sequences. We recently developed a dynamic programming algorithm for the local similarity problem that requires only space proportional to the sum of the two sequence lengths, whereas earlier methods use space proportional to the product of the lengths. In this paper, we describe how to parallelize the new algorithm and present results of experimental studies on an Intel hypercube. The parallel method provides rapid, high-resolution alignments for users of our software toolkit for pairwise sequence comparison, as illustrated here by a comparison of the chloroplast genomes of tobacco and liverwort.

Existing dynamic-programming algorithms for identifying similar regions of two sequences require time and space proportional to the product of the sequence lengths. Often this space requirement is more limiting than the time requirement. We describe a dynamic-programming local-similarity algorithm that needs only space proportional to the sum of the sequence lengths. The method can also find repeats within a single long sequence. To illustrate the algorithm's potential, we discuss comparison of a 73,360 nucleotide sequence containing the human beta-like globin gene cluster and a corresponding 44,594 nucleotide sequence for rabbit, a problem well beyond the capabilities of other dynamic-programming software.