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/

MOTIVATION: Gene regulation commonly involves interaction among DNA, proteins and biochemical conditions. Using chromatin immunoprecipitation (ChIP) technologies, protein-DNA interactions are routinely detected in the genome scale. Computational methods that detect weak protein-binding signals and simultaneously maintain a high specificity yet remain to be challenging. An attractive approach is to incorporate biologically relevant data, such as protein co-occupancy, to improve the power of protein-binding detection. We call the additional data related with the target protein binding as supporting tracks. RESULTS: We propose a novel but rigorous statistical method to identify protein occupancy in ChIP data using multiple supporting tracks (PASS2). We demonstrate that utilizing biologically related information can significantly increase the discovery of true protein-binding sites, while still maintaining a desired level of false positive calls. Applying the method to GATA1 restoration in mouse erythroid cell line, we detected many new GATA1-binding sites using GATA1 co-occupancy data. AVAILABILITY: http://stat.psu.edu/ approximately yuzhang/pass2.tar.

BACKGROUND: The most common application for the next-generation sequencing technologies is resequencing, where short reads from the genome of an individual are aligned to a reference genome sequence for the same species. These mappings can then be used to identify genetic differences among individuals in a population, and perhaps ultimately to explain phenotypic variation. Many algorithms capable of aligning short reads to the reference, and determining differences between them have been reported. Much less has been reported on how to use these technologies to determine genetic differences among individuals of a species for which a reference sequence is not available, which drastically limits the number of species that can easily benefit from these new technologies. RESULTS: We describe a computational pipeline, called DIAL (De novo Identification of Alleles), for identifying single-base substitutions between two closely related genomes without the help of a reference genome. The method works even when the depth of coverage is insufficient for de novo assembly, and it can be extended to determine small insertions/deletions. We evaluate the software's effectiveness using published Roche/454 sequence data from the genome of Dr. James Watson (to detect heterozygous positions) and recent Illumina data from orangutan, in each case comparing our results to those from computational analysis that uses a reference genome assembly. We also illustrate the use of DIAL to identify nucleotide differences among transcriptome sequences. CONCLUSIONS: DIAL can be used for identification of nucleotide differences in species for which no reference sequence is available. Our main motivation is to use this tool to survey the genetic diversity of endangered species as the identified sequence differences can be used to design genotyping arrays to assist in the species' management. The DIAL source code is freely available at http://www.bx.psu.edu/miller_lab/.

MOTIVATION: Genomic mutations and variations provide insightful information about the functionality of sequence elements and their association with human diseases. Traditionally, variations are identified through analysis of short DNA sequences, usually shorter than 1000 bp per fragment. Optical maps provide both faster and more cost-efficient means for detecting such differences, because a single map can span over 1 million bp. Optical maps are assembled to cover the whole genome, and the accuracy of assembly is critical. RESULTS: We present a computationally efficient model-based method for improving quality of such assemblies. Our method provides very high accuracy even with moderate coverage (<20 x). We utilize a hidden Markov model to represent the consensus map and use the expectation-Maximization algorithm to drive the refinement process. We also provide quality scores to assess the quality of the finished map. AVAILABILITY: Code is available from www.cmb.usc.edu/people/valouev/