MOTIVATION: Multiple high-throughput approaches have recently been developed and allowed the discovery of enhancers on a genome scale in a single experiment. However, the datasets generated from these approaches are not fully utilized by the research community due to technical challenges such as lack of consensus enhancer annotation and integrative analytic tools. RESULTS: We developed an interactive database, EnhancerAtlas, which contains an atlas of 2,534,123 enhancers for 105 cell/tissue types. A consensus enhancer annotation was obtained for each cell by summation of independent experimental datasets with the relative weights derived from a cross-validation approach. Moreover, EnhancerAtlas provides a set of useful analytic tools that allow users to query and compare enhancers in a particular genomic region or associated with a gene of interest, and assign enhancers and their target genes from a custom dataset. AVAILABILITY AND IMPLEMENTATION: The database with analytic tools is available at http://www.enhanceratlas.org/ CONTACT: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

Bioinformatics (2015) 31 (15), 2560–2564 doi: 10.1093/bioinformatics/btv158 The authors of the above article wish to alert the reader to the following availability link, as the link given in the published article is no longer active: Availability and Implementation: 4Dgenome is freely accessible at http://4dgenome.research.chop.edu/ . The authors apologize for this change.

Abstract Motivation: The 3D structure of the genome plays a critical role in regulating gene expression. Recent progress in mapping technologies for chromatin interactions has led to a rapid increase in this kind of interaction data. This trend will continue as research in this burgeoning field intensifies. Results: We describe the 4DGenome database that stores chromatin interaction data compiled through comprehensive literature curation. The database currently covers both low- and high-throughput assays, including 3C, 4C-Seq, 5C, Hi-C, ChIA-PET and Capture-C. To complement the set of interactions detected by experimental assays, we also include interactions predicted by a recently developed computational method with demonstrated high accuracy. The database currently contains ∼8 million records, covering 102 cell/tissue types in five organisms. Records in the database are described using a standardized file format, facilitating data exchange. The vast major of the interactions were assigned a confidence score. Using the web interface, users can query and download database records via a number of annotation dimensions. Query results can be visualized along with other genomics datasets via links to the UCSC genome browser. We anticipate that 4DGenome will be a valuable resource for investigating the spatial structure-and-function relationship of genomes. Availability and Implementation: 4Dgenome is freely accessible at http://4dgenome.int-med.uiowa.edu. The database and web interface are implemented in MySQL, Apache and JavaScript with all major browsers supported. Contact: [email protected] Supplementary Information: Supplementary Materials are available at Bioinformatics online.

Development of heart diseases is driven by dynamic changes in both the activity and connectivity of gene pathways. Understanding these dynamic events is critical for understanding pathogenic mechanisms and development of effective treatment. Currently, there is a lack of computational methods that enable analysis of multiple gene networks, each of which exhibits differential activity compared to the network of the baseline/healthy condition. We describe the iMDM algorithm to identify both unique and shared gene modules across multiple differential co-expression networks, termed M-DMs (multiple differential modules). We applied iMDM to a time-course RNA-Seq dataset generated using a murine heart failure model generated on two genotypes. We showed that iMDM achieves higher accuracy in inferring gene modules compared to using single or multiple co-expression networks. We found that condition-specific M-DMs exhibit differential activities, mediate different biological processes, and are enriched for genes with known cardiovascular phenotypes. By analyzing M-DMs that are present in multiple conditions, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that module dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Thus, pathway dynamics is a powerful measure for understanding pathogenesis. iMDM provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype. With the exponential growth of omics data, our method can aid in generating systems-level insights into disease progression.

MOTIVATION: Disease progression is driven by dynamic changes in both the activity and connectivity of molecular pathways. Understanding these dynamic events is critical for disease prognosis and effective treatment. Compared with activity dynamics, connectivity dynamics is poorly explored. RESULTS: We describe the M-module algorithm to identify gene modules with common members but varied connectivity across multiple gene co-expression networks (aka M-modules). We introduce a novel metric to capture the connectivity dynamics of an entire M-module. We find that M-modules with dynamic connectivity have distinct topological and biochemical properties compared with static M-modules and hub genes. We demonstrate that incorporation of module connectivity dynamics significantly improves disease stage prediction. We identify different sets of M-modules that are important for specific disease stage transitions and offer new insights into the molecular events underlying disease progression. Besides modeling disease progression, the algorithm and metric introduced here are broadly applicable to modeling dynamics of molecular pathways. AVAILABILITY AND IMPLEMENTATION: M-module is implemented in R. The source code is freely available at http://www.healthcare.uiowa.edu/labs/tan/M-module.zip.

While the annotation of human protein-coding sequences is now fairly comprehensive, the identification of regulatory sequences remains difficult. With higher resolution, fewer artifacts and greater coverage, short-read-sequencing-based technologies have made striking impact on genome research. Given the rapid accumulation of genome-wide chromatin state data, there is a pressing need for computational methods to analyze these data. In this paper, we developed a Multiple Layer Perceptron (MLP) framework to predict transcriptional enhancers by integrating multiple types of chromatin state maps, including histone modifications, DNase I cleavage, and DNA methylation. Comparisons with previous work using known enhancers from three cell types suggest that our algorithm is more robust and has higher precision and sensitivity. We anticipate that the new method will be a valuable tool for genome-wide mapping of various DNA regulatory elements in a wide variety of cell types, tissues and growth conditions.

MOTIVATION: Recent large-scale chromatin states mapping efforts have revealed characteristic chromatin modification signatures for various types of functional DNA elements. Given the important influence of chromatin states on gene regulation and the rapid accumulation of genome-wide chromatin modification data, there is a pressing need for computational methods to analyze these data in order to identify functional DNA elements. However, existing computational tools do not exploit data transformation and feature extraction as a means to achieve a more accurate prediction. RESULTS: We introduce a new computational framework for identifying functional DNA elements using chromatin signatures. The framework consists of a data transformation and a feature extraction step followed by a classification step using time-delay neural network. We implemented our framework in a software tool CSI-ANN (chromatin signature identification by artificial neural network). When applied to predict transcriptional enhancers in the ENCODE region, CSI-ANN achieved a 65.5% sensitivity and 66.3% positive predictive value, a 5.9% and 11.6% improvement, respectively, over the previously best approach. AVAILABILITY AND IMPLEMENTATION: CSI-ANN is implemented in Matlab. The source code is freely available at http://www.medicine.uiowa.edu/Labs/tan/CSIANNsoft.zip CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary Materials are available at Bioinformatics online.

BACKGROUND: Recent advances in proteomic technologies have enabled us to create detailed protein-protein interaction maps in multiple species and in both normal and diseased cells. As the size of the interaction dataset increases, powerful computational methods are required in order to effectively distil network models from large-scale interactome data. RESULTS: We present an algorithm, miPALM (Module Inference by Parametric Local Modularity), to infer protein complexes in a protein-protein interaction network. The algorithm uses a novel graph theoretic measure, parametric local modularity, to identify highly connected sub-networks as candidate protein complexes. Using gold standard sets of protein complexes and protein function and localization annotations, we show our algorithm achieved an overall improvement over previous algorithms in terms of precision, recall, and biological relevance of the predicted complexes. We applied our algorithm to predict and characterize a set of 138 novel protein complexes in S. cerevisiae. CONCLUSIONS: miPALM is a novel algorithm for detecting protein complexes from large protein-protein interaction networks with improved accuracy than previous methods. The software is implemented in Matlab and is freely available at http://www.medicine.uiowa.edu/Labs/tan/software.html.