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A novel method for discovering local spatial clusters of genomic regions with functional relationships from DNA contact maps.

ABSTRACT: The three-dimensional structure of genomes makes it possible for genomic regions not adjacent in the primary sequence to be spatially proximal. These DNA contacts have been found to be related to various molecular activities. Previous methods for analyzing DNA contact maps obtained from Hi-C experiments have largely focused on studying individual interactions, forming spatial clusters composed of contiguous blocks of genomic locations, or classifying these clusters into general categories based on some global properties of the contact maps.Here, we describe a novel computational method that can flexibly identify small clusters of spatially proximal genomic regions based on their local contact patterns. Using simulated data that highly resemble Hi-C data obtained from real genome structures, we demonstrate that our method identifies spatial clusters that are more compact than methods previously used for clustering genomic regions based on DNA contact maps. The clusters identified by our method enable us to confirm functionally related genomic regions previously reported to be spatially proximal in different species. We further show that each genomic region can be assigned a numeric affinity value that indicates its degree of participation in each local cluster, and these affinity values correlate quantitatively with DNase I hypersensitivity, gene expression, super enhancer activities and replication timing in a cell type specific manner. We also show that these cluster affinity values can precisely define boundaries of reported topologically associating domains, and further define local sub-domains within each domain.The source code of BNMF and tutorials on how to use the software to extract local clusters from contact maps are available at http://yiplab.cse.cuhk.edu.hk/bnmf/kevinyip@cse.cuhk.edu.hkSupplementary data are available at Bioinformatics online.

SUBMITTER: Hu X

PROVIDER: S-EPMC4908340 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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A novel method for discovering local spatial clusters of genomic regions with functional relationships from DNA contact maps.

Hu Xihao X Shi Christina Huan CH Yip Kevin Y KY

Bioinformatics (Oxford, England) 20160601 12

<h4>Motivation</h4>The three-dimensional structure of genomes makes it possible for genomic regions not adjacent in the primary sequence to be spatially proximal. These DNA contacts have been found to be related to various molecular activities. Previous methods for analyzing DNA contact maps obtained from Hi-C experiments have largely focused on studying individual interactions, forming spatial clusters composed of contiguous blocks of genomic locations, or classifying these clusters into genera ...[more]

PMID: 27307607

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Project description:BackgroundThe falling cost of next-generation sequencing technology has allowed deep sequencing across related species and of individuals within species. Whole genome assemblies from these data remain high time- and resource-consuming computational tasks, particularly if best solutions are sought using different assembly strategies and parameter sets. However, in many cases, the underlying research questions are not genome-wide but rather target specific genes or sets of genes. We describe a novel assembly tool, SRAssembler, that efficiently assembles only contigs containing potential homologs of a gene or protein query, thus enabling gene-specific genome studies over large numbers of short read samples.ResultsWe demonstrate the functionality of SRAssembler with examples largely drawn from plant genomics. The workflow implements a recursive strategy by which relevant reads are successively pulled from the input sets based on overlapping significant matches, resulting in virtual chromosome walking. The typical workflow behavior is illustrated with assembly of simulated reads. Applications to real data show that SRAssembler produces homologous contigs of equivalent quality to whole genome assemblies. Settings can be chosen to not only assemble presumed orthologs but also paralogous gene loci in distinct contigs. A key application is assembly of the same locus in many individuals from population genome data, which provides assessment of structural variation beyond what can be inferred from read mapping to a reference genome alone. SRAssembler can be used on modest computing resources or used in parallel on high performance computing clusters (most easily by invoking a dedicated Singularity image).ConclusionsSRAssembler offers an efficient tool to complement whole genome assembly software. It can be used to solve gene-specific research questions based on large genomic read samples from multiple sources and would be an expedient choice when whole genome assembly from the reads is either not feasible, too costly, or unnecessary. The program can also aid decision making on the depth of sequencing in an ongoing novel genome sequencing project or with respect to ultimate whole genome assembly strategies.

Project description:Mirror protein structures are often considered as artifacts in modeling protein structures. However, they may soon become a new branch of biochemistry. Moreover, methods of protein structure reconstruction, based on their residue-residue contact maps, need methodology to differentiate between models of native and mirror orientation, especially regarding the reconstructed backbones. We analyzed 130 500 structural protein models obtained from contact maps of 1 305 SCOP domains belonging to all 7 structural classes. On average, the same numbers of native and mirror models were obtained among 100 models generated for each domain. Since their structural features are often not sufficient for differentiating between the two types of model orientations, we proposed to apply various energy terms (ETs) from PyRosetta to separate native and mirror models. To automate the procedure for differentiating these models, the k-means clustering algorithm was applied. Using total energy did not allow to obtain appropriate clusters-the accuracy of the clustering for class A (all helices) was no more than 0.52. Therefore, we tested a series of different k-means clusterings based on various combinations of ETs. Finally, applying two most differentiating ETs for each class allowed to obtain satisfying results. To unify the method for differentiating between native and mirror models, independent of their structural class, the two best ETs for each class were considered. Finally, the k-means clustering algorithm used three common ETs: probability of amino acid assuming certain values of dihedral angles Φ and Ψ, Ramachandran preferences and Coulomb interactions. The accuracies of clustering with these ETs were in the range between 0.68 and 0.76, with sensitivity and selectivity in the range between 0.68 and 0.87, depending on the structural class. The method can be applied to all fully-automated tools for protein structure reconstruction based on contact maps, especially those analyzing big sets of models.

Dataset Information

A novel method for discovering local spatial clusters of genomic regions with functional relationships from DNA contact maps.

Publications

A novel method for discovering local spatial clusters of genomic regions with functional relationships from DNA contact maps.

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