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Classification of topological domains based on gene expression and regulation.

ABSTRACT: Tissue-specific gene expression is thought to be one of the major forces shaping mammalian gene order. A recent study that used whole-genome chromosome conformation assays has shown that the mammalian genome is divided into specific topological domains that are shared between different tissues and organisms. Here, we wanted to assess whether gene expression and regulation are involved in shaping these domains and can be used to classify them. We analyzed gene expression and regulation levels in these domains by using RNA-seq and enhancer-associated ChIP-seq datasets for 17 different mouse tissues. We found 162 domains that are active (high gene expression and regulation) in all 17 tissues. These domains are significantly shorter, contain less repeats, and have more housekeeping genes. In contrast, we found 29 domains that are inactive (low gene expression and regulation) in all analyzed tissues and are significantly longer, have more repeats, and gene deserts. Tissue-specific active domains showed some correlation with tissue-type and gene ontology. Domain temporal gene regulation and expression differences also displayed some gene ontology terms fitting their temporal function. Combined, our results provide a catalog of shared and tissue-specific topological domains and suggest that gene expression and regulation could have a role in shaping them.

SUBMITTER: Zhao J

PROVIDER: S-EPMC3873163 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Classification of topological domains based on gene expression and regulation.

Zhao Jingjing J Shi Hongbo H Ahituv Nadav N

Genome 20130701 7

Tissue-specific gene expression is thought to be one of the major forces shaping mammalian gene order. A recent study that used whole-genome chromosome conformation assays has shown that the mammalian genome is divided into specific topological domains that are shared between different tissues and organisms. Here, we wanted to assess whether gene expression and regulation are involved in shaping these domains and can be used to classify them. We analyzed gene expression and regulation levels in ...[more]

PMID: 24099394

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Project description:BACKGROUND: An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types. METHODOLOGY/PRINCIPAL FINDINGS: We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described. CONCLUSIONS/SIGNIFICANCE: The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers.

Dataset Information

Classification of topological domains based on gene expression and regulation.

Publications

Classification of topological domains based on gene expression and regulation.

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