Understanding tissue-specificity with human tissue-specific regulatory networks

Guo, Wei-Li; Zhu, Lin; Deng, Su-Ping; Zhao, Xing‐Ming; Huang, De-Shuang

doi:10.1007/s11432-016-5582-0

Cited by 12 publications

(8 citation statements)

References 43 publications

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“…The aging process contributes to the changes seen in the cardiovascular system in older people 43 . The tissue-specific regulations that are important for tissue identity 44 , and the fat and muscle specific gene modules would help us to assess the effects of age on functional and molecular phenotypes in fat, heart and muscle tissues. It is quite straightforward to hypothesize that functional and molecular phenotypes that vary with aging may be associated with specific gene modules, which consists of a list of genes that display different expression patterns between old and young organisms.…”

Section: Discussionmentioning

confidence: 99%

Matrix factorization reveals aging-specific co-expression gene modules in the fat and muscle tissues in nonhuman primates

Wang

Zhao

Zhou

2016

Sci Rep

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Accurate identification of coherent transcriptional modules (subnetworks) in adipose and muscle tissues is important for revealing the related mechanisms and co-regulated pathways involved in the development of aging-related diseases. Here, we proposed a systematically computational approach, called ICEGM, to Identify the Co-Expression Gene Modules through a novel mathematical framework of Higher-Order Generalized Singular Value Decomposition (HO-GSVD). ICEGM was applied on the adipose, and heart and skeletal muscle tissues in old and young female African green vervet monkeys. The genes associated with the development of inflammation, cardiovascular and skeletal disorder diseases, and cancer were revealed by the ICEGM. Meanwhile, genes in the ICEGM modules were also enriched in the adipocytes, smooth muscle cells, cardiac myocytes, and immune cells. Comprehensive disease annotation and canonical pathway analysis indicated that immune cells, adipocytes, cardiomyocytes, and smooth muscle cells played a synergistic role in cardiac and physical functions in the aged monkeys by regulation of the biological processes associated with metabolism, inflammation, and atherosclerosis. In conclusion, the ICEGM provides an efficiently systematic framework for decoding the co-expression gene modules in multiple tissues. Analysis of genes in the ICEGM module yielded important insights on the cooperative role of multiple tissues in the development of diseases.

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Section: Discussionmentioning

confidence: 99%

Matrix factorization reveals aging-specific co-expression gene modules in the fat and muscle tissues in nonhuman primates

Wang

Zhao

Zhou

2016

Sci Rep

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“…There are currently three established categories of mutations defined as high penetrance, moderate-risk and low-risk. These categories currently include a number of genes indicated in research including ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, FANCM, MLH1, MRE11A, MSH2, MSH6, MUTYH, NBN, PALB2, PMS1, PMS2, PTEN, RAD50, RAD51C, STK11 and TP53 genes [54] [55] [20][21][22][23][24][25][26][27].…”

Section: Breast Cancermentioning

confidence: 99%

A Novel Approach to Detecting Epistasis using Random Sampling Regularisation

Hind¹,

Lisboa

Hussain

et al. 2020

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Epistasis is a progressive approach that complements the 'common disease, common variant' hypothesis that highlights the potential for connected networks of genetic variants collaborating to produce a phenotypic expression. Epistasis is commonly performed as a pairwise or limitless-arity capacity that considers variant networks as either variant vs variant or as high order interactions. This type of analysis extends the number of tests that were previously performed in a standard approach such as Genome-Wide Association Study (GWAS), in which False Discovery Rate (FDR) is already an issue, therefore by multiplying the number of tests up to a factorial rate also increases the issue of FDR. Further to this, epistasis introduces its own limitations of computational complexity and intensity that are generated based on the analysis performed; to consider the most intense approach, a multivariate analysis introduces a time complexity of O(n!). Proposed in this paper is a novel methodology for the detection of epistasis using interpretable methods and best practice to outline interactions through filtering processes. Using a process of Random Sampling Regularisation which randomly splits and produces sample sets to conduct a voting system to regularise the significance and reliability of biological markers, SNPs. Preliminary results are promising, outlining a concise detection of interactions. Results for the detection of epistasis, in the classification of breast cancer patients, indicated eight outlined risk candidate interactions from five variants and a singular candidate variant with high protective association.

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“…The hierarchical prior in Eqs. (16) and 17implement the optimization problem as described in Eq. (15).…”

Section: The Bfdsem Algorithmmentioning

confidence: 99%

“…environments, tissues, diseases) changes. However, the differential analysis of GRNs under different conditions is also of significant importance to identify gene functions, discover biological mechanisms of different tissues and find genes related to diseases [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Bayesian differential analysis of gene regulatory networks exploiting genetic perturbations

Liu

et al. 2020

BMC Bioinformatics

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Background: Gene regulatory networks (GRNs) can be inferred from both gene expression data and genetic perturbations. Under different conditions, the gene data of the same gene set may be different from each other, which results in different GRNs. Detecting structural difference between GRNs under different conditions is of great significance for understanding gene functions and biological mechanisms. Results: In this paper, we propose a Bayesian Fused algorithm to jointly infer differential structures of GRNs under two different conditions. The algorithm is developed for GRNs modeled with structural equation models (SEMs), which makes it possible to incorporate genetic perturbations into models to improve the inference accuracy, so we name it BFDSEM. Different from the naive approaches that separately infer pair-wise GRNs and identify the difference from the inferred GRNs, we first re-parameterize the two SEMs to form an integrated model that takes full advantage of the two groups of gene data, and then solve the re-parameterized model by developing a novel Bayesian fused prior following the criterion that separate GRNs and differential GRN are both sparse. Conclusions: Computer simulations are run on synthetic data to compare BFDSEM to two state-of-the-art joint inference algorithms: FSSEM and ReDNet. The results demonstrate that the performance of BFDSEM is comparable to FSSEM, and is generally better than ReDNet. The BFDSEM algorithm is also applied to a real data set of lung cancer and adjacent normal tissues, the yielded normal GRN and differential GRN are consistent with the reported results in previous literatures. An open-source program implementing BFDSEM is freely available in Additional file 1.

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Understanding tissue-specificity with human tissue-specific regulatory networks

Cited by 12 publications

References 43 publications

Matrix factorization reveals aging-specific co-expression gene modules in the fat and muscle tissues in nonhuman primates

Matrix factorization reveals aging-specific co-expression gene modules in the fat and muscle tissues in nonhuman primates

A Novel Approach to Detecting Epistasis using Random Sampling Regularisation

Bayesian differential analysis of gene regulatory networks exploiting genetic perturbations

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