Network-Based Metric Space for Phenotypic Stratification of Samples Using Transcriptome Profiles

2021 ◽  
Author(s):  
Inyoung Sung ◽  
Dohoon Lee ◽  
Sangseon Lee ◽  
Sun Kim
Keyword(s):  
Metric Space ◽  
High Throughput Sequencing ◽  
Interaction Network ◽  
Gene Interaction ◽  
Computational Framework ◽  
Information Theoretic ◽  
Phenotypic Differences ◽  
Stratification Method ◽  
Network Propagation ◽  
Genetic Profiles

Abstract With the advancements of high-throughput sequencing technology, several recent studies addressed the clinical/phenotypic stratification of samples by utilizing transcriptome data. However, existing stratification methods lack efficient utilization of gene interaction information, and furthermore, handling more than 20,000 genes causes the curse of high dimensionality that hinders elucidating the linkage between genetic profiles and clinical/phenotypic differences. To overcome these challenges, we propose a network-based two-step computational framework. We first reduce dimensions of transcriptome to a few tens of dimensions by mapping transcriptome to protein interaction network followed by performing network propagation algorithm and clustering analysis. Then, each network is converted into a single numeric metric by utilizing information theoretic quantification of gene expression abnormality, which results in a single sample mapping to a metric space generated by each subnetwork in the form of vectors. The proposed network-based stratification method was used to analyses Pan-Caner dataset and Oryza sativa dataset. Extensive experiments showed that our method generates a metric space that captures data-specific biological functions and improves the stratification performance compared to existing methods. Therefore, the proposed method successfully stratified the samples, addressing the problem in the complex gene space. The proposed method is implemented in Python and available at https://github.com/Sunginyoung/net_stratification.

scholarly journals Driver Attribute Filling for Genes in Interaction Network via Modularity Subspace-Based Concept Learning from Small Samples

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fei Xie ◽  
Jianing Xi ◽  
Qun Duan
Keyword(s):  
Concept Learning ◽  
Interaction Network ◽  
Gene Interaction ◽  
Small Samples ◽  
Gene Interaction Network ◽  
Driver Genes ◽  
Cancer Driver ◽  
Gene Interaction Networks ◽  
Network Propagation ◽  
Network Properties

The aberrations of a gene can influence it and the functions of its neighbour genes in gene interaction network, leading to the development of carcinogenesis of normal cells. In consideration of gene interaction network as a complex network, previous studies have made efforts on the driver attribute filling of genes via network properties of nodes and network propagation of mutations. However, there are still obstacles from problems of small size of cancer samples and the existence of drivers without property of network neighbours, limiting the discovery of cancer driver genes. To address these obstacles, we propose an efficient modularity subspace based concept learning model. Our model can overcome the curse of dimensionality due to small samples via dimension reduction in the task of attribute concept learning and explore the features of genes through modularity subspace beyond the network neighbours. The evaluation analysis also demonstrates the superiority of our model in the task of driver attribute filling on two gene interaction networks. Generally, our model shows a promising prospect in the application of interaction network analysis of tumorigenesis.

Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

2019 ◽  
Vol 19 (4) ◽  
pp. 216-223 ◽  
Author(s):  
Tianyi Zhao ◽  
Donghua Wang ◽  
Yang Hu ◽  
Ningyi Zhang ◽  
Tianyi Zang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Feature Vector ◽  
Mirna Gene ◽  
Interaction Network ◽  
Gene Interaction ◽  
Proteinprotein Interaction ◽  
Synaptic Structures

Background: More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms. Objective: Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis. Materials and Methods: We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers). Results and Conclusion: We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

scholarly journals Community Detection in Large-Scale Bipartite Biological Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Genís Calderer ◽  
Marieke L. Kuijjer
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Interaction Network ◽  
Gene Interaction ◽  
Community Structures ◽  
Gene Interaction Network ◽  
Structure Detection ◽  
Wide Range ◽  
Disease Associations ◽  
Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

scholarly journals Bioinformatic Analysis of Neuroimmune Mechanism of Neuropathic Pain

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hao Yu ◽  
Yang Liu ◽  
Chao Li ◽  
Jianhao Wang ◽  
Bo Yu ◽  
...  
Keyword(s):  
Neuropathic Pain ◽  
Inflammatory Responses ◽  
Interaction Network ◽  
Gene Interaction ◽  
Bioinformatic Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Gene Interaction Network ◽  
Pathway Enrichment ◽  
Protein Protein Interaction

Background. Neuropathic pain (NP) is a devastating complication following nerve injury, and it can be alleviated by regulating neuroimmune direction. We aimed to explore the neuroimmune mechanism and identify some new diagnostic or therapeutic targets for NP treatment via bioinformatic analysis. Methods. The microarray GSE18803 was downloaded and analyzed using R. The Venn diagram was drawn to find neuroimmune-related differentially expressed genes (DEGs) in neuropathic pain. Gene Ontology (GO), pathway enrichment, and protein-protein interaction (PPI) network were used to analyze DEGs, respectively. Besides, the identified hub genes were submitted to the DGIdb database to find relevant therapeutic drugs. Results. A total of 91 neuroimmune-related DEGs were identified. The results of GO and pathway enrichment analyses were closely related to immune and inflammatory responses. PPI analysis showed two important modules and 8 hub genes: PTPRC, CD68, CTSS, RAC2, LAPTM5, FCGR3A, CD53, and HCK. The drug-hub gene interaction network was constructed by Cytoscape, and it included 24 candidate drugs and 3 hub genes. Conclusion. The present study helps us better understand the neuroimmune mechanism of neuropathic pain and provides some novel insights on NP treatment, such as modulation of microglia polarization and targeting bone resorption. Besides, CD68, CTSS, LAPTM5, FCGR3A, and CD53 may be used as early diagnostic biomarkers and the gene HCK can be a therapeutic target.

scholarly journals Venn-diaNet : venn diagram based network propagation analysis framework for comparing multiple biological experiments

2019 ◽  
Vol 20 (S23) ◽  
Author(s):  
Benjamin Hur ◽  
Dongwon Kang ◽  
Sangseon Lee ◽  
Ji Hwan Moon ◽  
Gung Lee ◽  
...  
Keyword(s):  
Interaction Network ◽  
Venn Diagram ◽  
Analysis Framework ◽  
Seed Selection ◽  
Systematic Analysis ◽  
Main Research ◽  
Protein Protein Interaction ◽  
Propagation Analysis ◽  
Network Propagation ◽  
Main Research Topic

Abstract Background The main research topic in this paper is how to compare multiple biological experiments using transcriptome data, where each experiment is measured and designed to compare control and treated samples. Comparison of multiple biological experiments is usually performed in terms of the number of DEGs in an arbitrary combination of biological experiments. This process is usually facilitated with Venn diagram but there are several issues when Venn diagram is used to compare and analyze multiple experiments in terms of DEGs. First, current Venn diagram tools do not provide systematic analysis to prioritize genes. Because that current tools generally do not fully focus to prioritize genes, genes that are located in the segments in the Venn diagram (especially, intersection) is usually difficult to rank. Second, elucidating the phenotypic difference only with the lists of DEGs and expression values is challenging when the experimental designs have the combination of treatments. Experiment designs that aim to find the synergistic effect of the combination of treatments are very difficult to find without an informative system. Results We introduce Venn-diaNet, a Venn diagram based analysis framework that uses network propagation upon protein-protein interaction network to prioritizes genes from experiments that have multiple DEG lists. We suggest that the two issues can be effectively handled by ranking or prioritizing genes with segments of a Venn diagram. The user can easily compare multiple DEG lists with gene rankings, which is easy to understand and also can be coupled with additional analysis for their purposes. Our system provides a web-based interface to select seed genes in any of areas in a Venn diagram and then perform network propagation analysis to measure the influence of the selected seed genes in terms of ranked list of DEGs. Conclusions We suggest that our system can logically guide to select seed genes without additional prior knowledge that makes us free from the seed selection of network propagation issues. We showed that Venn-diaNet can reproduce the research findings reported in the original papers that have experiments that compare two, three and eight experiments. Venn-diaNet is freely available at: http://biohealth.snu.ac.kr/software/venndianet

scholarly journals A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis

10.1186/gm404 ◽  
2012 ◽  
Vol 4 (12) ◽  
Author(s):  
Raymond J Louie ◽  
Jingyu Guo ◽  
John W Rodgers ◽  
Rick White ◽  
Najaf A Shah ◽  
...  
Keyword(s):  
Interaction Network ◽  
Gene Interaction ◽  
Gene Interaction Network ◽  
Protein Biogenesis

Rheumatoid arthritis–associated DNA methylation sites in peripheral blood mononuclear cells

2018 ◽  
Vol 78 (1) ◽  
pp. 36-42 ◽  
Author(s):  
Hong Zhu ◽  
Long-Fei Wu ◽  
Xing-Bo Mo ◽  
Xin Lu ◽  
Hui Tang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Mrna Expression ◽  
Mononuclear Cells ◽  
Interaction Network ◽  
Gene Interaction ◽  
Jurkat Cells ◽  
Gene Interaction Network ◽  
Peripheral Blood Mononuclear ◽  
Inference Tests ◽  
Inducible Gene

ObjectivesTo identify novel DNA methylation sites significant for rheumatoid arthritis (RA) and comprehensively understand their underlying pathological mechanism.MethodsWe performed (1) genome-wide DNA methylation and mRNA expression profiling in peripheral blood mononuclear cells from RA patients and health controls; (2) correlation analysis and causal inference tests for DNA methylation and mRNA expression data; (3) differential methylation genes regulatory network construction; (4) validation tests of 10 differential methylation positions (DMPs) of interest and corresponding gene expressions; (5) correlation between PARP9 methylation and its mRNA expression level in Jurkat cells and T cells from patients with RA; (6) testing the pathological functions of PARP9 in Jurkat cells.ResultsA total of 1046 DNA methylation positions were associated with RA. The identified DMPs have regulatory effects on mRNA expressions. Causal inference tests identified six DNA methylation–mRNA–RA regulatory chains (eg, cg00959259-PARP9-RA). The identified DMPs and genes formed an interferon-inducible gene interaction network (eg, MX1, IFI44L, DTX3L and PARP9). Key DMPs and corresponding genes were validated their differences in additional samples. Methylation of PARP9 was correlated with mRNA level in Jurkat cells and T lymphocytes isolated from patients with RA. The PARP9 gene exerted significant effects on Jurkat cells (eg, cell cycle, cell proliferation, cell activation and expression of inflammatory factor IL-2).ConclusionsThis multistage study identified an interferon-inducible gene interaction network associated with RA and highlighted the importance of PARP9 gene in RA pathogenesis. The results enhanced our understanding of the important role of DNA methylation in pathology of RA.

scholarly journals PCR-RFLP Detection and Genogroup Identification of Piscirickettsia salmonis in Field Samples

Pathogens ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 358
Author(s):  
Pamela Aravena ◽  
Rodrigo Pulgar ◽  
Javiera Ortiz-Severín ◽  
Felipe Maza ◽  
Alexis Gaete ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
In Silico Analysis ◽  
Cost Effective ◽  
Rrna Gene ◽  
Bacterial Composition ◽  
Bacterial Dna ◽  
Phenotypic Differences ◽  
Field Samples ◽  
Pcr Rflp ◽  
Digestion Pattern

Piscirickettsia salmons, the causative agent of piscirickettsiosis, is genetically divided into two genomic groups, named after the reference strains as LF-89-like or EM-90-like. Phenotypic differences have been detected between the P. salmonis genogroups, including antibiotic susceptibilities, host specificities and pathogenicity. In this study, we aimed to develop a rapid, sensitive and cost-effective assay for the differentiation of the P. salmonis genogroups. Using an in silico analysis of the P. salmonis 16S rDNA digestion patterns, we have designed a genogroup-specific assay based on PCR-restriction fragment length polymorphism (RFLP). An experimental validation was carried out by comparing the restriction patterns of 13 P. salmonis strains and 57 field samples obtained from the tissues of dead or moribund fish. When the bacterial composition of a set of field samples, for which we detected mixtures of bacterial DNA, was analyzed by a high-throughput sequencing of the 16S rRNA gene amplicons, a diversity of taxa could be identified, including pathogenic and commensal bacteria. Despite the presence of mixtures of bacterial DNA, the characteristic digestion pattern of the P. salmonis genogroups could be detected in the field samples without the need of a microbiological culture and bacterial isolation.

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