scholarly journals Spatial Analysis of Functional Enrichment (SAFE) in Large Biological Networks

2018 ◽  
pp. 249-268 ◽  
Author(s):  
Anastasia Baryshnikova
Keyword(s):  
Spatial Analysis ◽  
Biological Networks ◽  
Functional Enrichment

scholarly journals Spatial Analysis of Functional Enrichment (SAFE) in Large Biological Networks

2016 ◽  
Author(s):  
Anastasia Baryshnikova
Keyword(s):  
Spatial Analysis ◽  
Biological Networks ◽  
Biological Process ◽  
Genetic Interaction ◽  
Chemical Compounds ◽  
Functional Enrichment ◽  
Relative Positioning ◽  
Chemical Genomic ◽  
Genome Scale ◽  
Novel Drug

Summary/AbstractSpatial Analysis of Functional Enrichment (SAFE) is a systematic quantitative approach for annotating large biological networks. SAFE detects network regions that are statistically overrepresented for functional groups or quantitative phenotypes of interest, and provides an intuitive visual representation of their relative positioning within the network. In doing so, SAFE determines which functions are represented in a network, which parts of the network they are associated with and how they are potentially related to one another.Here, I provide a detailed stepwise description of how to perform a SAFE analysis. As an example, I use SAFE to annotate the genome-scale genetic interaction similarity network fromSaccharomyces cerevisiaewith Gene Ontology (GO) biological process terms. In addition, I show how integrating GO with chemical genomic data in SAFE can recapitulate known modes-of-action of chemical compounds and potentially identify novel drug mechanisms.

scholarly journals Meaningful Infrastructures in Biological Networks: Related Studies, Algorithms and Evaluation Parameters

2022 ◽  
Author(s):  
Lionel Alangeh Ngobesing ◽  
Yılmaz Atay
Keyword(s):  
Biological Networks ◽  
Network Science ◽  
Water Cycle ◽  
Label Propagation ◽  
Functional Enrichment ◽  
Iteration Algorithm ◽  
Water Cycle Algorithm ◽  
Normalized Mutual Information ◽  
Evaluation Parameters ◽  
Information Functional

Abstract: In network science and big data, the concept of finding meaningful infrastructures in networks has emerged as a method of finding groups of entities with similar properties within very complex systems. The whole concept is generally based on finding subnetworks which have more properties (links) amongst nodes belonging to the same cluster than nodes in other groups (A concept presented by Girvan and Newman, 2002). Today meaningful infrastructure identification is applied in all types of networks from computer networks, to social networks to biological networks. In this article we will look at how meaningful infrastructure identification is applied in biological networks. This concept is important in biological networks as it helps scientist discover patterns in proteins or drugs which helps in solving many medical mysteries. This article will encompass the different algorithms that are used for meaningful infrastructure identification in biological networks. These include Genetic Algorithm, Differential Evolution, Water Cycle Algorithm (WCA), Walktrap Algorithm, Connect Intensity Iteration Algorithm (CIIA), Firefly algorithms and Overlapping Multiple Label Propagation Algorithm. These al-gorithms are compared with using performance measurement parameters such as the Mod-ularity, Normalized Mutual Information, Functional Enrichment, Recall and Precision, Re-dundancy, Purity and Surprise, which we will also discuss here.

scholarly journals Kernel Differential Subgraph Analysis to Reveal the Key Period Affecting Glioblastoma

Biomolecules ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 318 ◽  
Author(s):  
Jiang Xie ◽  
Jiamin Sun ◽  
Jiatai Feng ◽  
Fuzhang Yang ◽  
Jiao Wang ◽  
...  
Keyword(s):  
Biological Networks ◽  
Cortical Neurons ◽  
Neural Differentiation ◽  
Primary Brain Tumor ◽  
Regulatory Genes ◽  
Functional Enrichment ◽  
Steiner Tree Problem ◽  
Literature Mining ◽  
Driver Genes ◽  
Kegg Pathways

Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs.

scholarly journals Systematic Functional Annotation and Visualization of Biological Networks

2015 ◽  
Author(s):  
Anastasia Baryshnikova
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Functional Organization ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Functional Enrichment ◽  
Functional Structure ◽  
Cancer Drug ◽  
Functional Connections ◽  
High Level

ABSTRACTLarge-scale biological networks map functional connections between most genes in the genome and can potentially uncover high level organizing principles governing cellular functions. These networks, however, are famously complex and often regarded as disordered masses of tangled interactions (“hairballs”) that are nearly impenetrable to biologists. As a result, our current understanding of network functional organization is very limited. To address this problem, I developed a systematic quantitative approach for annotating biological networks and examining their functional structure. This method, named Spatial Analysis of Functional Enrichment (SAFE), detects network regions that are statistically overrepresented for a functional group or a quantitative phenotype of interest, and provides an intuitive visual representation of their relative positioning within the network. By successfully annotating theSaccharomyces cerevisiaegenetic interaction network with Gene Ontology terms, SAFE proved to be sensitive to functional signals and robust to noise. In addition, SAFE annotated the network with chemical genomic data and uncovered a new potential mechanism of resistance to the anti-cancer drug bortezomib. Finally, SAFE showed that protein-protein interactions, despite their apparent complexity, also have a high level functional structure. These results demonstrate that SAFE is a powerful new tool for examining biological networks and advancing our understanding of the functional organization of the cell.

scholarly journals Bioinformatic Analysis Reveals Novel Hub Genes pathways associated with IgA nephropathy

2020 ◽  
Author(s):  
xue Jiang ◽  
Zhijie Xu ◽  
Yuanyuan Du ◽  
Hongyu Chen
Keyword(s):  
Inflammatory Response ◽  
Molecular Mechanism ◽  
Biological Networks ◽  
Tissue Sample ◽  
Bioinformatic Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Ppi Network ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. The aim of the study is to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism. Methods Based on the microarray data GSE93798 and GSE37460 were downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between IgAN samples and normal controls were identified. With DEGs, we further performed a series of functional enrichment analyses. Protein-protein interaction (PPI) networks of the DEGs were built with the STRING online search tool and visualized by using Cytoscape, then further identified the hub gene and most important module in DEGs, Biological Networks Gene Oncology tool (BiNGO) were then performed to elucidate the molecular mechanism of IgAN. Results A total of 148 DEGs were recognized, consisting of 53 upregulated genes and 95 downregulated genes. GO analysis indicates that DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fibroblast growth factor stimulus, inflammatory response, and innate immunity. The modules analysis showed that genes in the top 1 significant modules of PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and inflammatory response respectively. The top 10 hub genes were constructed in PPI network, which could well distinguish the IgAN and control group in monocytes sample and tissue sample. We finally identified ITGB2 and FCER1G gene may have important roles in the development of IgAN. Conclusions We identified a series of key genes along with the pathways that were most closely related with IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate genes targets of IgAN.

scholarly journals Identification of Condition-Specific Biomarker Systems in Uterine Cancer

2021 ◽  
Author(s):  
Allison R Hickman ◽  
Yuqing Hang ◽  
Rini Pauly ◽  
Frank A Feltus
Keyword(s):  
Biological Networks ◽  
Target Genes ◽  
Uterine Cancer ◽  
Differential Expression Analysis ◽  
Gaussian Mixture ◽  
Functional Enrichment ◽  
Specific Gene ◽  
Uterine Carcinosarcoma ◽  
Uterine Tissue ◽  
Clinical Biomarkers

Abstract Uterine cancer is the fourth most common cancer among women, projected to affect 66,000 US women in 2021. Uterine cancer often arises in the inner lining of the uterus, known as the endometrium, but can present as several different types of cancer, including endometrioid cancer, serous adenocarcinoma, and uterine carcinosarcoma. Previous studies have analyzed the genetic changes between normal and cancerous uterine tissue to identify specific genes of interest, including TP53 and PTEN. Here we used Gaussian Mixture Models to build condition-specific gene co-expression networks for endometrial cancer, uterine carcinosarcoma, and normal uterine tissue. We then incorporated uterine regulatory edges and investigated potential co-regulation relationships. These networks were further validated using differential expression analysis, functional enrichment, and a statistical analysis comparing the expression of transcription factors and their target genes across cancerous and normal uterine samples. These networks allow for a more comprehensive look into the biological networks and pathways affected in uterine cancer compared to previous singular gene analyses. We hope this study can be incorporated into existing knowledge surrounding the genetics of uterine cancer and soon become clinical biomarkers as a tool for better prognosis and treatment.

scholarly journals Bioinformatic Analysis of Gene Variants from Gastroschisis Recurrence Identifies Multiple Novel Pathogenetic Pathways: Implication for the Closure of the Ventral Body Wall

2019 ◽  
Vol 20 (9) ◽  
pp. 2295 ◽  
Author(s):  
Víctor M. Salinas-Torres ◽  
Hugo L. Gallardo-Blanco ◽  
Rafael A. Salinas-Torres ◽  
Ricardo M. Cerda-Flores ◽  
José J. Lugo-Trampe ◽  
...  
Keyword(s):  
Biological Networks ◽  
Body Wall ◽  
Molecular Mechanisms ◽  
De Novo ◽  
Developmental Process ◽  
Interaction Analysis ◽  
Regulation Of Gene Expression ◽  
Bioinformatic Analysis ◽  
Recessive Model ◽  
Functional Enrichment

We investigated whether likely pathogenic variants co-segregating with gastroschisis through a family-based approach using bioinformatic analyses were implicated in body wall closure. Gene Ontology (GO)/Panther functional enrichment and protein-protein interaction analysis by String identified several biological networks of highly connected genes in UGT1A3, UGT1A4, UGT1A5, UGT1A6, UGT1A7, UGT1A8, UGT1A9, UGT1A10, AOX1, NOTCH1, HIST1H2BB, RPS3, THBS1, ADCY9, and FGFR4. SVS–PhoRank identified a dominant model in OR10G4 (also as heterozygous de novo), ITIH3, PLEKHG4B, SLC9A3, ITGA2, AOX1, and ALPP, including a recessive model in UGT1A7, UGT1A6, PER2, PTPRD, and UGT1A3. A heterozygous compound model was observed in CDYL, KDM5A, RASGRP1, MYBPC2, PDE4DIP, F5, OBSCN, and UGT1A. These genes were implicated in pathogenetic pathways involving the following GO related categories: xenobiotic, regulation of metabolic process, regulation of cell adhesion, regulation of gene expression, inflammatory response, regulation of vascular development, keratinization, left-right symmetry, epigenetic, ubiquitination, and regulation of protein synthesis. Multiple background modifiers interacting with disease-relevant pathways may regulate gastroschisis susceptibility. Based in our findings and considering the plausibility of the biological pattern of mechanisms and gene network modeling, we suggest that the gastroschisis developmental process may be the consequence of several well-orchestrated biological and molecular mechanisms which could be interacting with gastroschisis predispositions within the first ten weeks of development.

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