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 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 Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interaction networks

2017 ◽  
Author(s):  
Scott W. Simpkins ◽  
Justin Nelson ◽  
Raamesh Deshpande ◽  
Sheena C. Li ◽  
Jeff S. Piotrowski ◽  
...  
Keyword(s):  
Biological Process ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Chemical Compounds ◽  
Genetic Interactions ◽  
Interaction Networks ◽  
Biological Processes ◽  
Functional Information ◽  
Chemical Genetic ◽  
Genetic Interaction Network

AbstractChemical-genetic interactions – observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes – contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. CG-TARGET compared favorably to a baseline enrichment approach across a variety of benchmarks, achieving similar accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. We applied CG-TARGET to a recent screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. Upon investigation of the compatibility of chemical-genetic and genetic interaction profiles, we observed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We present here a detailed characterization of the CG-TARGET method along with experimental validation of predicted biological process targets, focusing on inhibitors of tubulin polymerization and cell cycle progression. Our approach successfully demonstrates the use of genetic interaction networks in the functional annotation of compounds to biological processes.

scholarly journals Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pusheng Quan ◽  
Kai Wang ◽  
Shi Yan ◽  
Shirong Wen ◽  
Chengqun Wei ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Target ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Drug Candidates ◽  
Novel Drug

AbstractThis study aimed to identify potential novel drug candidates and targets for Parkinson’s disease. First, 970 genes that have been reported to be related to PD were collected from five databases, and functional enrichment analysis of these genes was conducted to investigate their potential mechanisms. Then, we collected drugs and related targets from DrugBank, narrowed the list by proximity scores and Inverted Gene Set Enrichment analysis of drug targets, and identified potential drug candidates for PD treatment. Finally, we compared the expression distribution of the candidate drug-target genes between the PD group and the control group in the public dataset with the largest sample size (GSE99039) in Gene Expression Omnibus. Ten drugs with an FDR < 0.1 and their corresponding targets were identified. Some target genes of the ten drugs significantly overlapped with PD-related genes or already known therapeutic targets for PD. Nine differentially expressed drug-target genes with p < 0.05 were screened. This work will facilitate further research into the possible efficacy of new drugs for PD and will provide valuable clues for drug design.

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 A review on drug repurposing applicable to COVID-19

2020 ◽  
Author(s):  
Serena Dotolo ◽  
Anna Marabotti ◽  
Angelo Facchiano ◽  
Roberto Tagliaferri
Keyword(s):  
Artificial Intelligence ◽  
Drug Target ◽  
Lower Cost ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Chemical Compounds ◽  
The Moment ◽  
New Applications ◽  
Novel Drug ◽  
Small Chemical

Abstract Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

ProbingTrypanosoma cruzibiology with DNA microarrays

Parasitology ◽  
2004 ◽  
Vol 128 (S1) ◽  
pp. S3-S10 ◽  
Author(s):  
B. A. BURLEIGH
Keyword(s):  
Drug Targets ◽  
Dna Microarrays ◽  
Sequence Information ◽  
Transcriptional Responses ◽  
Host Interactions ◽  
Treatment And Prevention ◽  
Molecular Events ◽  
Approaches To Study ◽  
Genome Scale ◽  
Novel Drug

The application of genome-scale approaches to studyTrypanosoma cruzi–host interactions at different stages of the infective process is becoming possible with sequencing and assembly of theT. cruzigenome nearing completion and sequence information available for both human and mouse genomes. Investigators have recently begun to exploit DNA microarray technology to analyze host transcriptional responses toT. cruziinfection and dissect developmental processes in the complexT. cruzilife-cycle. Collectively, information generated from these and future studies will provide valuable insights into the molecular requirements for establishment ofT. cruziinfection in the host and highlight the molecular events coinciding with disease progression. While the field is in its infancy, the availability of genomic information and increased accessibility to relatively high-throughput technologies represents a significant advancement toward identification of novel drug targets and vaccine candidates for the treatment and prevention of Chagas' disease.

scholarly journals Rewiring of genetic networks in response to modification of genetic background

2014 ◽  
Author(s):  
Djordje Bajic ◽  
Clara Moreno ◽  
Juan F Poyatos
Keyword(s):  
Genetic Interaction ◽  
Genetic Network ◽  
Wild Type ◽  
Alternative Pathways ◽  
In The Wild ◽  
Cellular Behaviour ◽  
Neutral Mutations ◽  
Genome Scale ◽  
Global Reduction ◽  
Molecular Circuitry

Genome-scale genetic interaction networks are progressively contributing to map the molecular circuitry that determines cellular behaviour. To what extent this mapping changes in response to different environmental or genetic conditions is however largely unknown. Here we assembled a genetic network using an in silico model of metabolism in yeast to explicitly ask how separate genetic backgrounds alter network structure. Backgrounds defined by single deletions of metabolically active enzymes induce strong rewiring when the deletion corresponds to a catabolic gene, evidencing a broad redistribution of fluxes to alternative pathways. We also show how change is more pronounced in interactions linking genes in distinct functional modules, and in those connections that present weak epistasis. These patterns reflect overall the distributed robustness of catabolism. In a second class of genetic backgrounds, in which a number of neutral mutations accumulate, we dominantly observe modifications in the negative interactions that together with an increase in the number of essential genes indicate a global reduction in buffering. Notably, neutral trajectories that originate considerable changes in the wild-type network comprise mutations that diminished the environmental plasticity of the corresponding metabolism, what emphasizes a mechanistic integration of genetic and environmental buffering. More generally, our work demonstrates how the specific mechanistic causes of robustness influence the architecture of multiconditional genetic interaction maps.

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