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{
"question": [
"How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?",
"What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?",
"How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?",
"What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?",
"How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?"
],
"answer": [
"Multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets by systematically integrating interactions of informational molecules, such as protein-protein interactions, protein-DNA interactions, protein-RNA interactions, RNA-RNA interactions, protein state information, methylation state, and interactions with metabolites. This comprehensive integration of data allows for a more accurate construction of genomics networks, leading to more reliable predictions of phenotypic traits. Additionally, the integration of genotype and gene-expression data in a single model can drastically reduce QTL candidate genes and infer the structure of networks from expression-regulatory polymorphisms to variation.",
"The computational challenges in analyzing large-scale transcriptomic data within GeneNetwork.org include handling the massive data sets, deciding how much confidence to place in QTL extracted from noisy array and proteomic platforms, and integrating multiple types of data across the biological scale. Solutions to these challenges involve the use of statistical tools to extract relevant patterns of covariation and causal relations, Bayesian and combinatorial network-analysis methods, and the integration of additional information such as SNP distributions. The GeneNetwork web service also provides workflows and methods to navigate massive multiscalar data sets and an extensive systems genetics toolkit for analysis and synthesis. Furthermore, machine-learning approaches like GANs can be used to generate larger expression datasets for network inference.",
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],
"contexts": [
[
"Until highly accurate genomics networks can beconstructed from more complete data, inferences drawnfrom biological networks should be considered as hypothesis that need to be further tested with experimental method, where the results can then illuminatethe representation of the biological system. The predictive power of the genomics networks could be enhancedby more systematically integrating interactions of informational molecules, such as protein-protein interactions, protein-DNA interactions, protein-RNA interactions, RNA-RNA interactions, protein state information, methylation state, and interactions with metabolites, as these types of data have become available(Schadt et al. 2009).",
"Until highly accurate genomics networks can beconstructed from more complete data, inferences drawnfrom biological networks should be considered as hypothesis that need to be further tested with experimental method, where the results can then illuminatethe representation of the biological system. The predictive power of the genomics networks could be enhancedby more systematically integrating interactions of informational molecules, such as protein-protein interactions, protein-DNA interactions, protein-RNA interactions, RNA-RNA interactions, protein state information, methylation state, and interactions with metabolites, as these types of data have become available(Schadt et al. 2009).",
"Tointegrate phenotypes in MKD with genomic datasets, wechose to include the Protein Family Database (Pfam) [17],Clusters of Orthologous Groups (COGs) [18,19], KyotoEncyclopedia of Genes and Genomes (KEGG) [20], andbiological concepts found in the Gene Ontology (GO)[21,22] which span multiple scales of biology. A key challenge of the post-genomic era is to conceive large-scalestudies of genomes and observable characteristics of organisms(phenotypes) and to interpret the data thus produced. The goal ofthis phenomic study is to improve our understanding of complexbiological systems in terms of their molecular underpinnings.Inaddition, the method has the potential to predict newcorrelations between phenotypes and biological systemsrepresented in GO as shown in the complete result datasetsat http://phenos.bsd.uchicago.edu/prok_phenotype. Whileprevious correlations studies had been completed on onlyfour phenotypes [5,6], we present an additional 38 phenotype-to-GO correlations. We propose that this methodpotentially enables a systems-biology approach to analyzegenomic datasets by providing a systematic view of themolecular mechanisms beneath phenotypes across differentclassications of genes (protein families, pathways, molecularfunctions, and biological processes).",
"Recent improvements toGeneNetwork have reinvigorated it, including the addition of data from 10 species, multi-omicsanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource forpredictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most commonfunctions on GeneNetwork, as a gateway to deeper analyses, demonstrating how a smallnumber of plausible candidate genes can be found for a typical immune phenotype.",
"Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006).By integrating genotypeand gene-expression data in a single model, Kulp and Jagular (2006) have drastically reduced QTL candidate genes, and perhaps inferred the structure of networksfrom expression-regulatory polymorphisms to variation. Combinatorial algorithmsare applied to networks that are drawn by thresholding the gene-expression correlation matrix by high-pass filtering criteria. Edges in the graph represent highgenetic correlation, and the vertices or nodes represent traits. Maximal cliques, thelargest possible sets of completely connected (perfectly intercorrelated) traits, and16.5 SYSTEMS GENETIC ANALYSIS401other dense subgraphs are extracted and annotated from these gene sets (Baldwinet al. , 2005).",
"Other studies applied gene network modeling algorithms to identify thepotential regulators in complex diseases, for example cardiomyopathy [79], hepatic steatosis [80], as well ascoronary artery disease [81]. Finally, there are many other integrative approaches available for the analysis of multi-omics data, buthave not yet been applied in mouse systems genetics studies. Examples include the transcriptome-wideassociation study (TWAS) that integrates GWAS with expression datasets from other independent cohorts toprioritize candidate gene for phenotypic traits.We also developed a set of methods to analyze the different layers ofomics data that contribute to complex traits. In particular, intermediate phenotypes, including transcripts,proteins, and metabolites [22, 46, 73] were exploited to consolidate G2P and GXE connections. Despite theirpotential, transcriptome-/proteome-wide association studies (T/PWAS), which test the associations between aphenotype and all transcripts or proteins of a given tissue, have not been fully explored [74, 75], largelybecause of the limited availability of cohorts with such data (see above).",
"Recent improvements toGeneNetwork have reinvigorated it, including the addition of data from 10 species, multi-omicsanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource forpredictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most commonfunctions on GeneNetwork, as a gateway to deeper analyses, demonstrating how a smallnumber of plausible candidate genes can be found for a typical immune phenotype.",
"Theseresults do not rule out the possibility that for other tissues, conditions or organisms, utilizing thealternative methods as part of the third stage of the GEMOT algorithm may enhance its performance. We next aimed to characterize GEMOTs utility for a large biological network that included groupsof traits that share the same causal transcripts. Accordingly, each synthetic network included100 traits, 200 transcripts and 100 variants, featuring five co-mapped sub-networks. A singe datacollection consists of 100 networks, each containing five co-mapped sub-networks that carry the samenumber of traits (Materials and methods).",
"Here we provide open access and availability tothese data by integrating them into the GeneNetwork, aweb-based analytical tool that has been designed for multiscale integration of networks of genes, transcripts andtraits and optimized for on-line analysis of traits controlled by a combination of allelic variants and environmental factors. GeneNetwork with its central module WebQTLfacilitates the exploitation of permanent genetic referencepopulations that are accompanied by genotypic, phenotypic and mRNA abundance datasets.",
"Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006).By integrating genotypeand gene-expression data in a single model, Kulp and Jagular (2006) have drastically reduced QTL candidate genes, and perhaps inferred the structure of networksfrom expression-regulatory polymorphisms to variation. Combinatorial algorithmsare applied to networks that are drawn by thresholding the gene-expression correlation matrix by high-pass filtering criteria. Edges in the graph represent highgenetic correlation, and the vertices or nodes represent traits. Maximal cliques, thelargest possible sets of completely connected (perfectly intercorrelated) traits, and16.5 SYSTEMS GENETIC ANALYSIS401other dense subgraphs are extracted and annotated from these gene sets (Baldwinet al. , 2005).",
"The integration of genotypic and expression and other data haverecently been shown, in a Bayesian network framework [76], to enhance the overallaccuracy of predictive networks [40, 5153]. We have also recently demonstratedhow this class of network can be used to inform associations identified in GWAstudies [40]. 9 SummaryThe significant challenge we face in the post-genome era is deciphering the biological function of individual genes, pathways, and networks that drive complexphenotypes like disease.",
"Data upload into open access databases (such asGeneNetwork) allows an integrated analysis of different resources,phenotypic, genomic, and proteomic traits as well as data derivedfrom different tissues and diseases. Fibrogenesis is a common pathway that is induced by injury in almost every tissue [34], whichimplies core fibrogenic pathways [35] and common systemic modifiers [2], but also tissue-specific mechanisms [36]. Hence, data integration enhances the chance to detect genuine modifiers acrossorgans. GeneNetwork is a valuable platform that can be used byresearchers without advanced skills of bioinformatics to performsystems genetics analyses.",
"Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006).",
"Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006).By integrating genotypeand gene-expression data in a single model, Kulp and Jagular (2006) have drastically reduced QTL candidate genes, and perhaps inferred the structure of networksfrom expression-regulatory polymorphisms to variation. Combinatorial algorithmsare applied to networks that are drawn by thresholding the gene-expression correlation matrix by high-pass filtering criteria. Edges in the graph represent highgenetic correlation, and the vertices or nodes represent traits. Maximal cliques, thelargest possible sets of completely connected (perfectly intercorrelated) traits, and16.5 SYSTEMS GENETIC ANALYSIS401other dense subgraphs are extracted and annotated from these gene sets (Baldwinet al. , 2005).",
"Integration ofthese data in a readily computable form is required for facileidentication of highly supported genephenotype, genegene and phenotypephenotype relations (Akil et al. 2011). Here we demonstrate the benet of data integration in theGeneWeaver software system that enabled us to identifya common biological basis for two phenotypic endpoints related to alcoholism. This convergent evidence enabled us toprioritize our search for a biological correlate of these behaviors for which we could then demonstrate functional andgenetic validity."
],
[
"These programs have the capability to integrate large datasets ofgenetic and expression data from humans and animalstudies. Notably, the GeneNetwork program (www.genenetwork.org) can combine expression data gathered fromdifferent brain regions and tissues and map these withdescribed traits as a way to build gene networks [65]. Evolving developments in bioinformatics promise muchmore; it is now feasible to adopt a new modular approach,known as systems biology. Systems biology is a mathematical modeling technique applied to complex biologicalorganizations or processes for the purposes of generatingpredictive models that are more representative of biological situations [66,67].",
"This approach requires the accumulation and integration of many types of data,and also requires the use of many types of statistical tools to extract relevant patterns ofcovariation and causal relations as a function of genetics, environment, stage, and treatment. Inthis protocol we explain how to use the GeneNetwork web service, a powerful and free onlineresource for systems genetics. We provide workflows and methods to navigate massive multiscalardata sets and we explain how to use an extensive systems genetics toolkit for analysis andsynthesis.",
"Our work has been largely motivated by the computational demands ofsystems like GeneWeaver [82, 86], a web-based software platform for the integrationof functional genomics data. GeneWeaver includes a database containing lists ofgenes from diverse sources, along with descriptive metadata associated with theselists. Through gene homology, the lists can be combined across species such thatgenes on the lists are translated to a common reference. This enables theconstruction of a bipartite graph, with vertices representing individual genes.",
"Thismachine-learning approach could be readily extended to theCell 173, June 14, 2018 1589multi-omics datasets that drive network biology. A simpleexample would be using GANs to generate dramatically largerexpression datasets that can be used in the context ofnetwork inference to generate predictive models of transcriptional regulation. The black box nature of most next-generation machinelearning models presents an additional challenge for biologicalapplications.",
"This approach requires the accumulation and integration of many types of data,and also requires the use of many types of statistical tools to extract relevant patterns ofcovariation and causal relations as a function of genetics, environment, stage, and treatment. Inthis protocol we explain how to use the GeneNetwork web service, a powerful and free onlineresource for systems genetics. We provide workflows and methods to navigate massive multiscalardata sets and we explain how to use an extensive systems genetics toolkit for analysis andsynthesis.",
"Readersmay refer [42] for a comprehensive review on various availablesoftware tools. GeneNetWeaver (GNW) [43] is a Java-based reverse engineering tool for generating synthetic benchmark expression datasetsfrom gold standard DREAM challenge network. E. coli and Yeasttranscriptional regulatory networks are integrated as test case forbenchmark. Comparative assessment of inference algorithmsagainst DREAM challenge data can also be performed with thehelp GNW. Cytoscape [44] is a powerful tool most suitable forlarge-scale network analysis.",
"Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006).",
"A large number of bioinformatics tools have been developed to predict genefunction based on sequence homology [143-145], protein structure [144-146], phylogenetic profiles [147-149],protein-protein interactions [150-152], genetic interactions [153-155], and co-expression [156-162]. With the development of transcriptome profiling technologies, thousands of high-throughput studies havegenerated a wealth of genome-wide data that has become a valuable resource for systems genetics analyses. A few web resources, including GEO [163], ArrayExpress [164], GeneNetwork [165], and Bgee [166] amongstothers, have created repositories of such expression data for curation, reuse, and integration.",
"Network effectsWith the technological developments that allow for samplingof genomic, transcriptomic and proteomic data for multiple targets in the same set of samples, it is now possible to performnetworking analyses. These techniques involve assessing the coexpression of transcripts or proteins and building maps of modulesof closely related proles. Within these network maps, verticesrepresent transcripts or proteins and the edges represent thesimilarity between expression proles of pairs of transcripts or proteins. The principle of this analysis is that genes involved in thesame functional pathway are linked via short paths within the coexpression network.",
"However, the accurate derivation of such high-throughput data andtheir analysis in terms of biological function has been critical to truly leveraging the postgenomicrevolution. This chapter will describe an approach that focuses on the use of gene networks to bothorganize and interpret genomic expression data. Such networks, derived from statistical analysisof large genomic datasets and the application of multiple bioinformatics data resources, potentially allow the identification of key control elements for networks associated with human disease,and thus may lead to derivation of novel therapeutic approaches., 2011; Rosen et al. , 2003, 2007). GeneNetwork alsoprovides links to the following external databases: NCBI Entrez Gene, Summary from onMendelian Inheritance in Man (OMIM), GenBank, HomoloGene, UCSC Genome Browser(UCSC), BioGPS, STRING, PANTHER, Gemma, the brain synapse database, and the AllenBrain Atlas. Int Rev Neurobiol. Author manuscript; available in PMC 2014 July 21. O'Brien et al. Page 134. BIOINFORMATICS APPROACHES IN BEHAVIORAL NEUROSCIENCENIH-PA Author ManuscriptDespite the various high-throughput technologies employed and plat-forms available toperform expression analysis, a unifying consequence is the generation of large-scaleexpression datasets.",
"One challenge facing investigators in the interpretation of the massive data sets on GeneNetworkand elsewhere is deciding how much confidence toplace in QTL extracted from still noisy array andproteomic platforms after having conducted manythousands of statistical tests with poorly understooddegrees of dependency. Statistical approaches toaddress these types of challenges have beendeveloped using either theoretically derived (Landerand Kruglyak 1995) or empirical (Churchill and Doerge 1994) p values as well as false discovery rates(Benjamini and Hochberg 1995; Benjamini and Yekutieli 2005; Storey and Tibshirani 2003).",
"Bioinformatics Data SourcesThe ability to identify bona fide candidate genes in any genetic study is limited by one's knowledge of the function of the genes that are determined to harbor the most risk of that disease.With more than 30,000 full-length genes and tens of thousands of transcript variants, micro-RNAs, and noncanonical open reading frames (ORFs), the human genome annotation is undergoing constant development and refinement.Much of this information is now publicly accessible in major database interfaces, including the Gene Ontology, InterPro, pFam, KEGG, UCSC, and NCBI sites.Additional information, of course, can be found in actual published accounts.One major challenge is to develop tools that can cross-reference the results of genetic and functional genomic studies with these massive data sources, to develop knowledge networks for inference testing.Through free software (such as Cytoscape) and commercial software (such as Ingenuity and Pathway Assist), this goal is now within reach.In fact, these software applications can even use natural language-based text mining algorithms to directly integrate author statements with experimental data.",
"Here we provide open access and availability tothese data by integrating them into the GeneNetwork, aweb-based analytical tool that has been designed for multiscale integration of networks of genes, transcripts andtraits and optimized for on-line analysis of traits controlled by a combination of allelic variants and environmental factors. GeneNetwork with its central module WebQTLfacilitates the exploitation of permanent genetic referencepopulations that are accompanied by genotypic, phenotypic and mRNA abundance datasets.",
"Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006).",
"The integration of genotypic and expression and other data haverecently been shown, in a Bayesian network framework [76], to enhance the overallaccuracy of predictive networks [40, 5153]. We have also recently demonstratedhow this class of network can be used to inform associations identified in GWAstudies [40]. 9 SummaryThe significant challenge we face in the post-genome era is deciphering the biological function of individual genes, pathways, and networks that drive complexphenotypes like disease.",
"Computations can be as simple as sets of correlations andAnderson et al. Open Source Web-Based ToolsJ. Neurosci. , February 3, 2021 41(5):927936 933volume and neuron number (Fig. 5D). This type of information is important, forexample, when extrapolating from MRIvolume differences in humans to potentialvariations in cell number (Hibar et al. ,2015). One exciting area of research enabledby GeneNetwork.org is the reanalysis ofphenotypes generated before 2010, whichwould greatly benefit from recent computational methods and datasets.",
"Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006).",
"Coupling a large multispecies repository curated and empirical functionalgenomics data to fast computational tools allows for the rapid integrative analysis of heterogeneous datafor interpreting and extrapolating systems genetics results. Key words IT-tools for systems genetics, GeneWeaver data base, Data mining, QTL candidate gene1IntroductionSystems genetics studies generate large volumes of gene expressionnetworks, and positional candidate genes. Resolving and prioritizing these results requires refinement of the causal variants, functional role of genes and gene products and relationships of genecoexpression networks to mechanistic biology.",
"Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006)."
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