GeneWeaver: a web-based system for integrative functional genomics

Baker, Erich J.; Jay, Jeremy J.; Bubier, Jason A.; Langston, Michael A.; Chesler, Elissa J.

doi:10.1093/nar/gkr968

Cited by 128 publications

(141 citation statements)

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“…We were able to refine this result using advanced resources for functional genomic data integration with high-precision tools and resources for analysis of mouse genetic diversity. Our GeneWeaver system (Baker et al 2012) enabled integration of disparate functional genomics experimental results to attack the question of what biological mechanism underlies both alcohol preference and withdrawal. This led us to a wealth of published QTL for these traits, several of which overlapped.…”

Section: Discussionmentioning

confidence: 99%

“…This system has been previously described in detail (Baker et al 2009(Baker et al , 2012. GeneWeaver tools each operate on bipartite graphs with two sets of vertices, or nodes, representing genes and the gene sets to which they belong (Baker et al 2009).…”

Section: Databasementioning

confidence: 99%

“…Of the 60,000 gene sets in the GeneWeaver database (Baker et al 2012), a query for gene sets containing reference to "alcoholism" in their meta-content revealed 2857 gene sets. A specific query for gene sets annotated with "alcohol preference" revealed 163 gene sets and a separate query for "alcohol withdrawal" revealed 122 gene sets.…”

Section: Bioinformatic Analysismentioning

confidence: 99%

“…The HiSim tool constructs the hierarchical network of all set-set intersections, and the HiSim Graph tool organizes gene sets based on the genes that they contain, presenting a network graph of hierarchical intersections among a collection of input gene sets and enabling discovery of the relationships among these sets. The network was produced from the overlap of maximal bicliques in discrete bipartite structures, and a directed acyclic graph of intersecting gene sets was produced (Baker et al 2009(Baker et al , 2012Zhang et al 2014). Default options were used, such that bootstrapping of branching was performed, homology was used to integrate gene overlap across species, and the minimum number of intersecting genes required to produce a node was set at 1.…”

mentioning

confidence: 99%

“…The diversity and intensiveness of statistical procedures required to re-analyze primary data and the cross-platform and cross-species data integration required for matching identifiers across various experimental platforms place the process out of reach for widespread application. We designed the GeneWeaver software system (Baker et al 2009(Baker et al , 2012 to address these problems by enabling users to integrate their own data with large numbers of experimental results across multiple species using combinatorial algorithms in a real-time, web-based environment. GeneWeaver contains multiple streams of genomic data, including ontological annotations from Generic Model Organism Databases, QTL positional candidates, transcriptomic results from published experimental studies, user-submitted functional genomics data sets, GWAS candidates, literature annotation, and other strategies.…”

mentioning

confidence: 99%

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Identification of a QTL in Mus musculus for Alcohol Preference, Withdrawal, and Ap3m2 Expression Using Integrative Functional Genomics and Precision Genetics

et al. 2014

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Extensive genetic and genomic studies of the relationship between alcohol drinking preference and withdrawal severity have been performed using animal models. Data from multiple such publications and public data resources have been incorporated in the GeneWeaver database with .60,000 gene sets including 285 alcohol withdrawal and preference-related gene sets. Among these are evidence for positional candidates regulating these behaviors in overlapping quantitative trait loci (QTL) mapped in distinct mouse populations. Combinatorial integration of functional genomics experimental results revealed a single QTL positional candidate gene in one of the loci common to both preference and withdrawal. Functional validation studies in Ap3m2 knockout mice confirmed these relationships. Genetic validation involves confirming the existence of segregating polymorphisms that could account for the phenotypic effect. By exploiting recent advances in mouse genotyping, sequence, epigenetics, and phylogeny resources, we confirmed that Ap3m2 resides in an appropriately segregating genomic region. We have demonstrated genetic and alcohol-induced regulation of Ap3m2 expression. Although sequence analysis revealed no polymorphisms in the Ap3m2-coding region that could account for all phenotypic differences, there are several upstream SNPs that could. We have identified one of these to be an H3K4me3 site that exhibits strain differences in methylation. Thus, by making cross-species functional genomics readily computable we identified a common QTL candidate for two related bio-behavioral processes via functional evidence and demonstrate sufficiency of the genetic locus as a source of variation underlying two traits. FINDING the genetic and genomic basis for complex disorders has been a major challenge, particularly for behavioral traits. This is because the disorders are highly heterogeneous in their manifestation and regulation, requiring a match of numerous genes to many aspects of the disorders. A compelling approach to this problem lies in integration of numerous disparate data sets across species, each aimed at characterizing the mechanistic biological underpinnings for the behaviors underlying these disorders as modeled in various species. Alcoholism and alcohol use-related disorders are complex, heritable traits that have been the subject of extensive genomic and genetic investigations (Morozova et al. 2012). These traits are particularly challenging because of their heterogeneity and the presence of multiple overlapping subsystems that subserve them (Gould and Gottesman 2006;Crabbe 2012). Many rodent quantitative trait loci (QTL) have been mapped for alcohol-related traits (Ehlers et al. 2010), gene expression analyses have been performed in a variety of populations, and a long history of alcoholism genetics in humans has led to numerous genome-wide association studies (GWAS) or linkage studies (Enoch 2013). QTL mapping studies historically had very low resolution, and many have been performed using populations for which...

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Section: Discussionmentioning

confidence: 99%

Section: Databasementioning

confidence: 99%

Section: Bioinformatic Analysismentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Identification of a QTL in Mus musculus for Alcohol Preference, Withdrawal, and Ap3m2 Expression Using Integrative Functional Genomics and Precision Genetics

et al. 2014

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A Vision for Twenty‐First Century Healthcare

Hood¹,

Price²,

Yurkovich³

2022

Holland‐Frei Cancer Medicine

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Overview The convergence of systems science, big data, and patient‐activated social networks is leading to the practice of predictive, preventive, personalized, and participatory (P4) Medicine. P4 Medicine has two central thrusts: optimizing wellness and demystifying disease. In this chapter, we will describe current research practices that embody P4 Medicine, providing examples of how these studies have and can continue to integrate with and transform the healthcare system. We will focus on the use of dense longitudinal profiling of an individual's physiological state—so‐called “deep phenotyping”—to gain key insights into wellness as well as the onset, progression, treatment, and prevention of disease. Such a systems approach will generate clinically actionable possibilities to enable individuals to optimize wellness and/or avoid disease. We believe that this approach will transform healthcare by decreasing costs, increasing healthcare quality, and promoting innovation in a new paradigm of medical practice.

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Integrating omics into the cardiac differentiation of human pluripotent stem cells

Rojas-Muñoz

Maurya

et al. 2014

WIREs Mechanisms of Disease

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Time-dependent extracellular manipulations of human pluripotent stem cells can yield as much as 90% pure populations of cardiomyocytes. While the extracellular control of differentiation generally entails dynamic regulation of well-known pathways such as Wnt, BMP, and Nodal signaling, the underlying genetic networks are far more complex and are poorly understood. Notably, the identification of these networks holds promise for understanding heart disease and regeneration. The availability of genome-wide experimentation, such as RNA and DNA sequencing, as well as high throughput surveying with small molecule and small interfering RNA libraries, now enables us to map the genetic interactions underlying cardiac differentiation on a global scale. Initial studies demonstrate the complexity of the genetic regulation of cardiac differentiation, exposing unanticipated novel mechanisms. However, the large datasets generated tend to be overwhelming and systematic approaches are needed to process the vast amount of data to improve our mechanistic understanding of the complex biology. Systems biology methods spur high hopes for parsing vast amounts of data into genetic interaction models that can be verified experimentally and ultimately yield functional networks that expose the genetic connections underlying biological processes.

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GeneWeaver: a web-based system for integrative functional genomics

Cited by 128 publications

References 31 publications

Identification of a QTL in Mus musculus for Alcohol Preference, Withdrawal, and Ap3m2 Expression Using Integrative Functional Genomics and Precision Genetics

Identification of a QTL in Mus musculus for Alcohol Preference, Withdrawal, and Ap3m2 Expression Using Integrative Functional Genomics and Precision Genetics

A Vision for Twenty‐First Century Healthcare

Integrating omics into the cardiac differentiation of human pluripotent stem cells

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