Dataset Information

GenoSets: visual analytic methods for comparative genomics.

ABSTRACT: Many important questions in biology are, fundamentally, comparative, and this extends to our analysis of a growing number of sequenced genomes. Existing genomic analysis tools are often organized around literal views of genomes as linear strings. Even when information is highly condensed, these views grow cumbersome as larger numbers of genomes are added. Data aggregation and summarization methods from the field of visual analytics can provide abstracted comparative views, suitable for sifting large multi-genome datasets to identify critical similarities and differences. We introduce a software system for visual analysis of comparative genomics data. The system automates the process of data integration, and provides the analysis platform to identify and explore features of interest within these large datasets. GenoSets borrows techniques from business intelligence and visual analytics to provide a rich interface of interactive visualizations supported by a multi-dimensional data warehouse. In GenoSets, visual analytic approaches are used to enable querying based on orthology, functional assignment, and taxonomic or user-defined groupings of genomes. GenoSets links this information together with coordinated, interactive visualizations for both detailed and high-level categorical analysis of summarized data. GenoSets has been designed to simplify the exploration of multiple genome datasets and to facilitate reasoning about genomic comparisons. Case examples are included showing the use of this system in the analysis of 12 Brucella genomes. GenoSets software and the case study dataset are freely available at http://genosets.uncc.edu. We demonstrate that the integration of genomic data using a coordinated multiple view approach can simplify the exploration of large comparative genomic data sets, and facilitate reasoning about comparisons and features of interest.

SUBMITTER: Cain AA

PROVIDER: S-EPMC3463605 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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GenoSets: visual analytic methods for comparative genomics.

Cain Aurora A AA Kosara Robert R Gibas Cynthia J CJ

PloS one 20121003 10

Many important questions in biology are, fundamentally, comparative, and this extends to our analysis of a growing number of sequenced genomes. Existing genomic analysis tools are often organized around literal views of genomes as linear strings. Even when information is highly condensed, these views grow cumbersome as larger numbers of genomes are added. Data aggregation and summarization methods from the field of visual analytics can provide abstracted comparative views, suitable for sifting l ...[more]

PMID: 23056299

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Project description:ObjectivePeroxisome proliferator-activated receptor γ (PPARγ) agonists have been shown to have anti-proliferative, anti-angiogenic, and proapoptotic effects, leading to interest in their use as cancer therapeutics. Pioglitazone, a U.S. Food and Drug Administration-approved type II diabetes medication and PPARγ agonist, may have a role in adjuvant head-and-neck squamous cell carcinoma treatment or prevention. Therefore, the purpose of this study was: 1) to treat oral cavity cancer cells with the PPARγ activator, pioglitazone, to analyze gene expression changes; and 2) to compare those changes with our preexisting genomic data for development of hypothesis-driven additional basic and clinical studies.Study designProspective in vitro.MethodsWe utilized microarray technology, as well as OCPlus (Bioconductor open source software) and Ingenuity Pathway Analysis (Qiagen, Redwood City, CA), to analyze differential gene expression in tumor and pioglitazone-treated tumor cells on a genome-wide level to demonstrate the feasibility of such an approach and determine appropriate sample size for future investigations.ResultsWe found that approximately 35 samples are required to adequately power future studies. We next discovered that pioglitazone significantly affects Inducible T-Cell Costimulator (iCOS)-Ligand for the T-cell-specific cell surface receptor ICOS (iCOSL) and type II diabetes mellitus pathways as a putative anti-cancer mechanism.ConclusionGenome-wide analysis is possible for the exploration of differential pathway modulation and rapid hypothesis generation. Both inflammation and type II diabetes pathways were significantly altered and therefore might provide unique hypothesis-driven pharmacodynamic parameters for future in vitro or in vivo studies utilizing thiazolidinediones. These techniques could be applied to microarray or other high throughput data from a variety of hypothesis-generating research scenarios in otolaryngology (e.g., middle ear proteomics, sinus microbiome studies).Level of evidenceNA. Laryngoscope, 127:E124-E131, 2017.

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GenoSets: visual analytic methods for comparative genomics.

Publications

GenoSets: visual analytic methods for comparative genomics.

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