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GenoSets: Visual Analytic Methods for Comparative Genomics
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 view...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463605/ https://www.ncbi.nlm.nih.gov/pubmed/23056299 http://dx.doi.org/10.1371/journal.pone.0046401 |
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author | Cain, Aurora A. Kosara, Robert Gibas, Cynthia J. |
author_facet | Cain, Aurora A. Kosara, Robert Gibas, Cynthia J. |
author_sort | Cain, Aurora A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3463605 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-34636052012-10-09 GenoSets: Visual Analytic Methods for Comparative Genomics Cain, Aurora A. Kosara, Robert Gibas, Cynthia J. PLoS One Research Article 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. Public Library of Science 2012-10-03 /pmc/articles/PMC3463605/ /pubmed/23056299 http://dx.doi.org/10.1371/journal.pone.0046401 Text en © 2012 Cain et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Cain, Aurora A. Kosara, Robert Gibas, Cynthia J. GenoSets: Visual Analytic Methods for Comparative Genomics |
title | GenoSets: Visual Analytic Methods for Comparative Genomics |
title_full | GenoSets: Visual Analytic Methods for Comparative Genomics |
title_fullStr | GenoSets: Visual Analytic Methods for Comparative Genomics |
title_full_unstemmed | GenoSets: Visual Analytic Methods for Comparative Genomics |
title_short | GenoSets: Visual Analytic Methods for Comparative Genomics |
title_sort | genosets: visual analytic methods for comparative genomics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463605/ https://www.ncbi.nlm.nih.gov/pubmed/23056299 http://dx.doi.org/10.1371/journal.pone.0046401 |
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