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ProGenExpress: Visualization of quantitative data on prokaryotic genomes

BACKGROUND: The integration of genomic information with quantitative experimental data is a key component of systems biology. An increasing number of microbial genomes are being sequenced, leading to an increasing amount of data from post-genomics technologies. The genomes of prokaryotes contain man...

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Detalles Bibliográficos
Autor principal: Watson, Michael
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1087476/
https://www.ncbi.nlm.nih.gov/pubmed/15829007
http://dx.doi.org/10.1186/1471-2105-6-98
Descripción
Sumario:BACKGROUND: The integration of genomic information with quantitative experimental data is a key component of systems biology. An increasing number of microbial genomes are being sequenced, leading to an increasing amount of data from post-genomics technologies. The genomes of prokaryotes contain many structures of interest, such as operons, pathogenicity islands and prophage sequences, whose behaviour is of interest during infection and disease. There is a need for simple and novel tools to display and analyse data from these integrated datasets, and we have developed ProGenExpress as a tool for visualising arbitrarily complex numerical data in the context of prokaryotic genomes. RESULTS: Here we describe ProGenExpress, an R package that allows researchers to easily and quickly visualize quantitative measurements, such as those produced by microarray experiments, in the context of the genome organization of sequenced prokaryotes. Data from microarrays, proteomics or other whole-genome technologies can be accurately displayed on the genome. ProGenExpress can also search for novel regions of interest that consist of groups of adjacent genes that show similar patterns across the experimental data set. We demonstrate ProGenExpress with microarray data from a time-course experiment involving Salmonella typhimurium. CONCLUSION: ProGenExpress can be used to visualize quantitative data from complex experiments in the context of the genome of sequenced prokaryotes, and to find novel regions of interest.