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RESOURCERER: a database for annotating and linking microarray resources within and across species

Microarray expression analysis is providing unprecedented data on gene expression in humans and mammalian model systems. Although such studies provide a tremendous resource for understanding human disease states, one of the significant challenges is cross-referencing the data derived from different...

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Autores principales: Tsai, Jennifer, Sultana, Razvan, Lee, Yudan, Pertea, Geo, Karamycheva, Svetlana, Antonescu, Valentin, Cho, Jennifer, Parvizi, Babak, Cheung, Foo, Quackenbush, John
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2001
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC138985/
https://www.ncbi.nlm.nih.gov/pubmed/16173164
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author Tsai, Jennifer
Sultana, Razvan
Lee, Yudan
Pertea, Geo
Karamycheva, Svetlana
Antonescu, Valentin
Cho, Jennifer
Parvizi, Babak
Cheung, Foo
Quackenbush, John
author_facet Tsai, Jennifer
Sultana, Razvan
Lee, Yudan
Pertea, Geo
Karamycheva, Svetlana
Antonescu, Valentin
Cho, Jennifer
Parvizi, Babak
Cheung, Foo
Quackenbush, John
author_sort Tsai, Jennifer
collection PubMed
description Microarray expression analysis is providing unprecedented data on gene expression in humans and mammalian model systems. Although such studies provide a tremendous resource for understanding human disease states, one of the significant challenges is cross-referencing the data derived from different species, across diverse expression analysis platforms, in order to properly derive inferences regarding gene expression and disease state. To address this problem, we have developed RESOURCERER, a microarray-resource annotation and cross-reference database built using the analysis of expressed sequence tags (ESTs) and gene sequences provided by the TIGR Gene Index (TGI) and TIGR Orthologous Gene Alignment (TOGA) databases [now called Eukaryotic Gene Orthologs (EGO)].
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spelling pubmed-1389852003-03-03 RESOURCERER: a database for annotating and linking microarray resources within and across species Tsai, Jennifer Sultana, Razvan Lee, Yudan Pertea, Geo Karamycheva, Svetlana Antonescu, Valentin Cho, Jennifer Parvizi, Babak Cheung, Foo Quackenbush, John Genome Biol Software Report Microarray expression analysis is providing unprecedented data on gene expression in humans and mammalian model systems. Although such studies provide a tremendous resource for understanding human disease states, one of the significant challenges is cross-referencing the data derived from different species, across diverse expression analysis platforms, in order to properly derive inferences regarding gene expression and disease state. To address this problem, we have developed RESOURCERER, a microarray-resource annotation and cross-reference database built using the analysis of expressed sequence tags (ESTs) and gene sequences provided by the TIGR Gene Index (TGI) and TIGR Orthologous Gene Alignment (TOGA) databases [now called Eukaryotic Gene Orthologs (EGO)]. BioMed Central 2001 2001-10-19 /pmc/articles/PMC138985/ /pubmed/16173164 Text en Copyright © 2001 Tsai et al., licensee BioMed Central Ltd
spellingShingle Software Report
Tsai, Jennifer
Sultana, Razvan
Lee, Yudan
Pertea, Geo
Karamycheva, Svetlana
Antonescu, Valentin
Cho, Jennifer
Parvizi, Babak
Cheung, Foo
Quackenbush, John
RESOURCERER: a database for annotating and linking microarray resources within and across species
title RESOURCERER: a database for annotating and linking microarray resources within and across species
title_full RESOURCERER: a database for annotating and linking microarray resources within and across species
title_fullStr RESOURCERER: a database for annotating and linking microarray resources within and across species
title_full_unstemmed RESOURCERER: a database for annotating and linking microarray resources within and across species
title_short RESOURCERER: a database for annotating and linking microarray resources within and across species
title_sort resourcerer: a database for annotating and linking microarray resources within and across species
topic Software Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC138985/
https://www.ncbi.nlm.nih.gov/pubmed/16173164
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