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GOBO: Gene Expression-Based Outcome for Breast Cancer Online.

Ringnér, Markus LU ; Fredlund, Erik LU ; Häkkinen, Jari LU ; Borg, Åke LU and Staaf, Johan LU (2011) In PLoS One 6(3).
Abstract
Microarray-based gene expression analysis holds promise of improving prognostication and treatment decisions for breast cancer patients. However, the heterogeneity of breast cancer emphasizes the need for validation of prognostic gene signatures in larger sample sets stratified into relevant subgroups. Here, we describe a multifunctional user-friendly online tool, GOBO (http://co.bmc.lu.se/gobo), allowing a range of different analyses to be performed in an 1881-sample breast tumor data set, and a 51-sample breast cancer cell line set, both generated on Affymetrix U133A microarrays. GOBO supports a wide range of applications including: 1) rapid assessment of gene expression levels in subgroups of breast tumors and cell lines, 2)... (More)
Microarray-based gene expression analysis holds promise of improving prognostication and treatment decisions for breast cancer patients. However, the heterogeneity of breast cancer emphasizes the need for validation of prognostic gene signatures in larger sample sets stratified into relevant subgroups. Here, we describe a multifunctional user-friendly online tool, GOBO (http://co.bmc.lu.se/gobo), allowing a range of different analyses to be performed in an 1881-sample breast tumor data set, and a 51-sample breast cancer cell line set, both generated on Affymetrix U133A microarrays. GOBO supports a wide range of applications including: 1) rapid assessment of gene expression levels in subgroups of breast tumors and cell lines, 2) identification of co-expressed genes for creation of potential metagenes, 3) association with outcome for gene expression levels of single genes, sets of genes, or gene signatures in multiple subgroups of the 1881-sample breast cancer data set. The design and implementation of GOBO facilitate easy incorporation of additional query functions and applications, as well as additional data sets irrespective of tumor type and array platform. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS One
volume
6
issue
3
publisher
Public Library of Science
external identifiers
  • WOS:000288613300021
  • PMID:21445301
  • Scopus:79952819724
ISSN
1932-6203
DOI
10.1371/journal.pone.0017911
project
CREATE Health
language
English
LU publication?
yes
id
d5246f30-95ac-462c-9f0b-141a8a57f1d0 (old id 1883344)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/21445301?dopt=Abstract
date added to LUP
2011-04-01 22:22:24
date last changed
2016-11-27 04:26:14
@misc{d5246f30-95ac-462c-9f0b-141a8a57f1d0,
  abstract     = {Microarray-based gene expression analysis holds promise of improving prognostication and treatment decisions for breast cancer patients. However, the heterogeneity of breast cancer emphasizes the need for validation of prognostic gene signatures in larger sample sets stratified into relevant subgroups. Here, we describe a multifunctional user-friendly online tool, GOBO (http://co.bmc.lu.se/gobo), allowing a range of different analyses to be performed in an 1881-sample breast tumor data set, and a 51-sample breast cancer cell line set, both generated on Affymetrix U133A microarrays. GOBO supports a wide range of applications including: 1) rapid assessment of gene expression levels in subgroups of breast tumors and cell lines, 2) identification of co-expressed genes for creation of potential metagenes, 3) association with outcome for gene expression levels of single genes, sets of genes, or gene signatures in multiple subgroups of the 1881-sample breast cancer data set. The design and implementation of GOBO facilitate easy incorporation of additional query functions and applications, as well as additional data sets irrespective of tumor type and array platform.},
  author       = {Ringnér, Markus and Fredlund, Erik and Häkkinen, Jari and Borg, Åke and Staaf, Johan},
  issn         = {1932-6203},
  language     = {eng},
  number       = {3},
  publisher    = {ARRAY(0x88ac888)},
  series       = {PLoS One},
  title        = {GOBO: Gene Expression-Based Outcome for Breast Cancer Online.},
  url          = {http://dx.doi.org/10.1371/journal.pone.0017911},
  volume       = {6},
  year         = {2011},
}