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Open AccessJournal ArticleDOI

Jetset: selecting the optimal microarray probe set to represent a gene

TLDR
This method provides a simple, unambiguous mapping to allow assessment of the expression levels of specific genes of interest and evaluated concordance between protein measurements and gene expression values.
Abstract
Background: Interpretation of gene expression microarrays requires a mapping from probe set to gene. On many Affymetrix gene expression microarrays, a given gene may be detected by multiple probe sets, which may deliver inconsistent or even contradictory measurements. Therefore, obtaining an unambiguous expression estimate of a pre-specified gene can be a nontrivial but essential task. Results: We developed scoring methods to assess each probe set for specificity, splice isoform coverage, and robustness against transcript degradation. We used these scores to select a single representative probe set for each gene, thus creating a simple one-to-one mapping between gene and probe set. To test this method, we evaluated concordance between protein measurements and gene expression values, and between sets of genes whose expression is known to be correlated. For both test cases, we identified genes that were nominally detected by multiple probe sets, and we found that the probe set chosen by our method showed stronger concordance. Conclusions: This method provides a simple, unambiguous mapping to allow assessment of the expression levels of specific genes of interest.

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Inconsistency in large pharmacogenomic studies

TL;DR: Genomic data are well correlated between studies; however, the measured drug response data are highly discordant, which has potential implications for using these outcome measures to assess gene–drug associations or select potential anticancer drugs on the basis of their reported results.
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Stemness of the hybrid Epithelial/Mesenchymal State in Breast Cancer and Its Association with Poor Survival.

TL;DR: The data support a novel model that links a mixed EM signature with stemness in individual cells, luminal and basal cell lines, in vivo xenograft mouse models, and in all breast cancer subtypes and suggest that targeting E/M heterogeneity by eliminating hybrid E-M cells and cooperation between E and M cell-types could improve breast cancer patient survival independent of breast cancer-subtype.
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Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer

TL;DR: In this article, a reference ranking of all survival related genes in chemotherapy treated basal and estrogen positive/HER2 negative breast cancer was presented, and the results help to neglect those with unlikely clinical significance and focus future research on the most promising candidates.
Journal ArticleDOI

TNMplot.com: A Web Tool for the Comparison of Gene Expression in Normal, Tumor and Metastatic Tissues.

TL;DR: In this article, the authors established an integrated database using available transcriptome-level datasets and created a web platform which enables the mining of this database by comparing normal, tumor and metastatic data across all genes in real time.
References
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Journal ArticleDOI

NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins

TL;DR: The National Center for Biotechnology Information Reference Sequence (RefSeq) database provides a non-redundant collection of sequences representing genomic data, transcripts and proteins that pragmatically includes sequence data that are currently publicly available in the archival databases.
Journal ArticleDOI

Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer.

TL;DR: The ability to identify patients who have a favourable prognosis could, after independent confirmation, allow clinicians to avoid adjuvant systemic therapy or to choose less aggressive therapeutic options.
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Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt.

TL;DR: It is demonstrated how to use the computational environment R to integrate and jointly analyze experimental datasets, employing BioMart web services to provide the molecule mappings.
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Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data.

TL;DR: This work reorganized probes on more than a dozen popular GeneChips into gene-, transcript- and exon-specific probe sets in light of up-to-date genome, cDNA/EST clustering and single nucleotide polymorphism information, and demonstrates that the original Affymetrix probe set definitions are inaccurate.
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