Jetset: selecting the optimal microarray probe set to represent a gene
Open Access
- 15 December 2011
- journal article
- Published by Springer Science and Business Media LLC in BMC Bioinformatics
- Vol. 12 (1), 474
- https://doi.org/10.1186/1471-2105-12-474
Abstract
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.Keywords
This publication has 23 references indexed in Scilit:
- GATExplorer: Genomic and Transcriptomic Explorer; mapping expression probes to gene loci, transcripts, exons and ncRNAsBMC Bioinformatics, 2010
- Optimization of the BLASTN substitution matrix for prediction of non-specific DNA microarray hybridizationNucleic Acids Research, 2009
- Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRtNature Protocols, 2009
- Transcript-Specific Expression Profiles Derived from Sequence-Based Analysis of Standard MicroarraysPLOS ONE, 2009
- Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signaturesBreast Cancer Research, 2008
- Correction of technical bias in clinical microarray data improves concordance with known biological informationGenome Biology, 2008
- Transcript-based redefinition of grouped oligonucleotide probe sets using AceView: High-resolution annotation for microarraysBMC Bioinformatics, 2007
- A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancersNature Genetics, 2006
- NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteinsNucleic Acids Research, 2004
- Chipping away at the chip bias: RNA degradation in microarray analysisNature Genetics, 2003