The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

By Maqc Consortium. Nature Biotechnology, 2006

2,181 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6f8182d4b807f877a2e1102ffc95e4392d37a6d0

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In Molecular Biology

Citations in the corpus

An Abundance of Ubiquitously Expressed Genes Revealed by Tissue Transcriptome Sequence Data (2009, neutral): “2 exons and only one annotated isoform in RefSeq whose expressions had been measured by the MicroArray Quality Control project [50] in the same two samples, UHR (universal human reference) RNA and Figure 4.”
voom: precision weights unlock linear model analysis tools for RNA-seq read counts (2014, result): “The groups are labeled A–D and are closely analogous to the similarly labeled RNA samples used in the earlier microarray quality control study [57].”
Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis (2010, background): “One of the major statistical paradigms in expression microarray analysis has been the “Fold change-ranking with a non-stringent p-value cutoff” [18-20].”
Fold change and p-value cutoffs significantly alter microarray interpretations (2012, background): “Array technology has improved these conditions yet problems of standardizing statistical analyses are lacking, along with observed differences when comparing microarray platforms [2], though others have found significant reproducibility [3].”
The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies (2008, methodology): “For more information about the MAQC project and the data sets, refer to Shi et al [13].”
The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote (2013, background): “The SEQC project, which is the third stage of the wellknown MAQC project (31), is producing benchmark nextgen sequencing data.”
Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures (2008, methodology): “Hybridization probes were mapped to Entrez GeneID [26] through sequence alignment against RefSeq mRNA in the (NM) subset, similar to the approach of Shi and colleagues [27], using RefSeq version 21 (2007.”
Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data (2013, methodology): “ray technology [11,12] as well as the SEQC effort to characterize RNA-seq technology and include close to 1,000 genes that were validated by TaqMan qPCR.”
An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients (2010, background): “The accuracy of microarray-based gene expression measurements has been evaluated by a wide array of diverse studies [6–8], leading to the general conclusion that it is a powerful surveyor of gene expression changes when its limitations are considered properly.”
Removing technical variability in RNA-seq data using conditional quantile normalization (2012, methodology): “The same samples have been assayed extensively on microarrays, and we used data from the MAQC Consortium (2006), in which each of the 2 samples was hybridized to 5 different Affymetrix U133 Plus 2.0 arrays (Affymetrix, Santa Clara, CA).”
Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines (2014, background): “One of the major concerns is that gene expression estimates, generated on different microarray platforms or even in different batches, are not always consistent [11].”
A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data (2014, methodology): “The MAQC dataset: MicroArray Quality Control Project (MAQC) data [31] includes biological samples for human brain reference RNA (hbr) and universal human reference RNA (uhr).”
Human Disease-Drug Network Based on Genomic Expression Profiles (2009, result): “05) were more reliable (and more consistent with the results from similar studies) than those simply based on P value or fold change alone [23].”
Direct multiplexed measurement of gene expression with color-coded probe pairs (2008, neutral): “NM_014484 MOCS3 206141_at Hs00819330_s1 183 (6) 83 (9) D/D 42 (2) 40 (2) D/U 28.”