Literature DB >> 16964225

Evaluation of DNA microarray results with quantitative gene expression platforms.

Roger D Canales1, Yuling Luo, James C Willey, Bradley Austermiller, Catalin C Barbacioru, Cecilie Boysen, Kathryn Hunkapiller, Roderick V Jensen, Charles R Knight, Kathleen Y Lee, Yunqing Ma, Botoul Maqsodi, Adam Papallo, Elizabeth Herness Peters, Karen Poulter, Patricia L Ruppel, Raymond R Samaha, Leming Shi, Wen Yang, Lu Zhang, Federico M Goodsaid.   

Abstract

We have evaluated the performance characteristics of three quantitative gene expression technologies and correlated their expression measurements to those of five commercial microarray platforms, based on the MicroArray Quality Control (MAQC) data set. The limit of detection, assay range, precision, accuracy and fold-change correlations were assessed for 997 TaqMan Gene Expression Assays, 205 Standardized RT (Sta)RT-PCR assays and 244 QuantiGene assays. TaqMan is a registered trademark of Roche Molecular Systems, Inc. We observed high correlation between quantitative gene expression values and microarray platform results and found few discordant measurements among all platforms. The main cause of variability was differences in probe sequence and thus target location. A second source of variability was the limited and variable sensitivity of the different microarray platforms for detecting weakly expressed genes, which affected interplatform and intersite reproducibility of differentially expressed genes. From this analysis, we conclude that the MAQC microarray data set has been validated by alternative quantitative gene expression platforms thus supporting the use of microarray platforms for the quantitative characterization of gene expression.

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Year:  2006        PMID: 16964225     DOI: 10.1038/nbt1236

Source DB:  PubMed          Journal:  Nat Biotechnol        ISSN: 1087-0156            Impact factor:   54.908


  246 in total

1.  Evaluating methods for ranking differentially expressed genes applied to microArray quality control data.

Authors:  Koji Kadota; Kentaro Shimizu
Journal:  BMC Bioinformatics       Date:  2011-06-06       Impact factor: 3.169

Review 2.  Genomics in mammalian cell culture bioprocessing.

Authors:  Diane M Wuest; Sarah W Harcum; Kelvin H Lee
Journal:  Biotechnol Adv       Date:  2011-11-04       Impact factor: 14.227

3.  RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues.

Authors:  Fay Wang; John Flanagan; Nan Su; Li-Chong Wang; Son Bui; Allissa Nielson; Xingyong Wu; Hong-Thuy Vo; Xiao-Jun Ma; Yuling Luo
Journal:  J Mol Diagn       Date:  2012-01       Impact factor: 5.568

Review 4.  Voluntary exploratory data submissions to the US FDA and the EMA: experience and impact.

Authors:  Federico M Goodsaid; Shashi Amur; Jiri Aubrecht; Michael E Burczynski; Kevin Carl; Jennifer Catalano; Rosane Charlab; Sandra Close; Catherine Cornu-Artis; Laurent Essioux; Albert J Fornace; Lois Hinman; Huixiao Hong; Ian Hunt; David Jacobson-Kram; Ansar Jawaid; David Laurie; Lawrence Lesko; Heng-Hong Li; Klaus Lindpaintner; James Mayne; Peter Morrow; Marisa Papaluca-Amati; Timothy W Robison; John Roth; Ina Schuppe-Koistinen; Leming Shi; Olivia Spleiss; Weida Tong; Sharada L Truter; Jacky Vonderscher; Agnes Westelinck; Li Zhang; Issam Zineh
Journal:  Nat Rev Drug Discov       Date:  2010-06       Impact factor: 84.694

5.  Transcriptional profiling of TLR-4/7/8-stimulated guinea pig splenocytes and whole blood by bDNA assay.

Authors:  Lance K Ching; Farah Mompoint; Jeffrey A Guderian; Alex Picone; Ian M Orme; Rhea N Coler; Steven G Reed; Susan L Baldwin
Journal:  J Immunol Methods       Date:  2011-08-03       Impact factor: 2.303

6.  CEDER: accurate detection of differentially expressed genes by combining significance of exons using RNA-Seq.

Authors:  Lin Wan; Fengzhu Sun
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2012 Sep-Oct       Impact factor: 3.710

7.  Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study.

Authors:  Sheng Li; Scott W Tighe; Charles M Nicolet; Deborah Grove; Shawn Levy; William Farmerie; Agnes Viale; Chris Wright; Peter A Schweitzer; Yuan Gao; Dewey Kim; Joe Boland; Belynda Hicks; Ryan Kim; Sagar Chhangawala; Nadereh Jafari; Nalini Raghavachari; Jorge Gandara; Natàlia Garcia-Reyero; Cynthia Hendrickson; David Roberson; Jeffrey Rosenfeld; Todd Smith; Jason G Underwood; May Wang; Paul Zumbo; Don A Baldwin; George S Grills; Christopher E Mason
Journal:  Nat Biotechnol       Date:  2014-08-24       Impact factor: 54.908

8.  Normalization of RNA-seq data using factor analysis of control genes or samples.

Authors:  Davide Risso; John Ngai; Terence P Speed; Sandrine Dudoit
Journal:  Nat Biotechnol       Date:  2014-08-24       Impact factor: 54.908

9.  Abnormal response of costal chondrocytes to acidosis in patients with chest wall deformity.

Authors:  A Asmar; I Semenov; R Kelly; M Stacey
Journal:  Exp Mol Pathol       Date:  2018-11-25       Impact factor: 3.362

Review 10.  Genomic markers for decision making: what is preventing us from using markers?

Authors:  Vicky M Coyle; Patrick G Johnston
Journal:  Nat Rev Clin Oncol       Date:  2009-12-15       Impact factor: 66.675

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