BMC Bioinformatics

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Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data

Kerby Shedden*, Wei Chen, Rork Kuick, Debashis Ghosh, James Macdonald, Kathleen R Cho, Thomas J Giordano, Stephen B Gruber, Eric R Fearon, Jeremy MG Taylor and Samir Hanash

BMC Bioinformatics 2005, 6:26 doi:10.1186/1471-2105-6-26

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Methodology   Open Access

Statistical evaluation of transcriptomic data generated using the Affymetrix one-cycle, two-cycle and IVT-Express RNA labelling protocols with the Arabidopsis ATH1 microarray

Tara J Holman, Michael H Wilson, Kim Kenobi, Ian L Dryden, T Hodgman, Andrew TA Wood, Michael J Holdsworth Plant Methods 2010, 6:9 (15 March 2010)

Methodology article   Open Access Highly Accessed

Probe set filtering increases correlation between Affymetrix GeneChip and qRT-PCR expression measurements

Jakub Mieczkowski, Magdalena E Tyburczy, Michal Dabrowski, Piotr Pokarowski BMC Bioinformatics 2010, 11:104 (24 February 2010)

Database   Open Access Highly Accessed

SiPaGene: A new repository for instant online retrieval, sharing and meta-analyses of GeneChip® expression data

Adriane Menßen, Götz Edinger, Joachim R Grün, Ulrike Haase, Ria Baumgrass, Andreas Grützkau, Andreas Radbruch, Gerd-R Burmester, Thomas Häupl BMC Genomics 2009, 10:98 (5 March 2009)

Methodology article   Open Access Highly Accessed

Comparison of small n statistical tests of differential expression applied to microarrays

Carl Murie, Owen Woody, Anna Y Lee, Robert Nadon BMC Bioinformatics 2009, 10:45 (3 February 2009)

Research article   Open Access Highly Accessed

Methods for evaluating gene expression from Affymetrix microarray datasets

Ning Jiang, Lindsey J Leach, Xiaohua Hu, Elena Potokina, Tianye Jia, Arnis Druka, Robbie Waugh, Michael J Kearsey, Zewei W Luo BMC Bioinformatics 2008, 9:284 (17 June 2008)

Comparison of the most commonly used statistical methods for assessing differential gene expression from Affymetrix microarrays on a high-quality dataset shows that PDNN is the best performer, clearly superior to the standard Affymetrix MAS5.0 method.

Proceedings   Open Access

Different normalization strategies for microarray gene expression traits affect the heritability estimation

Jun Ma, Zhaohui S Qin BMC Proceedings 2007, 1(Suppl 1):S154 (18 December 2007)

Research   Open Access Highly Accessed

Genetic subtraction profiling identifies genes essential for Arabidopsis reproduction and reveals interaction between the female gametophyte and the maternal sporophyte

Amal J Johnston, Patrick Meier, Jacqueline Gheyselinck, Samuel EJ Wuest, Michael Federer, Edith Schlagenhauf, Jörg D Becker, Ueli Grossniklaus Genome Biology 2007, 8:R204 (3 October 2007)

Genetic subtraction and expression profiling of wild-type Arabidopsis and a sporophytic mutant lacking an embryo sac identified 1,260 genes expressed in the embryo sac; a total of 527 genes were identified for their expression in ovules of mutants lacking an embryo sac.

Research article   Open Access

Transcriptional profiling of C57 and DBA strains of mice in the absence and presence of morphine

Dorothy E Grice, Ilkka Reenilä, Pekka T Männistö, Andrew I Brooks, George G Smith, Greg T Golden, Joseph D Buxbaum, Wade H Berrettini BMC Genomics 2007, 8:76 (16 March 2007)

Methodology article   Open Access

A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database

Simon Katz, Rafael A Irizarry, Xue Lin, Mark Tripputi, Mark W Porter BMC Bioinformatics 2006, 7:464 (23 October 2006)

Methodology article   Open Access

Robust computational reconstitution – a new method for the comparative analysis of gene expression in tissues and isolated cell fractions

Martin Hoffmann, Dirk Pohlers, Dirk Koczan, Hans-Jürgen Thiesen, Stefan Wölfl, Raimund W Kinne BMC Bioinformatics 2006, 7:369 (4 August 2006)

Research article   Open Access Highly Accessed

The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies

Roel GW Verhaak, Frank JT Staal, Peter JM Valk, Bob Lowenberg, Marcel JT Reinders, Dick de Ridder BMC Bioinformatics 2006, 7:105 (2 March 2006)

Preprocessing normalization methods can strongly affect the results of a small microarray study; low-level analysis focusing on expression levels of specific transcripts is affected more profoundly than high-level multivariate analysis.

Research article   Open Access Highly Accessed

Evaluation of methods for oligonucleotide array data via quantitative real-time PCR

Li-Xuan Qin, Richard P Beyer, Francesca N Hudson, Nancy J Linford, Daryl E Morris, Kathleen F Kerr BMC Bioinformatics 2006, 7:23 (17 January 2006)

Research article   Open Access Highly Accessed

Sources of variation in Affymetrix microarray experiments

Stanislav O Zakharkin, Kyoungmi Kim, Tapan Mehta, Lang Chen, Stephen Barnes, Katherine E Scheirer, Rudolph S Parrish, David B Allison, Grier P Page BMC Bioinformatics 2005, 6:214 (29 August 2005)

Variation in Affymetrix GeneChip microarrays was mostly (between one half and two thirds) due to biological variation, with around a quarter due to labeling, and with under 14% being the result of differences in hybridization.