Literature DB >> 17594235

What is the best reference RNA? And other questions regarding the design and analysis of two-color microarray experiments.

Kathleen F Kerr1, Kyle A Serikawa, Caimiao Wei, Mette A Peters, Roger E Bumgarner.   

Abstract

The reference design is a practical and popular choice for microarray studies using two-color platforms. In the reference design, the reference RNA uses half of all array resources, leading investigators to ask: What is the best reference RNA? We propose a novel method for evaluating reference RNAs and present the results of an experiment that was specially designed to evaluate three common choices of reference RNA. We found no compelling evidence in favor of any particular reference. In particular, a commercial reference showed no advantage in our data. Our experimental design also enabled a new way to test the effectiveness of pre-processing methods for two-color arrays. Our results favor using intensity normalization and foregoing background subtraction. Finally, we evaluate the sensitivity and specificity of data quality filters, and we propose a new filter that can be applied to any experimental design and does not rely on replicate hybridizations.

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Year:  2007        PMID: 17594235     DOI: 10.1089/omi.2007.0012

Source DB:  PubMed          Journal:  OMICS        ISSN: 1536-2310


  10 in total

1.  Finding differentially expressed genes in two-channel DNA microarray datasets: how to increase reliability of data preprocessing.

Authors:  Ana Rotter; Matjaz Hren; Spela Baebler; Andrej Blejec; Kristina Gruden
Journal:  OMICS       Date:  2008-09

2.  Storage products and transcriptional analysis of the endosperm of cultivated wheat and two wild wheat species.

Authors:  N K Uhlmann; D M Beckles
Journal:  J Appl Genet       Date:  2010       Impact factor: 3.240

3.  Effects of scanning sensitivity and multiple scan algorithms on microarray data quality.

Authors:  Andrew Williams; Errol M Thomson
Journal:  BMC Bioinformatics       Date:  2010-03-12       Impact factor: 3.169

4.  Transcriptional profiling identifies differentially expressed genes in developing turkey skeletal muscle.

Authors:  Kelly R B Sporer; Robert J Tempelman; Catherine W Ernst; Kent M Reed; Sandra G Velleman; Gale M Strasburg
Journal:  BMC Genomics       Date:  2011-03-08       Impact factor: 3.969

5.  Extended analysis of benchmark datasets for Agilent two-color microarrays.

Authors:  Kathleen F Kerr
Journal:  BMC Bioinformatics       Date:  2007-10-03       Impact factor: 3.169

6.  Transcriptome analyses of liver in newly-hatched chicks during the metabolic perturbation of fasting and re-feeding reveals THRSPA as the key lipogenic transcription factor.

Authors:  Larry A Cogburn; Nares Trakooljul; Xiaofei Wang; Laura E Ellestad; Tom E Porter
Journal:  BMC Genomics       Date:  2020-01-31       Impact factor: 3.969

7.  Toxicogenomic analysis of susceptibility to inhaled urban particulate matter in mice with chronic lung inflammation.

Authors:  Errol M Thomson; Andrew Williams; Carole L Yauk; Renaud Vincent
Journal:  Part Fibre Toxicol       Date:  2009-03-11       Impact factor: 9.400

8.  Characterisation and correction of signal fluctuations in successive acquisitions of microarray images.

Authors:  Annie Glatigny; Hervé Delacroix; Thomas Tang; Nicolas François; Lawrence Aggerbeck; Marie-Hélène Mucchielli-Giorgi
Journal:  BMC Bioinformatics       Date:  2009-03-30       Impact factor: 3.169

9.  The LO-BaFL method and ALS microarray expression analysis.

Authors:  Cristina Baciu; Kevin J Thompson; Jean-Luc Mougeot; Benjamin R Brooks; Jennifer W Weller
Journal:  BMC Bioinformatics       Date:  2012-09-24       Impact factor: 3.169

10.  Sexual differentiation of the zebra finch song system: potential roles for sex chromosome genes.

Authors:  Michelle L Tomaszycki; Camilla Peabody; Kirstin Replogle; David F Clayton; Robert J Tempelman; Juli Wade
Journal:  BMC Neurosci       Date:  2009-03-23       Impact factor: 3.288

  10 in total

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