Literature DB >> 11196312

Maximization of signal derived from cDNA microarrays.

S E Wildsmith1, G E Archer, A J Winkley, P W Lane, P J Bugelski.   

Abstract

Microarray technology is a powerful tool for generating expression data on a large number of genes simultaneously. However, as for any assay, it must be reproducible to give confidence in the results. Using a classical statistical method--the factorial design of experiments--we have assessed the effects of different experimental factors in our system. Significant effects on signal were seen when the standard components were substituted with a different enzyme, fluorescent label, or RNA purification method. This has led to the implementation of an improved procedure that maximizes signal without affecting the variability of the system, thus increasing the signal-to-noise ratio. In addition, we were able to quantify the variability between microarrays and replicates within microarrays.

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Year:  2001        PMID: 11196312     DOI: 10.2144/01301dd04

Source DB:  PubMed          Journal:  Biotechniques        ISSN: 0736-6205            Impact factor:   1.993


  12 in total

Review 1.  Microarrays under the microscope.

Authors:  S E Wildsmith; F J Elcock
Journal:  Mol Pathol       Date:  2001-02

2.  Identification and removal of contaminating fluorescence from commercial and in-house printed DNA microarrays.

Authors:  M Juanita Martinez; Anthony D Aragon; Angelina L Rodriguez; Jose M Weber; Jerilyn A Timlin; Michael B Sinclair; David M Haaland; Margaret Werner-Washburne
Journal:  Nucleic Acids Res       Date:  2003-02-15       Impact factor: 16.971

Review 3.  Microarrays, antiobesity and the liver.

Authors:  Fernando Castro-Chávez
Journal:  Ann Hepatol       Date:  2004 Oct-Dec       Impact factor: 2.400

4.  Parameters for lithium treatment are critical in its enhancement of fracture-healing in rodents.

Authors:  Joshua Bernick; Yufa Wang; Ian A Sigal; Benjamin A Alman; Cari M Whyne; Diane Nam
Journal:  J Bone Joint Surg Am       Date:  2014-12-03       Impact factor: 5.284

5.  Optimization of high-density cDNA-microarray protocols by 'design of experiments'.

Authors:  Gunnar Wrobel; Joerg Schlingemann; Lars Hummerich; Heidi Kramer; Peter Lichter; Meinhard Hahn
Journal:  Nucleic Acids Res       Date:  2003-06-15       Impact factor: 16.971

6.  Empirical array quality weights in the analysis of microarray data.

Authors:  Matthew E Ritchie; Dileepa Diyagama; Jody Neilson; Ryan van Laar; Alexander Dobrovic; Andrew Holloway; Gordon K Smyth
Journal:  BMC Bioinformatics       Date:  2006-05-19       Impact factor: 3.169

7.  Comparison of Alexa Fluor and CyDye for practical DNA microarray use.

Authors:  Joanne L Ballard; Violet K Peeva; Christopher J S deSilva; Jessica L Lynch; Nigel R Swanson
Journal:  Mol Biotechnol       Date:  2007-07       Impact factor: 2.860

8.  Comparison of statistical data models for identifying differentially expressed genes using a generalized likelihood ratio test.

Authors:  Kok-Yong Seng; Robb W Glenny; David K Madtes; Mary E Spilker; Paolo Vicini; Sina A Gharib
Journal:  Gene Regul Syst Bio       Date:  2008

9.  Evaluation of five different cDNA labeling methods for microarrays using spike controls.

Authors:  Azadeh Badiee; Hans Geir Eiken; Vidar M Steen; Roger Løvlie
Journal:  BMC Biotechnol       Date:  2003-12-11       Impact factor: 2.563

10.  Profound influence of microarray scanner characteristics on gene expression ratios: analysis and procedure for correction.

Authors:  Heidi Lyng; Azadeh Badiee; Debbie H Svendsrud; Eivind Hovig; Ola Myklebost; Trond Stokke
Journal:  BMC Genomics       Date:  2004-02-03       Impact factor: 3.969

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