Literature DB >> 14579749

Optimizing stringency for expression microarrays.

James E Korkola1, Anne L Estep, Sunanda Pejavar, Sandy DeVries, Ronald Jensen, Frederic M Waldman.   

Abstract

While several studies have reported methods to optimize expression microarray protocols, none have dealt directly with hybridization wash stringency. We designed a series of experiments to determine the optimal stringency conditions for microarray experiments, using reproducibility and magnitudes of log2 (test/reference) ratio values as measures of quality. Low-stringency wash conditions of cell line hybridizations led to nonspecific binding, resulting in increased intensities, decreased magnitude of ratios, and poor reproducibility. Relatively high-stringency wash conditions were found to give the best reproducibility and large magnitude ratio changes, although increasing the stringency beyond this point led to lower magnitude ratios and poorer reproducibility. The expression levels of the ERBB2 oncogene in the BT474 versus MCF7 cell lines showed that high-stringency wash conditions gave the best agreement with real-time quantitative PCR, although the magnitude of the changes by microarray was smaller than for real-time quantitative PCR. Analysis of a series of cell lines washed at the optimized stringency indicated that the rank order of relative expression levels for ERBB2 microarray clones agreed well with the rank order of ERBB2 levels, as measured by quantitative PCR. These results indicate that the optimization of stringency conditions will improve microarray reproducibility and give more representative expression values.

Entities:  

Mesh:

Substances:

Year:  2003        PMID: 14579749     DOI: 10.2144/03354mt04

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


  2 in total

Review 1.  Real-time DNA microarrays: reality check.

Authors:  Alexander Chagovetz; Steve Blair
Journal:  Biochem Soc Trans       Date:  2009-04       Impact factor: 5.407

2.  Identification of a robust gene signature that predicts breast cancer outcome in independent data sets.

Authors:  James E Korkola; Ekaterina Blaveri; Sandy DeVries; Dan H Moore; E Shelley Hwang; Yunn-Yi Chen; Anne L H Estep; Karen L Chew; Ronald H Jensen; Frederic M Waldman
Journal:  BMC Cancer       Date:  2007-04-11       Impact factor: 4.430

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.