Assessment of the relationship between signal intensities and transcript concentration for Affymetrix GeneChip®arrays

Chudin, Eugene; Walker, Randal; Kosaka, Alan; Wu, Sue X; Rabert, D K; Chang, Thomas K. H.; Kreder, Dirk

doi:10.1186/gb-2001-3-1-research0005

Cited by 69 publications

(12 citation statements)

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“…A more informative measure of sensitivity would be the response per binding event. The argument also can be made that the response per binding event is the more useful metric in many situations, since the target is often the limiting reagent, for example, in gene expression assays where transcript concentrations can be ∼1 pmol L −1 . In such situations, the response per binding event can directly predict the maximum possible signal realizable in the limit that all targets are bound.…”

Section: Resultsmentioning

confidence: 99%

Capacitive Monitoring of Morpholino-DNA Surface Hybridization: Experimental and Theoretical Analysis

2010

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Impedance and cyclic voltammetry methods, complemented by Poisson-Boltzmann (PB) modeling, are used to study hybridization of DNA analyte strands to monolayers of morpholino oligomers (MOs) immobilized by one end to mercaptopropanol-passivated gold electrodes. MOs, like peptide nucleic acids (PNAs), are uncharged molecules that recognize nucleic acids following conventional base-pairing rules. The capacitive response to hybridization, determined from realtime impedance measurements, is analyzed with emphasis on understanding the underlying structural changes and on providing a foundation for label-free diagnostics. The capacitive response is correlated with the instantaneous surface molecular populations by labeling DNA and MO strands with ferrocene tags and using cyclic voltammetry to monitor their respective coverages in real-time. This approach allows analysis of hybridization-induced changes in interfacial capacitance as a function of duplex coverage, the DC bias used for readout, buffer molarity, and probe coverage. The results indicate that unhybridized MO layers exist in a compact state on the solid support. For hybridized layers, the intrinsic signal per hybridization event is strongly enhanced at low ionic strengths but, interestingly, does not depend on the readout bias in the sampled range negative of the capacitive minimum. A PB model incorporating an effective medium description of the hybridizing films is used to establish how hybridization-derived changes in dielectric composition and charge distribution at the surface translate into experimentally-observed variations in interfacial capacitance.

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Section: Resultsmentioning

confidence: 99%

Capacitive Monitoring of Morpholino-DNA Surface Hybridization: Experimental and Theoretical Analysis

2010

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“…There is reason to believe that and can be approximated with linear functions ( 6 ). On the other hand, the nature of can vary significantly from triplet to triplet, and cannot be covered by a single class of continuous functions.…”

Section: Methodsmentioning

confidence: 99%

Discovering modulators of gene expression

Babur

Demir

Gönen

et al. 2010

Nucleic Acids Research

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Proteins that modulate the activity of transcription factors, often called modulators, play a critical role in creating tissue- and context-specific gene expression responses to the signals cells receive. GEM (Gene Expression Modulation) is a probabilistic framework that predicts modulators, their affected targets and mode of action by combining gene expression profiles, protein–protein interactions and transcription factor–target relationships. Using GEM, we correctly predicted a significant number of androgen receptor modulators and observed that most modulators can both act as co-activators and co-repressors for different target genes.

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“…It has been suggested that inter-array variance can be reduced by ignoring data from MM probes, or by using an alternative computation to take advantage of the MM data [13-16]. In previous versions of Microarray Suite, MM signals were always subtracted from PM signals, which often led to negative expression levels.…”

Section: Discussionmentioning

confidence: 99%

Computational method for reducing variance with Affymetrix microarrays

2002

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BackgroundAffymetrix microarrays are used by many laboratories to generate gene expression profiles. Generally, only large differences (> 1.7-fold) between conditions have been reported. Computational methods to reduce inter-array variability might be of value when attempting to detect smaller differences. We examined whether inter-array variability could be reduced by using data based on the Affymetrix algorithm for pairwise comparisons between arrays (ratio method) rather than data based on the algorithm for analysis of individual arrays (signal method). Six HG-U95A arrays that probed mRNA from young (21–31 yr old) human muscle were compared with six arrays that probed mRNA from older (62–77 yr old) muscle.ResultsDifferences in mean expression levels of young and old subjects were small, rarely > 1.5-fold. The mean within-group coefficient of variation for 4629 mRNAs expressed in muscle was 20% according to the ratio method and 25% according to the signal method. The ratio method yielded more differences according to t-tests (124 vs. 98 differences at P < 0.01), rank sum tests (107 vs. 85 differences at P < 0.01), and the Significance Analysis of Microarrays method (124 vs. 56 differences with false detection rate < 20%; 20 vs. 0 differences with false detection rate < 5%). The ratio method also improved consistency between results of the initial scan and results of the antibody-enhanced scan.ConclusionThe ratio method reduces inter-array variance and thereby enhances statistical power.

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Assessment of the relationship between signal intensities and transcript concentration for Affymetrix GeneChip®arrays

Cited by 69 publications

References 10 publications

Capacitive Monitoring of Morpholino-DNA Surface Hybridization: Experimental and Theoretical Analysis

Capacitive Monitoring of Morpholino-DNA Surface Hybridization: Experimental and Theoretical Analysis

Discovering modulators of gene expression

Computational method for reducing variance with Affymetrix microarrays

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