An improved physico-chemical model of hybridization on high-density oligonucleotide microarrays

Ono, Naoaki; Suzuki, Shingo; Furusawa, Chikara; Agata, Tomoharu; Kashiwagi, Akiko; Shimizu, Hiroshi; Yomo, Tetsuya

doi:10.1093/bioinformatics/btn109

Cited by 45 publications

(66 citation statements)

References 33 publications

(55 reference statements)

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“…The model fits well the data within two orders of magnitude in concentration. To fit the data in the low −∆G limit we added a background value (I 0 ) : the intensities at high binding affinity reach a saturation value below the chemical saturation, 23,24 without the need of invoking a nonequilibrium effect. However the depletion models of Refs.…”

Section: Custom Affymetrix Microarraymentioning

confidence: 99%

Nonequilibrium Effects in DNA Microarrays: A Multiplatform Study

et al. 2011

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It has recently been shown that in some DNA microarrays the time needed to reach thermal equilibrium may largely exceed the typical experimental time, which is about 15h in standard protocols (Hooyberghs et al. Phys. Rev. E 81, 012901 (2010)). In this paper we discuss how this breakdown of thermodynamic equilibrium could be detected in microarray experiments without resorting to real time hybridization data, which are difficult to implement in standard experimental conditions. The method is based on the analysis of the distribution of fluorescence intensities I from different spots for probes carrying base mismatches. In thermal equilibrium and at sufficiently low concentrations, log I is expected to be linearly related to the hybridization free energy ∆G with a slope equal to 1/RT exp , where T exp is the experimental temperature and R is the gas constant. The breakdown of equilibrium results in the deviation from this law. A model for hybridization kinetics explaining the observed experimental behavior is discussed, the so-called 3-state model. It predicts that deviations from equilibrium yield a proportionality of log I to ∆G/RT eff . Here, T eff is an "effective" temperature, higher than the experimental one. This behavior is indeed observed in some experiments on Agilent arrays. 16,20 We analyze experimental data from two other microarray platforms and discuss, on the basis of the results, the attainment of equilibrium in these cases. Interestingly, the same 3-state model predicts a (dynamical) saturation of the signal at values below the expected one at equilibrium.

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Section: Custom Affymetrix Microarraymentioning

confidence: 99%

Nonequilibrium Effects in DNA Microarrays: A Multiplatform Study

et al. 2011

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“…Up to now, many studies have reported that the hybridization between probes and sequences can be affected by factors as follows: (i) Probe specific binding affinity which relies on probe sequence. Prior studies [8,9,12,19,30,31] have shown that array intensities are subject to large variability from probe to probe and depend on not only the quantities of target sequences but also the probe binding affinity. (ii) Cross-hybridization of probe to sequences other than the desired target sequences.…”

Section: Introductionmentioning

confidence: 99%

A study of biases of DNA copy number estimation based on PICR model

Wang

Cheng

et al. 2011

Front. Math. China

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Affymetrix single-nucleotide polymorphism (SNP) arrays have been widely used for SNP genotype calling and copy number variation (CNV) studies, both of which are dependent on accurate DNA copy number estimation significantly. However, the methods for copy number estimation may suffer from kinds of difficulties: probe dependent binding affinity, crosshybridization of probes, and the whole genome amplification (WGA) of DNA sequences. The probe intensity composite representation (PICR) model, one former established approach, can cope with most complexities and achieve high accuracy in SNP genotyping. Nevertheless, the copy numbers estimated by PICR model still show array and site dependent biases for CNV studies. In this paper, we propose a procedure to adjust the biases and then make CNV inference based on both PICR model and our method. The comparison indicates that our correction of copy numbers is necessary for CNV studies.

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“…More elaborate models therefore include the effects of the secondary structures of the probe [8,18-20] and the target [9,21,22] on the overall binding efficiency. Another factor that reduces the efficiency of probe-target hybridization is the formation of probe-probe dimers [6].…”

Section: Introductionmentioning

confidence: 99%

Hybridization thermodynamics of NimbleGen Microarrays

Mueckstein

Leparc²,

Posekany³

et al. 2010

BMC Bioinformatics

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BackgroundWhile microarrays are the predominant method for gene expression profiling, probe signal variation is still an area of active research. Probe signal is sequence dependent and affected by probe-target binding strength and the competing formation of probe-probe dimers and secondary structures in probes and targets.ResultsWe demonstrate the benefits of an improved model for microarray hybridization and assess the relative contributions of the probe-target binding strength and the different competing structures. Remarkably, specific and unspecific hybridization were apparently driven by different energetic contributions: For unspecific hybridization, the melting temperature Tm was the best predictor of signal variation. For specific hybridization, however, the effective interaction energy that fully considered competing structures was twice as powerful a predictor of probe signal variation. We show that this was largely due to the effects of secondary structures in the probe and target molecules. The predictive power of the strength of these intramolecular structures was already comparable to that of the melting temperature or the free energy of the probe-target duplex.ConclusionsThis analysis illustrates the importance of considering both the effects of probe-target binding strength and the different competing structures. For specific hybridization, the secondary structures of probe and target molecules turn out to be at least as important as the probe-target binding strength for an understanding of the observed microarray signal intensities. Besides their relevance for the design of new arrays, our results demonstrate the value of improving thermodynamic models for the read-out and interpretation of microarray signals.

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An improved physico-chemical model of hybridization on high-density oligonucleotide microarrays

Cited by 45 publications

References 33 publications

Nonequilibrium Effects in DNA Microarrays: A Multiplatform Study

Nonequilibrium Effects in DNA Microarrays: A Multiplatform Study

A study of biases of DNA copy number estimation based on PICR model

Hybridization thermodynamics of NimbleGen Microarrays

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