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Within a single infected individual, a virus population can have a high genomic variability. In the case of HIV, several mutations can be present even in a small genomic window of 20-30 nucleotides. For diagnostics purposes, it is often needed to resequence genomic subsets where crucial mutations are known to occur. In this article, we address this issue using DNA microarrays and inputs from hybridization thermodynamics. Hybridization signals from multiple probes are analysed, including strong signals from perfectly matching (PM) probes and a large amount of weaker cross-hybridization signals from mismatching (MM) probes. The latter are crucial in the data analysis. Seven coded clinical samples (HIV-1) are analyzed, and the microarray results are in full concordance with Sanger sequencing data. Moreover, the thermodynamic analysis of microarray signals resolves inherent ambiguities in Sanger data of mixed samples and provides additional clinically relevant information. These results show the reliability and added value of DNA microarrays for point-of-care diagnostic purposes.

Hybridization of nucleic acids on solid surfaces is a key process involved in high-throughput technologies such as microarrays and, in some cases, next-generation sequencing (NGS). A physical understanding of the hybridization process helps to determine the accuracy of these technologies. The goal of a widespread research program is to develop reliable transformations between the raw signals reported by the technologies and individual molecular concentrations from an ensemble of nucleic acids. This research has inputs from many areas, from bioinformatics and biostatistics, to theoretical and experimental biochemistry and biophysics, to computer simulations. A group of leading researchers met in Ploen Germany in 2011 to discuss present knowledge and limitations of our physico-chemical understanding of high-throughput nucleic acid technologies. This meeting inspired us to write this summary, which provides an overview of the state-of-the-art approaches based on physico-chemical foundation to modeling of the nucleic acids hybridization process on solid surfaces. In addition, practical application of current knowledge is emphasized.

Background: Several preprocessing methods are available for the analysis of Affymetrix Genechips arrays. The most popular algorithms analyze the measured fluorescence intensities with statistical methods. Here we focus on a novel algorithm, AffyILM, available from Bioconductor, which relies on inputs from hybridization thermodynamics and uses an extended Langmuir isotherm model to compute transcript concentrations. These concentrations are then employed in the statistical analysis. We compared the performance of AffyILM and other traditional methods both in the old and in the newest generation of GeneChips., Results: Tissue mixture and Latin Square datasets (provided by Affymetrix) were used to assess the performances of the differential expression analysis depending on the preprocessing strategy. A correlation analysis conducted on the tissue mixture data reveals that the median-polish algorithm allows to best summarize AffyILM concentrations computed at the probe-level. Those correlation results are equivalent to the best correlations observed using popular preprocessing methods relying on intensity values. The performances of each tested preprocessing algorithm were quantified using the Latin Square HG-U133A dataset, thanks to the comparison of differential analysis results with the list of spiked genes. The figures of merit generated illustrates that the performances associated to AffyILM(medianpolish), inferred from the present statistical analysis, are comparable to the best performing strategies previously reported., Conclusions: Converting probe intensities to estimates of target concentrations prior to the statistical analysis, AffyILM(medianpolish) is one of the best performing strategy currently available. Using hybridization theory, probe-level estimates of target concentrations should be identically distributed. In the future, a probe-level multivariate analysis of the concentrations should be compared to the univariate analysis of probe-set summarized expression data.

We consider mixtures of two DNA sequences t and t' differing by a single nucleotide, which are analyzed by an Agilent custom DNA microarray. In particular we focus on the case in which t, the "wild type", is predominantly abundant and t' the "mutant" is at very low concentrations compared to t. We show that by using appropriately designed arrays it is possible to accurately quantify the presence of t' even at low relative concentrations (≈1%). The detection method is based on thermodynamic models of DNA hybridisation and on the analysis of a large number of hybridisation intensities from probes containing one or two mismatches with respect to t and t'., (Copyright © 2010 Elsevier B.V. All rights reserved.)

Background: An algorithm for the analysis of Affymetrix Genechips is presented. This algorithm, referred to as the Inverse Langmuir Method (ILM), estimates the binding of transcripts to complementary probes using DNA/RNA hybridization free energies, and the hybridization between partially complementary transcripts in solution using RNA/RNA free energies. The balance between these two competing reactions allows for the translation of background-subtracted intensities into transcript concentrations., Results: To validate the ILM, it is applied to publicly available microarray data from a multi-lab comparison study. Here, microarray experiments are performed on samples which deviate only in few genes. The log2 fold change between these two samples, as obtained from RT-PCR experiments, agrees well with the log2 fold change as obtained with the ILM, indicating that the ILM determines changes in the expression level accurately. We also show that the ILM allows for the identification of outlying probes, as it yields independent concentration estimates per probe., Conclusion: The ILM is robust and offers an interesting alternative to purely statistical algorithms for microarray data analysis.

Background: Affymetrix Genechips are characterized by probe pairs, a perfect match (PM) and a mismatch (MM) probe differing by a single nucleotide. Most of the data preprocessing algorithms neglect MM signals, as it was shown that MMs cannot be used as estimators of the non-specific hybridization as originally proposed by Affymetrix. The aim of this paper is to study in detail on a large number of experiments the behavior of the average PM/MM ratio. This is taken as an indicator of the quality of the hybridization and, when compared between different chip series, of the quality of the chip design., Results: About 250 different GeneChip hybridizations performed at the VIB Microarray Facility for Homo sapiens, Drosophila melanogaster, and Arabidopsis thaliana were analyzed. The investigation of such a large set of data from the same source minimizes systematic experimental variations that may arise from differences in protocols or from different laboratories. The PM/MM ratios are derived theoretically from thermodynamic laws and a link is made with the sequence of PM and MM probe, more specifically with their central nucleotide triplets., Conclusion: The PM/MM ratios subdivided according to the different central nucleotides triplets follow qualitatively those deduced from the hybridization free energies in solution. It is shown also that the PM and MM histograms are related by a simple scale transformation, in agreement with what is to be expected from hybridization thermodynamics. Different quantitative behavior is observed on the different chip organisms analyzed, suggesting that some organism chips have superior probe design compared to others.