Transcript-based redefinition of grouped oligonucleotide probe sets using AceView: High-resolution annotation for microarrays

BACKGROUND: Extracting biological information from high-density Affymetrix arrays is a multi-step process that begins with the accurate annotation of microarray probes. Shortfalls in the original Affymetrix probe annotation have been described; however, few studies have provided rigorous solutions for routine data analysis. RESULTS: Using AceView, a comprehensive human transcript database, we have reannotated the probes by matching them to RNA transcripts instead of genes. Based on this transcript-level annotation, a new probe set definition was created in which every probe in a probe set maps to a common set of AceView gene transcripts. In addition, using artificial data sets we identified that a minimal probe set size of 4 is necessary for reliable statistical summarization. We further demonstrate that applying the new probe set definition can detect specific transcript variants contributing to differential expression and it also improves cross-platform concordance. CONCLUSION: We conclude that our transcript-level reannotation and redefinition of probe sets complement the original Affymetrix design. Redefinitions introduce probe sets whose sizes may not support reliable statistical summarization; therefore, we advocate using our transcript-level mapping redefinition in a secondary analysis step rather than as a replacement. Knowing which specific transcripts are differentially expressed is important to properly design probe/primer pairs for validation purposes. For convenience, we have created custom chip-description-files (CDFs) and annotation files for our new probe set definitions that are compatible with Bioconductor, Affymetrix Expression Console or third party software

Background correction using dinucleotide affinities improves the performance of GCRMA

BACKGROUND: High-density short oligonucleotide microarrays are a primary research tool for assessing global gene expression. Background noise on microarrays comprises a significant portion of the measured raw data, which can have serious implications for the interpretation of the generated data if not estimated correctly. RESULTS: We introduce an approach to calculate probe affinity based on sequence composition, incorporating nearest-neighbor (NN) information. Our model uses position-specific dinucleotide information, instead of the original single nucleotide approach, and adds up to 10% to the total variance explained (R(2)) when compared to the previously published model. We demonstrate that correcting for background noise using this approach enhances the performance of the GCRMA preprocessing algorithm when applied to control datasets, especially for detecting low intensity targets. CONCLUSION: Modifying the previously published position-dependent affinity model to incorporate dinucleotide information significantly improves the performance of the model. The dinucleotide affinity model enhances the detection of differentially expressed genes when implemented as a background correction procedure in GeneChip preprocessing algorithms. This is conceptually consistent with physical models of binding affinity, which depend on the nearest-neighbor stacking interactions in addition to base-pairing

Studies on the relationships between oligonucleotide probe properties and hybridization signal intensities

Microarray technology is a commonly used tool in biomedical research for assessing global gene expression, surveying DNA sequence variations, and studying alternative gene splicing. Given the wide range of applications of this technology, comprehensive understanding of its underlying mechanisms is of importance. The focus of this work is on contributions from microarray probe properties (probe secondary structure: ?Gss, probe-target binding energy: ?G, probe-target mismatch) to the signal intensity. The benefits of incorporating or ignoring these properties to the process of microarray probe design and selection, as well as to microarray data preprocessing and analysis, are reported. Four related studies are described in this thesis. In the first, probe secondary structure was found to account for up to 3% of all variation on Affymetrix microarrays. In the second, a dinucleotide affinity model was developed and found to enhance the detection of differentially expressed genes when implemented as a background correction procedure in GeneChip preprocessing algorithms. This model is consistent with physical models of binding affinity of the probe target pair, which depends on the nearest-neighbor stacking interactions in addition to base-pairing. In the remaining studies, the importance of incorporating biophysical factors in both the design and the analysis of microarrays ‘percent bound’, predicted by equilibrium models of hybridization, is a useful factor in predicting and assessing the behavior of long oligonucleotide probes. However, a universal probe-property-independent three-parameter Langmuir model has also been tested, and this simple model has been shown to be as, or more, effective as complex, computationally expensive models developed for microarray target concentration estimation. The simple, platform-independent model can equal or even outperform models that explicitly incorporate probe properties, such as the model incorporating probe percent bound developed in Chapter Three. This suggests that with a “spiked-in” concentration series targeting as few as 5-10 genes, reliable estimation of target concentration can be achieved for the entire microarray

The effect of target secondary structure on microarray data quality

DNA? microarrays? have? become? an? invaluable? high? throughput? biotechnology? method,? which? allows? a? parallel? investigation? of? thousands? of? cellular? events? in? a? single?experiment.?The?principle?behind?the?technology?is?very?simple:?fluorescently? labeled? single? stranded? target? molecules? bind? to? their? specific? probes? deposited? on? the? microarray? surface.? However,? the? microarray? data? rarely? represent? a? yes? or? no? answer? to? a? biological? community,? but? rather? provide? a? direction? for? further? investigation.? There? is? a? complicated? quantitative? relationship? between? a? detected? spot? signal? and? the? amount? of? target? present? in? the? unknown? mixture.? We? hypothesize? that? physical? characteristics? of? probe? and? target? molecules? complicate? the?binding?reaction?between?target?and?probe.?To?test?this?hypothesis,?we?designed? a? controlled? microarray? experiment? in? which? the? amount? and? stability? of? the? secondary? structure? present? in? the? probe-binding? regions? of? target? as? biophysical? properties? of? nucleic? acids? varies? in? a? known? way.? ? Based? on? computational? simulations? of? hybridization,? we? hypothesize? that? secondary? structure? formation? in? the? target? can? result? in? considerable? interference? with? the? process? of? probe-target? binding.? ? This? interference? will? have? the? effect? of? lowering? the? spot? signal? intensity.?? We? simulated? hybridization? between? probe? and? target? and? analyzed? the? simulation? data? to? predict? how? much? the? microarray? signal? is? affected? by? folding? of? the? target? molecule,? for? the? purpose? of? developing? a? new? generation? of? microarray? design? and? analysis?software.