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The impact of quantitative optimization of hybridization conditions on gene expression analysis.

ABSTRACT: With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarray protocols.As shown in this study, suboptimal conditions can have considerable impact on biologically relevant observations. For example, deviation from the optimal temperature by one degree Celsius lead to a loss of up to 44% of differentially expressed genes identified. While genes from thousands of Gene Ontology categories were affected, transcription factors and other low-copy-number regulators were disproportionately lost. Calibrated protocols are thus required in order to take full advantage of the large dynamic range of microarrays.For an objective optimization of protocols we introduce an approach that maximizes the amount of information obtained per experiment. A comparison of two typical samples is sufficient for this calibration. We can ensure, however, that optimization results are independent of the samples and the specific measures used for calibration. Both simulations and spike-in experiments confirmed an unbiased determination of generally optimal experimental conditions.Well calibrated hybridization conditions are thus easily achieved and necessary for the efficient detection of differential expression. They are essential for the sensitive pro filing of low-copy-number molecules. This is particularly critical for studies of transcription factor expression, or the inference and study of regulatory networks.

SUBMITTER: Sykacek P

PROVIDER: S-EPMC3065421 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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The impact of quantitative optimization of hybridization conditions on gene expression analysis.

Sykacek Peter P Kreil David P DP Meadows Lisa A LA Auburn Richard P RP Fischer Bettina B Russell Steven S Micklem Gos G

BMC bioinformatics 20110314

<h4>Background</h4>With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarray protocols.<h4>Results</h4>As shown in this study, suboptimal conditions can have considerable impact on biologically relevant observations. For example, deviation from the optimal temperature ...[more]

PMID: 21401920

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Project description:Background: With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarray protocols. Results: As shown in this study, suboptimal conditions can have considerable impact on biologically relevant observations. For example, deviation from the optimal temperature by one degree Celsius lead to a loss of 44% of differentially expressed genes identified. While genes from thousands of Gene Ontology categories were affected, transcription factors and other low-copy-number regulators were disproportionately lost. Calibrated protocols are thus required in order to take full advantage of the large dynamic range of microarrays. For an objective optimization of protocols we introduce an approach that maximizes the amount of information obtained per experiment. A comparison of two typical samples is sufficient for this calibration. We ensure, however, that optimization results are independent of the samples and the specific measures used for calibration. Both simulations and spike-in experiments confirm an unbiased determination of generally optimal experimental conditions. Conclusions: Well calibrated hybridization conditions are thus easily achieved and necessary for the efficient detection of differential expression. They are essential for the sensitive profiling of low-copy-number molecules. This is particularly critical for studies of transcription factor expression, or the inference and study of regulatory networks. Supporting material, including source code and data, is available at http://bioinf.boku.ac.at/pub/optMA2010/. Optimization of hybridization temperature via an assessment of differential expression between two samples (male vs female Drosophila melanogaster) in 6 technical replicates (3 regular + 3 dye-swaps) for hybridizations at different temperatures (in two batches of 50, 52, 54, and 56; and 47, 49, 50, and 51 degree Celsius, with the repeated hybridization at 50 degree Celsius serving to demonstrate batch-to-batch stability).

Project description:Anthropogenic hybridization is an increasing conservation threat worldwide. In South Africa, recent hybridization is threatening numerous ungulate taxa. For example, the genetic integrity of the near-threatened bontebok (Damaliscus pygargus pygargus) is threatened by hybridization with the more common blesbok (D. p. phillipsi). Identifying nonadmixed parental and admixed individuals is challenging based on the morphological traits alone; however, molecular analyses may allow for accurate detection. Once hybrids are identified, population simulation software may assist in determining the optimal conservation management strategy, although quantitative evaluation of hybrid management is rarely performed. In this study, our objectives were to describe species-wide and localized rates of hybridization in nearly 3,000 individuals based on 12 microsatellite loci, quantify the accuracy of hybrid assignment software (STRUCTURE and NEWHYBRIDS), and determine an optimal threshold of bontebok ancestry for management purposes. According to multiple methods, we identified 2,051 bontebok, 657 hybrids, and 29 blesbok. More than two-thirds of locations contained at least some hybrid individuals, with populations varying in the degree of introgression. HYBRIDLAB was used to simulate four generations of coexistence between bontebok and blesbok, and to optimize a threshold of ancestry, where most hybrids will be detected and removed, and the fewest nonadmixed bontebok individuals misclassified as hybrids. Overall, a threshold Q-value (admixture coefficient) of 0.90 would remove 94% of hybrid animals, while a threshold of 0.95 would remove 98% of hybrid animals but also 8% of nonadmixed bontebok. To this end, a threshold of 0.90 was identified as optimal and has since been implemented in formal policy by a provincial nature conservation agency. Due to widespread hybridization, effective conservation plans should be established and enforced to conserve native populations that are genetically unique.

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The impact of quantitative optimization of hybridization conditions on gene expression analysis.

Publications

The impact of quantitative optimization of hybridization conditions on gene expression analysis.

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