Hallgeir Bergum

Norwegian University of Science and Technology, Trondheim, Sor-Trondelag Fylke, Norway

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Publications (5)15.45 Total impact

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    Respiratory Medicine 04/2007; 101(3):679; author reply 680. · 2.59 Impact Factor
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    ABSTRACT: The measurement of gene expression using microarray technology is a complicated process in which a large number of factors can be varied. Due to the lack of standard calibration samples such as are used in traditional chemical analysis it may be a problem to evaluate whether changes done to the microarray procedure actually improve the identification of truly differentially expressed genes. The purpose of the present work is to report the optimization of several steps in the microarray process both in laboratory practices and in data processing using criteria that do not rely on external standards. We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a null distribution obtained from the self-self experiment. The proposed high-contrast versus self-self method (HCSSM) requires only four microarrays per evaluation. The effects of blocking reagent dose, filtering, and background corrections methodologies were investigated. In our experiments a dose of 250 ng LNA (locked nucleic acid) dT blocker, no background correction and weight based filtering gave the largest number of differentially expressed genes. The choice of background correction method had a stronger impact on the estimated number of differentially expressed genes than the choice of filtering method. Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real. The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards. Our optimizing method is highly applicable to both long oligo-probe microarrays which have become commonly used for well characterized organisms such as man, mouse and rat, as well as to cDNA microarrays which are still of importance for organisms with incomplete genome sequence information such as many bacteria, plants and fish.
    BMC Genomics 02/2007; 8:377. · 4.40 Impact Factor
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    ABSTRACT: Modern biology has shifted from "one gene" approaches to methods for genomic-scale analysis like microarray technology, which allow simultaneous measurement of thousands of genes. This has created a need for tools facilitating interpretation of biological data in "batch" mode. However, such tools often leave the investigator with large volumes of apparently unorganized information. To meet this interpretation challenge, gene-set, or cluster testing has become a popular analytical tool. Many gene-set testing methods and software packages are now available, most of which use a variety of statistical tests to assess the genes in a set for biological information. However, the field is still evolving, and there is a great need for "integrated" solutions. GeneTools is a web-service providing access to a database that brings together information from a broad range of resources. The annotation data are updated weekly, guaranteeing that users get data most recently available. Data submitted by the user are stored in the database, where it can easily be updated, shared between users and exported in various formats. GeneTools provides three different tools: i) NMC Annotation Tool, which offers annotations from several databases like UniGene, Entrez Gene, SwissProt and GeneOntology, in both single- and batch search mode. ii) GO Annotator Tool, where users can add new gene ontology (GO) annotations to genes of interest. These user defined GO annotations can be used in further analysis or exported for public distribution. iii) eGOn, a tool for visualization and statistical hypothesis testing of GO category representation. As the first GO tool, eGOn supports hypothesis testing for three different situations (master-target situation, mutually exclusive target-target situation and intersecting target-target situation). An important additional function is an evidence-code filter that allows users, to select the GO annotations for the analysis. GeneTools is the first "all in one" annotation tool, providing users with a rapid extraction of highly relevant gene annotation data for e.g. thousands of genes or clones at once. It allows a user to define and archive new GO annotations and it supports hypothesis testing related to GO category representations. GeneTools is freely available through www.genetools.no
    BMC Bioinformatics 02/2006; 7:470. · 3.02 Impact Factor
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    ABSTRACT: RNA dot blot hybridization is a commonly used technique for gene expression assays. However, membrane based RNA dot/slot blot hybridization is time consuming, requires large amounts of RNA, and is less suited for parallel assays of more than one gene at a time. Here, we describe a glass-slide based miniaturized RNA dot blot (RNA array) procedure for rapid and parallel gene expression analysis using fluorescently labeled probes. RNA arrays were prepared by simple manual spotting of RNA onto amino-silane coated microarray glass slides, and used for two-color fluorescent hybridization with specific probes labeled with Cy3 and 18S ribosomal RNA house-keeping gene probe labeled with Cy5 fluorescent dyes. After hybridization, arrays were scanned on a fluorescent microarray scanner and images analyzed using microarray image analysis software. We demonstrate that this method gives comparable results to Northern blot analysis, and enables high throughput quantification of transcripts from nanogram quantities of total RNA in hundreds of samples. RNA array on glass slide and detection by fluorescently labeled probes can be used for rapid and parallel gene expression analysis. The method is particularly well suited for gene expression assays that involve quantitation of many transcripts in large numbers of samples.
    BMC Biotechnology 07/2004; 4:12. · 2.17 Impact Factor
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    ABSTRACT: Oxidative stress may initiate lipid peroxidation that generates ethane. Ethane, at low concentrations, is eliminated by pulmonary exhalation. Previous methods have not allowed frequent sampling, thus ethane kinetics has not been studied in man. A validated method over the range 3.8-100,000 ppb with a limit of quantitation of 3.8 ppb (CV 9.3%) based on cryofocusing technique of a 60 ml breath sample allowed frequent sampling. Due to a rapid analytical procedure batches of more than 100 samples may be analyzed. In human volunteers (24-55 years) uptake was studied for up to 23 min (n = 9), elimination was studied for 210 min (n = 9). Ethane was inhaled (concentrations varied from 16 to 29 ppm (parts per million)) through a non-rebreathing system; sampling was performed with short intervals from the expiratory limb. Samples were also drawn from the inhalatory limb. Ninety-five percent of steady state (inspired) concentration was reached within 1.75 min. Five percent of the initially inhaled concentrations was found in exhaled air 1.5 min after termination of inhalation. A terminal mean half life of 31 min for ethane was also observed. The data indicate that frequent sampling will be necessary to capture relevant changes in breath ethane.
    Free Radical Research 09/2003; 37(8):815-21. · 3.28 Impact Factor