Biostatistical Considerations of the Use of Genomic DNA Reference in Microarrays

Using genomic DNA as common reference in microarray experiments has recently been tested by different laboratories (2, 3, 5, 7, 9, 20, 24-26). While some reported that experimental results of microarrays using genomic DNA reference conformed nicely to those obtained by cDNA: cDNA co-hybridization method, others acquired poor results. We hypothesized that these conflicting reports could be resolved by biostatistical analyses. To test it, microarray experiments were performed in a 4 proteobacterium Shewanella oneidensis. Pair-wise comparison of three experimental conditions was obtained either by direct cDNA: cDNA co-hybridization, or by indirect calculation through a Shewanella genomic DNA reference. Several major biostatistical techniques were exploited to reduce the amount of inconsistency between both methods and the results were assessed. We discovered that imposing the constraint of minimal number of replicates, logarithmic transformation and random error analyses significantly improved the data quality. These findings could potentially serve as guidelines for microarray data analysis using genomic DNA as reference

Performance evaluation of DNA copy number segmentation methods

A number of bioinformatic or biostatistical methods are available for analyzing DNA copy number profiles measured from microarray or sequencing technologies. In the absence of rich enough gold standard data sets, the performance of these methods is generally assessed using unrealistic simulation studies, or based on small real data analyses. We have designed and implemented a framework to generate realistic DNA copy number profiles of cancer samples with known truth. These profiles are generated by resampling real SNP microarray data from genomic regions with known copy-number state. The original real data have been extracted from dilutions series of tumor cell lines with matched blood samples at several concentrations. Therefore, the signal-to-noise ratio of the generated profiles can be controlled through the (known) percentage of tumor cells in the sample. In this paper, we describe this framework and illustrate some of the benefits of the proposed data generation approach on a practical use case: a comparison study between methods for segmenting DNA copy number profiles from SNP microarrays. This study indicates that no single method is uniformly better than all others. It also helps identifying pros and cons for the compared methods as a function of biologically informative parameters, such as the fraction of tumor cells in the sample and the proportion of heterozygous markers. Availability: R package jointSeg: http://r-forge.r-project.org/R/?group\_id=156

Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling Strategies

High-throughput post-genomic studies are now routinely and promisingly investigated in biological and biomedical research. The main statistical approach to select genes differentially expressed between two groups is to apply a t-test, which is subject of criticism in the literature. Numerous alternatives have been developed based on different and innovative variance modeling strategies. However, a critical issue is that selecting a different test usually leads to a different gene list. In this context and given the current tendency to apply the t-test, identifying the most efficient approach in practice remains crucial. To provide elements to answer, we conduct a comparison of eight tests representative of variance modeling strategies in gene expression data: Welch's t-test, ANOVA [1], Wilcoxon's test, SAM [2], RVM [3], limma [4], VarMixt [5] and SMVar [6]. Our comparison process relies on four steps (gene list analysis, simulations, spike-in data and re-sampling) to formulate comprehensive and robust conclusions about test performance, in terms of statistical power, false-positive rate, execution time and ease of use. Our results raise concerns about the ability of some methods to control the expected number of false positives at a desirable level. Besides, two tests (limma and VarMixt) show significant improvement compared to the t-test, in particular to deal with small sample sizes. In addition limma presents several practical advantages, so we advocate its application to analyze gene expression data

arrayMap: A Reference Resource for Genomic Copy Number Imbalances in Human Malignancies

Background: The delineation of genomic copy number abnormalities (CNAs) from cancer samples has been instrumental for identification of tumor suppressor genes and oncogenes and proven useful for clinical marker detection. An increasing number of projects have mapped CNAs using high-resolution microarray based techniques. So far, no single resource does provide a global collection of readily accessible oncoge- nomic array data. Methodology/Principal Findings: We here present arrayMap, a curated reference database and bioinformatics resource targeting copy number profiling data in human cancer. The arrayMap database provides a platform for meta-analysis and systems level data integration of high-resolution oncogenomic CNA data. To date, the resource incorporates more than 40,000 arrays in 224 cancer types extracted from several resources, including the NCBI's Gene Expression Omnibus (GEO), EBIs ArrayExpress (AE), The Cancer Genome Atlas (TCGA), publication supplements and direct submissions. For the majority of the included datasets, probe level and integrated visualization facilitate gene level and genome wide data re- view. Results from multi-case selections can be connected to downstream data analysis and visualization tools. Conclusions/Significance: To our knowledge, currently no data source provides an extensive collection of high resolution oncogenomic CNA data which readily could be used for genomic feature mining, across a representative range of cancer entities. arrayMap represents our effort for providing a long term platform for oncogenomic CNA data independent of specific platform considerations or specific project dependence. The online database can be accessed at http://www.arraymap.org.Comment: 17 pages, 5 inline figures, 3 tables, supplementary figures/tables split into 4 PDF files; manuscript submitted to PLoS ON

The EC4 European syllabus for post-graduate training in clinical chemistry and laboratory medicine : Version 4-2012

Laboratory medicine’s practitioners across the European community include medical, scientific and pharmacy trained specialists whose contributions to health and healthcare is in the application of diagnostic tests for screening and early detection of disease, differential diagnosis, monitoring, management and treatment of patients, and their prognostic assessment. In submitting a revised common syllabus for post-graduate education and training across the 27 member states an expectation is set for harmonised, high quality, safe practice. In this regard an extended ‘Core knowledge, skills and competencies’ division embracing all laboratory medicine disciplines is described. For the first time the syllabus identifies the competencies required to meet clinical leadership demands for defining, directing and assuring the efficiency and effectiveness of laboratory services as well as expectations in translating knowledge and skills into ability to practice. In a ‘Specialist knowledge’ division, the expectations from the individual disciplines of Clinical Chemistry/Immunology, Haematology/Blood Transfusion, Microbiology/ Virology, Genetics and In Vitro Fertilisation are described. Beyond providing a common platform of knowledge, skills and competency, the syllabus supports the aims of the European Commission in providing safeguards to increasing professional mobility across European borders at a time when demand for highly qualified professionals is increasing and the labour force is declining. It continues to act as a guide for the formulation of national programmes supplemented by the needs of individual country priorities.peer-reviewe

High-throughput profiling for discovery of non-coding RNA biomarkers of lung disease.

In respiratory medicine there is a need for clinical biomarkers for diagnosis, prognosis and assessment of response to therapy. Noncoding RNA (ncRNA) is expressed in all human cells; two major classes - long ncRNA and microRNA - are detectable extracellularly in the circulation and other biofluids. Altered ncRNA expression is associated with lung disease; collectively this indicates that ncRNA represents a potential biomarker class. This article presents and compares existing platforms for detection and quantification of ncRNA, specifically hybridization, qRT-PCR and RNA sequencing, and outlines methods for data interpretation and normalization. Each approach has merits and shortcomings, which can affect the choice of method when embarking on a biomarker study. Biomarker properties and pre-analytical considerations for ncRNA profiling are also presented. Since a variety of profiling approaches are available, careful study and experimental design are important. Finally, challenges and goals for reliable, standardized high-throughput ncRNA profiling in biofluids as lung disease biomarkers are reviewed

Design of a Database Driven System to Compile and Define Local Bioinformatics Resources at the University of North Carolina at Chapel Hill.

This master's project answered the need for a comprehensive database of bioinformatics resources, services, and faculty members at the University of North Carolina at Chapel Hill. This paper traces the development of the database from its inception at the Health Sciences Library as a Field Experience project through design and documentation as the database took shape. User groups are listed along with their tasks, and the special considerations these groups required during design of the Web interface for the database. Finally, preliminary results from a survey of potential users of this database are included with future plans for the database after graduation

High-Resolution Mutation Mapping Reveals Parallel Experimental Evolution in Yeast

Understanding the genetic basis of evolutionary adaptation is limited by our ability to efficiently identify the genomic locations of adaptive mutations. Here we describe a method that can quickly and precisely map the genetic basis of naturally and experimentally evolved complex traits using linkage analysis. A yeast strain that expresses the evolved trait is crossed to a distinct strain background and DNA from a large pool of progeny that express the trait of interest is hybridized to oligonucleotide microarrays that detect thousands of polymorphisms between the two strains. Adaptive mutations are detected by linkage to the polymorphisms from the evolved parent. We successfully tested our method by mapping five known genes to a precision of 0.2–24 kb (0.1–10 cM), and developed computer simulations to test the effect of different factors on mapping precision. We then applied this method to four yeast strains that had independently adapted to a fluctuating glucose–galactose environment. All four strains had acquired one or more missense mutations in GAL80, the repressor of the galactose utilization pathway. When transferred into the ancestral strain, the gal80 mutations conferred the fitness advantage that the evolved strains show in the transition from glucose to galactose. Our results show an example of parallel adaptation caused by mutations in the same gene