CiteULike: jyuh microarray

SNP-specific array-based allele-specific expression analysis.

Hans T Bjornsson — 2008-03-31T08:09:11-00:00

Genome Res (27 March 2008)

We have developed an optimized array-based approach for customizable allele-specific gene expression (ASE) analysis. The central features of the approach are the ability to select SNPs at will for detection, and the absence of need to PCR amplify the target. A surprisingly long probe length (39-49 nt) was needed for allelic discrimination. Reconstitution experiments demonstrate linearity of ASE over a broad range. Using this approach, we have discovered at least two novel imprinted genes, NLRP2, which encodes a member of the inflammasome, and OSBPL1A, which encodes a presumed oxysterol-binding protein, were both preferentially expressed from the maternal allele. In contrast, ERAP2, which encodes an aminopeptidase, did not show preferential parent-of-origin expression, but rather, cis-acting nonimprinted differential allelic control. The approach is scalable to the whole genome and can be used for discovery of functional epigenetic modifications in patient samples.

New Onto-Tools: Promoter-Express, nsSNPCounter and Onto-Translate.

P Khatri — 2006-08-23T13:03:17-00:00

Nucleic Acids Res, Vol. 34, No. Web Server issue. (1 July 2006)

The Onto-Tools suite is composed of an annotation database and eight complementary, web-accessible data mining tools: Onto-Express, Onto-Compare, Onto-Design, Onto-Translate, Onto-Miner, Pathway-Express, Promoter-Express and nsSNPCounter. Promoter-Express is a new tool added to the Onto-Tools ensemble that facilitates the identification of transcription factor binding sites active in specific conditions. nsSNPCounter is another new tool that allows computation and analysis of synonymous and non-synonymous codon substitutions for studying evolutionary rates of protein coding genes. Onto-Translate has also been enhanced to expand its scope and accuracy by fully utilizing the capabilities of the Onto-Tools database. Currently, Onto-Translate allows arbitrary mappings between 28 types of IDs for 53 organisms. Onto-Tools are freely available at http://vortex.cs.wayne.edu/Projects.html.

Seeded Bayesian Networks: constructing genetic networks from microarray data

Amira Djebbari — 2008-07-07T15:05:15-00:00

BMC Systems Biology, Vol. 2, No. 1. (2008)

BACKGROUND:DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes - often represented as networks - in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results. RESULTS:Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data. CONCLUSIONS:The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package <http://compbio.dfci.harvard.edu/paper.html>.

GeneCAT--novel webtools that combine BLAST and co-expression analyses.

Marek Mutwil — 2008-05-18T21:56:00-00:00

Nucleic acids research (14 May 2008)

The gene co-expression analysis toolbox (GeneCAT) introduces several novel microarray data analyzing tools. First, the multigene co-expression analysis, combined with co-expressed gene networks, provides a more powerful data mining technique than standard, single-gene co-expression analysis. Second, the high-throughput Map-O-Matic tool matches co-expression pattern of multiple query genes to genes present in user-defined subdatabases, and can therefore be used for gene mapping in forward genetic screens. Third, Rosetta combines co-expression analysis with BLAST and can be used to find 'true' gene orthologs in the plant model organisms Arabidopsis thaliana and Hordeum vulgare (Barley). GeneCAT is equipped with expression data for the model plant A. thaliana, and first to introduce co-expression mining tools for the monocot Barley. GeneCAT is available at http://genecat.mpg.de.

GeConT 2: gene context analysis for orthologous proteins, conserved domains and metabolic pathways.

CE Martinez-Guerrero — 2008-07-18T01:06:35-00:00

Nucleic acids research, Vol. 36, No. Web Server issue. (1 July 2008)

The Gene Context Tool (GeConT) allows users to visualize the genomic context of a gene or a group of genes and their orthologous relationships within fully sequenced bacterial genomes. The new version of the server incorporates information from the COG, Pfam and KEGG databases, allowing users to have an integrated graphical representation of the function of genes at multiple levels, their phylogenetic distribution and their genomic context. The sequence of any of the genes can be easily retrieved, as well as the 5' or 3' regulatory regions, greatly facilitating further types of analysis. GeConT 2 is available at: http://bioinfo.ibt.unam.mx/gecont.

DAVID: Database for Annotation, Visualization, and Integrated Discovery.

G Dennis — 2008-02-01T13:04:23-00:00

Genome Biol, Vol. 4, No. 5. (2003)

BACKGROUND: Functional annotation of differentially expressed genes is a necessary and critical step in the analysis of microarray data. The distributed nature of biological knowledge frequently requires researchers to navigate through numerous web-accessible databases gathering information one gene at a time. A more judicious approach is to provide query-based access to an integrated database that disseminates biologically rich information across large datasets and displays graphic summaries of functional information. RESULTS: Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://www.david.niaid.nih.gov) addresses this need via four web-based analysis modules: 1) Annotation Tool - rapidly appends descriptive data from several public databases to lists of genes; 2) GoCharts - assigns genes to Gene Ontology functional categories based on user selected classifications and term specificity level; 3) KeggCharts - assigns genes to KEGG metabolic processes and enables users to view genes in the context of biochemical pathway maps; and 4) DomainCharts - groups genes according to PFAM conserved protein domains. CONCLUSIONS: Analysis results and graphical displays remain dynamically linked to primary data and external data repositories, thereby furnishing in-depth as well as broad-based data coverage. The functionality provided by DAVID accelerates the analysis of genome-scale datasets by facilitating the transition from data collection to biological meaning.

Evaluation of automated and conventional microarray hybridization: a question of data quality and best practice?

VK Peeva — 2008-07-17T16:31:16-00:00

Biotechnology and applied biochemistry, Vol. 50, No. Pt 4. (August 2008), pp. 181-190.

Microarray is a widely used technique to study gene expression. The increasing interest in the technology has resulted in increased availability of commercial accessory reagents and instrumentation. In principle, commercial advances in reagents, kits and equipment should greatly improve assay performance, since they each bring a measure of quality assurance and uniformity to the data above that which may be obtained from the original manual hybridization processes. However, independent validation of this perceived benefit remains an essential part of the adoption process and, in microarrays, this has often been overlooked for want of immediate convenience. We describe here the comparative evaluation of two automated hybridization instruments, namely the MAUI(R) (microarray user interface) hybridization system and the GeneTAC HybStation, against the conventional manual coverslip hybridization methodology. Our results show that there is a significant advantage in using automated hybridization instrumentation over the diffusion-based coverslip methodology. We observed an enhancement of the mean signal, signal-to-noise ratio, and reproducibility between replicates when using both the MAUI(R) hybridization system and the GeneTAC HybStation. Automation further reduced labour time, offered simplicity and greater reproducibility and accuracy in the results. The present study has independently validated the benefits automation brings to the microarray hybridization process and highlights the differences between the instruments examined. We further comment on the higher quality of spot morphology when hybridization is performed at a lower temperature in combination with our buffer of choice.

Efficient non-unique probes selection algorithms for DNA microarray.

P Deng — 2008-06-17T16:43:01-00:00

BMC genomics, Vol. 9 Suppl 1 (2008)

BACKGROUND: Temperature and salt concentration are very helpful experimental conditions for a probe to hybridize uniquely to its intended target. In large families of closely related target sequences, the high degree of similarity makes it impossible to find a unique probe for every target. We studied how to select a minimum set of non-unique probes to identify the presence of at most d targets in a sample where each non-unique probe can hybridize to a set of targets. RESULTS: We proposed efficient algorithms based on Integer Linear Programming to select a minimum number of non-unique probes using d-disjunct matrices. Our non-unique probes selection can also identify up to d targets in a sample with at most k experimental errors. The decoding complexity of our algorithms is as simple as O(n). The experimental results show that the decoding time is much faster than that of the methods using d-separable matrices while running time and solution size are comparable. CONCLUSIONS: Since finding unique probes is often not easy, we make use of non-unique probes. Minimizing the number of non-unique probes will result in a smaller DNA microarry design which leads to a smaller chip and considerable reduction of cost. While minimizing the probe set, the decoding ability should not be diminished. Our non-unique probes selection algorithms can identify up to d targets with error tolerance and the decoding complexity is O(n).

Toward improved biochips based on rolling circle amplification--influences of the microenvironment on the fluorescence properties of labeled DNA oligonucleotides.

E Mayer-Enthart — 2008-07-17T13:04:36-00:00

Annals of the New York Academy of Sciences, Vol. 1130 (July 2008), pp. 287-292.

Microarrays have become an increasingly important tool for biotechnology and molecular diagnostics. Despite many advantages, their sensitivity is still insufficient for such tasks as the analysis of small sample quantities and for the detection of alterations in gene expression of low-abundance genes. Accordingly, amplification strategies are necessary. Approaches to amplify the signal intensity include the increase of the number of dye molecules per target through either particle labels or rolling circle amplification, as used for this study.

MultiPrimer: a system for microarray PCR primer design.

R Fernandes — 2008-07-17T08:22:15-00:00

Methods in molecular biology (Clifton, N.J.), Vol. 402 (2007), pp. 305-314.

To construct full-genome spotted microarrays, a large number of PCR primers that amplify the required DNA need to be synthesized. We describe an algorithmic technique that allows one to use fewer primers to achieve this goal. This can reduce the expense of constructing full-genome spotted microarrays considerably. PCR primers are usually designed, so that each primer occurs uniquely in the genome. This condition is unnecessarily strong for selective amplification, because only the primer pair associated with each amplification needs be unique. We also describe the interface to our software, MultiPrimer, that computes a small set of primers for amplification of a given gene set.

PDTD: a web-accessible protein database for drug target identification

Zhenting Gao — 2008-02-20T02:00:30-00:00

BMC Bioinformatics, Vol. 9 (19 February 2008), 104.

MAID : An effect size based model for microarray data integration across laboratories and platforms

Ivan Borozan — 2008-07-11T02:48:26-00:00

BMC Bioinformatics, Vol. 9 (10 July 2008), 305.

GEPAS, a web-based tool for microarray data analysis and interpretation

Joaquin Tarraga — 2008-06-02T13:50:41-00:00

Nucl. Acids Res. (28 May 2008), gkn303.

Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org. 10.1093/nar/gkn303

Fuzzy association rules for biological data analysis: a case study on yeast

Francisco Lopez — 2008-02-19T13:11:46-00:00

BMC Bioinformatics, Vol. 9, No. 1. (2008)

BACKGROUND:Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Integration of the information available in the various databases is required to unveil possible associations relating already known data. Biological data are often imprecise and noisy. Fuzzy set theory is specially suitable to model imprecise data while association rules are very appropriate to integrate heterogeneous data.RESULTS:In this work we propose a novel fuzzy methodology based on a fuzzy association rule mining method for biological knowledge extraction. We apply this methodology over a yeast genome dataset containing heterogeneous information regarding structural and functional genome features. A number of association rules have been found, many of them agreeing with previous research in the area. In addition, a comparison between crisp and fuzzy results proves the fuzzy associations to be more reliable than crisp ones.CONCLUSIONS:An integrative approach as the one carried out in this work can unveil significant knowledge which is currently hidden and dispersed through the existing biological databases. It is shown that fuzzy association rules can model this knowledge in an intuitive way by using linguistic labels and few easy-understandable parameters.

Evolving fuzzy rules to model gene expression.

Ricardo Linden — 2006-08-25T20:26:25-00:00

Biosystems (30 April 2006)

This paper develops an algorithm that extracts explanatory rules from microarray data, which we treat as time series, using genetic programming (GP) and fuzzy logic. Reverse polish notation is used (RPN) to describe the rules and to facilitate the GP approach. The algorithm also allows for the insertion of prior knowledge, making it possible to find sets of rules that include the relationships between genes already known. The algorithm proposed is applied to problems arising in the construction of gene regulatory networks, using two different sets of real data from biological experiments on the Arabidopsis thaliana cold response and the rat central nervous system, respectively. The results show that the proposed technique can fit data to a pre-defined precision even in situations where the data set has thousands of features but only a limited number of points in time are available, a situation in which traditional statistical alternatives encounter difficulties, due to the scarcity of time points.

Effect of data normalization on fuzzy clustering of DNA microarray data.

SY Kim — 2008-07-17T04:05:21-00:00

BMC bioinformatics, Vol. 7 (2006)

BACKGROUND: Microarray technology has made it possible to simultaneously measure the expression levels of large numbers of genes in a short time. Gene expression data is information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. Clustering is an important tool for finding groups of genes with similar expression patterns in microarray data analysis. However, hard clustering methods, which assign each gene exactly to one cluster, are poorly suited to the analysis of microarray datasets because in such datasets the clusters of genes frequently overlap. RESULTS: In this study we applied the fuzzy partitional clustering method known as Fuzzy C-Means (FCM) to overcome the limitations of hard clustering. To identify the effect of data normalization, we used three normalization methods, the two common scale and location transformations and Lowess normalization methods, to normalize three microarray datasets and three simulated datasets. First we determined the optimal parameters for FCM clustering. We found that the optimal fuzzification parameter in the FCM analysis of a microarray dataset depended on the normalization method applied to the dataset during preprocessing. We additionally evaluated the effect of normalization of noisy datasets on the results obtained when hard clustering or FCM clustering was applied to those datasets. The effects of normalization were evaluated using both simulated datasets and microarray datasets. A comparative analysis showed that the clustering results depended on the normalization method used and the noisiness of the data. In particular, the selection of the fuzzification parameter value for the FCM method was sensitive to the normalization method used for datasets with large variations across samples. CONCLUSION: Lowess normalization is more robust for clustering of genes from general microarray data than the two common scale and location adjustment methods when samples have varying expression patterns or are noisy. In particular, the FCM method slightly outperformed the hard clustering methods when the expression patterns of genes overlapped and was advantageous in finding co-regulated genes. Thus, the FCM approach offers a convenient method for finding subsets of genes that are strongly associated to a given cluster.

Development of a single tube 640-plex genotyping method for detection of nucleic acid variations on microarrays.

K Krjutskov — 2008-07-16T12:53:32-00:00

Nucleic acids research, Vol. 36, No. 12. (July 2008)

Detection of DNA sequence variation is critical to biomedical applications, including disease genetic identification, diagnosis and treatment, drug discovery and forensic analysis. Here, we describe an arrayed primer extension-based genotyping method (APEX-2) that allows multiplex (640-plex) DNA amplification and detection of single nucleotide polymorphisms (SNPs) and mutations on microarrays via four-color single-base primer extension. The founding principle of APEX-2 multiplex PCR requires two oligonucleotides per SNP/mutation to generate amplicons containing the position of interest. The same oligonucleotides are then subsequently used as immobilized single-base extension primers on a microarray. The method described here is ideal for SNP or mutation detection analysis, molecular diagnostics and forensic analysis. This robust genetic test has minimal requirements: two primers, two spots on the microarray and a low cost four-color detection system for the targeted site; and provides an advantageous alternative to high-density platforms and low-density detection systems.

Calibrating the Performance of SNP Arrays for Whole-Genome Association Studies

Ke Hao — 2008-06-30T00:41:47-00:00

PLoS Genet, Vol. 4, No. 6. (27 June 2008), e1000109.

To facilitate whole-genome association studies (WGAS), several high-density SNP genotyping arrays have been developed. Genetic coverage and statistical power are the primary benchmark metrics in evaluating the performance of SNP arrays. Ideally, such evaluations would be done on a SNP set and a cohort of individuals that are both independently sampled from the original SNPs and individuals used in developing the arrays. Without utilization of an independent test set, previous estimates of genetic coverage and statistical power may be subject to an overfitting bias. Additionally, the SNP arrays' statistical power in WGAS has not been systematically assessed on real traits. One robust setting for doing so is to evaluate statistical power on thousands of traits measured from a single set of individuals. In this study, 359 newly sampled Americans of European descent were genotyped using both Affymetrix 500K (Affx500K) and Illumina 650Y (Ilmn650K) SNP arrays. From these data, we were able to obtain estimates of genetic coverage, which are robust to overfitting, by constructing an independent test set from among these genotypes and individuals. Furthermore, we collected liver tissue RNA from the participants and profiled these samples on a comprehensive gene expression microarray. The RNA levels were used as a large-scale set of quantitative traits to calibrate the relative statistical power of the commercial arrays. Our genetic coverage estimates are lower than previous reports, providing evidence that previous estimates may be inflated due to overfitting. The Ilmn650K platform showed reasonable power (50% or greater) to detect SNPs associated with quantitative traits when the signal-to-noise ratio (SNR) is greater than or equal to 0.5 and the causal SNP's minor allele frequency (MAF) is greater than or equal to 20% (N = 359). In testing each of the more than 40,000 gene expression traits for association to each of the SNPs on the Ilmn650K and Affx500K arrays, we found that the Ilmn650K yielded 15% times more discoveries than the Affx500K at the same false discovery rate (FDR) level.

Empirical Bayes screening of many p-values with applications to microarray studies.

S Datta — 2007-11-12T19:42:43-00:00

Bioinformatics, Vol. 21, No. 9. (1 May 2005), pp. 1987-1994.

MOTIVATION: Statistical tests for the detection of differentially expressed genes lead to a large collection of p-values one for each gene comparison. Without any further adjustment, these p-values may lead to a large number of false positives, simply because the number of genes to be tested is huge, which might mean wastage of laboratory resources. To account for multiple hypotheses, these p-values are typically adjusted using a single step method or a step-down method in order to achieve an overall control of the error rate (the so-called familywise error rate). In many applications, this may lead to an overly conservative strategy leading to too few genes being flagged. RESULTS: In this paper we introduce a novel empirical Bayes screening (EBS) technique to inspect a large number of p-values in an effort to detect additional positive cases. In effect, each case borrows strength from an overall picture of the alternative hypotheses computed from all the p-values, while the entire procedure is calibrated by a step-down method so that the familywise error rate at the complete null hypothesis is still controlled. It is shown that the EBS has substantially higher sensitivity than the standard step-down approach for multiple comparison at the cost of a modest increase in the false discovery rate (FDR). The EBS procedure also compares favorably when compared with existing FDR control procedures for multiple testing. The EBS procedure is particularly useful in situations where it is important to identify all possible potentially positive cases which can be subjected to further confirmatory testing in order to eliminate the false positives. We illustrated this screening procedure using a data set on human colorectal cancer where we show that the EBS method detected additional genes related to colon cancer that were missed by other methods.This novel empirical Bayes procedure is advantageous over our earlier proposed empirical Bayes adjustments due to the following reasons: (i) it offers an automatic screening of the p-values the user may obtain from a univariate (i.e., gene by gene) analysis package making it extremely easy to use for a non-statistician, (ii) since it applies to the p-values, the tests do not have to be t-tests; in particular they could be F-tests which might arise in certain ANOVA formulations with expression data or even nonparametric tests, (iii) the empirical Bayes adjustment uses nonparametric function estimation techniques to estimate the marginal density of the transformed p-values rather than using a parametric model for the prior distribution and is therefore robust against model mis-specification. AVAILABILITY: R code for EBS is available from the authors upon request. SUPPLEMENTARY INFORMATION: http://www.stat.uga.edu/~datta/EBS/supp.htm

Genomic outlier profile analysis: mixture models, null hypotheses, and nonparametric estimation.

Debashis Ghosh — 2008-07-14T03:51:24-00:00

Biostatistics (Oxford, England) (6 June 2008)

In most analyses of large-scale genomic data sets, differential expression analysis is typically assessed by testing for differences in the mean of the distributions between 2 groups. A recent finding by Tomlins and others (2005) is of a different type of pattern of differential expression in which a fraction of samples in one group have overexpression relative to samples in the other group. In this work, we describe a general mixture model framework for the assessment of this type of expression, called outlier profile analysis. We start by considering the single-gene situation and establishing results on identifiability. We propose 2 nonparametric estimation procedures that have natural links to familiar multiple testing procedures. We then develop multivariate extensions of this methodology to handle genome-wide measurements. The proposed methodologies are compared using simulation studies as well as data from a prostate cancer gene expression study.

Does this band make sense? Limits to expression based cancer studies.

Timothy K Williams — 2008-07-14T03:44:37-00:00

Cancer letters (3 July 2008)

Cancer researchers commonly employ reverse transcriptase polymerase chain reaction (RT-PCR) for gene expression analysis of cancer cells. While this technique is facile and reproducible, it is not without limitations. The human genome contains abundant nearly identical sequences (e.g. pseudogenes) to mRNA transcript sequences, which amplify when performing RT-PCR on samples with even trace amounts of genomic DNA. Such sequences include housekeeping transcripts such as beta-actin and GAPDH. This is also true for numerous gene products whose expression is altered in disease states such as cancer (e.g. pp32). Moreover, we describe that amplification of undesirable sequences is not simply avoided by designing primers spanning multiple exons. We also found that template-specific reverse transcriptase reactions lack the specificity necessary to definitively determine the sense or anti-sense orientation of an mRNA transcript. Given the above mentioned caveats and limitations of expression analysis studies, we encourage cancer investigators to test for the existence of intronless genomic sequences that are similar to the specific transcript of the gene being studied. Further, RNA samples should be completely genomic DNA-free prior to performing RT-PCR based assays. Finally, to ensure reliability of RT-PCR or array results, we recommend not utilizing the widely accepted loading controls, GAPDH and/or beta-actin.

Technical variables in high-throughput miRNA expression profiling: Much work remains to be done.

Peter T Nelson — 2008-07-14T03:45:51-00:00

Biochimica et biophysica acta (7 April 2008)

MicroRNA (miRNA) gene expression profiling has provided important insights into plant and animal biology. However, there has not been ample published work about pitfalls associated with technical parameters in miRNA gene expression profiling. One source of pertinent information about technical variables in gene expression profiling is the separate and more well-established literature regarding mRNA expression profiling. However, many aspects of miRNA biochemistry are unique. For example, the cellular processing and compartmentation of miRNAs, the differential stability of specific miRNAs, and aspects of global miRNA expression regulation require specific consideration. Additional possible sources of systematic bias in miRNA expression studies include the differential impact of pre-analytical variables, substrate specificity of nucleic acid processing enzymes used in labeling and amplification, and issues regarding new miRNA discovery and annotation. We conclude that greater focus on technical parameters is required to bolster the validity, reliability, and cultural credibility of miRNA gene expression profiling studies.

Multiplexed detection methods for profiling microRNA expression in biological samples.

AW Wark — 2008-07-14T03:48:40-00:00

Angewandte Chemie (International ed. in English), Vol. 47, No. 4. (2008), pp. 644-652.

The recent discovery of short, non-protein coding RNA molecules, such as microRNA molecules (miRNAs), that can control gene expression has unveiled a whole new layer of complexity in the regulation of cell function. Since 2001, there has been a surge of interest in understanding the regulatory role of the hundreds to thousands of miRNAs expressed in both plants and animals. Significant progress in this area requires the development of quantitative bioanalytical methods for the rapid, multiplexed detection of all miRNAs that are present in a particular cell or tissue sample. In this Minireview, we discuss some of the latest methods for high-throughput miRNA profiling and the unique technological challenges that must be surmounted in this endeavor.

The methodology used to measure differential gene expression affects the outcome.

Y Ding — 2008-06-13T14:18:59-00:00

Journal of biomolecular techniques : JBT, Vol. 18, No. 5. (December 2007), pp. 321-330.

Confirmation of gene expression by a second methodology is critical in order to detect false-positive findings associated with microarrays. However, the impact of methodology upon the measurement of gene expression has not been rigorously evaluated. In the current study, we compared differential gene expression between PC3 and PC3-M human prostate cancer cell lines using three separate methods: microarray, quantitative RT/PCR (qRT/PCR), and Northern blotting. The PC3 to PC3-M ratio of gene expression was determined for each of 24 different genes evaluated, by each of the three methods. Comparison of gene expression ratios between Northern and microarray, Northern and qRT/PCR, and microarray and qRT/PCR, gave correlation coefficients (r) of 0.72, 0.39, and 0.63, respectively. In each instance, one to two outlier genes were apparent. Their exclusion from analysis gave r values of 0.79, 0.72, and 0.83, respectively. These findings demonstrate that the assessment of differential gene expression is dependent upon the methodology used in each situation where outcome between different methodologies was compared, the presence of a relatively limited number of outlier genes precludes high overall correlation between the methods. Validation of gene expression by different methods should be performed whenever possible.

Highly sensitive and specific microRNA expression profiling using BeadArray technology.

Jing Chen — 2008-06-30T10:12:29-00:00

Nucleic acids research (25 June 2008)

We have developed a highly sensitive, specific and reproducible method for microRNA (miRNA) expression profiling, using the BeadArraytrade mark technology. This method incorporates an enzyme-assisted specificity step, a solid-phase primer extension to distinguish between members of miRNA families. In addition, a universal PCR is used to amplify all targets prior to array hybridization. Currently, assay probes are designed to simultaneously analyse 735 well-annotated human miRNAs. Using this method, highly reproducible miRNA expression profiles were generated with 100-200 ng total RNA input. Furthermore, very similar expression profiles were obtained with total RNA and enriched small RNA species (R(2) >/= 0.97). The method has a 3.5-4 log (10(5)-10(9) molecules) dynamic range and is able to detect 1.2- to 1.3-fold-differences between samples. Expression profiles generated by this method are highly comparable to those obtained with RT-PCR (R(2) = 0.85-0.90) and direct sequencing (R = 0.87-0.89). This method, in conjunction with the 96-sample array matrix should prove useful for high-throughput expression profiling of miRNAs in large numbers of tissue samples.

MicroRNA expression profiling using microarrays.

CG Liu — 2008-05-21T12:41:03-00:00

Nature protocols, Vol. 3, No. 4. (2008), pp. 563-578.

Microarray technology is a powerful high-throughput tool capable of monitoring the expression of thousands of small noncoding RNAs at once within tens of samples processed in parallel in a single experiment. To conduct a genome-wide analysis of miRNA expression of normal and disease samples, such as cancer, and to distinguish expression signatures associated with diagnosis, prognosis and therapeutic interventions, we have developed a unique miRNA microarray assay on a CodeLink platform. The miRNA array consists of 4,104 probes printed in duplicate. This array can simultaneously profile more than 1,500 mature miRNAs and their corresponding precursors from 474 human and 373 mouse miRNA genes. The full protocol details of the miRNA microarray assay developed by our group are described here, including miRNA oligo probe design, array fabrication and miRNA target preparation (by reverse transcription of total RNA), target-probe hybridization on array, signal detection and data analysis. The assay is simple, can be easily standardized and allows the reproducible profiling of up to 24 total RNA samples within 24 h.

nuID: A universal naming schema of oligonucleotides for Illumina, Affymetrix, and other microarrays

Pan Du — 2007-06-01T07:35:05-00:00

Biology Direct, Vol. 2 (31 May 2007), 16.

Model-based variance-stabilizing transformation for Illumina microarray data

Simon Lin — 2008-01-25T02:22:20-00:00

Nucl. Acids Res. (4 January 2008), gkm1075.

Variance stabilization is a step in the preprocessing of microarray data that can greatly benefit the performance of subsequent statistical modeling and inference. Due to the often limited number of technical replicates for Affymetrix and cDNA arrays, achieving variance stabilization can be difficult. Although the Illumina microarray platform provides a larger number of technical replicates on each array (usually over 30 randomly distributed beads per probe), these replicates have not been leveraged in the current log2 data transformation process. We devised a variance-stabilizing transformation (VST) method that takes advantage of the technical replicates available on an Illumina microarray. We have compared VST with log2 and Variance-stabilizing normalization (VSN) by using the Kruglyak bead-level data (2006) and Barnes titration data (2005). The results of the Kruglyak data suggest that VST stabilizes variances of bead-replicates within an array. The results of the Barnes data show that VST can improve the detection of differentially expressed genes and reduce false-positive identifications. We conclude that although both VST and VSN are built upon the same model of measurement noise, VST stabilizes the variance better and more efficiently for the Illumina platform by leveraging the availability of a larger number of within-array replicates. The algorithms and Supplementary Data are included in the lumi package of Bioconductor, available at: www.bioconductor.org. 10.1093/nar/gkm1075

beadarray: R classes and methods for Illumina bead-based data

Mark Dunning — 2008-05-20T23:50:49-00:00

Bioinformatics, Vol. 23, No. 16. (15 August 2007), pp. 2183-2184.

Summary: The R/Bioconductor package beadarray allows raw data from Illumina experiments to be read and stored in convenient R classes. Users are free to choose between various methods of image processing, background correction and normalization in their analysis rather than using the defaults in Illumina's; proprietary software. The package also allows quality assessment to be carried out on the raw data. The data can then be summarized and stored in a format which can be used by other R/Bioconductor packages to perform downstream analyses. Summarized data processed by Illumina's; BeadStudio software can also be read and analysed in the same manner. Availability: The beadarray package is available from the Bioconductor web page at www.bioconductor.org. A user's; guide and example data sets are provided with the package. Contact: md392@cam.ac.uk 10.1093/bioinformatics/btm311

Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution

Brian Wilhelm — 2008-05-19T01:16:04-00:00

Nature (18 May 2008)

RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays

John Marioni — 2008-06-11T20:56:53-00:00

Genome Res. (11 June 2008), gr.079558.108.

Ultra high-throughput sequencing is emerging as an attractive alternative to microarrays for genotyping, analysis of methylation patterns and identification of transcription factor binding sites. Here, we describe an application of the Illumina sequencing platform to study mRNA expression levels. Our goals were to estimate technical variance associated with Illumina sequencing in this context and to compare its ability to identify differentially expressed genes with existing array technologies. To do so, we estimated gene expression differences between liver and kidney RNA samples using multiple sequencing replicates, and compared the sequencing data to results obtained from Affymetrix arrays using the same RNA samples. We find that the Illumina sequencing data are highly replicable, with relatively little technical variation, and so, for many purposes, it may suffice to sequence each mRNA sample only once (i.e., using one lane). The information in a single lane of Illumina sequencing data appears comparable to that in a single array in enabling identification of differentially expressed genes, while allowing for additional analyses such as detection of low-expressed genes, alternative splice variants, and novel transcripts. Based on our observations, we propose an empirical protocol and a statistical framework for the analysis of gene expression using ultra high-throughput sequencing technology. 10.1101/gr.079558.108

Assessment of gene expression in many samples using vertical arrays.

RA Risques — 2008-07-14T03:12:47-00:00

Nucleic acids research, Vol. 36, No. 10. (June 2008)

Microarrays and high-throughput sequencing methods can be used to measure the expression of thousands of genes in a biological sample in a few days, whereas PCR-based methods can be used to measure the expression of a few genes in thousands of samples in about the same amount of time. These methods become more costly as the number of biological samples increases or as the number of genes of interest increases, respectively, and these factors constrain experimental design. To address these issues, we introduced 'vertical arrays' in which RNA from each biological sample is converted into multiple, overlapping cDNA subsets and spotted on glass slides. These vertical arrays can be queried with single gene probes to assess the expression behavior in thousands of biological samples in a single hybridization reaction. The spotted subsets are less complex than the original RNA from which they derive, which improves signal-to-noise ratios. Here, we demonstrate the quantitative capabilities of vertical arrays, including the sensitivity and accuracy of the method and the number of subsets needed to achieve this accuracy for most expressed genes.

GenePattern 2.0

Michael Reich — 2006-04-27T04:38:54-00:00

Nature Genetics, Vol. 38, No. 5., pp. 500-501.

MD-SeeGH: a platform for integrative analysis of multi-dimensional genomic data

Bryan Chi — 2008-05-22T03:59:01-00:00

BMC Bioinformatics, Vol. 9 (20 May 2008), 243.

Comparison of Modern Techniques for Saliva Screening*

Jarrah R Myers — 2008-07-14T02:52:56-00:00

Journal of forensic sciences (22 May 2008)

Saliva stains present a unique challenge in the forensic setting, often challenging the analyst to weigh the value of presumptive indication of the fluid versus the potential for DNA analysis to yield identification information. There are many situations in which determining the presence of a body fluid is probative and further corroborates DNA evidence. That said, even a minute portion of sample consumed by a screening test could mean the difference between a full, partial, or null profile obtained through DNA analysis. The basis of presumptive testing or screening of saliva has historically been based on the presence of amylase, a component found in relatively high concentrations in human saliva versus other body fluids and substances. Though the current available methods for the screening of saliva in a forensic application have grown in number, the popularity of these methods seemingly has not. This study attempts to identify a specific and sensitive saliva screening test by comparing three modern techniques-the recently released SALIgAE((R)), Phadebas((R)), and starch-iodine mini-centrifuge test-on the basis of sensitivity, specificity, mixtures, and simulated casework samples while also considering sample consumption. The Phadebas((R)) method for presumptive saliva testing detected dilutions of neat saliva down to 1:200 versus considerably less sensitive results with SALIgAE((R)) and the starch-iodine mini-centrifuge test. Utilizing a screening test with a high degree of sensitivity, such as Phadebas((R)), allows an analyst to gain a maximum amount of information in the form of body fluid indication and DNA results because of the consumption of a small portion of sample.

New markers for old stains: stable mRNA markers for blood and saliva identification from up to 16-year-old stains.

Dmitry Zubakov — 2008-07-14T02:42:35-00:00

International journal of legal medicine (2 July 2008)

In forensic science, the unequivocal identification of the cellular origin of crime scene samples used for DNA profiling can provide crucial information for crime scene reconstruction. We have previously shown that various mRNA markers from genes with expression patterns specific for blood and saliva can be established from whole-genome expression analysis of time-wise degraded samples and were stable enough to specifically identify blood and saliva stains up to 180 days of age. Here, we showed that nine blood-specific and five saliva-specific mRNA markers can be amplified successfully and reliably in much older blood (13-16 years) and saliva (2-6 years) stains, respectively, suggesting their suitability for tissue identification in forensic case work. Moreover, our findings imply that forensic RNA testing can be reliable and robust if degraded samples are considered in the marker ascertainment procedure, with promising expectations beyond tissue identification purposes.

Salivary diagnostics: enhancing disease detection and making medicine better.

A Segal — 2008-07-14T02:40:34-00:00

European journal of dental education : official journal of the Association for Dental Education in Europe, Vol. 12 Suppl 1 (February 2008), pp. 22-29.

To monitor health status, disease onset and progression, and treatment outcome non-invasively is a most desirable goal in the health care delivery and health research. There are three prerequisites necessary to reach this goal: 1. A non-invasive method for collecting biological samples. 2. Specific biomarkers associated with health or disease. 3. A technology platform to rapidly discriminate the biomarkers. An initiative catalysed by the National Institute of Dental and Craniofacial Research (NIDCR) has created a roadmap to achieve this goal through the use of oral fluids as the diagnostic medium to scrutinize the health and disease status. This is an ideal opportunity to bridge state-of-the-art saliva-based biosensors and disease-discriminatory salivary biomarkers in diagnostic applications. Oral fluid, often called the mirror of the body, is a perfect medium to be explored for health and disease surveillance. The translational applications and opportunities are enormous. This review presents the translational value of saliva as a credible clinical diagnostic fluid and the scientific rationale for such use.

Characterization of salivary RNA by cDNA library analysis

Noh Park — 2006-10-20T13:49:50-00:00

Archives of Oral Biology, Vol. In Press, Corrected Proof

Oral fluid (saliva) meets the demands for a noninvasive and accessible diagnostic medium. Recent reports by our group and others described the presence and use of human RNA in saliva as a diagnostic or forensic tool, including the use for oral cancer detection. To gain insights into the integrity of salivary RNA, we examined in detail the integrity of salivary RNA by generating a cDNA library from pooled supernatant saliva of 10 healthy donors. From a library with a primary library titer of 1.3 x 106 cfu/mL of which 95% of the clones had inserts, we successfully sequenced 117 random colonies containing recombinant clones. BLAST search results indicated that all of these clones contained sequences of human origin. Most of the salivary RNAs appeared to be endonucleolytically cleaved at random positions as indicated by comparisons to respective full length parental RNAs from the Genbank. Twelve of the insert sequences matched to the normal salivary core transcriptome sequences, which are highly abundant mRNAs present in healthy individuals. This study provides an in-depth molecular analysis of the saliva transcriptome and should be a useful resource for future basic and translational studies of RNA in human saliva. In addition, this paper presents unequivocal evidence for the presence of RNA in saliva as determined by the use of diverse techniques such as reverse transcriptase quantitative polymerase chain reaction (RT-qPCR), in vitro translation, and the construction of a salivary cDNA library.

SIMAGE: simulation of DNA-microarray gene expression data.

CJ Albers — 2008-07-14T02:16:12-00:00

BMC bioinformatics, Vol. 7 (2006)

BACKGROUND: Simulation of DNA-microarray data serves at least three purposes: (i) optimizing the design of an intended DNA microarray experiment, (ii) comparing existing pre-processing and processing methods for best analysis of a given DNA microarray experiment, (iii) educating students, lab-workers and other researchers by making them aware of the many factors influencing DNA microarray experiments. RESULTS: Our model has multiple layers of factors influencing the experiment. The relative influence of such factors can differ significantly between labs, experiments within labs, etc. Therefore, we have added a module to roughly estimate their parameters from a given data set. This guarantees that our simulated data mimics real data as closely as possible. CONCLUSION: We introduce a model for the simulation of dual-dye cDNA-microarray data closely resembling real data and coin the model and its software implementation "SIMAGE" which stands for simulation of microarray gene expression data. The software is freely accessible at: http://bioinformatics.biol.rug.nl/websoftware/simage.

Analysis of the real EADGENE data set: Comparison of methods and guidelines for data normalisation and selection of differentially expressed genes (Open Access publication).

F Jaffrézic — 2008-01-12T22:10:17-00:00

Genet Sel Evol, Vol. 39, No. 6. (c 2007), pp. 633-650.

A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE (European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two different mastitis causing bacteria: Escherichia coli and Staphylococcus aureus. It was reassuring to see that most of the teams found the same main biological results. In fact, most of the differentially expressed genes were found for infection by E. coli between uninfected and 24 h challenged udder quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised a biological problem of cross-talk between infected and uninfected quarters which will have to be dealt with for further microarray studies.

Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication).

M Watson — 2008-01-12T22:12:22-00:00

Genet Sel Evol, Vol. 39, No. 6. (c 2007), pp. 669-683.

Microarrays allow researchers to measure the expression of thousands of genes in a single experiment. Before statistical comparisons can be made, the data must be assessed for quality and normalisation procedures must be applied, of which many have been proposed. Methods of comparing the normalised data are also abundant, and no clear consensus has yet been reached. The purpose of this paper was to compare those methods used by the EADGENE network on a very noisy simulated data set. With the a priori knowledge of which genes are differentially expressed, it is possible to compare the success of each approach quantitatively. Use of an intensity-dependent normalisation procedure was common, as was correction for multiple testing. Most variety in performance resulted from differing approaches to data quality and the use of different statistical tests. Very few of the methods used any kind of background correction. A number of approaches achieved a success rate of 95% or above, with relatively small numbers of false positives and negatives. Applying stringent spot selection criteria and elimination of data did not improve the false positive rate and greatly increased the false negative rate. However, most approaches performed well, and it is encouraging that widely available techniques can achieve such good results on a very noisy data set.

Analysis of the real EADGENE data set: Multivariate approaches and post analysis (Open Access publication).

P Sørensen — 2008-01-12T22:07:22-00:00

Genet Sel Evol, Vol. 39, No. 6. (c 2007), pp. 651-668.

The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC) or principal component analysis (PCA) to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus). The main result from these analyses was that HC and PCA were able to separate tissue samples taken at 24 h following E. coli infection from the other samples. The second approach identified groups of differentially co-expressed genes, by identifying clusters of genes highly correlated when animals were infected with E. coli but not correlated more than expected by chance when the infective pathogen was S. aureus. The third approach looked at differential expression of predefined gene sets. Gene sets were defined based on information retrieved from biological databases such as Gene Ontology. Based on these annotation sources the teams used either the GlobalTest or the Fisher exact test to identify differentially expressed gene sets. The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed.

Making a new technology work: the standardization and regulation of microarrays.

S Rogers — 2008-07-14T02:09:02-00:00

The Yale journal of biology and medicine, Vol. 80, No. 4. (December 2007), pp. 165-178.

The translation of laboratory innovations into clinical tools is dependent upon the development of regulatory arrangements designed to ensure that the new technology will be used reliably and consistently. A case study of a key post-genomic technology, gene chips or microarrays, exemplifies this claim. The number of microarray publications and patents has increased exponentially during the last decade and diagnostic microarray tests already are making their way into the clinic. Yet starting in the mid-1990s, scientific journals were overrun with criticism concerning the ambiguities involved in interpreting most of the assumptions of a microarray experiment. Questions concerning platform comparability and statistical calculations were and continue to be raised, in spite of the emergence by 2001 of an initial set of standards concerning several components of a microarray experiment. This article probes the history and ongoing efforts aimed at turning microarray experimentation into a viable, meaningful, and consensual technology by focusing on two related elements:1) The history of the development of the Microarray Gene Expression Data Society (MGED), a remarkable bottom-up initiative that brings together different kinds of specialists from academic, commercial, and hybrid settings to produce, maintain, and update microarray standards; and 2) The unusual mix of skills and expertise involved in the development and use of microarrays. The production, accumulation, storage, and mining of microarray data remain multi-skilled endeavors bridging together different types of scientists who embody a diversity of scientific traditions. Beyond standardization, the interfacing of these different skills has become a key issue for further development of the field.

A disattenuated correlation estimate when variables are measured with error: illustration estimating cross-platform correlations.

KJ Archer — 2008-04-17T17:14:02-00:00

Statistics in medicine, Vol. 27, No. 7. (30 March 2008), pp. 1026-1039.

Previous cross-platform reproducibility studies have compared consistency of intensities as well as consistency of fold changes across different platforms using Pearson's correlation coefficient. In this study, we propose the use of measurement error models for estimating gene-specific correlations. Additionally, gene-specific reliability estimates are shown to be useful in prioritizing clones for sequence verification rather than selecting clones using a simple random sample. The proposed 'disattenuated' correlation may prove useful in a wide variety of studies when both X and Y are measured with error, such as in confirmation studies of microarray gene expression values, wherein more reliable laboratory assays such as real-time polymerase chain reaction are used.

Nonparametric relevance-shifted multiple testing procedures for the analysis of high-dimensional multivariate data with small sample sizes

Cornelia Froemke — 2008-01-27T12:35:31-00:00

BMC Bioinformatics, Vol. 9, No. 1. (2008)

BACKGROUND:In many research areas it is necessary to find differences between treatment groups with several variables. For example, studies of microarray data seek to find a significant difference in location parameters from zero or one for ratios thereof for each variable. However, in some studies a significant deviation of the difference in locations from zero (or 1 in terms of the ratio) is biologically meaningless. A relevant difference or ratio is sought in such cases.RESULTS:This article addresses the use of relevance-shifted tests on ratios for a multivariate parallel two-sample group design. Two empirical procedures are proposed which embed the relevance-shifted test on ratios. As both procedures test a hypothesis for each variable, the resulting multiple testing problem has to be considered. Hence, the procedures include a multiplicity correction. Both procedures are extensions of available procedures for point null hypotheses achieving exact control of the familywise error rate. Whereas the shift of the null hypothesis alone would give straightforward solutions, the problems that are the reason for the empirical considerations discussed here arise by the fact that the shift is considered in both directions and the whole parameter space in between these two limits has to be accepted as null hypothesis.CONCLUSIONS:The first algorithm to be discussed uses a permutation algorithm, and is appropriate for designs with a moderately large number of observations. However, many experiments have limited sample sizes. Then the second procedure might be more appropriate, where multiplicity is corrected according to a concept of data-driven order of hypotheses.

Required sample size and nonreplicability thresholds for heterogeneous genetic associations.

R Moonesinghe — 2008-07-11T03:41:52-00:00

Proceedings of the National Academy of Sciences of the United States of America, Vol. 105, No. 2. (15 January 2008), pp. 617-622.

Many gene-disease associations proposed to date have not been consistently replicated across different populations. Nonreplication often reflects false positives in the original claims. However, occasionally, nonreplication may be due to heterogeneity due to biases or even genuine diversity of the genetic effects in different populations. Here, we propose methods for estimating the required sample size to replicate an association across many studies with different amounts of between-study heterogeneity, when data are summarized through metaanalysis. We demonstrate thresholds of between-study heterogeneity (tau(0)(2)) above which one cannot reach adequate power to replicate a proposed association at a specified level of statistical significance when k studies are performed (regardless of how large these studies are). Based on empirical evidence from 91 proposed gene-disease associations (50 on candidate genes and 41 from genome-wide association efforts), the observed between-study heterogeneity is often close to or even surpasses nonreplicability thresholds. With more modest between-study heterogeneity, the required sample size increases considerably compared with when no between-study heterogeneity exists. Increases are steep as tau(0)(2) is approached. Therefore, some true associations may not be practically possible to replicate with consistency, no matter how large studies are conducted. Efforts should be made to minimize between-study heterogeneity in targeted genetic effects.

Innovative integrated system for real-time measurement of hybridization and melting on standard format microarrays.

Y Marcy — 2008-07-11T03:36:54-00:00

BioTechniques, Vol. 44, No. 7. (June 2008), pp. 913-920.

Despite the great popularity and potential of microarrays, their use for research and clinical applications is still hampered by lengthy and costly design and optimization processes, mainly because the technology relies on the end point measurement of hybridization. Thus, the ability to monitor many hybridization events on a standard microarray slide in real time would greatly expand the use and benefit of this technology, as it would give access to better prediction of probe performance and improved optimization of hybridization parameters. Although real-time hybridization and thermal denaturation measurements have been reported, a complete walk-away system compatible with the standard format of microarrays is still unavailable. To address this issue, we have designed a biochip tool that combines a hybridization station with active mixing capability and temperature control together with a fluorescence reader in a single compact benchtop instrument. This integrated live hybridization machine (LHM) allows measuring in real time the hybridization of target DNA to thousands of probes simultaneously and provides excellent levels of detection and superior sequence discrimination. Here we show on an environmental single nucleotide polymorphism (SNP) model system that the LHM enables a variety of experiments unachievable with conventional biochip tools.

Messenger RNA expression profiling using DNA microarray technology: diagnostic tool, scientific analysis or un-interpretable data?

MS Walker — 2008-07-11T03:32:18-00:00

International journal of molecular medicine, Vol. 21, No. 1. (January 2008), pp. 13-17.

Analysis of the transcriptome using DNA microarrays has become a standard approach for investigation of the molecular basis of human disease in both clinical and experimental settings. However, drawing conclusions from the wealth of data obtained has remained problematic. There have been difficulties with accurate reporting of results, with experimental reproducibility and with identifying and interpreting the biologically relevant information. In this review we discuss the successful use of DNA microarray technology in molecular medical research, and we highlight methods of addressing the issues of both reproducibility and biological interpretation.

Evaluating Statistical Methods Using Plasmode Data Sets in the Age of Massive Public Databases: An Illustration Using False Discovery Rates

Gary Gadbury — 2008-06-21T00:13:13-00:00

PLoS Genet, Vol. 4, No. 6. (20 June 2008), e1000098.

Plasmode is a term coined several years ago to describe data sets that are derived from real data but for which some truth is known. Omic techniques, most especially microarray and genomewide association studies, have catalyzed a new zeitgeist of data sharing that is making data and data sets publicly available on an unprecedented scale. Coupling such data resources with a science of plasmode use would allow statistical methodologists to vet proposed techniques empirically (as opposed to only theoretically) and with data that are by definition realistic and representative. We illustrate the technique of empirical statistics by consideration of a common task when analyzing high dimensional data: the simultaneous testing of hundreds or thousands of hypotheses to determine which, if any, show statistical significance warranting follow-on research. The now-common practice of multiple testing in high dimensional experiment (HDE) settings has generated new methods for detecting statistically significant results. Although such methods have heretofore been subject to comparative performance analysis using simulated data, simulating data that realistically reflect data from an actual HDE remains a challenge. We describe a simulation procedure using actual data from an HDE where some truth regarding parameters of interest is known. We use the procedure to compare estimates for the proportion of true null hypotheses, the false discovery rate (FDR), and a local version of FDR obtained from 15 different statistical methods.

Improving the power for detecting overlapping genes from multiple DNA microarray-derived gene lists.

X Deng — 2008-07-11T03:23:10-00:00

BMC bioinformatics, Vol. 9 Suppl 6 (2008)

BACKGROUND: In DNA microarray gene expression profiling studies, a fundamental task is to extract statistically significant genes that meet certain research hypothesis. Currently, Venn diagram is a frequently used method for identifying overlapping genes that meet the investigator's research hypotheses. However this simple operation of intersecting multiple gene lists, known as the Intersection-Union Tests (IUTs), is performed without knowing the incurred changes in Type 1 error rate and can lead to loss of discovery power. RESULTS: We developed an IUT adjustment procedure, called Relaxed IUT (RIUT), which is proved to be less conservative and more powerful for intersecting independent tests than the traditional Venn diagram approach. The advantage of the RIUT procedure over traditional IUT is demonstrated by empirical Monte-Carlo simulation and two real toxicogenomic gene expression case studies. Notably, the enhanced power of RIUT enables it to identify overlapping gene sets leading to identification of certain known related pathways which were not detected using the traditional IUT method. CONCLUSION: We showed that traditional IUT via a Venn diagram is generally conservative, which may lead to loss discovery power in DNA microarray studies. RIUT is proved to be a more powerful alternative for performing IUTs in identifying overlapping genes from multiple gene lists derived from microarray gene expression profiling.