Comparison of different approaches for comparative genetic analysis using microarray hybridization

A robust analysis of comparative genomic microarray data is critical for meaningful genomic comparison studies. In this paper, we compare our method (implemented in a new software tool, GENCOM, freely available at ) with three commonly used analysis methods: GACK (freely available at ), an empirical cut-off value of twofold difference between the fluorescence intensities after LOWESS normalization or after AVERAGE normalization in which the fluorescence intensity is divided by the average fluorescence intensity of the entire data set. Each method was tested using data sets from real experiments with prior knowledge of conserved and divergent genes. GENCOM and GACK were superior when a high proportion of genes were divergent. GENCOM was the most suitable method for the data set in which the relationship between the fluorescence intensities was not linear. GENCOM has proved robust in an analysis of all the data sets tested.     

Bayesian meta-analysis models for microarray data: a comparative study

Background

With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods.

Results

Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets were pre-scaled.

Conclusion

The Bayesian meta-analysis model that combines probabilities across studies does not aggregate gene expression measures, thus an inter-study variability parameter is not included in the model. This results in a simpler modeling approach than aggregating expression measures, which accounts for variability across studies. The probability integration model identified more true discovered genes and fewer true omitted genes than combining expression measures, for our data sets.     

AffyTrees: facilitating comparative analysis of Affymetrix plant microarray chips

Microarrays measure the expression of large numbers of genes simultaneously and can be used to delve into interaction networks involving many genes at a time. However, it is often difficult to decide to what extent knowledge about the expression of genes gleaned in one model organism can be transferred to other species. This can be examined either by measuring the expression of genes of interest under comparable experimental conditions in other species, or by gathering the necessary data from comparable microarray experiments. However, it is essential to know which genes to compare between the organisms. To facilitate comparison of expression data across different species, we have implemented a Web-based software tool that provides information about sequence orthologs across a range of Affymetrix microarray chips. AffyTrees provides a quick and easy way of assigning which probe sets on different Affymetrix chips measure the expression of orthologous genes. Even in cases where gene or genome duplications have complicated the assignment, groups of comparable probe sets can be identified. The phylogenetic trees provide a resource that can be used to improve sequence annotation and detect biases in the sequence complement of Affymetrix chips. Being able to identify sequence orthologs and recognize biases in the sequence complement of chips is necessary for reliable cross-species microarray comparison. As the amount of work required to generate a single phylogeny in a nonautomated manner is considerable, AffyTrees can greatly reduce the workload for scientists interested in large-scale cross-species comparisons.     

Design of a seven-genome Escherichia coli microarray for comparative genomic profiling

We describe the design and evaluate the use of a high-density oligonucleotide microarray covering seven sequenced Escherichia coli genomes in addition to several sequenced E. coli plasmids, bacteriophages, pathogenicity islands, and virulence genes. Its utility is demonstrated for comparative genomic profiling of two unsequenced strains, O175:H16 D1 and O157:H7 3538 (Deltastx(2)::cat) as well as two well-known control strains, K-12 W3110 and O157:H7 EDL933. By using fluorescently labeled genomic DNA to query the microarrays and subsequently analyze common virulence genes and phage elements and perform whole-genome comparisons, we observed that O175:H16 D1 is a K-12-like strain and confirmed that its phi3538 (Deltastx(2)::cat) phage element originated from the E. coli 3538 (Deltastx(2)::cat) strain, with which it shares a substantial proportion of phage elements. Moreover, a number of genes involved in DNA transfer and recombination was identified in both new strains, providing a likely explanation for their capability to transfer phi3538 (Deltastx(2)::cat) between them. Analyses of control samples demonstrated that results using our custom-designed microarray were representative of the true biology, e.g., by confirming the presence of all known chromosomal phage elements as well as 98.8 and 97.7% of queried chromosomal genes for the two control strains. Finally, we demonstrate that use of spatial information, in terms of the physical chromosomal locations of probes, improves the analysis.     

Evaluation of normalization methods for microarray data

Background

Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. This novel technique helps us to understand gene regulation as well as gene by gene interactions more systematically. In the microarray experiment, however, many undesirable systematic variations are observed. Even in replicated experiment, some variations are commonly observed. Normalization is the process of removing some sources of variation which affect the measured gene expression levels. Although a number of normalization methods have been proposed, it has been difficult to decide which methods perform best. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization.

Results

In this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data.

Conclusions

Our results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.

     

Evaluation and comparison of gene clustering methods in microarray analysis

MOTIVATION: Microarray technology has been widely applied in biological and clinical studies for simultaneous monitoring of gene expression in thousands of genes. Gene clustering analysis is found useful for discovering groups of correlated genes potentially co-regulated or associated to the disease or conditions under investigation. Many clustering methods including hierarchical clustering, K-means, PAM, SOM, mixture model-based clustering and tight clustering have been widely used in the literature. Yet no comprehensive comparative study has been performed to evaluate the effectiveness of these methods. RESULTS: In this paper, six gene clustering methods are evaluated by simulated data from a hierarchical log-normal model with various degrees of perturbation as well as four real datasets. A weighted Rand index is proposed for measuring similarity of two clustering results with possible scattered genes (i.e. a set of noise genes not being clustered). Performance of the methods in the real data is assessed by a predictive accuracy analysis through verified gene annotations. Our results show that tight clustering and model-based clustering consistently outperform other clustering methods both in simulated and real data while hierarchical clustering and SOM perform among the worst. Our analysis provides deep insight to the complicated gene clustering problem of expression profile and serves as a practical guideline for routine microarray cluster analysis.     

Copasetic analysis: a framework for the blind analysis of microarray imagery

From its conception, bioinformatics has been a multidisciplinary field which blends domain expert knowledge with new and existing processing techniques, all of which are focused on a common goal. Typically, these techniques have focused on the direct analysis of raw microarray image data. Unfortunately, this fails to utilise the image's full potential and in practice, this results in the lab technician having to guide the analysis algorithms. This paper presents a dynamic framework that aims to automate the process of microarray image analysis using a variety of techniques. An overview of the entire framework process is presented, the robustness of which is challenged throughout with a selection of real examples containing varying degrees of noise. The results show the potential of the proposed framework in its ability to determine slide layout accurately and perform analysis without prior structural knowledge. The algorithm achieves approximately, a 1 to 3 dB improved peak signal-to-noise ratio compared to conventional processing techniques like those implemented in GenePix when used by a trained operator. As far as the authors are aware, this is the first time such a comprehensive framework concept has been directly applied to the area of microarray image analysis.     

Evaluation of viral genome assembly and diversity estimation in deep metagenomes

Background

Viruses have unique properties, small genome and regions of high similarity, whose effects on metagenomic assemblies have not been characterized so far. This study uses diverse in silico simulated viromes to evaluate how extensively genomes can be assembled using different sequencing platforms and assemblers. Further, it investigates the suitability of different methods to estimate viral diversity in metagenomes.

Results

We created in silico metagenomes mimicking various platforms at different sequencing depths. The CLC assembler revealed subpar compared to IDBA_UD and CAMERA , which are metagenomic-specific. Up to a saturation point, Illumina platforms proved more capable of reconstructing large portions of viral genomes compared to 454. Read length was an important factor for limiting chimericity, while scaffolding marginally improved contig length and accuracy. The genome length of the various viruses in the metagenomes did not significantly affect genome reconstruction, but the co-existence of highly similar genomes was detrimental. When evaluating diversity estimation tools, we found that PHACCS results were more accurate than those from CatchAll and clustering, which were both orders of magnitude above expected.

Conclusions

Assemblers designed specifically for the analysis of metagenomes should be used to facilitate the creation of high-quality long contigs. Despite the high coverage possible, scientists should not expect to always obtain complete genomes, because their reconstruction may be hindered by co-existing species bearing highly similar genomic regions. Further development of metagenomics-oriented assemblers may help bypass these limitations in future studies. Meanwhile, the lack of fully reconstructed communities keeps methods to estimate viral diversity relevant. While none of the three methods tested had absolute precision, only PHACCS was deemed suitable for comparative studies.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-989) contains supplementary material, which is available to authorized users.     

AFM 4.0: a toolbox for DNA microarray analysis

We have developed a series of programs, collectively packaged as Array File Maker 4.0 (AFM), that manipulate and manage DNA microarray data. AFM 4.0 is simple to use, applicable to any organism or microarray, and operates within the familiar confines of Microsoft Excel. Given a database of expression ratios, AFM 4.0 generates input files for clustering, helps prepare colored figures and Venn diagrams, and can uncover aneuploidy in yeast microarray data. AFM 4.0 should be especially useful to laboratories that do not have access to specialized commercial or in-house software.     

Comparison of different microarray data analysis programs and description of a database for microarray data management

Data analysis and management represent a major challenge for gene expression studies using microarrays. Here, we compare different methods of analysis and demonstrate the utility of a personal microarray database. Gene expression during HIV infection of cell lines was studied using Affymetrix U-133 A and B chips. The data were analyzed using Affymetrix Microarray Suite and Data Mining Tool, Silicon Genetics GeneSpring, and dChip from Harvard School of Public Health. A small-scale database was established with FileMaker Pro Developer to manage and analyze the data. There was great variability among the programs in the lists of significantly changed genes constructed from the same data. Similarly choices of different parameters for normalization, comparison, and standardization greatly affected the outcome. As many probe sets on the U133 chip target the same Unigene clusters, the Unigene information can be used as an internal control to confirm and interpret the probe set results. Algorithms used for the determination of changes in gene expression require further refinement and standardization. The use of a personal database powered with Unigene information can enhance the analysis of gene expression data.     

Isolation and identification of fecal bacteria from adult swine.

An examination of the fecal microflora of adult swine was made with regard to the efficiency of several roll tube media in enumeration and recovery of anaerobes, the effects of medium constituents on recovery, and the isolation and identification of the predominant kinds of bacteria. Total number of organisms by microscopic bacterial counts varied among fecal samples from 4.48 X 10(10) to 7.40 X 10(10) bacteria/g (wet weight). Comparison of different nonselective roll tube media indicated that about 30% of the fecal bacteria could be recovered with a rumen fluid (40%, vol/vol) medium (M98-5). Recoveries of 21 and 15%, respectively, were obtained with M10 and rumen fluid-glucose-cellobiose agar (RGCA) media. Rumen fluid, Trypticase, sugars, and CO2 gas phase were important components required for maximum recovery with this medium. Similar high recoveries of anaerobes were also obtained with M98-5 containing swine cecal extract of place in rumen fluid or M10 plus swine cecal extract. Significantly lower recoveries were observed with RCGA, media supplemented with swine fecal extracts, reinforced clostridial medium, brain heart infusion agar, and prereduced blood agar. Ninety percent of the bacteria isolated from roll tube media were gram positive and consisted of facultatively anaerobic streptococci, Eubacterium sp., Clostridium sp., and Propionibacterium acnes. The remainder of the flora (8%) included several other species of anaerobes and Escherichia coli. Rumen fluid (or volatile fatty acids), Trypticase, and yeast extract additions to basal media stimulated the growth of anaerobic strains. Variation in the relative proportions of the predominant fecal microflora was observed. This work indicates that satisfactory enumeration, isolation and cultivation of the predominant microflora in swine feces can be obtained when strict anaerobic culture methods and a rumen fluid medium are used.     

Comparative analysis of comparative genomic hybridization microarray technologies: report of a workshop sponsored by the Wellcome Trust

BACKGROUND: Array-comparative genomic hybridization (CGH), although providing much higher resolution compared with conventional CGH, has not yet become a widely applied method for the analysis of genomic gains and losses. METHODS: In January 2002, the Wellcome Trust sponsored a workshop where many of the laboratories developing this technology met to compare different methodologies for array-CGH. Fourteen groups participated, comprising 11 from Europe and 3 from the United States. To facilitate objective analysis, each laboratory constructed arrays using the same anonymous clones and performed a series of test hybridizations using identical genomic DNAs. RESULTS: A figure of merit (FM) was developed to summarize entire collections of data from each laboratory in a single measurement. The FMs consistently showed that a few groups produced quantitative array hybridization data of high quality, whereas a majority achieved a lower standard. CONCLUSIONS: The conclusions of the workshop were that polymerase chain reaction-based methods for the amplification of large insert clones for arraying were effective for array-CGH. It was also concluded that hybridizations performed under coverslips or in automated hybridization apparatus were less effective than hybridizations performed in simple wells with gentle rocking. A common experience by the participants was the batch-to-batch variability of commercial Cot1 preparations in their ability to suppress hybridization to repeat sequences. (Supplementary material for this article can be found in the online issue, which is available at http://www.interscience.wiley.com/jpages/0196-4763/suppmat/49_2/v49.43.html or at http://www.sanger.ac.uk/HGP/Cytogenetics/Publications/Cytometry Sept 2002/Supplemental.pdf.)     

Evaluation of bone-derived and marrow-derived vascular endothelial cells by microarray analysis

This study focused on the differential expression levels of proteins that may exist between bone-derived and marrow-derived vascular endothelial cells (BVEC and MVEC). The vascular cells were isolated from trabecular bone regions and central marrow cavity regions of mouse long bones. Cells were cultured for 1 week to expand the population then separated from non-vascular cells using biotinylated isolectin B4, streptavidin-coated metallic microbeads, and a magnetic column. After an additional week of culture time, RNA was isolated from both cell types and compared using microarray analysis. RT-PCR was used to confirm and relatively quantitate the RNA messages. The bone-derived cells expressed more aldehyde dehydrogenase 3A1 (ALDH3A1), Secreted Modular Calcium-2 (SMOC-2), CCAAT enhancer binding protein (C/EBP-beta), matrix metalloproteinase 13 (MMP-13), and annexin 8 (ANX8) than the marrow-derived cells. Spalpha and matrix GLA-protein (MGP) were produced in greater abundance by the marrow-derived cells. This study reveals that there are profound and unique differences between the vasculature of the metaphysis as compared to that of the central marrow cavity. The unique array of proteins expressed by the bone-derived endothelial cells may support growth of tumors from cancer cells that frequently metastasize and lodge in the trabecular bone regions.