Assessing sources of variability in microarray gene expression data

Experiments using microarrays abound in genomic research, yet one factor remains in question. Without replication, how much stock can we put into the findings of microarray experiments? In addition, there is a growing desire to integrate microarray data with other molecular databases. To accomplish this in a scientifically acceptable manner, we must be able to measure the validity and quality of microarray data. Otherwise, it would be the weakest link in any integration process. Validating and evaluating the quality of data requires the ability to determine the reproducibility of results. Data obtained from a microarray experiment designed as a feasibility test provided a unique opportunity to partition and quantify several sources of variation that are likely to be present in most microarray experiments. We use this opportunity to discuss the origins of variability observed in microarray experiments and provide some suggestions for how to minimize or avoid them when designing an experiment.     

Workflow management systems for gene sequence analysis and evolutionary studies – A Review

Post ‘omic’ era has resulted in the development of many primary, secondary and derived databases. Many analytical and visualization bioinformatics tools have been developed to manage and analyze the data available through large sequencing projects. Availability of heterogeneous databases and tools make it difficult for researchers to access information from varied sources and run different bioinformatics tools to get desired analysis done. Building integrated bioinformatics platforms is one of the most challenging tasks that bioinformatics community is facing. Integration of various databases, tools and algorithm is a challenging problem to deal with. This article describes the bioinformatics analysis workflow management systems that are developed in the area of gene sequence analysis and phylogeny. This article will be useful for biotechnologists, molecular biologists, computer scientists and statisticians engaged in computational biology and bioinformatics research.     

FlyGEM, a full transcriptome array platform for the Drosophila community

We have constructed a DNA microarray to monitor expression of predicted genes in Drosophila. By using homotypic hybridizations, we show that the array performs reproducibly, that dye effects are minimal, and that array results agree with systematic northern blotting. The array gene list has been extensively annotated and linked-out to other databases. Incyte and the NIH have made the platform available to the community via academic microarray facilities selected by an NIH committee.     

Sharing gene expression data: an array of options

Sharing of microarray data has many advantages for the scientific and biomedical community, and should be advocated by neuroscience journals. The goals of sharing are manifold, and include improving analysis and confidence in results, and facilitating global comparisons between experiments, while at the same time, not penalizing those who share. The sharing of microarray data poses unique challenges relative to more generic data such as DNA sequences. These challenges are surmountable, and various sharing formats are possible. Centralized non-commercial databases are being developed to facilitate this process.     

EzArray: A web-based highly automated Affymetrix expression array data management and analysis system

Background  

Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. Most microarray programs require users to have sophisticated knowledge of mathematics, statistics and computer skills for usage. With accumulating microarray data deposited in public databases, easy-to-use programs to re-analyze previously published microarray data are in high demand.     

Development of a Common Oligonucleotide Reference Standard for Microarray Data Normalization and Comparison across Different Microbial Communities

High-density functional gene arrays have become a powerful tool for environmental microbial detection and characterization. However, microarray data normalization and comparison for this type of microarray remain a challenge in environmental microbiology studies because some commonly used normalization methods (e.g., genomic DNA) for the study of pure cultures are not applicable. In this study, we developed a common oligonucleotide reference standard (CORS) method to address this problem. A unique 50-mer reference oligonucleotide probe was selected to co-spot with gene probes for each array feature. The complementary sequence was synthesized and labeled for use as the reference target, which was then spiked and cohybridized with each sample. The signal intensity of this reference target was used for microarray data normalization and comparison. The optimal amount or concentration were determined to be ca. 0.5 to 2.5% of a gene probe for the reference probe and ca. 0.25 to 1.25 fmol/μl for the reference target based on our evaluation with a pilot array. The CORS method was then compared to dye swap and genomic DNA normalization methods using the Desulfovibrio vulgaris whole-genome microarray, and significant linear correlations were observed. This method was then applied to a functional gene array to analyze soil microbial communities, and the results demonstrated that the variation of signal intensities among replicates based on the CORS method was significantly lower than the total intensity normalization method. The developed CORS provides a useful approach for microarray data normalization and comparison for studies of complex microbial communities.Microarray-based technology has become a robust genomic tool to detect, track, and profile hundreds to thousands of different microbial populations simultaneously in complex environments such as soils and sediments. For example, GeoChip, a comprehensive functional gene array, has been developed for investigating biogeochemical, ecological, and environmental processes (12, 18, 23, 27, 29, 32). Although a massive amount of microarray data can be generated rapidly, one of the bottlenecks in using microarrays for environmental microbial community studies is the lack of an appropriate standard for data comparison and normalization (6). Currently, it is difficult to compare microarray data across different sites, experiments, laboratories, and/or time periods (10). This limits the power of the technology to address ecological and environmental questions.In pure culture-based functional genomics studies, genomic DNAs (gDNAs) have been used as a common reference for hybridizations in which the same amount of gDNAs are used to cohybridize with each target cDNA sample and then to normalize different target cDNAs based on the gDNA standard (4, 5, 8, 9, 19, 21, 23). Several normalization methods such as scale normalization, quantile normalization, and Lowess normalization have been used for gene expression studies (2). Using the gDNA standard method can minimize or eliminate differences in target cDNA quantity, spot morphology, uneven hybridization, labeling, and sequence-specific hybridization behaviors (5), and this allows the comparison of microarray data across different sites, laboratories, experiments, and/or times. The main rationale for gDNA as a common reference is that it provides complete coverage for all genes represented on the array because the DNA composition from a particular organism should be identical across different treatment samples even though RNA expression is different (8). However, this approach is not applicable to microbial community studies because not all communities have identical DNA compositions. Pooling of equal amounts of gDNA or RNA from every target sample to make a common sample could be used as an alternative reference for cohybridization (1, 22). However, the disadvantage of the sample pooling approach is that samples do not provide large amounts of DNA or RNA in a reliable and reproducible way. For example, groundwater samples usually have a very low biomass and thus would not provide enough DNA for pooling. In addition, the sample pool itself is uncharacterized, and gene abundance may be diluted out so that insufficient DNA is present to result in a positive signal some array features, especially for those genes in low abundance. Moreover, a new sample pool would be required for every new experiment, making comparison across experiments difficult. Thus, other approaches need to be developed for microbial community studies.Dudley et al. (7) used a 25-mer oligonucleotide that matched a small portion of the parental EST clone vector contained in every PCR product printed on the array for normalization of pure culture RNA expression. Although the oligonucleotide generated a stable hybridization signal on every array feature, this method requires a universal sequence tag as a “capture” sequence, limiting its general use in microbial community studies. Thus, in the present study, we developed a common oligonucleotide reference standard (CORS) approach by co-spotting a common oligonucleotide with each array feature to improve the accuracy and comparability of microarray data for microbial community studies. This method was evaluated by using a pilot array, a whole-genome array, and a functional gene array, and all results demonstrate that the developed CORS is a reliable and reproducible method for microarray data normalization and comparison for microbial community studies.     

Molecular Genetics Information System (MOLGENIS): alternatives in developing local experimental genomics databases

MOTIVATION: Genomic research laboratories need adequate infrastructure to support management of their data production and research workflow. But what makes infrastructure adequate? A lack of appropriate criteria makes any decision on buying or developing a system difficult. Here, we report on the decision process for the case of a molecular genetics group establishing a microarray laboratory. RESULTS: Five typical requirements for experimental genomics database systems were identified: (i) evolution ability to keep up with the fast developing genomics field; (ii) a suitable data model to deal with local diversity; (iii) suitable storage of data files in the system; (iv) easy exchange with other software; and (v) low maintenance costs. The computer scientists and the researchers of the local microarray laboratory considered alternative solutions for these five requirements and chose the following options: (i) use of automatic code generation; (ii) a customized data model based on standards; (iii) storage of datasets as black boxes instead of decomposing them in database tables; (iv) loosely linking to other programs for improved flexibility; and (v) a low-maintenance web-based user interface. Our team evaluated existing microarray databases and then decided to build a new system, Molecular Genetics Information System (MOLGENIS), implemented using code generation in a period of three months. This case can provide valuable insights and lessons to both software developers and a user community embarking on large-scale genomic projects. AVAILABILITY: http://www.molgenis.nl     

Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability

Rapidly growing public gene expression databases contain a wealth of data for building an unprecedentedly detailed picture of human biology and disease. This data comes from many diverse measurement platforms that make integrating it all difficult. Although RNA-sequencing (RNA-seq) is attracting the most attention, at present, the rate of new microarray studies submitted to public databases far exceeds the rate of new RNA-seq studies. There is clearly a need for methods that make it easier to combine data from different technologies. In this paper, we propose a new method for processing RNA-seq data that yields gene expression estimates that are much more similar to corresponding estimates from microarray data, hence greatly improving cross-platform comparability. The method we call PREBS is based on estimating the expression from RNA-seq reads overlapping the microarray probe regions, and processing these estimates with standard microarray summarisation algorithms. Using paired microarray and RNA-seq samples from TCGA LAML data set we show that PREBS expression estimates derived from RNA-seq are more similar to microarray-based expression estimates than those from other RNA-seq processing methods. In an experiment to retrieve paired microarray samples from a database using an RNA-seq query sample, gene signatures defined based on PREBS expression estimates were found to be much more accurate than those from other methods. PREBS also allows new ways of using RNA-seq data, such as expression estimation for microarray probe sets. An implementation of the proposed method is available in the Bioconductor package “prebs.”     

Re-thinking organisms: The impact of databases on model organism biology

Community databases have become crucial to the collection, ordering and retrieval of data gathered on model organisms, as well as to the ways in which these data are interpreted and used across a range of research contexts. This paper analyses the impact of community databases on research practices in model organism biology by focusing on the history and current use of four community databases: FlyBase, Mouse Genome Informatics, WormBase and The Arabidopsis Information Resource. We discuss the standards used by the curators of these databases for what counts as reliable evidence, acceptable terminology, appropriate experimental set-ups and adequate materials (e.g., specimens). On the one hand, these choices are informed by the collaborative research ethos characterising most model organism communities. On the other hand, the deployment of these standards in databases reinforces this ethos and gives it concrete and precise instantiations by shaping the skills, practices, values and background knowledge required of the database users. We conclude that the increasing reliance on community databases as vehicles to circulate data is having a major impact on how researchers conduct and communicate their research, which affects how they understand the biology of model organisms and its relation to the biology of other species.     

Microarray expression profiling resources for plant genomics

Large volumes of genomic data have been generated for several plant species over the past decade, including structural sequence data and functional annotation at the genome level. Various technologies such as expressed sequence tags (ESTs), massively parallel signature sequencing (MPSS) and microarrays have been used to study gene expression and to provide functional data for many genes simultaneously. This review focuses on recent advances in the application of microarrays in plant genomic research and in gene expression databases available for plants. Large sets of Arabidopsis microarray data are publicly available. Recently developed array platforms are currently being used to generate genome-wide expression profiles for several crop species. Coupled to these platforms are public databases that provide access to these large-scale expression data, which can be used to aid the functional discovery of gene function.     

DNA microarray experiments: biological and technological aspects

DNA microarray technologies, such as cDNA and oligonucleotide microarrays, promise to revolutionize biological research and further our understanding of biological processes. Due to the complex nature and sheer amount of data produced from microarray experiments, biologists have sought the collaboration of experts in the analytical sciences, including statisticians, among others. However, the biological and technical intricacies of microarray experiments are not easily accessible to analytical experts. One aim for this review is to provide a bridge to some of the relevant biological and technical aspects involved in microarray experiments. While there is already a large literature on the broad applications of the technology, basic research on the technology itself and studies to understand process variation remain in their infancy. We emphasize the importance of basic research in DNA array technologies to improve the reliability of future experiments.     

Anvaya: a workflows environment for automated genome analysis

Anvaya is a workflow environment for automated genome analysis that provides an interface for several bioinformatics tools and databases, loosely coupled together in a coordinated system, enabling the execution of a set of analyses tools in series or in parallel. It is a client-server workflow environment that has an advantage over existing software as it enables extensive pre & post processing of biological data in an efficient manner. "Anvaya" offers the user, novel functionalities to carry out exhaustive comparative analysis via "custom tools," which are tools with new functionality not available in standard tools, and "built-in PERL parsers," which automate data-flow between tools that hitherto, required manual intervention. It also provides a set of 11 pre-defined workflows for frequently used pipelines in genome annotation and comparative genomics ranging from EST assembly and annotation to phylogenetic reconstruction and microarray analysis. It provides a platform that serves as a single-stop solution for biologists to carry out hassle-free and comprehensive analysis, without being bothered about the nuances involved in tool installation, command line parameters, format conversions required to connect tools and manage/process multiple data sets at a single instance.