Maintaining integrity

Research on genome stability and integrity now extends far beyond the biochemistry of DNA repair to encompass signal transduction pathways that span numerous aspects of cellular life. Derailed genomic integrity pathways can result in debilitating genetic disorders, premature ageing, predisposition to cancer and degenerative conditions. Current progress in this rapidly expanding field was the subject of an EMBO workshop, Maintenance of Genomic Integrity, that took place in June 2004 in Galway, Ireland. Top     

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.     

Maintaining replication fork integrity in UV-irradiated Escherichia coli cells

In dividing cells, the stalling of replication fork complexes by impediments to DNA unwinding or by template imperfections that block synthesis by the polymerase subunits is a serious threat to genomic integrity and cell viability. What happens to stalled forks depends on the nature of the offending obstacle. In UV-irradiated Escherichia coli cells DNA synthesis is delayed for a considerable period, during which forks undergo extensive processing before replication can resume. Thus, restart depends on factors needed to load the replicative helicase, indicating that the replisome may have dissociated. It also requires the RecFOR proteins, which are known to load RecA recombinase on single-stranded DNA, implying that template strands are exposed. To gain a further understanding of how UV irradiation affects replication and how replication resumes after a block, we used fluorescence microscopy and BrdU or radioisotope labelling to examine chromosome replication and cell cycle progression. Our studies confirm that RecFOR promote efficient reactivation of stalled forks and demonstrate that they are also needed for productive replication initiated at the origin, or triggered elsewhere by damage to the DNA. Although delayed, all modes of replication do recover in the absence of these proteins, but nascent DNA strands are degraded more extensively by RecJ exonuclease. However, these strands are also degraded in the presence of RecFOR when restart is blocked by other means, indicating that RecA loading is not sufficient to stabilise and protect the fork. This is consistent with the idea that RecA actively promotes restart. Thus, in contrast to eukaryotic cells, there may be no factor in bacterial cells acting specifically to stabilise stalled forks. Instead, nascent strands may be protected by the simple expedient of promoting restart. We also report that the efficiency of fork reactivation is not affected in polB mutants.     

CGO: utilizing and integrating gene expression microarray data in clinical research and data management

Clinical GeneOrganizer (CGO) is a novel windows-based archiving, organization and data mining software for the integration of gene expression profiling in clinical medicine. The program implements various user-friendly tools and extracts data for further statistical analysis. This software was written for Affymetrix GeneChip *.txt files, but can also be used for any other microarray-derived data. The MS-SQL server version acts as a data mart and links microarray data with clinical parameters of any other existing database and therefore represents a valuable tool for combining gene expression analysis and clinical disease characteristics.     

Development of public toxicogenomics software for microarray data management and analysis

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarray, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data and the availability of functional information for data interpretation. At the FDA's National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack. ArrayTrack is Minimum Information About a Microarray Experiment (MIAME) supportive for storing both microarray data and experiment parameters associated with a toxicogenomics study. A quality control mechanism is implemented to assure the fidelity of entered expression data. ArrayTrack also provides a rich collection of functional information about genes, proteins and pathways drawn from various public biological databases for facilitating data interpretation. In addition, several data analysis and visualization tools are available with ArrayTrack, and more tools will be available in the next released version. Importantly, gene expression data, functional information and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. ArrayTrack is publicly available online and the prospective user can also request a local installation version by contacting the authors.     

Mining mouse microarray data

Microarrays of mouse genes are now available from several sources, and they have so far given new insights into gene expression in embryonic development, regions of the brain and during apoptosis. Microarray data posted on the internet can be reanalyzed to study a range of questions.     

BASE - 2nd generation software for microarray data management and analysis

Background  

Microarray experiments are increasing in size and samples are collected asynchronously over long time. Available data are re-analysed as more samples are hybridized. Systematic use of collected data requires tracking of biomaterials, array information, raw data, and assembly of annotations. To meet the information tracking and data analysis challenges in microarray experiments we reimplemented and improved BASE version 1.2.     

Pre-processing Agilent microarray data

Background  

Pre-processing methods for two-sample long oligonucleotide arrays, specifically the Agilent technology, have not been extensively studied. The goal of this study is to quantify some of the sources of error that affect measurement of expression using Agilent arrays and to compare Agilent's Feature Extraction software with pre-processing methods that have become the standard for normalization of cDNA arrays. These include log transformation followed by loess normalization with or without background subtraction and often a between array scale normalization procedure. The larger goal is to define best study design and pre-processing practices for Agilent arrays, and we offer some suggestions.     

MAD: a suite of tools for microarray data management and processing

SUMMARY: Microarray data management and processing (MAD) is a set of Windows integrated software for microarray analysis. It consists of a relational database for data storage with many user-interfaces for data manipulation, several text file parsers and Microsoft Excel macros for automation of data processing, and a generator to produce text files that are ready for cluster analysis. AVAILABILITY: Executable is available free of charge on http://pompous.swmed.edu. The source code is also available upon request.     

Propagating uncertainty in microarray data analysis

Microarray technology is associated with many sources of experimentaluncertainty. In this review we discuss a number of approachesfor dealing with this uncertainty in the processing of datafrom microarray experiments. We focus here on the analysis ofhigh-density oligonucleotide arrays, such as the popular AffymetrixGeneChip® array, which contain multiple probes for eachtarget. This set of probes can be used to determine an estimatefor the target concentration and can also be used to determinethe experimental uncertainty associated with this measurement.This measurement uncertainty can then be propagated throughthe downstream analysis using probabilistic methods. We giveexamples showing how these credibility intervals can be usedto help identify differential expression, to combine informationfrom replicated experiments and to improve the performance ofprincipal component analysis.      

Query-driven module discovery in microarray data

MOTIVATION: Existing (bi)clustering methods for microarray data analysis often do not answer the specific questions of interest to a biologist. Such specific questions could be derived from other information sources, including expert prior knowledge. More specifically, given a set of seed genes which are believed to have a common function, we would like to recruit genes with similar expression profiles as the seed genes in a significant subset of experimental conditions. RESULTS: We introduce QDB, a novel Bayesian query-driven biclustering framework in which the prior distributions allow introducing knowledge from a set of seed genes (query) to guide the pattern search. In two well-known yeast compendia, we grow highly functionally enriched biclusters from small sets of seed genes using a resolution sweep approach. In addition, relevant conditions are identified and modularity of the biclusters is demonstrated, including the discovery of overlapping modules. Finally, our method deals with missing values naturally, performs well on artificial data from a recent biclustering benchmark study and has a number of conceptual advantages when compared to existing approaches for focused module search.     

Maintaining the integrity of human immunodeficiency virus sequence databases.

Human immunodeficiency virus type 1 (HIV-1) sequences are accumulating in the literature at a rapid pace. For this ever-expanding resource to be maximally useful, it is critical that researchers strive to maintain a high level of quality assurance, both in experimental design and conduct and in analyses. Here we present detailed analyses of problematic sets of HIV-1 sequences in the database that include sequence anomalies suggestive of mislabeling or sample contamination problems. These data are examined in the context of currently available HIV-1 sequence information to provide an example of how to identify potentially flawed data. Indicators of potential problems with sequences are (i) sequences that are nearly identical that are supposed to be derived from unlinked individuals and that are markedly distinct from other sequences from the putative source or (ii) sequences that are nearly identical to those of laboratory strains. We provide an outline of methods that researchers can use to perform preliminary laboratory and computational analyses that could help identify problematic data and thus help ensure the integrity of sequence databases.     

Maintaining corneal integrity how the "window" stays clear

The anterior surface of the eye is composed of the cornea, conjunctiva, and the zone between the two called the limbus. The cornea must maintain optical clarity to retain good vision. However, the ocular surface is vulnerable to trauma, microbial infection, and exposure to environmental toxins. This places the cornea, especially, at risk for disruptions of the epithelial barrier and subsequent immunopathological events. Cell-cell and cell-matrix attachment junctions incorporating adhesion molecules ensure that the epithelial barrier remains intact. Protein components of the basement membrane, including laminins, are vital to the adhesion of corneal epithelial cells to the underlying stroma and function to enhance the strength of the bond between epithelium and connective tissue. Epithelial cells also play an early and crucial role in the initiation of ocular surface responses should a potentially antigenic molecule enter into deeper corneal tissues. For example, epithelial cells may produce and release cytokines such as interleukin-1 (IL-1). The delicate balance between the matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) are central to mechanisms regulating dissolution of the extracellular matrix that may be a consequence of infection or wound healing. Adhesion molecules, cytokines and chemokines, and MMPs and TIMPs thus participate in the corneal response to immunologic challenge or wounding. They may also be involved in corneal pathologies associated with genetic diseases, diabetes, and vitamin A deficiency. In addition these molecules are components of cellular pathways underlying the clinical complications often observed with contact lens wear and refractive surgeries used to improve visual acuity.     

Analyzing microarray data using CLANS

Analysis of microarray experiments is complicated by the huge amount of data involved. Searching for groups of co-expressed genes is akin to searching for protein families in a database as, in both cases, small subsets of genes with similar features are to be found within vast quantities of data. CLANS was originally developed to find protein families in large sets of amino acid sequences where the amount of data involved made phylogenetic approaches overly cumbersome. We present a number of improvements that greatly extend the previous version of CLANS and show its application to microarray data as well as its ability of incorporating additional information to facilitate interactive analysis. AVAILABILITY: The program is available for download from: http://bioinfoserver.rsbs.anu.edu.au/downloads/clans/     

Normalization of cDNA microarray data

Normalization means to adjust microarray data for effects which arise from variation in the technology rather than from biological differences between the RNA samples or between the printed probes. This paper describes normalization methods based on the fact that dye balance typically varies with spot intensity and with spatial position on the array. Print-tip loess normalization provides a well-tested general purpose normalization method which has given good results on a wide range of arrays. The method may be refined by using quality weights for individual spots. The method is best combined with diagnostic plots of the data which display the spatial and intensity trends. When diagnostic plots show that biases still remain in the data after normalization, further normalization steps such as plate-order normalization or scale-normalization between the arrays may be undertaken. Composite normalization may be used when control spots are available which are known to be not differentially expressed. Variations on loess normalization include global loess normalization and two-dimensional normalization. Detailed commands are given to implement the normalization techniques using freely available software.     

Mayday--a microarray data analysis workbench

Mayday is a workbench for visualization, analysis and storage of microarray data. It features a graphical user interface and supports the development and integration of existing and new analysis methods. Besides the infrastructural core functionality, Mayday offers a variety of plug-ins, such as various interactive viewers, a connection to the R statistical environment, a connection to SQL-based databases and different data mining methods, including WEKA-library based methods for classification and various clustering methods. In addition, so-called meta information objects are provided for annotation of the microarray data allowing integration of data from different sources, which is a feature that, for instance, is employed in the enhanced heatmap visualization. Supplementary information: The software and more detailed information including screenshots and a user guide as well as test data can be found on the Mayday home page http://www.zbit.uni-tuebingen.de/pas/mayday. The core is published under the GPL (GNU Public License) and the associated plug-ins under the LGPL (Lesser GNU Public License).