SpliceCenter: a suite of web-based bioinformatic applications for evaluating the impact of alternative splicing on RT-PCR, RNAi, microarray, and peptide-based studies.

BACKGROUND: Over 60% of protein-coding genes in vertebrates express mRNAs that undergo alternative splicing. The resulting collection of transcript isoforms poses significant challenges for contemporary biological assays. For example, RT-PCR validation of gene expression microarray results may be unsuccessful if the two technologies target different splice variants. Effective use of sequence-based technologies requires knowledge of the specific splice variant(s) that are targeted. In addition, the critical roles of alternative splice forms in biological function and in disease suggest that assay results may be more informative if analyzed in the context of the targeted splice variant. RESULTS: A number of contemporary technologies are used for analyzing transcripts or proteins. To enable investigation of the impact of splice variation on the interpretation of data derived from those technologies, we have developed SpliceCenter. SpliceCenter is a suite of user-friendly, web-based applications that includes programs for analysis of RT-PCR primer/probe sets, effectors of RNAi, microarrays, and protein-targeting technologies. Both interactive and high-throughput implementations of the tools are provided. The interactive versions of SpliceCenter tools provide visualizations of a gene's alternative transcripts and probe target positions, enabling the user to identify which splice variants are or are not targeted. The high-throughput batch versions accept user query files and provide results in tabular form. When, for example, we used SpliceCenter's batch siRNA-Check to process the Cancer Genome Anatomy Project's large-scale shRNA library, we found that only 59% of the 50,766 shRNAs in the library target all known splice variants of the target gene, 32% target some but not all, and 9% do not target any currently annotated transcript. CONCLUSION: SpliceCenter http://discover.nci.nih.gov/splicecenter provides unique, user-friendly applications for assessing the impact of transcript variation on the design and interpretation of RT-PCR, RNAi, gene expression microarrays, antibody-based detection, and mass spectrometry proteomics. The tools are intended for use by bench biologists as well as bioinformaticists.

Microarray for molecular typing of Salmonella enterica serovars.

We describe the development of a spotted array for the delineation of the most common 14 disease-causing Salmonella serovars in the United States. Our array consists of 414 70 mers targeting core genes of Salmonella enterica, subspecies I specific genes, fimbrial genes, pathogenicity islands, Gifsy elements and other variable genes. Using this array we were able to identify a unique gene presence/absence profile for each of the targeted serovar which was used as the serovar differentiating criteria. Based on this profile, we developed a Matlab programme that compares the profile of an unknown sample to all 14 reference serovar profiles and give out the closest serovar match. Since we have included probes targeting most of the virulence genes and variable genes in Salmonella, in addition to using for serovar detection this array could also be used for studying the virulence gene content and also for evaluating the genetic relation between different isolates of Salmonella.

A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies.

Over the last decade, gene expression microarrays have had a profound impact on biomedical research. The diversity of platforms and analytical methods available to researchers have made the comparison of data from multiple platforms challenging. In this study, we describe a framework for comparisons across platforms and laboratories. We have attempted to include nearly all the available commercial and 'in-house' platforms. Using probe sequences matched at the exon level improved consistency of measurements across the different microarray platforms compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values as confirmed by quantitative real-time (QRT)-PCR. Concordance of measurements was higher between laboratories on the same platform than across platforms. We demonstrate that, after stringent preprocessing, commercial arrays were more consistent than in-house arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms.

Quality control for microarray experiments.

OBJECTIVES: In this paper we give an overview of post-hybridization quality control methods for gene expression chips, including methods for the gene/spot level, the hybridization/chip level and the process level. We present quality control methods that can be applied after hybridization and image analysis, i.e. that use data from the chip experiment itself. Wet lab quality control steps, which should be applied before the probe is measured on a chip, are not discussed. This review is aimed towards statisticians and data analysts. METHODS: We give examples of some of the quality control measures available for spotted cDNA and Affymetrix GeneChips, the most common chip types. As quality control measures are technology and design-dependent, we will stress on methods that have the potential to be applied platform-independently. RESULTS: Quality control should identify poor quality chips or hybridizations, as well as faulty measurements for individual genes/spots. Additionally, high throughput laboratories processing several tens or hundreds of microarrays per week have the need for an appropriate process control to be able to identify changes in the production process as early as possible. CONCLUSION: Microarrays have become a standard research tool for biologists and medical researchers. As a consequence, there is a great need for standardized quality control, as false findings due to problem in data quality can lead to a substantial loss of resources.

Comparison of the predictive accuracy of DNA array-based multigene classifiers across cDNA arrays and Affymetrix GeneChips.

We examined how well differentially expressed genes and multigene outcome classifiers retain their class-discriminating values when tested on data generated by different transcriptional profiling platforms. RNA from 33 stage I-III breast cancers was hybridized to both Affymetrix GeneChip and Millennium Pharmaceuticals cDNA arrays. Only 30% of all corresponding gene expression measurements on the two platforms had Pearson correlation coefficient r >or= 0.7 when UniGene was used to match probes. There was substantial variation in correlation between different Affymetrix probe sets matched to the same cDNA probe. When cDNA and Affymetrix probes were matched by basic local alignment tool (BLAST) sequence identity, the correlation increased substantially. We identified 182 genes in the Affymetrix and 45 in the cDNA data (including 17 common genes) that accurately separated 91% of cases in supervised hierarchical clustering in each data set. Cross-platform testing of these informative genes resulted in lower clustering accuracy of 45 and 79%, respectively. Several sets of accurate five-gene classifiers were developed on each platform using linear discriminant analysis. The best 100 classifiers showed average misclassification error rate of 2% on the original data that rose to 19.5% when tested on data from the other platform. Random five-gene classifiers showed misclassification error rate of 33%. We conclude that multigene predictors optimized for one platform lose accuracy when applied to data from another platform due to missing genes and sequence differences in probes that result in differing measurements for the same gene.

Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations.

BACKGROUND: Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations. RESULTS: In this study, expression measurements for 10,763 genes uniquely represented on Affymetrix U133A/B GeneChips and Amersham CodeLink UniSet Human 20 K microarrays were compared. For each microarray platform, five technical replicates, derived from the same total RNA samples, were labeled, hybridized, and quantified according to each manufacturers' standard protocols. The correlation coefficient (r) of differential expression ratios for the entire set of 10,763 overlapping genes was 0.62 between platforms. However, the correlation improved significantly (r = 0.79) when genes within noise were excluded. In addition to levels of inter-platform correlation, we evaluated precision, statistical-significance profiles, power, and noise levels for each microarray platform. Accuracy of differential expression was measured against real-time PCR for 25 genes and both platforms correlated well with r values of 0.92 and 0.79 for CodeLink and GeneChip, respectively. CONCLUSIONS: As a result of this study, we recommend using only genes called 'present' in cross-platform correlations. However, as in this study, a large number of genes may be lost from the correlation due to differing levels of noise between platforms. This is an important consideration given the apparent difference in sensitivity of the two platforms. Data from microarray analysis need to be interpreted cautiously and therefore, we provide guidelines for making cross-platform correlations. In all, this study represents the most comprehensive and specifically designed comparison of short-oligonucleotide microarray platforms to date using the largest set of overlapping genes.

Effect of secondary structure on single nucleotide polymorphism detection with a porous microarray matrix; implications for probe selection.

Oligonucleotide arrays capable of detecting single nucleotide polymorphisms (SNPs) from amplified nucleic acid have many applications. The expected SNP is usually placed approximately in the center of the probe to ensure the maximum shift in Tm between complementary and SNP sequences. Unfortunately, different short probes (< 30 bases) selected using widely accepted criteria do not perform consistently in this type of assay. Here we present a systematic study on the effect of secondary structure on the ability of oligonucleotide probes to detect an SNP, using real-time array monitoring of a porous microarray substrate that incorporates a novel intra-array mixing system. These results demonstrate that, although positioning of an SNP in the middle of the probe is highly destabilizing, the effect of stable secondary structure on the signal obtained is so dramatic that such probes may be very insensitive. Therefore, if the SNP flanking sequence contains significant secondary structure, then more sensitive probes with good specificity may be obtained by positioning the mutation towards one end of the probe.

The importance of thermodynamic equilibrium for high throughput gene expression arrays.

We present an analysis of physical chemical constraints on the accuracy of DNA micro-arrays under equilibrium and nonequilibrium conditions. At the beginning of the article we describe an algorithm for choosing a probe set with high specificity for targeted genes under equilibrium conditions. The algorithm as well as existing methods is used to select probes from the full Saccharomyces cerevisiae genome, and these probe sets, along with a randomly selected set, are used to simulate array experiments and identify sources of error. Inasmuch as specificity and sensitivity are maximum at thermodynamic equilibrium, we are particularly interested in the factors that affect the approach to equilibrium. These are analyzed later in the article, where we develop and apply a rapidly executable method to simulate the kinetics of hybridization on a solid phase support. Although the difference between solution phase and solid phase hybridization is of little consequence for specificity and sensitivity when equilibrium is achieved, the kinetics of hybridization has a pronounced effect on both. We first use the model to estimate the effects of diffusion, crosshybridization, relaxation time, and target concentration on the hybridization kinetics, and then investigate the effects of the most important kinetic parameters on specificity. We find even when using probe sets that have high specificity at equilibrium that substantial crosshybridization is present under nonequilibrium conditions. Although those complexes that differ from perfect complementarity by more than a single base do not contribute to sources of error at equilibrium, they slow the approach to equilibrium dramatically and confound interpretation of the data when they dissociate on a time scale comparable to the time of the experiment. For the best probe set, our simulation shows that steady-state behavior is obtained in a relaxation time of approximately 12-15 h for experimental target concentrations approximately (10(-13) - 10(-14))M, but the time is greater for lower target concentrations in the range (10(-15)-10(-16))M. The result points to an asymmetry in the accuracy with which up- and downregulated genes are identified.

Selecting signature oligonucleotides to identify organisms using DNA arrays.

MOTIVATION: DNA arrays are a very useful tool to quickly identify biological agents present in some given sample, e.g. to identify viruses causing disease, for quality control in the food industry, or to determine bacteria contaminating drinking water. The selection of specific oligos to attach to the array surface is a relevant problem in the experiment design process. Given a set S of genomic sequences (the target sequences), the task is to find at least one oligonucleotide, called probe, for each sequence in S. This probe will be attached to the array surface, and must be chosen in a way that it will not hybridize to any other sequence but the intended target. Furthermore, all probes on the array must hybridize to their intended targets under the same reaction conditions, most importantly at the temperature T at which the experiment is conducted. RESULTS: We present an efficient algorithm for the probe design problem. Melting temperatures are calculated for all possible probe-target interactions using an extended nearest-neighbor model, allowing for both non-Watson-Crick base-pairing and unpaired bases within a duplex. To compute temperatures efficiently, a combination of suffix trees and dynamic programming based alignment algorithms is introduced. Additional filtering steps during preprocessing increase the speed of the computation. The practicability of the algorithms is demonstrated by two case studies: The identification of HIV-1 subtypes, and of 28S rDNA sequences from >or=400 organisms.

DNA hybridization to mismatched templates: a chip study.

High-density oligonucleotide arrays are among the most rapidly expanding technologies in biology today. In the GeneChip system, the reconstruction of the sample mRNA concentrations depends upon the differential signal generated by hybridizing the RNA to two nearly identical templates: a perfect match probe (PM) containing the exact biological sequence; and a single mismatch (MM) differing from the PM by a single base substitution. It has been observed that a large fraction of MMs repetitively bind targets better than the PMs, against the obvious expectation of sequence specificity. We examine this problem via statistical analysis of a large set of microarray experiments. We classify the probes according to their signal to noise (S/N) ratio, defined as the eccentricity of a (PM,MM) pair's "trajectory" across many experiments. Of those probes having large S/N (>3) only a fraction behave consistently with the commonly assumed hybridization model. Our results imply that the physics of DNA hybridization in microarrays is more complex than expected, and suggest estimators for the target RNA concentration.

A new class of homogeneous nucleic acid probes based on specific displacement hybridization.

We have developed a new class of probes for homogeneous nucleic acid detection based on the proposed displacement hybridization. Our probes consist of two complementary oligodeoxyribonucleotides of different length labeled with a fluorophore and a quencher in close proximity in the duplex. The probes on their own are quenched, but they become fluorescent upon displacement hybridization with the target. These probes display complete discrimination between a perfectly matched target and single nucleotide mismatch targets. A comparison of double-stranded probes with corresponding linear probes confirms that the presence of the complementary strand significantly enhances their specificity. Using four such probes labeled with different color fluorophores, each designed to recognize a different target, we have demonstrated that multiple targets can be distinguished in the same solution, even if they differ from one another by as little as a single nucleotide. Double-stranded probes were used in real-time nucleic acid amplifications as either probes or as primers. In addition to its extreme specificity and flexibility, the new class of probes is simple to design and synthesize, has low cost and high sensitivity and is accessible to a wide range of labels. This class of probes should find applications in a variety of areas wherever high specificity of nucleic acid hybridization is relevant.

Locked on target: strategies for future gene diagnostics.

A rapidly expanding number of genetic mechanisms contributing to disease can be accessed via molecular analysis. In order to take full advantage of this potential for genetic diagnostics, and to monitor treatment by gene therapy and other emerging methods, a new generation of diagnostic techniques will be required. Of central importance in future analyses is the possibility to perform very large numbers of simultaneous genetic analyses in analytical devices of small linear dimensions. We have developed a new molecular probe design that is amenable to highly multiplex, specific analyses of total genomic DNA or of RNA molecules expressed in tissue samples. Here, a brief history of gene analytical procedures is presented, followed by a discussion of the properties, means of synthesis and applications of this new class of gene analytic reagents, padlock probes.

Analysis of DNA probes for the prenatal diagnosis of cystic fibrosis.

Cystic fibrosis is a common autosomal recessive disease in white persons. Prenatal diagnosis by DNA analysis became possible in families with a child who is affected by cystic fibrosis when the probes pJ3.11, metH and metD, which are linked closely to the cystic fibrosis gene (CF) were described. The recent description of the XV-2c and KM.19 probes has improved the prenatal diagnosis of cystic fibrosis greatly. The KM.19 probe alone was informative in eight of 12 families that were studied while XV-2c was informative in eight of 12 families that were studied while XV-2c was informative in only two of the 12 families. In contrast, the use of the pJ3.11, metH and metD probes in combination allowed full diagnosis in six of the 12 families. The combined use of the CF-linked probes produced informative data for all 12 families. Therefore, in most families with at least one affected living child, the first-trimester diagnosis of cystic fibrosis is possible with fetal DNA that has been prepared from chorionic villous samples. Strong linkage disequilibrium was found with both the KM.19-PstI polymorphism and the XV-2c-TaqI polymorphism and the CF gene.

A family of small inducible proteins secreted by leukocytes are members of a new superfamily that includes leukocyte and fibroblast-derived inflammatory agents, growth factors, and indicators of various activation processes.

We have isolated and characterized four cDNA clones that encode mRNA expressed more abundantly in Con A-activated mouse helper T cells than by resting T cells. One mRNA encoded a approximately 14-kDa protein with a hydrophobic N-terminal sequence and was abundantly expressed by the Th 2 subset of Th cells, but was not expressed by Th 1 cells. The remaining three mRNA encoded related approximately 8-kDa secreted proteins that are part of a family of small, secreted, and inducible mouse and human proteins. This family of proteins is itself distantly related to another family of growth and inflammatory factors that are associated with various lymphoid and fibroblast activation phenomena. One of the small, inducible, secreted proteins has a predicted mature N terminus identical to that of the previously described macrophage inflammatory protein.