Multi-focus cluster labeling.

Document collections resulting from searches in the biomedical literature, for instance, in PubMed, are often so large that some organization of the returned information is necessary. Clustering is an efficient tool for organizing search results. To help the user to decide how to continue the search for relevant documents, the content of each cluster can be characterized by a set of representative keywords or cluster labels. As different users may have different interests, it can be desirable with solutions that make it possible to produce labels from a selection of different topical categories. We therefore introduce the concept of multi-focus cluster labeling to give users the possibility to get an overview of the contents through labels from multiple viewpoints. The concept for multi-focus cluster labeling has been established and has been demonstrated on three different document collections. We illustrate that multi-focus visualizations can give an overview of clusters along axes that general labels are not able to convey. The approach is generic and should be applicable to any biomedical (or other) domain with any selection of foci where appropriate focus vocabularies can be established. A user evaluation also indicates that such a multi-focus concept is useful.

CellLineMiner: a knowledge portal for human cell lines.

UNLABELLED: Experimental models of human tissues and disease phenotypes frequently rely upon immortalized cell lines, which are easily accessible and simple to use due to their infinite capability of cell division. For decades, cell lines have been used to investigate cellular mechanisms of disease and the efficacy of drugs, most prominently for human cancers. However, the large body of knowledge with respect to human cell lines exists primarily in an unstructured fashion, that is, as free text in the scientific literature. Here we present CellLineMiner, a novel text mining-based web database that provides a comprehensive view of human cell line knowledge. The application offers a simple search in all indexed cell lines, accompanied by a rapid display of all identified literature associations. The CellLineMiner is intended to serve as a knowledge resource companion to the cellular model systems used in biomedical research. AVAILABILITY: CellLineMiner is accessible at http://dev.pubgene.com/cellmine.

Performance comparison of multiple microarray platforms for gene expression profiling.

With genome-wide gene expression microarrays being increasingly applied in various areas of biomedical research, the diversity of platforms and analytical methods has made comparison of data from multiple platforms very challenging. In this chapter, we describe a generalized framework for systematic comparisons across gene expression profiling platforms, which could accommodate both the available commercial arrays and "in-house" platforms, with both one-dye and two-dye platforms. It includes experimental design, data preprocessing protocols, cross-platform gene matching approaches, measures of data consistency comparisons, and considerations in biological validation. In the design of this framework, we considered the variety of platforms available, the need for uniform quality control procedures, real-world practical limitations, statistical validity, and the need for flexibility and extensibility of the framework. Using this framework, we studied ten diverse microarray platforms, and we conclude that using probe sequences matched at the exon level is important to improve cross-platform data consistency compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values, as confirmed by QRT-PCR. 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.

Comparison of hybridization-based and sequencing-based gene expression technologies on biological replicates.

BACKGROUND: High-throughput systems for gene expression profiling have been developed and have matured rapidly through the past decade. Broadly, these can be divided into two categories: hybridization-based and sequencing-based approaches. With data from different technologies being accumulated, concerns and challenges are raised about the level of agreement across technologies. As part of an ongoing large-scale cross-platform data comparison framework, we report here a comparison based on identical samples between one-dye DNA microarray platforms and MPSS (Massively Parallel Signature Sequencing). RESULTS: The DNA microarray platforms generally provided highly correlated data, while moderate correlations between microarrays and MPSS were obtained. Disagreements between the two types of technologies can be attributed to limitations inherent to both technologies. The variation found between pooled biological replicates underlines the importance of exercising caution in identification of differential expression, especially for the purposes of biomarker discovery. CONCLUSION: Based on different principles, hybridization-based and sequencing-based technologies should be considered complementary to each other, rather than competitive alternatives for measuring gene expression, and currently, both are important tools for transcriptome profiling.

The human genomic melting map.

In a living cell, the antiparallel double-stranded helix of DNA is a dynamically changing structure. The structure relates to interactions between and within the DNA strands, and the array of other macromolecules that constitutes functional chromatin. It is only through its changing conformations that DNA can organize and structure a large number of cellular functions. In particular, DNA must locally uncoil, or melt, and become single-stranded for DNA replication, repair, recombination, and transcription to occur. It has previously been shown that this melting occurs cooperatively, whereby several base pairs act in concert to generate melting bubbles, and in this way constitute a domain that behaves as a unit with respect to local DNA single-strandedness. We have applied a melting map calculation to the complete human genome, which provides information about the propensities of forming local bubbles determined from the whole sequence, and present a first report on its basic features, the extent of cooperativity, and correlations to various physical and biological features of the human genome. Globally, the melting map covaries very strongly with GC content. Most importantly, however, cooperativity of DNA denaturation causes this correlation to be weaker at resolutions fewer than 500 bps. This is also the resolution level at which most structural and biological processes occur, signifying the importance of the informational content inherent in the genomic melting map. The human DNA melting map may be further explored at http://meltmap.uio.no.

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.

Constitutive expression of the AP-1 transcription factors c-jun, junD, junB, and c-fos and the marginal zone B-cell transcription factor Notch2 in splenic marginal zone lymphoma.

Splenic marginal zone lymphoma (SMZL) is a lymphoma type of putative marginal zone B-cell origin. No specific genetic alterations have yet been demonstrated in SMZL. Clinically, SMZL is a low-grade B-cell non-Hodgkin lymphoma. However, the presence of p53 mutation, 7q22-7q32 deletion or the absence of somatic hypermutations of immunoglobulin genes has been correlated with a worse prognosis. In this study, we analyzed genome-wide gene expression of 24 cases of SMZL using the microarray technique. The AP-1 transcription factors c-jun, junD, junB, and c-fos as well as Notch2 were found to be specifically up-regulated. These data were confirmed by real-time PCR and immunohistochemical staining of tissue sections. The absence of concordant high expression of the MAP kinases, the signaling cascade leading to AP-1 up-regulation, suggests autoregulation of the AP-1 transcription factors and an important role in SMZL oncogenesis. High expression of Notch2, a transcription factor that induces marginal zone B-cell differentiation, is highly suggestive for a marginal zone B-cell origin of SMZL. In addition, SMZL with the 7q deletion showed high expression of TGF-beta1 and low expression of the DNA helicase XPB, a crucial part of the nucleotide excision repair complex, possibly explaining the more aggressive clinical course of those cases.

A primer on gene expression and microarrays for machine learning researchers.

Data originating from biomedical experiments has provided machine learning researchers with an important source of motivation for developing and evaluating new algorithms. A new wave of algorithmic development has been initiated with the publication of gene expression data derived from microarrays. Microarray data analysis is particularly challenging given the large number of measurements (typically in the order of thousands) that are reported for relatively few samples (typically in the order of dozens). Many data sets are now available on the web. It is important that machine learning researchers understand how data are obtained and which assumptions are necessary in the analysis. Microarray data have the potential to cause significant impact in machine learning research, not just as a rich and realistic source of cases for testing new algorithms, as has been the UCI machine learning repository in the past decades, but also as a main motivation for their development. In this article, we briefly review the biology underlying microarrays, the process of obtaining gene expression measurements, and the rationale behind the common types of analyses involved in a microarray experiment. We outline the main challenges and reiterate critical considerations regarding the construction of supervised learning models that use this type of data. The goal of this article is to familiarize machine learning researchers with data originated from gene expression microarrays.

FigSearch: a figure legend indexing and classification system.

UNLABELLED: FigSearch is a prototype text-mining and classification system for figures from any corpus of full-text biological papers. The system allows users to search for figures that contain genes of interest and illustrate protein interactions. The retrieved figures are ranked by a score representing the likelihood to be of a certain type, in this case, schematic illustrations of protein interactions and signaling events. The system contains a Web interface for search, a module for classification of figures based on vector representations of figure legends and a module for indexing gene names. In a preliminary validation, the FigSearch system showed satisfactory performance according to domain experts in providing the most relevant graphical representations. This strategy may be easily extended to other figure types. Moreover, as more full-text data become available, such a system will find increased usefulness in identifying and presenting compressed biological knowledge. AVAILABILITY: A searchable Web interface, FigSearch, is accessible via http://pubgeneserver.uio.no/figsearch/ for all figures from the available corpus.

Deciphering gene expression profiles generated from DNA microarrays and their applications in oral medicine.

Genome-wide monitoring of gene expression profiles using DNA microarrays provides a unique approach to exploring the biological processes underlying oral diseases and disorders by providing a comprehensive survey of a cell's or tissue's transcriptional mapping. This revolutionary technology allows for the simultaneous assessment of the transcription levels of tens of thousands of genes, and of their relative expression between normal and diseased cells. As microarray data analysis evolves, there is a widespread hope that microarrays will significantly impact our ability to explore the genetic changes associated with disease etiology and development, ultimately leading to the discovery of new biomarkers for disease diagnosis and prognosis prediction as well as new therapeutic tools. The goal of this manuscript is to review 2 of the most commonly used microarray technologies, provide an overview of data analyses involved in a typical microarray experiment, and comment upon the application of microarrays to oral medicine.

Speed-up of DNA melting algorithm with complete nearest neighbor properties.

We describe an optimized algorithm, which is faster and more accurate compared to previously described algorithms, for computing the statistical mechanics of denaturation of nucleic acid sequences according to the classical Poland-Scheraga type of model. Nearest neighbor thermodynamics has been included in a complete and general way, by rigorously treating nearest neighbor interactions, helix end interactions, and isolated base-pairs. This avoids the simplifications of previous approaches and achieves full generality and controllability with respect to thermodynamic modeling. The algorithm computes subchain partition functions by recursion, from which various quantitative aspects of the melting process are easily derived, for example the base-pairing probability profiles. The algorithm represents an optimization with respect to algorithmic complexity of the partition function algorithm of Yeramian et al. (Biopolymers 1990, 30, 481-497): we reduce the computation time for a base-pairing probability profile from O(N2) to O(N), where N is the sequence length. This speed-up comes in addition to the speed-up due to a multiexponential approximation of the loop entropy factor as introduced by Fixman and Freire22 and applied by Yeramian et al. The speed-up, however, is independent of the multiexponential approximation and reduces time from O(N3) to O(N2) in the exact case. A method for representing very large numbers is described, which avoids numerical overflow in the partition functions for genomic length sequences. In addition to calculating the standard base-pairing probability profiles, we propose to use the algorithm to calculate various other probabilities (loops, helices, tails) for a more direct view of the melting regions and their positions and sizes. This can provide a better understanding of the physics of denaturation and the biology of genomes.

Microarrays and clinical dentistry.

BACKGROUND: The Human Genome Project, or HGP, has inspired a great deal of exciting biology recently by enabling the development of new technologies that will be essential for understanding the different types of abnormalities in diseases related to the oral cavity. LITERATURE REVIEWED: The authors review current literature pertaining to the advanced microarray technologies arising from the HGP and how they can contribute to dentistry. This technology has become a standard tool for monitoring activities of genes at both academic and pharmaceutical research institutions. RESULTS: With the availability of the DNA sequences for the entire human genome, attention now is focused on understanding various diseases at the genome level. Deciphering the molecular behavior of genetically encoded proteins is crucial to obtaining a more comprehensive picture of disease processes. Important progress has been made using microarrays, which have been shown to be effective in identifying gene expression patterns and variations that correlate with cellular development, physiology and function. Arrays can be used to classify tissue samples accurately based on molecular profiles and to select candidate genes related to a number of cancers, including oral cancer. This type of oral genetic approach will aid in the understanding of disease progression, thus improving diagnosis and treatment for patients. CLINICAL IMPLICATIONS: Microarrays hold much promise for the analysis of diseases in the oral cavity. As the technology evolves, dentists may see these tools as screening tests for better managing patients' dental care.

Comparing imperfect measurements with the Bland-Altman technique: application in gene expression analysis.

Several problems in medicine and biology involve the comparison of two measurements made on the same set of cases. The problem differs from a calibration problem because no gold standard can be identified. Testing the null hypothesis of no relationship using measures of association is not optimal since the measurements are made on the same cases, and therefore correlation coefficients will tend to be significant. The descriptive Bland-Altman method can be used in exploratory analysis of this problem, allowing the visualization of gross systematic differences between the two sets of measurements. We utilize the method on three sets of matched observations and demonstrate its usefulness in detecting systematic variations between two measurement technologies to assess gene expression.

Gene expression levels in different stages of progression in oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is one of the most common cancer types worldwide. The prognosis for patients with this disease is generally poor and little is known about its progression. Gene expression studies may provide important insights to the molecular mechanisms of this disease. We analyzed gene expression data from a small panel of patients diagnosed with OSCC. Even with only 13 patient samples we were able to find genes with significant differences in expression levels between normal, dysplasia, and cancer samples. The largest differences in expression were generally found between normal and cancer samples, but significant differences were also found for several genes between dysplasia and the other two sample types. We also represent the significance levels of differentially expressed genes on the chromosome domain. The genes and genetic features we examine are potentially important factors on the molecular level in the progression of OSCC.

Gene expression profiling by DNA microarrays and its application to dental research.

UNLABELLED: DNA microarray technology has been used for genome-wide gene expression studies that incorporate molecular genetics and computer science skills on massive levels. The technology permits the simultaneous analysis of tens of thousands of genes for the purposes of gene discovery, disease diagnosis. improved drug development, and therapeutics tailored to specific disease processes. OBJECTIVE: In this review, the two most common microarray technologies and their potential application to dental research will be discussed. The authors review current articles pertaining to the technologies and analysis of mRNA expression using DNA micro-arrays and its application to dental research. Since many genes contribute to normal functioning, research efforts are moving from the search for a disease specific gene to the understanding of the biochemical and molecular functioning of a variety of genes and how complicated networks of interaction can lead to a disease state, such as oral cancer. With the incorporation of DNA micro-array based research, we can look forward to more accurate diagnosis and surgical treatment/drug-delivery therapy based on an individual patient's genetic profile.

Analysis of repeatability in spotted cDNA microarrays.

We report a strategy for analysis of data quality in cDNA microarrays based on the repeatability of repeatedly spotted clones. We describe how repeatability can be used to control data quality by developing adaptive filtering criteria for microarray data containing clones spotted in multiple spots. We have applied the method on five publicly available cDNA microarray data sets and one previously unpublished data set from our own laboratory. The results demonstrate the feasibility of the approach as a foundation for data filtering, and indicate a high degree of variation in data quality, both across the data sets and between arrays within data sets.

Analysis of matched mRNA measurements from two different microarray technologies.

MOTIVATION: [corrected] The existence of several technologies for measuring gene expression makes the question of cross-technology agreement of measurements an important issue. Cross-platform utilization of data from different technologies has the potential to reduce the need to duplicate experiments but requires corresponding measurements to be comparable. METHODS: A comparison of mRNA measurements of 2895 sequence-matched genes in 56 cell lines from the standard panel of 60 cancer cell lines from the National Cancer Institute (NCI 60) was carried out by calculating correlation between matched measurements and calculating concordance between cluster from two high-throughput DNA microarray technologies, Stanford type cDNA microarrays and Affymetrix oligonucleotide microarrays. RESULTS: In general, corresponding measurements from the two platforms showed poor correlation. Clusters of genes and cell lines were discordant between the two technologies, suggesting that relative intra-technology relationships were not preserved. GC-content, sequence length, average signal intensity, and an estimator of cross-hybridization were found to be associated with the degree of correlation. This suggests gene-specific, or more correctly probe-specific, factors influencing measurements differently in the two platforms, implying a poor prognosis for a broad utilization of gene expression measurements across platforms.