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At least 361 records · Page 20Linked to original sources
PubMed2006

Using local gene expression similarities to discover regulatory binding site modules.

BACKGROUND: We present an approach designed to identify gene regulation patterns using sequence and expression data collected for Saccharomyces cerevisae. Our main goal is to relate the combinations of transcription factor binding sites (also referred to as binding site modules) identified in gene promoters to the expression of these genes. The novel aspects include local expression similarity clustering and an exact IF-THEN rule inference algorithm. We also provide a method of rule generalization to include genes with unknown expression profiles. RESULTS: We have implemented the proposed framework and tested it on publicly available datasets from yeast S. cerevisae. The testing procedure consists of thorough statistical analyses of the groups of genes matching the rules we infer from expression data against known sets of co-regulated genes. For this purpose we have used published ChIP-Chip data and Gene Ontology annotations. In order to make these tests more objective we compare our results with recently published similar studies. CONCLUSION: Results we obtain show that local expression similarity clustering greatly enhances overall quality of the derived rules, both in terms of enrichment of Gene Ontology functional annotation and coherence with ChIP-Chip binding data. Our approach thus provides reliable hypotheses on co-regulation that can be experimentally verified. An important feature of the method is its reliance only on widely accessible sequence and expression data. The same procedure can be easily applied to other microbial organisms.

Binding Sites↗
PubMed2005

Towards precise classification of cancers based on robust gene functional expression profiles.

BACKGROUND: Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level. RESULTS: Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s) for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles. CONCLUSION: This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge has facilitated the interpretation of the underlying molecular mechanisms for complex human diseases at the modular level.

Algorithms↗
PubMed2002

Extension and integration of the gene ontology (GO): combining GO vocabularies with external vocabularies.

Structured vocabulary development enhances the management of information in biological databases. As information grows, handling the complexity of vocabularies becomes difficult. Defined methods are needed to manipulate, expand and integrate complex vocabularies. The Gene Ontology (GO) project provides the scientific community with a set of structured vocabularies to describe domains of molecular biology. The vocabularies are used for annotation of gene products and for computational annotation of sequence data sets. The vocabularies focus on three concepts universal to living systems, biological process, molecular function and cellular component. As the vocabularies expand to incorporate terms needed by diverse annotation communities, species-specific terms become problematic. In particular, the use of species-specific anatomical concepts remains unresolved. We present a method for expansion of GO into areas outside of the three original universal concept domains. We combine concepts from two orthogonal vocabularies to generate a larger, more specific vocabulary. The example of mammalian heart development is presented because it addresses two issues that challenge GO; inclusion of organism-specific anatomical terms, and proliferation of terms and relationships. The combination of concepts from orthogonal vocabularies provides a robust representation of relevant terms and an opportunity for evaluation of hypothetical concepts.

Animals↗
PubMed1999

Metabolism and genetics of Helicobacter pylori: the genome era.

The publication of the complete sequence of Helicobacter pylori 26695 in 1997 and more recently that of strain J99 has provided new insight into the biology of this organism. In this review, we attempt to analyze and interpret the information provided by sequence annotations and to compare these data with those provided by experimental analyses. After a brief description of the general features of the genomes of the two sequenced strains, the principal metabolic pathways are analyzed. In particular, the enzymes encoded by H. pylori involved in fermentative and oxidative metabolism, lipopolysaccharide biosynthesis, nucleotide biosynthesis, aerobic and anaerobic respiration, and iron and nitrogen assimilation are described, and the areas of controversy between the experimental data and those provided by the sequence annotation are discussed. The role of urease, particularly in pH homeostasis, and other specialized mechanisms developed by the bacterium to maintain its internal pH are also considered. The replicational, transcriptional, and translational apparatuses are reviewed, as is the regulatory network. The numerous findings on the metabolism of the bacteria and the paucity of gene expression regulation systems are indicative of the high level of adaptation to the human gastric environment. Arguments in favor of the diversity of H. pylori and molecular data reflecting possible mechanisms involved in this diversity are presented. Finally, we compare the numerous experimental data on the colonization factors and those provided from the genome sequence annotation, in particular for genes involved in motility and adherence of the bacterium to the gastric tissue.

Gene Expression Regulation, Bacterial↗
PubMed2006

Computer-assisted reader software versus expert reviewers for polyp detection on CT colonography.

OBJECTIVE: The purpose of our study was to assess the sensitivity of computer-assisted reader (CAR) software for polyp detection compared with the performance of expert reviewers. MATERIALS AND METHODS: A library of colonoscopically validated CT colonography cases were collated and separated into training and test sets according to the time of accrual. Training data sets were annotated in consensus by three expert radiologists who were aware of the colonoscopy report. A subset of 45 training cases containing 100 polyps underwent batch analysis using ColonCAR version 1.2 software to determine the optimum polyp enhancement filter settings for polyp detection. Twenty-five consecutive positive test data sets were subsequently interpreted individually by each expert, who was unaware of the endoscopy report, and before generation of the annotated reference via an unblinded consensus interpretation. ColonCAR version 1.2 software was applied to the test cases, at optimized polyp enhancement filter settings, to determine diagnostic performance. False-positive findings were classified according to importance. RESULTS: The 25 test cases contained 32 nondiminutive polyps ranging from 6 to 35 mm in diameter. The ColonCAR version 1.2 software identified 26 (81%) of 32 polyps compared with an average sensitivity of 70% for the expert reviewers. Eleven (92%) of 12 polyps > or = 10 mm were detected by ColonCAR version 1.2. All polyps missed by experts 1 (n = 4) and 2 (n = 3) and 12 (86%) of 14 polyps missed by expert 3 were detected by ColonCAR version 1.2. The median number of false-positive highlights per case was 13, of which 91% were easily dismissed. CONCLUSION: ColonCAR version 1.2 is sensitive for polyp detection, with a clinically acceptable false-positive rate. ColonCAR version 1.2 has a synergistic effect to the reviewer alone, and its standalone performance may exceed even that of experts.

Adult↗
PubMed2005

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.

Arabidopsis↗
PubMed1996

The Genome Sequence DataBase (GSDB): meeting the challenge of genomic sequencing.

The genome sequence database (GSDB) is a complete, publicly available relational database of DNA sequences and annotation maintained by the National Center for Genome Resources (NCGR) under a Cooperative Agreement with the US Department of Energy (DOE). GSDB provides direct, client- server access to the database for data contributions, community annotation and SQL queries. The GSDB Annotator, a multi-platform graphic user interface, is freely available. Automatically updated relational replicates of GSDB are also freely available.

Amino Acid Sequence↗
PubMed2005

A rigorous method for multigenic families' functional annotation: the peptidyl arginine deiminase (PADs) proteins family example.

BACKGROUND: large scale and reliable proteins' functional annotation is a major challenge in modern biology. Phylogenetic analyses have been shown to be important for such tasks. However, up to now, phylogenetic annotation did not take into account expression data (i.e. ESTs, Microarrays, SAGE, ...). Therefore, integrating such data, like ESTs in phylogenetic annotation could be a major advance in post genomic analyses. We developed an approach enabling the combination of expression data and phylogenetic analysis. To illustrate our method, we used an example protein family, the peptidyl arginine deiminases (PADs), probably implied in Rheumatoid Arthritis. RESULTS: the analysis was performed as follows: we built a phylogeny of PAD proteins from the NCBI's NR protein database. We completed the phylogenetic reconstruction of PADs using an enlarged sequence database containing translations of ESTs contigs. We then extracted all corresponding expression data contained in EST database This analysis allowed us 1/To extend the spectrum of homologs-containing species and to improve the reconstruction of genes' evolutionary history. 2/To deduce an accurate gene expression pattern for each member of this protein family. 3/To show a correlation between paralogous sequences' evolution rate and pattern of tissular expression. CONCLUSION: coupling phylogenetic reconstruction and expression data is a promising way of analysis that could be applied to all multigenic families to investigate the relationship between molecular and transcriptional evolution and to improve functional annotation.

Animals↗
PubMed2006

Gene Class expression: analysis tool of Gene Ontology terms with gene expression data.

Serial analysis of gene expression (SAGE) technology produces large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in these gene sets. We present an interactive web-based tool, called Gene Class, which allows functional annotation of SAGE data using the Gene Ontology (GO) database. This tool performs searches in the GO database for each SAGE tag, making associations in the selected GO category for a level selected in the hierarchy. This system provides user-friendly data navigation and visualization for mapping SAGE data onto the gene ontology structure. This tool also provides graphical visualization of the percentage of SAGE tags in each GO category, along with confidence intervals and hypothesis testing.

Animals↗
PubMed2004

Parasite genome databases and web-based resources.

In the last decade, high-throughput genome sequencing and complementary techniques such as microarray and proteomics have generated, and will continue to generate, ever-increasing amounts of data. These technologies of gene discovery, expression, and functional analysis have been applied to a vast array of organisms, including parasites. In most instances, the data are freely available via the Internet, and researchers are becoming increasingly reliant on up-to-date, centralized data repositories to complement wet bench science. This chapter presents an overview of resources relevant to researchers with an interest in para-site genomics and biology. After briefly touching on some of the publicly available nucleotide and protein sequence as well as domain databases, the focus turns to parasite genome projects and associated Web-based resources. A list of parasite sequencing projects current at the time of writing, including relevant Web site addresses, is provided. The available resources range from network sites and project pages at sequencing institutes to databases that integrate and curate sequence data and associated annotation with diverse biological datasets. Particular attention is given to three databases, GeneDB (http://www.genedb.org/), PlasmoDB (http://plasmodb. org/), and tigr db, detailing the scope of each database and the tools available for data querying and retrieval.

Animals↗
PubMed2002

Genome annotation techniques: new approaches and challenges.

As more of the human genome draft sequence is finished, and genomes from other organisms begin to be sequenced, the demand for accurate and reliable genome annotation will increase significantly. To facilitate this industrial-scale genome annotation, automated bioinformatics solutions are increasingly required. As a result, automatic genome annotation systems have become more important in gene discovery within recent years. The design of such large-scale bioinformatics systems is an evolving and dynamic field, based on central cores of bioinformatics software tools and relational databases. Not only must these systems efficiently manage and integrate large volumes of genomic data, but they must also deliver accurate gene predictions and effectively distribute annotation data to the biosciences community.

Computational Biology↗
PubMed2006

The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo.

We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory interactions, and apply the method to predict a large portion of the regulatory network of the archaeon Halobacterium NRC-1. The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data. The learned network successfully predicted Halobacterium's global expression under novel perturbations with predictive power similar to that seen over training data. Several specific regulatory predictions were experimentally tested and verified.

Adenosine Triphosphatases↗
PubMed2006

Annotated regions of significance of SELDI-TOF-MS spectra for detecting protein biomarkers.

Peak detection is a key step in the analysis of SELDI-TOF-MS spectra, but the current default method has low specificity and poor peak annotation. To improve data quality, scientists still have to validate the identified peaks visually, a tedious and time-consuming process, especially for large data sets. Hence, there is a genuine need for methods that minimize manual validation. We have previously reported a multi-spectral signal detection method, called RS for 'region of significance', with improved specificity. Here we extend it to include a peak quantification algorithm based on annotated regions of significance (ARS). For each spectral region flagged as significant by RS, we first identify a dominant spectrum for determining the number of peaks and the m/z region of these peaks. From each m/z region of peaks, a peak template is extracted from all spectra via the principal component analysis. Finally, with the template, we estimate the amplitude and location of the peak in each spectrum with the least-squares method and refine the estimation of the amplitude via the mixture model. We have evaluated the ARS algorithm on patient samples from a clinical study. Comparison with the standard method shows that ARS (i) inherits the superior specificity of RS, and (ii) gives more accurate peak annotations than the standard method. In conclusion, we find that ARS alleviates the main problems in the preprocessing of SELDI-TOF spectra. The R-package ProSpect that implements ARS is freely available for academic use at http://www.meb.ki.se/ yudpaw.

Adenocarcinoma↗
PubMed2005

Collaborative biomedical data exploration in distributed virtual environments.

Imaging techniques such as MRI, fMRI, CT and PET have provided doctors with a means to acquire high-resolution biomedical images that serve as the foundation for the diagnosis and treatment of diseases. Experts with a multitude of backgrounds, including radiologists, anatomists, psychiatrists and neuroscientists now collaboratively analyze the same images to extract a better understanding of the encoded information. Unfortunately, access to these specialists at the same physical location is not always possible and new tools and techniques are required to facilitate simultaneous and collaborative exploration of volumetric data between spatially separated domain experts. This paper presents CVMED, a collaborative visualization environment for volumetric biomedical data-sets, supporting heterogeneous hardware, rendering and display systems connected via heterogeneous networks. CVMED provides the user with the algorithms and tools for stereoscopic as well as monoscopic data visualization and annotation along with the middleware needed to exchange the resulting visuals between all participants in real-time.

Biomedical Technology↗
PubMed2003

Protein complexes and functional modules in molecular networks.

Proteins, nucleic acids, and small molecules form a dense network of molecular interactions in a cell. Molecules are nodes of this network, and the interactions between them are edges. The architecture of molecular networks can reveal important principles of cellular organization and function, similarly to the way that protein structure tells us about the function and organization of a protein. Computational analysis of molecular networks has been primarily concerned with node degree [Wagner, A. & Fell, D. A. (2001) Proc. R. Soc. London Ser. B 268, 1803-1810; Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. (2000) Nature 407, 651-654] or degree correlation [Maslov, S. & Sneppen, K. (2002) Science 296, 910-913], and hence focused on single/two-body properties of these networks. Here, by analyzing the multibody structure of the network of protein-protein interactions, we discovered molecular modules that are densely connected within themselves but sparsely connected with the rest of the network. Comparison with experimental data and functional annotation of genes showed two types of modules: (i) protein complexes (splicing machinery, transcription factors, etc.) and (ii) dynamic functional units (signaling cascades, cell-cycle regulation, etc.). Discovered modules are highly statistically significant, as is evident from comparison with random graphs, and are robust to noise in the data. Our results provide strong support for the network modularity principle introduced by Hartwell et al. [Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. (1999) Nature 402, C47-C52], suggesting that found modules constitute the "building blocks" of molecular networks.

Biophysical Phenomena↗
PubMed2004

NetAffx Gene Ontology Mining Tool: a visual approach for microarray data analysis.

SUMMARY: The NetAffx Gene Ontology (GO) Mining Tool is a web-based, interactive tool that permits traversal of the GO graph in the context of microarray data. It accepts a list of Affymetrix probe sets and renders a GO graph as a heat map colored according to significance measurements. The rendered graph is interactive, with nodes linked to public web sites and to lists of the relevant probe sets. The GO Mining Tool provides visualization combining biological annotation with expression data, encompassing thousands of genes in one interactive view. AVAILABILITY: GO Mining Tool is freely available at http://www.affymetrix.com/analysis/query/go_analysis.affx

Abstracting and Indexing↗
PubMed1994

GenBank.

The GenBank sequence database continues to expand its data coverage, quality control, annotation content and retrieval services for the scientific community. Besides handling direct submissions of sequence data from authors, GenBank also incorporates DNA sequences from all available public sources; an integrated retrieval system, known as Entrez, also makes available data from the major protein sequence and structural databases, and from U.S. and European patents. MIDLINE abstracts from published articles describing the sequences are also included as an additional source of biological annotation for sequence entries. GenBank supports distribution of the data via FTP, CD-ROM, and E-mail servers. Network server-client programs provide access to an integrated database for literature retrieval and sequence similarity searching.

Amino Acid Sequence↗