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At least 433 records · Page 24Linked to original sources
PubMed2002

Mining microarray expression data by literature profiling.

BACKGROUND: The rapidly expanding fields of genomics and proteomics have prompted the development of computational methods for managing, analyzing and visualizing expression data derived from microarray screening. Nevertheless, the lack of efficient techniques for assessing the biological implications of gene-expression data remains an important obstacle in exploiting this information. RESULTS: To address this need, we have developed a mining technique based on the analysis of literature profiles generated by extracting the frequencies of certain terms from thousands of abstracts stored in the Medline literature database. Terms are then filtered on the basis of both repetitive occurrence and co-occurrence among multiple gene entries. Finally, clustering analysis is performed on the retained frequency values, shaping a coherent picture of the functional relationship among large and heterogeneous lists of genes. Such data treatment also provides information on the nature and pertinence of the associations that were formed. CONCLUSIONS: The analysis of patterns of term occurrence in abstracts constitutes a means of exploring the biological significance of large and heterogeneous lists of genes. This approach should contribute to optimizing the exploitation of microarray technologies by providing investigators with an interface between complex expression data and large literature resources.

Cluster Analysis↗
PubMed2002

Mining putative regulatory elements in promoter regions of Saccharomyces cerevisiae.

The availability of genome-wide gene expression data provides a unique set of genes from which we can decipher the mechanisms underlying the common transcriptional response. Transcription factors, which can bind to specific DNA sites, cooperatively regulate the transcription of genes. This study attempts to mine putative binding sites to investigate how combinations of the sites predicted from known sites and over-represented repetitive elements are distributed in the promoter regions of groups of functionally related genes. The over-represented repetitive elements appearing in the associations are possible transcription factor binding sites. The deduced association rules would facilitate to predict putative regulatory elements and to identify genes which are potentially co-regulated by the putative regulatory elements. Our proposed approach is applied to Saccharomyces cerevisiae and the promoter regions of yeast ORFs.

Base Sequence↗
PubMed2003

Genome informatics: current status and future prospects.

This article reviews recent advances in genomics and informatics relevant to cardiovascular research. In particular, we review the status of (1) whole genome sequencing efforts in human, mouse, rat, zebrafish, and dog; (2) the development of data mining and analysis tools; (3) the launching of the National Heart, Lung, and Blood Institute Programs for Genomics Applications and Proteomics Initiative; (4) efforts to characterize the cardiac transcriptome and proteome; and (5) the current status of computational modeling of the cardiac myocyte. In each instance, we provide links to relevant sources of information on the World Wide Web and critical appraisals of the promises and the challenges of an expanding and diverse information landscape.

Animals↗
PubMed2005

Linking tumor cell cytotoxicity to mechanism of drug action: an integrated analysis of gene expression, small-molecule screening and structural databases.

An integrated, bioinformatic analysis of three databases comprising tumor-cell-based small molecule screening data, gene expression measurements, and PDB (Protein Data Bank) ligand-target structures has been developed for probing mechanism of drug action (MOA). Clustering analysis of GI50 profiles for the NCI's database of compounds screened across a panel of tumor cells (NCI60) was used to select a subset of unique cytotoxic responses for about 4000 small molecules. Drug-gene-PDB relationships for this test set were examined by correlative analysis of cytotoxic response and differential gene expression profiles within the NCI60 and structural comparisons with known ligand-target crystallographic complexes. A survey of molecular features within these compounds finds thirteen conserved Compound Classes, each class exhibiting chemical features important for interactions with a variety of biological targets. Protein targets for an additional twelve Compound Classes could be directly assigned using drug-protein interactions observed in the crystallographic database. Results from the analysis of constitutive gene expressions established a clear connection between chemo-resistance and overexpression of gene families associated with the extracellular matrix, cytoskeletal organization, and xenobiotic metabolism. Conversely, chemo-sensitivity implicated overexpression of gene families involved in homeostatic functions of nucleic acid repair, aryl hydrocarbon metabolism, heat shock response, proteasome degradation and apoptosis. Correlations between chemo-responsiveness and differential gene expressions identified chemotypes with nonselective (i.e., many) molecular targets from those likely to have selective (i.e., few) molecular targets. Applications of data mining strategies that jointly utilize tumor cell screening, genomic, and structural data are presented for hypotheses generation and identifying novel anticancer candidates.

Antineoplastic Agents↗
PubMed2005

A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset.

Development of a robust and efficient approach for extracting useful information from microarray data continues to be a significant and challenging task. Microarray data are characterized by a high dimension, high signal-to-noise ratio, and high correlations between genes, but with a relatively small sample size. Current methods for dimensional reduction can further be improved for the scenario of the presence of a single (or a few) high influential gene(s) in which its effect in the feature subset would prohibit inclusion of other important genes. We have formalized a robust gene selection approach based on a hybrid between genetic algorithm and support vector machine. The major goal of this hybridization was to exploit fully their respective merits (e.g., robustness to the size of solution space and capability of handling a very large dimension of feature genes) for identification of key feature genes (or molecular signatures) for a complex biological phenotype. We have applied the approach to the microarray data of diffuse large B cell lymphoma to demonstrate its behaviors and properties for mining the high-dimension data of genome-wide gene expression profiles. The resulting classifier(s) (the optimal gene subset(s)) has achieved the highest accuracy (99%) for prediction of independent microarray samples in comparisons with marginal filters and a hybrid between genetic algorithm and K nearest neighbors.

Algorithms↗
PubMedDate not supplied

ONCOMINE: a cancer microarray database and integrated data-mining platform.

DNA microarray technology has led to an explosion of oncogenomic analyses, generating a wealth of data and uncovering the complex gene expression patterns of cancer. Unfortunately, due to the lack of a unifying bioinformatic resource, the majority of these data sit stagnant and disjointed following publication, massively underutilized by the cancer research community. Here, we present ONCOMINE, a cancer microarray database and web-based data-mining platform aimed at facilitating discovery from genome-wide expression analyses. To date, ONCOMINE contains 65 gene expression datasets comprising nearly 48 million gene expression measurements form over 4700 microarray experiments. Differential expression analyses comparing most major types of cancer with respective normal tissues as well as a variety of cancer subtypes and clinical-based and pathology-based analyses are available for exploration. Data can be queried and visualized for a selected gene across all analyses or for multiple genes in a selected analysis. Furthermore, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-drug target pairs to facilitate the discovery of novel biomarkers and therapeutic targets.

Databases, Genetic↗
PubMed2006

Discovering disease-genes by topological features in human protein-protein interaction network.

MOTIVATION: Mining the hereditary disease-genes from human genome is one of the most important tasks in bioinformatics research. A variety of sequence features and functional similarities between known human hereditary disease-genes and those not known to be involved in disease have been systematically examined and efficient classifiers have been constructed based on the identified common patterns. The availability of human genome-wide protein-protein interactions (PPIs) provides us with new opportunity for discovering hereditary disease-genes by topological features in PPIs network. RESULTS: This analysis reveals that the hereditary disease-genes ascertained from OMIM in the literature-curated (LC) PPIs network are characterized by a larger degree, tendency to interact with other disease-genes, more common neighbors and quick communication to each other whereas those properties could not be detected from the network identified from high-throughput yeast two-hybrid mapping approach (EXP) and predicted interactions (PDT) PPIs network. KNN classifier based on those features was created and on average gained overall prediction accuracy of 0.76 in cross-validation test. Then the classifier was applied to 5262 genes on human genome and predicted 178 novel disease-genes. Some of the predictions have been validated by biological experiments.

Chromosome Mapping↗
PubMed2006

QTL MatchMaker: a multi-species quantitative trait loci (QTL) database and query system for annotation of genes and QTL.

Identifying genes that underlie quantitative trait loci (QTL) is a challenging task. Here, we present a new QTL software system, named QTL MatchMaker. The system is designed to integrate and mine QTL information across human, mouse and rat genomes and to annotate functional genomic data. It combines and organizes information from relevant public databases and publications and integrates QTL, physical, genetic and cytogenetic maps across human, mouse and rat. To make this application available to the research community we have developed a website for high-throughput mapping of expressed sequences to QTL and for selection of candidate genes in the physiological genomics context of complex traits. QTL MatchMaker is accessible at http://pmrc.med.mssm.edu:9090/QTL/jsp/qtlhome.jsp.

Animals↗
PubMed2005

Selecting stable molecular targets for treatment and prevention of AIDS.

The common consideration in approaching protection against HIV, whether by a vaccine or therapeutic, is identification of suitable targets. Among the central criteria for suitability is target stability; i.e. resistance to mutation. In this paper we address the problem of stability, and develop methods for identifying stable targets. The targets that we focus on are structures formed by viral peptides and products of the class I major histocompatibility complex, the target of the immune system. The method mines the large databases of fully sequenced HIV genomes and MHC binding peptides, and takes account of human polymorphism to construct hundreds of subpopulation specific stable targets, each consisting of combinations of 3-5 complexes.

Acquired Immunodeficiency Syndrome↗
PubMed2002

[Brucella at the dawn of the third milenium: genomic organization and pathogenesis].

Bacteria of the genus Brucella, responsible for brucellosis, are pathogenic for animals and occasionally for humans. The cost of this widespread zoonotic infection is still very high for the community. Over the last few years, there have been advances in two main domains. First, the Brucella genome has been shown to be complex, with two circular chromosomes. Second, recent data on the virulence of Brucella suggest common mechanisms shared with plant pathogens and endosymbionts of the alpha-proteobacteria. Understanding virulence will have practical repercussions in the realms of vaccine development and, perhaps, development of new antibiotics. Two complete Brucella genome sequences are now available and will be a gold mine of information to guide future research.

Animals↗
PubMed1999

Microbial genomes.

Microbial genome sequencing is driven by the need to understand and control pathogens and to exploit extremophiles and their enzymes in bioremediation and industry. It is hard for the traditional bacteriologist to grasp the scale and pace of the venture. Around two dozen microbial genomes have now been completed and, within a decade, genomes from every significant species of bacterial pathogen of humans, animals and plants will have been sequenced. Indeed, we will often have more than one sequence from a species or genus--for example, we already have sequences from two strains of Helicobacter pylori, from two strains of Mycobacterium tuberculosis and from three species of Pyrococcus. However, genome sequencing risks becoming expensive molecular stamp-collecting without the tools to mine the data and fuel hypothesis-driven laboratory-based research. Bioinformatics, twinned with the new experimental approaches forming functional genomics', provides some of the needed tools. Nonetheless, there will be an increasing need for us to explore the detailed implications of genomic findings. Microbial genome sequencing thus represents not a threat, but an exciting opportunity for molecular microbiologists.

Computational Biology↗
PubMedDate not supplied

Genome-wide profile of oxidoreductases in viruses, prokaryotes, and eukaryotes.

Enzymes that utilize nicotinamide adenine dinucleotide (NAD) or its 2'-phosphate derivative (NADP) are found throughout the kingdoms of life. These enzymes are fundamental to many biochemical pathways, including central intermediary metabolism and mechanisms for cell survival and defense. The complete genomes of 25 organisms representing bacteria, protists, fungi, plants, and animals, and 811 viruses, were mined to identify and classify NAD(P)-dependent enzymes. An average of 3.4% of the proteins in these genomes was categorized as NAD(P)-utilizing proteins, with highest prevalence in the medium-chain oxidoreductase and short-chain oxidoreductase families. In general, the distribution of these enzymes by oxidoreductase family was correlated to the number of different catalytic mechanisms in each family. Organisms with smaller genomes encoded a larger proportion of NAD(P)-dependent enzymes in their proteome (approximately 6%) as compared to the larger genomes of eukaryotes (approximately 3%). Among viruses, those with large, double-strand DNA genomes were shown to encode oxidoreductases. Gram-positive and gram-negative bacteria showed some differences in the distribution of NAD(P)-dependent proteins. Several organisms such as M. tuberculosis, P. falciparum, and A. thaliana showed unique distributions of oxidoreductases corresponding to some phenotypic features.

Animals↗
PubMed2004

Cancer proteomics: Serum diagnostics for tumor marker discovery.

Cancer proteomics is an exciting field that is witnessing many new developments in recent years. It is hoped that these advances will result in decreased cancer death rates, which have not declined dramatically in the last several decades. Some of the problems with current tumor markers include the lack of sensitivity and specificity, factors that prevent their use in population-based screening of disease. Thus, there is an urgent need to identify novel biomarkers that can faithfully detect the disease state. As we are now in the post-genome era, many opportunities have been created. Genomic sequence data are available for human, as well as several other species. We are now poised to mine these data and to determine the functions of the encoded proteins constituting the human genome. Proteomics affords this opportunity by providing enhanced procedures and tools for discovery and also a framework for understanding these components in terms of pathogenesis. New technologies and improvements in existing methodologies will allow for the rapid growth in the identification and characterization of peptides and proteins that are unique to various clinical states. This technology can be successfully applied to clinical specimens for the identification of new tumor markers.

Algorithms↗
PubMed2004

Comparative genomics of rice and Arabidopsis. Analysis of 727 cytochrome P450 genes and pseudogenes from a monocot and a dicot.

Data mining methods have been used to identify 356 Cyt P450 genes and 99 related pseudogenes in the rice (Oryza sativa) genome using sequence information available from both the indica and japonica strains. Because neither of these genomes is completely available, some genes have been identified in only one strain, and 28 genes remain incomplete. Comparison of these rice genes with the 246 P450 genes and 26 pseudogenes in the Arabidopsis genome has indicated that most of the known plant P450 families existed before the monocot-dicot divergence that occurred approximately 200 million years ago. Comparative analysis of P450s in the Pinus expressed sequence tag collections has identified P450 families that predated the separation of gymnosperms and flowering plants. Complete mapping of all available plant P450s onto the Deep Green consensus plant phylogeny highlights certain lineage-specific families maintained (CYP80 in Ranunculales) and lineage-specific families lost (CYP92 in Arabidopsis) in the course of evolution.

Arabidopsis↗
PubMed2005

In silico identification of breast cancer genes by combined multiple high throughput analyses.

Publicly available human genomic sequence data provide an unprecedented opportunity for researchers to decode the functionality of human genome. Such information is extremely valuable in cancer prevention diagnosis and treatment. Cancer Genome Anatomy Project (CGAP) and Gene Expression Omnibus (GEO) are two bioinformatic infrastructures for studying functional genomics. The goal of this study is to explore the feasibility of incorporating the Internet-available bioinformatic databases to discover human breast cancer-related genes. Several tools including the Gene Finder, Virtual Northern (vNorthern) and SAGE digital gene expression displayer (DGED) were used to analyze differential gene expression between benign and malignant breast tissues. A pilot study was performed using both EST and SAGE vNorthern to analyze the expression of a panel of known genes, including high abundance genes beta-actin and G3PDH, low abundance genes BRCA1 and p53, tissue-specific genes CEA and PSA and two breast cancer-related genes Her2/neu and MUC1. We found a high expression of beta-actin and G3PDH and a low expression of BRCA1 and p53 across different types of tissues as well as a tissue-specific expression of CEA in colon and PSA in prostate. A further analysis of 30 known breast cancer-related genes in breast cancer tissues by vNorthern demonstrated a high expression of oncogenes and low expression of tumor suppressor genes. An open-end analysis of two pools of breast cancer and benign breast tissue libraries by SAGE DGED produced 53 differentially expressed genes according to the screening criteria of a >five-fold difference and p<0.01. Further analysis by EST vNorthern and virtual microarray analysis reduced the candidate genes to six, with four down-regulated genes, ANXA1, CAV1, KRT5 and MMP7, and two up-regulated genes, ERBB2 and G1P3 in breast cancer. These findings were validated by a real-time RT-PCR analysis in eight paired human breast cancer tissue samples. We conclude that the combined multiple high throughput analyses is an effective data mining strategy in cancer gene identification. This approach may improve the usage of public available genomic data through strategic data mining of high throughput analysis.

Blotting, Northern↗
PubMed2005

Exploring relationships and mining data with the UCSC Gene Sorter.

In parallel with the human genome sequencing and assembly effort, many tools have been developed to examine the structure and function of the human gene set. The University of California Santa Cruz (UCSC) Gene Sorter has been created as a gene-based counterpart to the chromosome-oriented UCSC Genome Browser to facilitate the study of gene function and evolution. This simple, but powerful tool provides a graphical display of related genes that can be sorted and filtered based on a variety of criteria. Genes may be ordered based on such characteristics as expression profiles, proximity in genome, shared Gene Ontology (GO) terms, and protein similarity. The display can be restricted to a gene set meeting a specific set of constraints by filtering on expression levels, gene name or ID, chromosomal position, and so on. The default set of information for each gene entry-gene name, selected expression data, a BLASTP E-value, genomic position, and a description-can be configured to include many other types of data, including expanded expression data, related accession numbers and IDs, orthologs in other species, GO terms, and much more. The Gene Sorter, a CGI-based Web application written in C with a MySQL database, is tightly integrated with the other applications in the UCSC Genome Browser suite. Available on a selected subset of the genome assemblies found in the Genome Browser, it further enhances the usefulness of the UCSC tool set in interactive genomic exploration and analysis.

Computational Biology↗
PubMed2001

Parasite genome initiatives.

During 1993-1994, scientists from developing and developed countries planned and initiated a number of parasite genome projects and several consortiums for the mapping and sequencing of these medium-sized genomes were established, often based on already ongoing scientific collaborations. Financial and other support came from WHO/TDR, Wellcome Trust and other funding agencies. Thus, the genomes of Plasmodium falciparum, Schistosoma mansoni, Trypanosoma cruzi, Leishmania major, Trypanosoma brucei, Brugia malayi and other pathogenic nematodes are now under study. From an initial phase of network formation, mapping efforts and resource building (EST, GSS, phage, cosmid, BAC and YAC library constructions), sequencing was initiated in gene discovery projects but soon also on a small chromosome, and now on a fully fledged genome scale. Proteomics, functional analysis, genetic manipulation and microarray analysis are ongoing to different degrees in the respective genome initiatives, and as the funding for the whole genome sequencing becomes secured, most of the participating laboratories, apart from larger sequencing centres, become oriented to post-genomics. Bioinformatics networks are being expanded, including in developing countries, for data mining, annotation and in-depth analysis.

Animals↗