PAZAR: a framework for collection and dissemination of cis-regulatory sequence annotation

PAZAR is an open-access and open-source database of transcription factor and regulatory sequence annotation with associated web interface and programming tools for data submission and extraction.

PAZAR is an open-access and open-source database of transcription factor and regulatory sequence annotation with associated web interface and programming tools for data submission and extraction. Curated boutique data collections can be maintained and disseminated through the unified schema of the mall-like PAZAR repository. The Pleiades Promoter Project collection of brain-linked regulatory sequences is introduced to demonstrate the depth of annotation possible within PAZAR. PAZAR, located at , is open for business.

WebGestalt: an integrated system for exploring gene sets in various biological contexts

High-throughput technologies have led to the rapid generation of large-scale datasets about genes and gene products. These technologies have also shifted our research focus from 'single genes' to 'gene sets'. We have developed a web-based integrated data mining system, WebGestalt (http://genereg.ornl.gov/webgestalt/), to help biologists in exploring large sets of genes. WebGestalt is composed of four modules: gene set management, information retrieval, organization/visualization, and statistics. The management module uploads, saves, retrieves and deletes gene sets, as well as performs Boolean operations to generate the unions, intersections or differences between different gene sets. The information retrieval module currently retrieves information for up to 20 attributes for all genes in a gene set. The organization/visualization module organizes and visualizes gene sets in various biological contexts, including Gene Ontology, tissue expression pattern, chromosome distribution, metabolic and signaling pathways, protein domain information and publications. The statistics module recommends and performs statistical tests to suggest biological areas that are important to a gene set and warrant further investigation. In order to demonstrate the use of WebGestalt, we have generated 48 gene sets with genes over-represented in various human tissue types. Exploration of all the 48 gene sets using WebGestalt is available for the public at http://genereg.ornl.gov/webgestalt/wg_enrich.php.

GeneKeyDB: a lightweight, gene-centric, relational database to support data mining environments

Background

The analysis of biological data is greatly enhanced by existing or emerging databases. Most existing databases, with few exceptions are not designed to easily support large scale computational analysis, but rather offer exclusively a web interface to the resource. We have recognized the growing need for a database which can be used successfully as a backend to computational analysis tools and pipelines. Such database should be sufficiently versatile to allow easy system integration.

Results

GeneKeyDB is a gene-centered relational database developed to enhance data mining in biological data sets. The system provides an underlying data layer for computational analysis tools and visualization tools. GeneKeyDB relies primarily on existing database identifiers derived from community databases (NCBI, GO, Ensembl, et al.) as well as the known relationships among those identifiers. It is a lightweight, portable, and extensible platform for integration with computational tools and analysis environments.

Conclusion

GeneKeyDB can enable analysis tools and users to manipulate the intersections, unions, and differences among different data sets.

Large-scale mutagenesis of the mouse to understand the genetic bases of nervous system structure and function

N-ethyl-N-nitrosourea (ENU) mutagenesis is presented as a powerful approach to developing models for human disease. The efforts of three NIH Mutagenesis Centers established for the detection of neuroscience-related phenotypes are described. Each center has developed an extensive panel of phenotype screens that assess nervous system structure and function. In particular, these screens focus on complex behavioral traits from drug and alcohol responses to circadian rhythms to epilepsy. Each of these centers has developed a bioinformatics infrastructure to track the extensive number of transactions that are inherent in these large-scale projects. Over 100 new mouse mutant lines have been defined through the efforts of these three mutagenesis centers and are presented to the research community via the centralized Web presence of the Neuromice.org consortium (http://www.neuromice.org). This community resource provides visitors with the ability to search for specific mutant phenotypes, to view the genetic and phenotypic details of mutant mouse lines, and to order these mice for use in their own research program.

The knockout mouse project

Mouse knockout technology provides a powerful means of elucidating gene function in vivo, and a publicly available genome-wide collection of mouse knockouts would be significantly enabling for biomedical discovery. To date, published knockouts exist for only about 10% of mouse genes. Furthermore, many of these are limited in utility because they have not been made or phenotyped in standardized ways, and many are not freely available to researchers. It is time to harness new technologies and efficiencies of production to mount a high-throughput international effort to produce and phenotype knockouts for all mouse genes, and place these resources into the public domain.

Gene expression profiling in human preadipocytes and adipocytes by microarray analysis

Uncontrolled expansion of adipose tissue leads to obesity, a public health epidemic affecting >30% of adult Americans. Adipose mass increases in part through the recruitment and differentiation of an existing pool of preadipocytes (PA) into adipocytes (AD). Most studies investigating adipogenesis used primarily murine cell lines; much less is known about the relevant processes that occur in humans. Therefore, characterization of genes associated with adipocyte development is key to understanding the pathogenesis of obesity and developing treatments for this disorder. To address this issue, we performed large-scale analyses of human adipose gene expression using microarray technology. Differential gene expression between PA and AD was analyzed in 6 female patients using human cDNA microarray slides and data analyzed using the Stanford Microarray Database. Statistical analysis for the gene expression was performed using the SAS mixed models. Compared with PA, several genes involved in lipid metabolism were overexpressed in AD, including fatty acid binding protein, adipose differentiation-related protein, lipoprotein lipase, perilipin, and adipose most abundant transcript 1. Novel genes expressed in adipocytes included E2F5 transcriptional factor and SMARC (SWI/SNF-related, matrix associated, actin-dependent regulator of chromatin). PA predominantly expressed genes encoding extracellular matrix components such as fibronectin, matrix metalloprotein, and novel proteins such as lysyl oxidase. Despite the high differential expression of some of these genes, many did not differ significantly likely due to high variability and limited statistical power. A comprehensive list of differential gene expression is presented according to cellular function. In conclusion, these studies offer an overview of the gene expression profiles in PA and AD and identify new genes with potentially important functions in adipose tissue development and obesity that merit further investigation.

GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies

Background

Microarray and other high-throughput technologies are producing large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in the gene sets.

Results

We have created a web-based tool for data analysis and data visualization for sets of genes called GOTree Machine (GOTM). This tool was originally intended to analyze sets of co-regulated genes identified from microarray analysis but is adaptable for use with other gene sets from other high-throughput analyses. GOTree Machine generates a GOTree, a tree-like structure to navigate the Gene Ontology Directed Acyclic Graph for input gene sets. This system provides user friendly data navigation and visualization. Statistical analysis helps users to identify the most important Gene Ontology categories for the input gene sets and suggests biological areas that warrant further study. GOTree Machine is available online at .

Conclusion

GOTree Machine has a broad application in functional genomic, proteomic and other high-throughput methods that generate large sets of interesting genes; its primary purpose is to help users sort for interesting patterns in gene sets.

MuTrack: a genome analysis system for large-scale mutagenesis in the mouse

Background

Modern biological research makes possible the comprehensive study and development of heritable mutations in the mouse model at high-throughput. Using techniques spanning genetics, molecular biology, histology, and behavioral science, researchers may examine, with varying degrees of granularity, numerous phenotypic aspects of mutant mouse strains directly pertinent to human disease states. Success of these and other genome-wide endeavors relies on a well-structured bioinformatics core that brings together investigators from widely dispersed institutions and enables them to seamlessly integrate data, observations and discussions.

Description

MuTrack was developed as the bioinformatics core for a large mouse phenotype screening effort. It is a comprehensive collection of on-line computational tools and tracks thousands of mutagenized mice from birth through senescence and death. It identifies the physical location of mice during an intensive phenotype screening process at several locations throughout the state of Tennessee and collects raw and processed experimental data from each domain. MuTrack's statistical package allows researchers to access a real-time analysis of mouse pedigrees for aberrant behavior, and subsequent recirculation and retesting. The end result is the classification of potential and actual heritable mutant mouse strains that become immediately available to outside researchers who have expressed interest in the mutant phenotype.

Conclusion

MuTrack demonstrates the effectiveness of using bioinformatics techniques in data collection, integration and analysis to identify unique result sets that are beyond the capacity of a solitary laboratory. By employing the research expertise of investigators at several institutions for a broad-ranging study, the TMGC has amplified the effectiveness of any one consortium member. The bioinformatics strategy presented here lends future collaborative efforts a template for a comprehensive approach to large-scale analysis.

Structure-activity relationships of trans-3,4-dimethyl-4-(3-hydroxyphenyl)piperidine antagonists for mu- and kappa-opioid receptors

A series of racemic N-substituted trans-3,4-dimethyl-4-(3-hydroxyphenyl)piperidines were evaluated for opioid agonist and antagonist activity at mu and kappa receptors. Several highly potent mu and kappa antagonists were discovered; however, no compounds with high selectivity for either the mu or kappa receptor were identified. Importantly, no derivative was found to have significant opioid agonist activity. Two derivatives were resolved, and the activities of the enantiomers were investigated. Only a limited stereochemical effect on opioid receptor selectivities was observed. The structure-activity relationships described establish the existence of an important lipophilic binding site distal to the nitrogen for both mu and kappa receptors and confirm the pure opioid antagonist pharmacophore nature of the trans-3,4-dimethyl-4-(3-hydroxyphenyl)piperidine structure.

DNA sequence determination by hybridization: a strategy for efficient large-scale sequencing

The concept of sequencing by hybridization (SBH) makes use of an array of all possible n-nucleotide oligomers (n-mers) to identify n-mers present in an unknown DNA sequence. Computational approaches can then be used to assemble the complete sequence. As a validation of this concept, the sequences of three DNA fragments, 343 base pairs in length, were determined with octamer oligonucleotides. Possible applications of SBH include physical mapping (ordering) of overlapping DNA clones, sequence checking, DNA fingerprinting comparisons of normal and disease-causing genes, and the identification of DNA fragments with particular sequence motifs in complementary DNA and genomic libraries. The SBH techniques may accelerate the mapping and sequencing phases of the human genome project.

Interferons as gene activators. Indications for repeated gene duplication during the evolution of a cluster of interferon-activatable genes on murine chromosome 1

We have described earlier a gene cluster, including at least six interferon-activatable genes closely linked to the erythroid alpha spectrin locus and the serum amyloid P-component locus on murine chromosome 1. Here, we report that sequences of three genes from the cluster (the 201, 202, and 204 genes) are very similar in a segment extending from at least 550 nucleotides upstream of the 3' end of the transcription initiation region to beyond the first exon intron border (96% similarity between the 202 and 204 genes and 89% similarity between the 201 and 204 genes). This region contains the following two types of interferon-responsive enhancers: a GA box and a Friedman Stark sequence. The proteins coded for by the 202 gene (51 kDa) and the 204 gene (72 kDa) are hydrophilic. The amino acids have been conserved in the two proteins in 47% of the sequence. Each protein includes two apparently contiguous, approximately 200-amino acid long segments with much sequence similarity (27% in the 202 protein and 34% in the 204 protein). These segments are preceded in the 204 protein only by a segment including four perfect and three imperfect repeats of a 7 amino acid sequence. These and other data suggest that the evolution of the gene cluster involved the duplication of a DNA segment generating a double length transcription unit and subsequent divergence and duplication of this unit giving rise to at least two interferon-activatable genes (the 202 and 204 genes).

Physical mapping of a family of interferon-activated genes, serum amyloid P-component, and alpha-spectrin on mouse chromosome 1

This report defines genetic and physical relationships among alpha-spectrin (Spna-1), serum amyloid P-component (Sap), and a family of interferon-activated genes, provisionally designated Ifi202, Ifi203, and Ifi204. By linkage analysis using a large panel of interspecific backcross mice, Sap, Ifi202, and Ifi204 were shown to be tightly linked to Spna-1 on distal mouse chromosome 1. By pulsed field electrophoresis, a genomic restriction map of 6400 kb of distal mouse chromosome 1 was generated, linking genes encoding Sap, (Ifi202, Ifi203, Ifi204), and Spna-1 in that order within 450-1000 kb (where the genes within brackets were not ordered). The interferon-activated genes Ifi202, Ifi203, and Ifi204 were linked within 75-150 kb. Furthermore, genes transcriptionally activated by cytokines, namely Sap, Ifi202, Ifi203, and Ifi204, were located within 450 kb. These studies suggest the possibility that selective pressure may maintain in physical proximity gene clusters which are under coordinate transcriptional control.

Interferons as gene activators: a cluster of six interferon-activatable genes is linked to the erythroid alpha-spectrin locus on murine chromosome 1

Several interferon-activatable murine genes were mapped to murine chromosomes by hybridizing cDNA probes to Southern blots of genomic DNA samples from a panel of mouse-hamster somatic cell hybrid lines. The 12 gene is located on chromosome 12 and it specifies a 3.6-kb mRNA. The 204 gene (specifying a 2.5-kb mRNA), and three genes of the 203 gene family (hybridizing to five mRNAs of sizes between 2 and 4.5 kb), together with the 202 gene (specifying a 2-kb mRNA) are located on murine chromosome 1. By restriction fragment length polymorphism analysis of DNA samples prepared from a panel of recombinant inbred mouse lines (C57BL/6J D DBA/2J) and from 85 [C3H/HeJ-gld/gld x Mus spretus) F1 X C3H/HeJ-gld/gld] backcross mice we established a close linkage of the 202, 203, and 204 genes to the erythroid alpha-spectrin gene (Spna-1) on distal murine chromosome 1. Cosmids containing the 202, 203, and 204 genes were isolated from a library derived from AKR mouse DNA. Southern blot analysis of such cosmids revealed: (a) hybridization of a partial 203 cDNA to three genes of the 203 gene family; (b) cross-hybridization of the 202 and 204 genes with one another and with a third gene (designated as 201 gene), and (c) a close linkage of genes of the 203 family with the 201, 202, and 204 genes. These results indicate the existence of a cluster of at least six closely linked, interferon-activatable genes on distal murine chromosome 1 in the vicinity of the Spna-1 locus and also of the Minor lymphocyte stimulating locus (Mlsa).

Interferons as gene activators: close linkage of two interferon-activatable murine genes

Previously we have identified a mouse genomic clone (clone 5) which specifies the 5' flanking region and the 5' terminal exon of an interferon-activatable gene (202 gene). Here, we show that about 5 kb upstream from this flanking region in clone 5 there occurs a 3' terminal region of a second interferon-activatable gene (203 gene). The two genes are transcribed in the same direction. Segments from the 203 gene can be hybridized to a set of five interferon-inducible RNAs. The 203 mRNAs are induced about 15-fold in interferon-treated Ehrlich ascites tumor cells.