Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

/pdf/gene-ontology-tool-for-the-unification-of-biology-ppsktrcye3.pdf

Gene Ontology: tool for the unification of biology

We explored genomic expression patterns in the yeast Saccharomyces cerevisiae responding to diverse environmental transitions. DNA microarrays were used to measure changes in transcript levels over time for almost every yeast gene, as cells responded to temperature shocks, hydrogen peroxide, the superoxide-generating drug menadione, the sulfhydryl-oxidizing agent diamide, the disulfide-reducing agent dithiothreitol, hyper- and hypo-osmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase. A large set of genes (approximately 900) showed a similar drastic response to almost all of these environmental changes. Additional features of the genomic responses were specialized for specific conditions. Promoter analysis and subsequent characterization of the responses of mutant strains implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators. Physiological themes in the genomic responses to specific environmental stresses provided insights into the effects of those stresses on the cell.

/pdf/genomic-expression-programs-in-the-response-of-yeast-cells-4xrnicxf7t.pdf

Genomic expression programs in the response of yeast cells to environmental changes.

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE
 .

/pdf/an-automated-method-for-finding-molecular-complexes-in-large-1xr24lgr4a.pdf

An automated method for finding molecular complexes in large protein interaction networks.

Pseudomonas aeruginosa is a ubiquitous environmental bacterium that is one of the top three causes of opportunistic human infections. A major factor in its prominence as a pathogen is its intrinsic resistance to antibiotics and disinfectants. Here we report the complete sequence of P. aeruginosa strain PAO1. At 6.3 million base pairs, this is the largest bacterial genome sequenced, and the sequence provides insights into the basis of the versatility and intrinsic drug resistance of P. aeruginosa. Consistent with its larger genome size and environmental adaptability, P. aeruginosa contains the highest proportion of regulatory genes observed for a bacterial genome and a large number of genes involved in the catabolism, transport and efflux of organic compounds as well as four potential chemotaxis systems. We propose that the size and complexity of the P. aeruginosa genome reflect an evolutionary adaptation permitting it to thrive in diverse environments and resist the effects of a variety of antimicrobial substances.

/pdf/complete-genome-sequence-of-pseudomonas-aeruginosa-pao1-an-u88d4e2n3p.pdf

Complete genome sequence of Pseudomonas aeruginosa PAO1, an opportunistic pathogen.

The Saccharomyces Genome Database (SGD) stores and organizes information about the nearly 6200 genes in the yeast genome. The information is organized around the ‘locus page’ and directs users to the detailed information they seek. SGD is endeavoring to integrate the existing information about yeast genes with the large volume of data generated by functional analyses that are beginning to appear in the literature and on web sites. New features will include searches of systematic analyses and Gene Summary Paragraphs that succinctly review the literature for each gene. In addition to current information, such as gene product and phenotype descriptions, the new locus page will also describe a gene product’s cellular process, function and localization using a controlled vocabulary developed in collaboration with two other model organism databases. We describe these developments in SGD through the newly reorganized locus page. The SGD is accessible via the WWW at http://genome-www. stanford. edu/Saccharomyces/

/pdf/integrating-functional-genomic-information-into-the-1tg5xpv7xh.pdf

Integrating functional genomic information into the Saccharomyces Genome Database

The Saccharomyces Genome Database (SGD) collects and organizes information about the molecular biology and genetics of the yeast Saccharomyces cerevisiae. The latest protein structure and comparison tools available at SGD are presented here. With the completion of the yeast sequence and the Caenorhabditis elegans sequence soon to follow, comparison of proteins from complete eukaryotic proteomes will be an extremely powerful way to learn more about a particular protein's structure, its function, and its relationships with other proteins. SGD can be accessed through the World Wide Web at http://genome-www.stanford.edu/Saccharomyces/

/pdf/using-the-saccharomyces-genome-database-sgd-for-analysis-of-4kh6i4y2q2.pdf

Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure.

We have made a DNA microarray that includes not only all the open reading frames (ORFs) and other features in the yeast genome, but also all the intergenic regions. We are using this as a tool to construct genome-wide maps of DNA-protein interactions for proteins that interact directly or indirectly with DNA or chromatin in vivo. Proteins are crosslinked to DNA in vivo using formaldehyde and the crosslinked DNA is extracted and sheared. DNA specifically associated with any protein of interest is immunoprecipitated using a specific antibody against the protein or an epitope tag that may be fused to the protein. The selected DNA, representing loci that the protein interacts with in vivo, can be identified by fluorescently labelling it and hybridizing it to the microarray along with an appropriate reference probe. This approach is being used to map the genome-wide interactions of sequence-specific DNA binding proteins, components of the transcription machinery and chromatin components, under a variety of conditions.

/pdf/genome-wide-maps-of-dna-protein-interactions-using-a-yeast-40i7n2gpxt.pdf

Charles R. Scafe

Papers

Integrating functional genomic information into the Saccharomyces Genome Database

Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure.

Genome-wide maps of DNA-protein interactions using a yeast ORF and intergenic microarray