Thu, 21 Aug 2008 01:49:30 BST CiteULike: Author Dolinski http://www.citeulike.org/author/Dolinski CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/group/894/article/2800782 <i>Nature reviews. Genetics (13 May 2008)</i><br /><br />The Gene Ontology (GO) project is a collaboration among model organism databases to describe gene products from all organisms using a consistent and computable language. GO produces sets of explicitly defined, structured vocabularies that describe biological processes, molecular functions and cellular components of gene products in both a computer- and human-readable manner. Here we describe key aspects of GO, which, when overlooked, can cause erroneous results, and address how these pitfalls can be avoided. Use and misuse of the gene ontology annotations. Seung Yon Rhee Valerie Wood Kara Dolinski Sorin Draghici doi:10.1038/nrg2363 Nature reviews. Genetics (13 May 2008) 2008-05-15T05:58:28-00:00 2008 Nature reviews. Genetics 1471-0064 genetics genome methods ontology programming http://www.citeulike.org/group/894/article/212874 <i>Nat Genet, Vol. 25, No. 1. (May 2000), pp. 25-29.</i> Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. M Ashburner CA Ball JA Blake D Botstein H Butler JM Cherry AP Davis K Dolinski SS Dwight JT Eppig MA Harris DP Hill L Issel-Tarver A Kasarskis S Lewis JC Matese JE Richardson M Ringwald GM Rubin G Sherlock doi:10.1038/75556 Nat Genet, Vol. 25, No. 1. (May 2000), pp. 25-29. 2005-05-27T12:30:22-00:00 2000 Nat Genet 1061-4036 25 1 25 29 databases data-mining genetics genome ontology protein proteins proteomics http://www.citeulike.org/user/jyuh/article/2423596 <i>Nucleic Acids Res, Vol. 36, No. Database issue. (January 2008)</i><br /><br />The Biological General Repository for Interaction Datasets (BioGRID) database (http://www.thebiogrid.org) was developed to house and distribute collections of protein and genetic interactions from major model organism species. BioGRID currently contains over 198 000 interactions from six different species, as derived from both high-throughput studies and conventional focused studies. Through comprehensive curation efforts, BioGRID now includes a virtually complete set of interactions reported to date in the primary literature for both the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe. A number of new features have been added to the BioGRID including an improved user interface to display interactions based on different attributes, a mirror site and a dedicated interaction management system to coordinate curation across different locations. The BioGRID provides interaction data with monthly updates to Saccharomyces Genome Database, Flybase and Entrez Gene. Source code for the BioGRID and the linked Osprey network visualization system is now freely available without restriction. The BioGRID Interaction Database: 2008 update. BJ Breitkreutz C Stark T Reguly L Boucher A Breitkreutz M Livstone R Oughtred DH Lackner J Bähler V Wood K Dolinski M Tyers Nucleic Acids Res, Vol. 36, No. Database issue. (January 2008) 2008-02-24T22:29:12-00:00 2008 Nucleic Acids Res 1362-4962 36 Database issue database interactome http://www.citeulike.org/user/cerca/article/3026072 <i>Annual Review of Genetics, Vol. 41, No. 1. (2007), pp. 465-507.</i><br /><br />In recent years, it has become clear that all of the organisms on the Earth are related to each other in ways that can be documented by molecular sequence comparison. In this review, we focus on the evolutionary relationships among the proteins of the eukaryotes, especially those that allow inference of function from one species to another. Data and illustrations are derived from specific comparison of eight species: Homo sapiens, Mus musculus, Arabidopsis thaliana, Caenorhabditis elegans, Danio rerio, Saccharomyces cerevisiae, and Plasmodium falciparum. Orthology and Functional Conservation in Eukaryotes Kara Dolinski David Botstein doi:10.1146/annurev.genet.40.110405.090439 Annual Review of Genetics, Vol. 41, No. 1. (2007), pp. 465-507. 2008-07-22T04:58:10-00:00 2007 Annual Review of Genetics 41 1 465 507 orthology http://www.citeulike.org/user/AbnerCYH/article/2609417 <i>Genome Biol, Vol. 6, No. 13. (2005)</i><br /><br />We have developed a general probabilistic system for query-based discovery of pathway-specific networks through integration of diverse genome-wide data. This framework was validated by accurately recovering known networks for 31 biological processes in Saccharomyces cerevisiae and experimentally verifying predictions for the process of chromosomal segregation. Our system, bioPIXIE, a public, comprehensive system for integration, analysis, and visualization of biological network predictions for S. cerevisiae, is freely accessible over the worldwide web. Discovery of biological networks from diverse functional genomic data. CL Myers D Robson A Wible MA Hibbs C Chiriac CL Theesfeld K Dolinski OG Troyanskaya doi:10.1186/gb-2005-6-13-r114 Genome Biol, Vol. 6, No. 13. (2005) 2008-03-28T20:41:18-00:00 2005 Genome Biol 1465-6914 6 13 algorithms biology complex graph http://www.citeulike.org/user/chasmand/article/694902 <i>Journal of Biology, Vol. 5, No. 4. (08 June 2006), 11.</i><br /><br />BACKGROUND:The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference.RESULTS:We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID (http://www.thebiogrid.org) and SGD (http://www.yeastgenome.org/) databases.CONCLUSION:Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks. Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae Teresa Reguly Ashton Breitkreutz Lorrie Boucher Bobby-Joe Breitkreutz Gary Hon Chad Myers Ainslie Parsons Helena Friesen Rose Oughtred Amy Tong Chris Stark Yuen Ho David Botstein Brenda Andrews Charles Boone Olga Troyanskya Trey Ideker Kara Dolinski Nizar Batada Mike Tyers doi:10.1186/jbiol36 Journal of Biology, Vol. 5, No. 4. (08 June 2006), 11. 2006-06-13T14:14:14-00:00 2006 Journal of Biology 1475-4924 5 4 11 biogrid databases http://www.citeulike.org/user/frohike/article/307432 <i>Proc Natl Acad Sci U S A, Vol. 100, No. 14. (8 July 2003), pp. 8348-8353.</i><br /><br />Genomic sequencing is no longer a novelty, but gene function annotation remains a key challenge in modern biology. A variety of functional genomics experimental techniques are available, from classic methods such as affinity precipitation to advanced high-throughput techniques such as gene expression microarrays. In the future, more disparate methods will be developed, further increasing the need for integrated computational analysis of data generated by these studies. We address this problem with MAGIC (Multisource Association of Genes by Integration of Clusters), a general framework that uses formal Bayesian reasoning to integrate heterogeneous types of high-throughput biological data (such as large-scale two-hybrid screens and multiple microarray analyses) for accurate gene function prediction. The system formally incorporates expert knowledge about relative accuracies of data sources to combine them within a normative framework. MAGIC provides a belief level with its output that allows the user to vary the stringency of predictions. We applied MAGIC to Saccharomyces cerevisiae genetic and physical interactions, microarray, and transcription factor binding sites data and assessed the biological relevance of gene groupings using Gene Ontology annotations produced by the Saccharomyces Genome Database. We found that by creating functional groupings based on heterogeneous data types, MAGIC improved accuracy of the groupings compared with microarray analysis alone. We describe several of the biological gene groupings identified. A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). OG Troyanskaya K Dolinski AB Owen RB Altman D Botstein doi:10.1073/pnas.0832373100 Proc Natl Acad Sci U S A, Vol. 100, No. 14. (8 July 2003), pp. 8348-8353. 2005-08-30T17:52:41-00:00 2003 Proc Natl Acad Sci U S A 0027-8424 100 14 8348 8353 bayesian_integration bayesian_network gene_ontology http://www.citeulike.org/user/carlk/article/2220660 <i>Nucl. Acids Res. (3 November 2007), gkm909.</i><br /><br />The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) collects and organizes biological information about the chromosomal features and gene products of the budding yeast Saccharomyces cerevisiae. Although published data from traditional experimental methods are the primary sources of evidence supporting Gene Ontology (GO) annotations for a gene product, high-throughput experiments and computational predictions can also provide valuable insights in the absence of an extensive body of literature. Therefore, GO annotations available at SGD now include high-throughput data as well as computational predictions provided by the GO Annotation Project (GOA UniProt; http://www.ebi.ac.uk/GOA/). Because the annotation method used to assign GO annotations varies by data source, GO resources at SGD have been modified to distinguish data sources and annotation methods. In addition to providing information for genes that have not been experimentally characterized, GO annotations from independent sources can be compared to those made by SGD to help keep the literature-based GO annotations current. 10.1093/nar/gkm909 Gene Ontology annotations at SGD: new data sources and annotation methods Eurie Hong Rama Balakrishnan Qing Dong Karen Christie Julie Park Gail Binkley Maria Costanzo Selina Dwight Stacia Engel Dianna Fisk Jodi Hirschman Benjamin Hitz Cynthia Krieger Michael Livstone Stuart Miyasato Robert Nash Rose Oughtred Marek Skrzypek Shuai Weng Edith Wong Kathy Zhu Kara Dolinski David Botstein Michael Cherry doi:10.1093/nar/gkm909 Nucl. Acids Res. (3 November 2007), gkm909. 2008-01-11T19:56:32-00:00 2007 Nucl. Acids Res. gkm909 go yeast http://www.citeulike.org/user/michaelbarton/article/2007416 <i>Mol Biol Cell, Vol. 16, No. 5. (May 2005), pp. 2503-2517.</i><br /><br />We studied the physiological response to glucose limitation in batch and steady-state (chemostat) cultures of Saccharomyces cerevisiae by following global patterns of gene expression. Glucose-limited batch cultures of yeast go through two sequential exponential growth phases, beginning with a largely fermentative phase, followed by an essentially completely aerobic use of residual glucose and evolved ethanol. Judging from the patterns of gene expression, the state of the cells growing at steady state in glucose-limited chemostats corresponds most closely with the state of cells in batch cultures just before they undergo this "diauxic shift." Essentially the same pattern was found between chemostats having a fivefold difference in steady-state growth rate (the lower rate approximating that of the second phase respiratory growth rate in batch cultures). Although in both cases the cells in the chemostat consumed most of the glucose, in neither case did they seem to be metabolizing it primarily through respiration. Although there was some indication of a modest oxidative stress response, the chemostat cultures did not exhibit the massive environmental stress response associated with starvation that also is observed, at least in part, during the diauxic shift in batch cultures. We conclude that despite the theoretical possibility of a switch to fully aerobic metabolism of glucose in the chemostat under conditions of glucose scarcity, homeostatic mechanisms are able to carry out metabolic adjustment as if fermentation of the glucose is the preferred option until the glucose is entirely depleted. These results suggest that some aspect of actual starvation, possibly a component of the stress response, may be required for triggering the metabolic remodeling associated with the diauxic shift. Homeostatic adjustment and metabolic remodeling in glucose-limited yeast cultures. MJ Brauer AJ Saldanha K Dolinski D Botstein doi:10.1091/mbc.E04-11-0968 Mol Biol Cell, Vol. 16, No. 5. (May 2005), pp. 2503-2517. 2007-11-28T20:52:32-00:00 2005 Mol Biol Cell 1059-1524 16 5 2503 2517 diauxic-shift gene-expression yeast http://www.citeulike.org/user/fishtank/article/580622 <i>Genome Res, Vol. 15, No. 12. (December 2005), pp. 1611-1619.</i><br /><br />Research with budding yeast (Saccharomyces cerevisiae) has been transformed by the publication, nearly a decade ago, of the entire genome DNA sequence. The introduction of this first eukaryotic genomic sequence changed the yeast research environment significantly, not just because of dramatic progress in technical means but also because the sequence made accessible a new class of scientific questions. A central goal of yeast research remains the determination of the biological role of every sequence feature in the yeast genome. The most remarkable change has been the shift in perspective from focus on individual genes and functionalities to a more global view of how the cellular networks and systems interact and function together to produce the highly evolved organism we see today. Changing perspectives in yeast research nearly a decade after the genome sequence. K Dolinski D Botstein doi:10.1101/gr.3727505 Genome Res, Vol. 15, No. 12. (December 2005), pp. 1611-1619. 2006-04-09T02:32:05-00:00 2005 Genome Res 1088-9051 15 12 1611 1619 yeast http://www.citeulike.org/user/michaelbarton/article/1205307 <i>PLoS ONE, Vol. 2 (2007)</i><br /><br />Ten years have passed since the genome of Saccharomyces cerevisiae-more precisely, the S288c strain-was completely sequenced. However, experimental work in yeast is commonly performed using strains that are of unknown genetic relationship to S288c. Here, we characterized the nucleotide-level similarity between S288c and seven commonly used lab strains (A364A, W303, FL100, CEN.PK, summation operator1278b, SK1 and BY4716) using 25mer oligonucleotide microarrays that provide complete and redundant coverage of the approximately 12 Mb Saccharomyces cerevisiae genome. Using these data, we assessed the frequency and distribution of nucleotide variation in comparison to the sequenced reference genome. These data allow us to infer the relationships between experimentally important strains of yeast and provide insight for experimental designs that are sensitive to sequence variation. We propose a rational approach for near complete sequencing of strains related to the reference using these data and directed re-sequencing. These data and new visualization tools are accessible online in a new resource: the Yeast SNPs Browser (YSB; http://gbrowse.princeton.edu/cgi-bin/gbrowse/yeast_strains_snps) that is available to all researchers. Genome-Wide Analysis of Nucleotide-Level Variation in Commonly Used Saccharomyces cerevisiae Strains. J Schacherer DM Ruderfer D Gresham K Dolinski D Botstein L Kruglyak doi:10.1371/journal.pone.0000322 PLoS ONE, Vol. 2 (2007) 2007-04-04T01:06:20-00:00 2007 PLoS ONE 1932-6203 2 2008-2-mdb 2008-3-mdb genetic-variation insertion-deletion mutation reviewed snp tiling-array yeast http://www.citeulike.org/user/talponer/article/256350 <i>Nucleic Acids Res, Vol. 32, No. Database issue. (1 January 2004)</i><br /><br />The Gene Ontology (GO) project (http://www. geneontology.org/) provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses. The Gene Ontology (GO) database and informatics resource. MA Harris J Clark A Ireland J Lomax M Ashburner R Foulger K Eilbeck S Lewis B Marshall C Mungall J Richter GM Rubin JA Blake C Bult M Dolan H Drabkin JT Eppig DP Hill L Ni M Ringwald R Balakrishnan JM Cherry KR Christie MC Costanzo SS Dwight S Engel DG Fisk JE Hirschman EL Hong RS Nash A Sethuraman CL Theesfeld D Botstein K Dolinski B Feierbach T Berardini S Mundodi SY Rhee R Apweiler D Barrell E Camon E Dimmer V Lee R Chisholm P Gaudet W Kibbe R Kishore EM Schwarz P Sternberg M Gwinn L Hannick J Wortman M Berriman V Wood N de la Cruz P Tonellato P Jaiswal T Seigfried R White Nucleic Acids Res, Vol. 32, No. Database issue. (1 January 2004) 2005-07-14T22:06:49-00:00 2004 Nucleic Acids Res 1362-4962 32 Database issue tesi http://www.citeulike.org/user/schulman/article/2332891 <i>Pers Soc Psychol Bull, Vol. 27, No. 11. (1 November 2001), pp. 1395-1406.</i><br /><br />When a request is preceded by a casual dialogue, the approached person is more likely to comply than when the same request follows a monologue. This effect appeared to be strong and replicable in a series of field studies. Across experiments, the issues discussed in conversations between the confederates and the participants and the nature of the critical request varied, suggesting that the effect is generalized. In social situations, the two basic modes of communication (dialogue and monologue) are characteristic of different types of interactions. Dialogue is characteristic of encounters with one's friends and acquaintances, whereas monologue is more prevalent in contacts with strangers. As a result of social learning, a dialogue makes us prone to treat a stranger as someone we know and thus become more eager to comply with her or his requests. Although the results obtained in the studies are consisted with this model, alternative explanations and limitations of the research also are discussed. 10.1177/01461672012711001 Dialogue Involvement as a Social Influence Technique Dariusz Dolinski Magdalena Nawrat Izabela Rudak doi:10.1177/01461672012711001 Pers Soc Psychol Bull, Vol. 27, No. 11. (1 November 2001), pp. 1395-1406. 2008-02-05T01:47:54-00:00 2001 Pers Soc Psychol Bull 27 11 1395 1406 persuasion social-dialogue http://www.citeulike.org/user/jyuh/article/1070582 <i>American Journal of Pathology, Vol. 170, No. 1. (1 January 2007), pp. 110-125.</i> v6 Integrin Regulates Renal Fibrosis and Inflammation in Alport Mouse Hahm Kyungmin Lukashev E Matvey Luo Yi Yang J William Dolinski M Brian Weinreb H Paul Simon J Kenneth Chun Wang Li Leone R Diane Lobb R Roy Mccrann J Donald Allaire E Normand Horan S Gerald Fogo Agnes Kalluri Raghu Shield F Charles Sheppard Dean Gardner A Humphrey Violette M Shelia doi:10.2353/ajpath.2007.060158 American Journal of Pathology, Vol. 170, No. 1. (1 January 2007), pp. 110-125. 2007-01-27T11:11:33-00:00 2007 American Journal of Pathology 0002-9440 170 1 110 125 American Society for Investigative Pathology no-tag http://www.citeulike.org/user/jyuh/article/1969816 <i>PLoS ONE, Vol. 2, No. 1. (2007)</i><br /><br />Many biological databases that provide comparative genomics information and tools are now available on the internet. While certainly quite useful, to our knowledge none of the existing databases combine results from multiple comparative genomics methods with manually curated information from the literature. Here we describe the Princeton Protein Orthology Database (P-POD, http://ortholog.princeton.edu), a user-friendly database system that allows users to find and visualize the phylogenetic relationships among predicted orthologs (based on the OrthoMCL method) to a query gene from any of eight eukaryotic organisms, and to see the orthologs in a wider evolutionary context (based on the Jaccard clustering method). In addition to the phylogenetic information, the database contains experimental results manually collected from the literature that can be compared to the computational analyses, as well as links to relevant human disease and gene information via the OMIM, model organism, and sequence databases. Our aim is for the P-POD resource to be extremely useful to typical experimental biologists wanting to learn more about the evolutionary context of their favorite genes. P-POD is based on the commonly used Generic Model Organism Database (GMOD) schema and can be downloaded in its entirety for installation on one's own system. Thus, bioinformaticians and software developers may also find P-POD useful because they can use the P-POD database infrastructure when developing their own comparative genomics resources and database tools. The Princeton Protein Orthology Database (P-POD): a comparative genomics analysis tool for biologists. S Heinicke MS Livstone C Lu R Oughtred F Kang SV Angiuoli O White D Botstein K Dolinski doi:10.1371/journal.pone.0000766 PLoS ONE, Vol. 2, No. 1. (2007) 2007-11-24T03:39:49-00:00 2007 PLoS ONE 1932-6203 2 1 no-tag http://www.citeulike.org/user/roys/article/1895625 <i>Science, Vol. 282, No. 5396. (11 December 1998), pp. 2022-2028.</i><br /><br />10.1126/science.282.5396.2022 Comparison of the Complete Protein Sets of Worm and Yeast: Orthology and Divergence Stephen Chervitz L Aravind Gavin Sherlock Catherine Ball Eugene Koonin Selina Dwight Midori Harris Kara Dolinski Scott Mohr Temple Smith Shuai Weng Michael Cherry David Botstein doi:10.1126/science.282.5396.2022 Science, Vol. 282, No. 5396. (11 December 1998), pp. 2022-2028. 2007-11-10T19:19:20-00:00 1998 Science 282 5396 2022 2028 proteome_comparison http://www.citeulike.org/user/dullhunk/article/134619 <i>Briefings in Bioinformatics, Vol. 5, No. 1. (March 2004), pp. 9-22.</i><br /><br />A scientific database can be a powerful tool for biologists in an era where large-scale genomic analysis, combined with smaller-scale scientific results, provides new insights into the roles of genes and their products in the cell. However, the collection and assimilation of data is, in itself, not enough to make a database useful. The data must be incorporated into the database and presented to the user in an intuitive and biologically significant manner. Most importantly, this presentation must be driven by the user's point of view; that is, from a biological perspective. The success of a scientific database can therefore be measured by the response of its users - statistically, by usage numbers and, in a less quantifiable way, by its relationship with the community it serves and its ability to serve as a model for similar projects. Since its inception ten years ago, the Saccharomyces Genome Database (SGD) has seen a dramatic increase in its usage, has developed and maintained a positive working relationship with the yeast research community, and has served as a template for at least one other database. The success of SGD, as measured by these criteria, is due in large part to philosophies that have guided its mission and organisation since it was established in 1993. This paper aims to detail these philosophies and how they shape the organisation and presentation of the database. Saccharomyces genome database: underlying principles and organisation. SS Dwight R Balakrishnan KR Christie MC Costanzo K Dolinski SR Engel B Feierbach DG Fisk J Hirschman EL Hong L Issel-Tarver RS Nash A Sethuraman B Starr CL Theesfeld R Andrada G Binkley Q Dong C Lane M Schroeder S Weng D Botstein JM Cherry Briefings in Bioinformatics, Vol. 5, No. 1. (March 2004), pp. 9-22. 2005-03-22T11:37:00-00:00 2004 Briefings in Bioinformatics 1467-5463 5 1 9 22 bib sgd http://www.citeulike.org/user/jpoyatos/article/1066500 <i>Nucleic Acids Res, Vol. 29, No. 1. (1 January 2001), pp. 80-81.</i><br /><br />Upon the completion of the SACCHAROMYCES: cerevisiae genomic sequence in 1996 [Goffeau,A. et al. (1997) NATURE:, 387, 5], several creative and ambitious projects have been initiated to explore the functions of gene products or gene expression on a genome-wide scale. To help researchers take advantage of these projects, the SACCHAROMYCES: Genome Database (SGD) has created two new tools, Function Junction and Expression Connection. Together, the tools form a central resource for querying multiple large-scale analysis projects for data about individual genes. Function Junction provides information from diverse projects that shed light on the role a gene product plays in the cell, while Expression Connection delivers information produced by the ever-increasing number of microarray projects. WWW access to SGD is available at genome-www.stanford. edu/Saccharomyces/. Saccharomyces Genome Database provides tools to survey gene expression and functional analysis data. CA Ball H Jin G Sherlock S Weng JC Matese R Andrada G Binkley K Dolinski SS Dwight MA Harris L Issel-Tarver M Schroeder D Botstein JM Cherry Nucleic Acids Res, Vol. 29, No. 1. (1 January 2001), pp. 80-81. 2007-01-25T10:29:09-00:00 2001 Nucleic Acids Res 1362-4962 29 1 80 81 no-tag http://www.citeulike.org/user/nuke2005/article/544 <i>Nucleic Acids Res, Vol. 32 Database issue (1 January 2004)</i><br /><br />The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/), a scientific database of the molecular biology and genetics of the yeast Saccharomyces cerevisiae, has recently developed several new resources that allow the comparison and integration of information on a genome-wide scale, enabling the user not only to find detailed information about individual genes, but also to make connections across groups of genes with common features and across different species. The Fungal Alignment Viewer displays alignments of sequences from multiple fungal genomes, while the Sequence Similarity Query tool displays PSI-BLAST alignments of each S.cerevisiae protein with similar proteins from any species whose sequences are contained in the non-redundant (nr) protein data set at NCBI. The Yeast Biochemical Pathways tool integrates groups of genes by their common roles in metabolism and displays the metabolic pathways in a graphical form. Finally, the Find Chromosomal Features search interface provides a versatile tool for querying multiple types of information in SGD. Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms. KR Christie S Weng R Balakrishnan MC Costanzo K Dolinski SS Dwight SR Engel B Feierbach DG Fisk JE Hirschman EL Hong L Issel-Tarver R Nash A Sethuraman B Starr CL Theesfeld R Andrada G Binkley Q Dong C Lane M Schroeder D Botstein JM Cherry Nucleic Acids Res, Vol. 32 Database issue (1 January 2004) 2004-11-22T00:17:30-00:00 2004 Nucleic Acids Res 1362-4962 32 Database issue no-tag http://www.citeulike.org/user/dragonrez/article/541 <i>Nucleic Acids Res, Vol. 30, No. 1. (1 January 2002), pp. 69-72.</i><br /><br />The Saccharomyces Genome Database (SGD) resources, ranging from genetic and physical maps to genome-wide analysis tools, reflect the scientific progress in identifying genes and their functions over the last decade. As emphasis shifts from identification of the genes to identification of the role of their gene products in the cell, SGD seeks to provide its users with annotations that will allow relationships to be made between gene products, both within Saccharomyces cerevisiae and across species. To this end, SGD is annotating genes to the Gene Ontology (GO), a structured representation of biological knowledge that can be shared across species. The GO consists of three separate ontologies describing molecular function, biological process and cellular component. The goal is to use published information to associate each characterized S.cerevisiae gene product with one or more GO terms from each of the three ontologies. To be useful, this must be done in a manner that allows accurate associations based on experimental evidence, modifications to GO when necessary, and careful documentation of the annotations through evidence codes for given citations. Reaching this goal is an ongoing process at SGD. For information on the current progress of GO annotations at SGD and other participating databases, as well as a description of each of the three ontologies, please visit the GO Consortium page at http://www.geneontology.org. SGD gene associations to GO can be found by visiting our site at http://genome-www.stanford.edu/Saccharomyces/. Saccharomyces Genome Database (SGD) provides secondary gene annotation using the Gene Ontology (GO). SS Dwight MA Harris K Dolinski CA Ball G Binkley KR Christie DG Fisk L Issel-Tarver M Schroeder G Sherlock A Sethuraman S Weng D Botstein JM Cherry Nucleic Acids Res, Vol. 30, No. 1. (1 January 2002), pp. 69-72. 2004-11-22T00:17:30-00:00 2002 Nucleic Acids Res 1362-4962 30 1 69 72 no-tag http://www.citeulike.org/user/irenas/article/543 <i>Nucleic Acids Res, Vol. 31, No. 1. (1 January 2003), pp. 216-218.</i><br /><br />The Saccharomyces Genome Database (SGD: http://genome-www.stanford.edu/Saccharomyces/) has recently developed new resources to provide more complete information about proteins from the budding yeast Saccharomyces cerevisiae. The PDB Homologs page provides structural information from the Protein Data Bank (PDB) about yeast proteins and/or their homologs. SGD has also created a resource that utilizes the eMOTIF database for motif information about a given protein. A third new resource is the Protein Information page, which contains protein physical and chemical properties, such as molecular weight and hydropathicity scores, predicted from the translated ORF sequence. Saccharomyces Genome Database (SGD) provides biochemical and structural information for budding yeast proteins. S Weng Q Dong R Balakrishnan K Christie M Costanzo K Dolinski SS Dwight S Engel DG Fisk E Hong L Issel-Tarver A Sethuraman C Theesfeld R Andrada G Binkley C Lane M Schroeder D Botstein J Michael Cherry Nucleic Acids Res, Vol. 31, No. 1. (1 January 2003), pp. 216-218. 2004-11-22T00:17:30-00:00 2003 Nucleic Acids Res 1362-4962 31 1 216 218 db yeast