Sat, 26 Jul 2008 08:09:58 BST CiteULike: sgoetz's library [110 articles] http://www.citeulike.org/user/sgoetz CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/sgoetz/article/197344 <i>Bioinformatics, Vol. 20, No. 2. (22 January 2004), pp. 170-179.</i><br /><br />MOTIVATION: Protein families evolve a multiplicity of functions through gene duplication, speciation and other processes. As a number of studies have shown, standard methods of protein function prediction produce systematic errors on these data. Phylogenomic analysis--combining phylogenetic tree construction, integration of experimental data and differentiation of orthologs and paralogs--has been proposed to address these errors and improve the accuracy of functional classification. The explicit integration of structure prediction and analysis in this framework, which we call structural phylogenomics, provides additional insights into protein superfamily evolution. RESULTS: Results of protein functional classification using phylogenomic analysis show fewer expected false positives overall than when pairwise methods of functional classification are employed. We present an overview of the motivations and fundamental principles of phylogenomic analysis, new methods developed for the key tasks, benchmark datasets for these tasks (when available) and suggest procedures to increase accuracy. We also discuss some of the methods used in the Celera Genomics high-throughput phylogenomic classification of the human genome. AVAILABILITY: Software tools from the Berkeley Phylogenomics Group are available at http://phylogenomics.berkeley.edu Phylogenomic inference of protein molecular function: advances and challenges. K Sjölander doi:10.1093/bioinformatics/bth021 Bioinformatics, Vol. 20, No. 2. (22 January 2004), pp. 170-179. 2005-05-11T21:27:13-00:00 2004 Bioinformatics 1367-4803 20 2 170 179 phylogenomics http://www.citeulike.org/user/sgoetz/article/3041331 <i>Nucleic acids research, Vol. 35, No. Web Server issue. (July 2007)</i><br /><br />The DBAli tools use a comprehensive set of structural alignments in the DBAli database to leverage the structural information deposited in the Protein Data Bank (PDB). These tools include (i) the DBAlit program that allows users to input the 3D coordinates of a protein structure for comparison by MAMMOTH against all chains in the PDB; (ii) the AnnoLite and AnnoLyze programs that annotate a target structure based on its stored relationships to other structures; (iii) the ModClus program that clusters structures by sequence and structure similarities; (iv) the ModDom program that identifies domains as recurrent structural fragments and (v) an implementation of the COMPARER method in the SALIGN command in MODELLER that creates a multiple structure alignment for a set of related protein structures. Thus, the DBAli tools, which are freely accessible via the World Wide Web at http://salilab.org/DBAli/, allow users to mine the protein structure space by establishing relationships between protein structures and their functions. DBAli tools: mining the protein structure space. MA Marti-Renom U Pieper MS Madhusudhan A Rossi N Eswar FP Davis F Al-Shahrour J Dopazo A Sali Nucleic acids research, Vol. 35, No. Web Server issue. (July 2007) 2008-07-24T22:15:39-00:00 2007 Nucleic acids research 1362-4962 35 Web Server issue no-tag http://www.citeulike.org/user/sgoetz/article/2123439 <i>Genome Res., Vol. 8, No. 3. (1 March 1998), pp. 163-167.</i><br /><br />10.1101/gr.8.3.163 Phylogenomics: Improving Functional Predictions for Uncharacterized Genes by Evolutionary Analysis Jonathan Eisen doi:10.1101/gr.8.3.163 Genome Res., Vol. 8, No. 3. (1 March 1998), pp. 163-167. 2007-12-15T10:17:25-00:00 1998 Genome Res. 8 3 163 167 no-tag http://www.citeulike.org/user/sgoetz/article/1611017 <i>BMC Bioinformatics, Vol. 8 Suppl 4 (2007)</i><br /><br />BACKGROUND: Advances in structural biology, including structural genomics, have resulted in a rapid increase in the number of experimentally determined protein structures. However, about half of the structures deposited by the structural genomics consortia have little or no information about their biological function. Therefore, there is a need for tools for automatically and comprehensively annotating the function of protein structures. We aim to provide such tools by applying comparative protein structure annotation that relies on detectable relationships between protein structures to transfer functional annotations. Here we introduce two programs, AnnoLite and AnnoLyze, which use the structural alignments deposited in the DBAli database. DESCRIPTION: AnnoLite predicts the SCOP, CATH, EC, InterPro, PfamA, and GO terms with an average sensitivity of ~90% and average precision of ~80%. AnnoLyze predicts ligand binding site and domain interaction patches with an average sensitivity of ~70% and average precision of ~30%, correctly localizing binding sites for small molecules in ~95% of its predictions. CONCLUSION: The AnnoLite and AnnoLyze programs for comparative annotation of protein structures can reliably and automatically annotate new protein structures. The programs are fully accessible via the Internet as part of the DBAli suite of tools at http://salilab.org/DBAli/. The AnnoLite and AnnoLyze programs for comparative annotation of protein structures. MA Marti-Renom A Rossi F Al-Shahrour FP Davis U Pieper J Dopazo A Sali doi:10.1186/1471-2105-8-S4-S4 BMC Bioinformatics, Vol. 8 Suppl 4 (2007) 2007-08-31T16:53:00-00:00 2007 BMC Bioinformatics 1471-2105 8 Suppl 4 no-tag http://www.citeulike.org/user/sgoetz/article/935546 <i>(September 1997), 9008.</i><br /><br />This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy JJ Jiang DW Conrath (September 1997), 9008. 2006-11-07T14:53:47-00:00 1997 9008 no-tag http://www.citeulike.org/user/sgoetz/article/1238 <i>(1998), pp. 296-304.</i><br /><br />Similarity is an important and widely used concept. Previous definitions of similarity are tied to a particular application or a form of knowledge representation. We present an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model. We demonstrate how our definition can be used to measure the similarity in a number of different domains. 1 Introduction Similarity is a fundamental and widely used concept. Many similarity measures have been... An information-theoretic definition of similarity Dekang Lin (1998), pp. 296-304. 2004-12-01T09:41:43-00:00 1998 296 304 Morgan Kaufmann, San Francisco, CA no-tag http://www.citeulike.org/user/sgoetz/article/879855 <i>(1995), pp. 448-453.</i><br /><br />This paper presents a new measure of semantic similarity in an is-a taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity judgments, with an upper bound of r = 0.90 for human subjects performing the same task), and significantly better than the traditional edge counting approach (r = 0.66). Using Information Content to Evaluate Semantic Similarity in a Taxonomy Philip Resnik (1995), pp. 448-453. 2006-10-01T01:01:30-00:00 1995 448 453 no-tag http://www.citeulike.org/user/sgoetz/article/2274656 <i>Nucleic Acids Res, Vol. 36, No. Database issue. (January 2008)</i><br /><br />GenBank (R) is a comprehensive database that contains publicly available nucleotide sequences for more than 260 000 named organisms, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the web-based BankIt or standalone Sequin programs and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the European Molecular Biology Laboratory Nucleotide Sequence Database in Europe and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through NCBI's retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI Homepage: www.ncbi.nlm.nih.gov. GenBank. DA Benson I Karsch-Mizrachi DJ Lipman J Ostell DL Wheeler doi:10.1093/nar/gkm929 Nucleic Acids Res, Vol. 36, No. Database issue. (January 2008) 2008-01-22T15:50:34-00:00 2008 Nucleic Acids Res 1362-4962 36 Database issue no-tag http://www.citeulike.org/user/sgoetz/article/3014333 <i>The Plant Journal, Vol. 35, No. 6. (2003), pp. 675-692.</i><br /><br />Plant nutrition critically depends on the activity of membrane transporters that translocate minerals from the soil into the plant and are responsible for their intra- and intercellular distribution. Most plant membrane transporters are encoded by multigene families whose members often exhibit overlapping expression patterns and a high degree of sequence homology. Furthermore, many inorganic nutrients are transported by more than one transporter family. These considerations, coupled with a large number of so-far non-annotated putative transporter genes, hamper our progress in understanding how the activity of specific transporters is integrated into a response to fluctuating conditions. We designed an oligonucleotide microarray representing 1096 Arabidopsis transporter genes and analysed the root transporter transcriptome over a 96-h period with respect to 80 mm NaCl, K+ starvation and Ca2+ starvation. Our data show that cation stress led to changes in transcript level of many genes across most transporter gene families. Analysis of transcriptionally modulated genes across all functional groups of transporters revealed families such as V-type ATPases and aquaporins that responded to all treatments, and families 2013 which included putative non-selective cation channels for the NaCl treatment and metal transporters for Ca2+ starvation conditions 2013 that responded to specific ionic environments. Several gene families including primary pumps, antiporters and aquaporins were analysed in detail with respect to the mRNA levels of different isoforms during ion stress. Cluster analysis allowed identification of distinct expression profiles, and several novel putative regulatory motifs were discovered within sets of co-expressed genes. Transcriptome analysis of root transporters reveals participation of multiple gene families in the response to cation stress Frans Maathuis Victor Filatov Pawel Herzyk Gerard Krijger Kristian Axelsen Sixue Chen Brian Green Yi Li Kathryn Madagan Rocío Sánchez-Fernández Brian Forde Michael Palmgren Philip Rea Lorraine Williams Dale Sanders Anna Amtmann doi:10.1046/j.1365-313X.2003.01839.x The Plant Journal, Vol. 35, No. 6. (2003), pp. 675-692. 2008-07-17T13:22:50-00:00 2003 The Plant Journal 35 6 675 692 no-tag http://www.citeulike.org/user/sgoetz/article/2206362 <i>Brief Bioinform, Vol. 9, No. 1. (1 January 2008), pp. 75-90.</i><br /><br />The information explosion in biology makes it difficult for researchers to stay abreast of current biomedical knowledge and to make sense of the massive amounts of online information. Ontologiesspecifications of the entities, their attributes and relationships among the entities in a domain of discourseare increasingly enabling biomedical researchers to accomplish these tasks. In fact, bio-ontologies are beginning to proliferate in step with accruing biological data. The myriad of ontologies being created enables researchers not only to solve some of the problems in handling the data explosion but also introduces new challenges. One of the key difficulties in realizing the full potential of ontologies in biomedical research is the isolation of various communities involved: some workers spend their career developing ontologies and ontology-related tools, while few researchers (biologists and physicians) know how ontologies can accelerate their research. The objective of this review is to give an overview of biomedical ontology in practical terms by providing a functional perspectivedescribing how bio-ontologies can and are being used. As biomedical scientists begin to recognize the many different ways ontologies enable biomedical research, they will drive the emergence of new computer applications that will help them exploit the wealth of research data now at their fingertips. 10.1093/bib/bbm059 Biomedical ontologies: a functional perspective Daniel Rubin Nigam Shah Natalya Noy doi:10.1093/bib/bbm059 Brief Bioinform, Vol. 9, No. 1. (1 January 2008), pp. 75-90. 2008-01-08T03:05:43-00:00 2008 Brief Bioinform 9 1 75 90 no-tag http://www.citeulike.org/user/sgoetz/article/1230118 <i>Protein Eng, Vol. 12, No. 2. (February 1999), pp. 85-94.</i><br /><br />Sequence alignments unambiguously distinguish between protein pairs of similar and non-similar structure when the pairwise sequence identity is high (>40% for long alignments). The signal gets blurred in the twilight zone of 20-35% sequence identity. Here, more than a million sequence alignments were analysed between protein pairs of known structures to re-define a line distinguishing between true and false positives for low levels of similarity. Four results stood out. (i) The transition from the safe zone of sequence alignment into the twilight zone is described by an explosion of false negatives. More than 95% of all pairs detected in the twilight zone had different structures. More precisely, above a cut-off roughly corresponding to 30% sequence identity, 90% of the pairs were homologous; below 25% less than 10% were. (ii) Whether or not sequence homology implied structural identity depended crucially on the alignment length. For example, if 10 residues were similar in an alignment of length 16 (>60%), structural similarity could not be inferred. (iii) The 'more similar than identical' rule (discarding all pairs for which percentage similarity was lower than percentage identity) reduced false positives significantly. (iv) Using intermediate sequences for finding links between more distant families was almost as successful: pairs were predicted to be homologous when the respective sequence families had proteins in common. All findings are applicable to automatic database searches. Twilight zone of protein sequence alignments. B Rost doi:10.1093/protein/12.2.85 Protein Eng, Vol. 12, No. 2. (February 1999), pp. 85-94. 2007-04-16T15:35:28-00:00 1999 Protein Eng 0269-2139 12 2 85 94 no-tag http://www.citeulike.org/user/sgoetz/article/2861660 <i>Nucl. Acids Res., Vol. 29, No. 1. (1 January 2001), pp. 37-40.</i><br /><br />Signature databases are vital tools for identifying distant relationships in novel sequences and hence for inferring protein function. InterPro is an integrated documentation resource for protein families, domains and functional sites, which amalgamates the efforts of the PROSITE, PRINTS, Pfam and ProDom database projects. Each InterPro entry includes a functional description, annotation, literature references and links back to the relevant member database(s). Release 2.0 of InterPro (October 2000) contains over 3000 entries, representing families, domains, repeats and sites of post-translational modification encoded by a total of 6804 different regular expressions, profiles, fingerprints and Hidden Markov Models. Each InterPro entry lists all the matches against SWISS-PROT and TrEMBL (more than 1 000 000 hits from 462 500 proteins in SWISS-PROT and TrEMBL). The database is accessible for text- and sequence-based searches at http://www.ebi.ac.uk/interpro/. Questions can be emailed to interhelp@ebi.ac.uk. 10.1093/nar/29.1.37 The InterPro database, an integrated documentation resource for protein families, domains and functional sites R Apweiler TK Attwood A Bairoch A Bateman E Birney M Biswas P Bucher L Cerutti F Corpet MDR Croning R Durbin L Falquet W Fleischmann J Gouzy H Hermjakob N Hulo I Jonassen D Kahn A Kanapin Y Karavidopoulou R Lopez B Marx NJ Mulder TM Oinn M Pagni F Servant CJA Sigrist EM Zdobnov doi:10.1093/nar/29.1.37 Nucl. Acids Res., Vol. 29, No. 1. (1 January 2001), pp. 37-40. 2008-06-04T15:55:30-00:00 2001 Nucl. Acids Res. 29 1 37 40 no-tag http://www.citeulike.org/user/sgoetz/article/1288547 <i>Genome Res., Vol. 12, No. 5. (1 May 2002), pp. 785-794.</i><br /><br />Recent progress in genomic sequencing, computational biology, and ontology development has presented an opportunity to investigate biological systems from a unique perspective, that is, examining genomes and transcriptomes through the multiple and hierarchical structure of Gene Ontology (GO). We report here our development of GO Engine, a computational platform for GO annotation, and analysis of the resultant GO annotations of human proteins. Protein annotation was centered on sequence homology with GO-annotated proteins and protein domain analysis. Text information analysis and a multiparameter cellular localization predictive tool were also used to increase the annotation accuracy, and to predict novel annotations. The majority of proteins corresponding to full-length mRNA in GenBank, and the majority of proteins in the NR database (nonredundant database of proteins) were annotated with one or more GO nodes in each of the three GO categories. The annotations of GenBank and SWISS-PROT proteins are available to the public at the GO Consortium web site. 10.1101/gr.86902 Large-Scale Protein Annotation through Gene Ontology Hanqing Xie Alon Wasserman Zurit Levine Amit Novik Vladimir Grebinskiy Avi Shoshan Liat Mintz doi:10.1101/gr.86902 Genome Res., Vol. 12, No. 5. (1 May 2002), pp. 785-794. 2007-05-10T15:36:22-00:00 2002 Genome Res. 12 5 785 794 no-tag http://www.citeulike.org/user/sgoetz/article/2814600 <i>International journal of plant genomics, Vol. 2008 (2008)</i><br /><br />Functional annotation of novel sequence data is a primary requirement for the utilization of functional genomics approaches in plant research. In this paper, we describe the Blast2GO suite as a comprehensive bioinformatics tool for functional annotation of sequences and data mining on the resulting annotations, primarily based on the gene ontology (GO) vocabulary. Blast2GO optimizes function transfer from homologous sequences through an elaborate algorithm that considers similarity, the extension of the homology, the database of choice, the GO hierarchy, and the quality of the original annotations. The tool includes numerous functions for the visualization, management, and statistical analysis of annotation results, including gene set enrichment analysis. The application supports InterPro, enzyme codes, KEGG pathways, GO direct acyclic graphs (DAGs), and GOSlim. Blast2GO is a suitable tool for plant genomics research because of its versatility, easy installation, and friendly use. Blast2GO: A Comprehensive Suite for Functional Analysis in Plant Genomics. A Conesa S Götz International journal of plant genomics, Vol. 2008 (2008) 2008-05-20T01:12:42-00:00 2008 International journal of plant genomics 1687-5370 2008 no-tag http://www.citeulike.org/user/sgoetz/article/1883204 <i>Bioinformatics (11 September 2007)</i><br /><br />MOTIVATION: High-throughput experiments such as microarray hybridizations often yield long lists of genes found to share a certain characteristic such as differential expression. Exploring Gene Ontology (GO) annotations for such lists of genes has become a widespread practice to get first insights into the potential biological meaning of the experiment. The standard statistical approach to measuring overrepresentation of GO terms cannot cope with the dependencies resulting from the structure of GO because they analyze each term in isolation. Especially the fact that annotations are inherited from more specific descendant terms can result in certain types of false-positive results with potentially misleading biological interpretation, a phenomenon which we term the inheritance problem. RESULTS: We present here a novel approach to analysis of GO term overrepresentation that determines overrepresentation of terms in the context of annotations to the term's parents. This approach reduces the dependencies between the individual term's measurements, and thereby avoids producing false-positive results owing to the inheritance problem. ROC analysis using study sets with overrepresented GO terms showed a clear advantage for our approach over the standard algorithm with respect to the inheritance problem. Although there can be no gold standard for exploratory methods such as analysis of GO term overrepresentation, analysis of biological datasets suggests that our algorithm tends to identify the core GO terms that are most characteristic of the dataset being analyzed. AVAILABILITY: The Ontologizer can be found at the project homepage http://www.charite.de/ch/medgen/ontologizer CONTACT: vingron@molgen.mpg.de. Improved Detection of Overrepresentation of Gene-Ontology Annotations with Parent-Child Analysis. Steffen Grossmann Sebastian Bauer Peter N Robinson Martin Vingron doi:10.1093/bioinformatics/btm440 Bioinformatics (11 September 2007) 2007-11-08T05:56:07-00:00 2007 Bioinformatics 1460-2059 no-tag http://www.citeulike.org/user/sgoetz/article/722983 <i>Bioinformatics, Vol. 22, No. 13. (1 July 2006), pp. 1600-1607.</i> Improved scoring of functional groups from gene expression data by decorrelating GO graph structure Alexa Adrian Rahnenfuhrer Jorg Lengauer Thomas doi:10.1093/bioinformatics/btl140 Bioinformatics, Vol. 22, No. 13. (1 July 2006), pp. 1600-1607. 2006-07-02T09:51:29-00:00 2006 Bioinformatics 1367-4803 22 13 1600 1607 Oxford University Press no-tag http://www.citeulike.org/user/sgoetz/article/2857008 <i>Nucl. Acids Res. (28 May 2008), gkn303.</i><br /><br />Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org. 10.1093/nar/gkn303 GEPAS, a web-based tool for microarray data analysis and interpretation Joaquin Tarraga Ignacio Medina Jose Carbonell Jaime Huerta-Cepas Pablo Minguez Eva Alloza Fatima Al-Shahrour Susana Vegas-Azcarate Stefan Goetz Pablo Escobar Francisco Garcia-Garcia Ana Conesa David Montaner Joaquin Dopazo doi:10.1093/nar/gkn303 Nucl. Acids Res. (28 May 2008), gkn303. 2008-06-02T13:50:41-00:00 2008 Nucl. Acids Res. gkn303 no-tag http://www.citeulike.org/user/sgoetz/article/2853945 <i>Nucl. Acids Res. (31 May 2008), gkn318.</i><br /><br />We present a new version of Babelomics, a complete suite of web tools for the functional profiling of genome scale experiments, with new and improved methods as well as more types of functional definitions. Babelomics includes different flavours of conventional functional enrichment methods as well as more advanced gene set analysis methods that makes it a unique tool among the similar resources available. In addition to the well-known functional definitions (GO, KEGG), Babelomics includes new ones such as Biocarta pathways or text mining-derived functional terms. Regulatory modules implemented include transcriptional control (Transfac, CisRed) and other levels of regulation such as miRNA-mediated interference. Moreover, Babelomics allows for sub-selection of terms in order to test more focused hypothesis. Also gene annotation correspondence tables can be imported, which allows testing with user-defined functional modules. Finally, a tool for the de novo' functional annotation of sequences has been included in the system. This allows using yet unannotated organisms in the program. Babelomics has been extensively re-engineered and now it includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. Babelomics is available at http://www.babelomics.org 10.1093/nar/gkn318 Babelomics: advanced functional profiling of transcriptomics, proteomics and genomics experiments Fatima Al-Shahrour Jose Carbonell Pablo Minguez Stefan Goetz Ana Conesa Joaquin Tarraga Ignacio Medina Eva Alloza David Montaner Joaquin Dopazo doi:10.1093/nar/gkn318 Nucl. Acids Res. (31 May 2008), gkn318. 2008-06-01T06:01:43-00:00 2008 Nucl. Acids Res. gkn318 no-tag http://www.citeulike.org/user/sgoetz/article/467312 <i>Nucleic Acids Res, Vol. 33, No. Web Server issue. (1 July 2005), pp. W460-W464.</i><br /><br />We present Babelomics, a complete suite of web tools for the functional analysis of groups of genes in high-throughput experiments, which includes the use of information on Gene Ontology terms, interpro motifs, KEGG pathways, Swiss-Prot keywords, analysis of predicted transcription factor binding sites, chromosomal positions and presence in tissues with determined histological characteristics, through five integrated modules: FatiGO (fast assignment and transference of information), FatiWise, transcription factor association test, GenomeGO and tissues mining tool, respectively. Additionally, another module, FatiScan, provides a new procedure that integrates biological information in combination with experimental results in order to find groups of genes with modest but coordinate significant differential behaviour. FatiScan is highly sensitive and is capable of finding significant asymmetries in the distribution of genes of common function across a list of ordered genes even if these asymmetries were not extreme. The strong multiple-testing nature of the contrasts made by the tools is taken into account. All the tools are integrated in the gene expression analysis package GEPAS. Babelomics is the natural evolution of our tool FatiGO (which analysed almost 22,000 experiments during the last year) to include more sources on information and new modes of using it. Babelomics can be found at http://www.babelomics.org. BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments. F Al-Shahrour P Minguez JM Vaquerizas L Conde J Dopazo Nucleic Acids Res, Vol. 33, No. Web Server issue. (1 July 2005), pp. W460-W464. 2006-01-17T19:10:47-00:00 2005 Nucleic Acids Res 1362-4962 33 Web Server issue W460 W464 no-tag http://www.citeulike.org/user/sgoetz/article/691342 <i>Bioinformatics, Vol. 20, No. 4. (1 March 2004), pp. 578-580.</i><br /><br />Summary: We present a simple but powerful procedure to extract Gene Ontology (GO) terms that are significantly over- or under-represented in sets of genes within the context of a genome-scale experiment (DNA microarray, proteomics, etc.). Said procedure has been implemented as a web application, FatiGO, allowing for easy and interactive querying. FatiGO, which takes the multiple-testing nature of statistical contrast into account, currently includes GO associations for diverse organisms (human, mouse, fly, worm and yeast) and the TrEMBL/Swissprot GOAnnotations@EBI correspondences from the European Bioinformatics Institute. Availability: http://fatigo.bioinfo.cnio.es 10.1093/bioinformatics/btg455 FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes Fatima Al-Shahrour Ramon Diaz-Uriarte Joaquin Dopazo doi:10.1093/bioinformatics/btg455 Bioinformatics, Vol. 20, No. 4. (1 March 2004), pp. 578-580. 2006-06-09T18:22:46-00:00 2004 Bioinformatics 20 4 578 580 no-tag http://www.citeulike.org/user/sgoetz/article/2813261 <i>Nucleic acids research, Vol. 36, No. Database issue. (January 2008), pp. D289-D292.</i><br /><br />Protein sequences are the most important source of evolutionary and functional information for new proteins. In order to facilitate the computationally intensive tasks of sequence analysis, the Similarity Matrix of Proteins (SIMAP) database aims to provide a comprehensive and up-to-date dataset of the pre-calculated sequence similarity matrix and sequence-based features like InterPro domains for all proteins contained in the major public sequence databases. As of September 2007, SIMAP covers approximately 17 million proteins and more than 6 million non-redundant sequences and provides a complete annotation based on InterPro 16. Novel features of SIMAP include a new, portlet-based web portal providing multiple, structured views on retrieved proteins and integration of protein clusters and a unique search method for similar domain architectures. Access to SIMAP is freely provided for academic use through the web portal for individuals at http://mips.gsf.de/simap/and through Web Services for programmatic access at http://mips.gsf.de/webservices/services/SimapService2.0?wsdl. SIMAP--structuring the network of protein similarities. T Rattei P Tischler R Arnold F Hamberger J Krebs J Krumsiek B Wachinger V Stümpflen W Mewes Nucleic acids research, Vol. 36, No. Database issue. (January 2008), pp. D289-D292. 2008-05-19T14:13:36-00:00 2008 Nucleic acids research 1362-4962 36 Database issue D289 D292 simap2go http://www.citeulike.org/user/sgoetz/article/2733895 <i>Nucl. Acids Res. (29 April 2008), gkn176.</i><br /><br />Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data. 10.1093/nar/gkn176 High-throughput functional annotation and data mining with the Blast2GO suite Stefan Götz Juan Garcia-Gomez Javier Terol Tim Williams Shivashankar Nagaraj Maria Nueda Montserrat Robles Manuel Talon Joaquin Dopazo Ana Conesa doi:10.1093/nar/gkn176 Nucl. Acids Res. (29 April 2008), gkn176. 2008-04-29T11:39:18-00:00 2008 Nucl. Acids Res. gkn176 annotation b2g blast2go gene_ontology_tool my_papers http://www.citeulike.org/user/sgoetz/article/2797878 <i>International Journal of Plant Genomics, Vol. 2008, No. 2008. (2008), pp. 1-12.</i><br /><br />Functional annotation of novel sequence data is a primary requirement for the utilization of functional genomics approaches in plant research. In this paper, we describe the Blast2GO suite as a comprehensive bioinformatics tool for functional annotation of sequences and data mining on the resulting annotations, primarily based on the gene ontology (GO) vocabulary. Blast2GO optimizes function transfer from homologous sequences through an elaborate algorithm that considers similarity, the extension of the homology, the database of choice, the GO hierarchy, and the quality of the original annotations. The tool includes numerous functions for the visualization, management, and statistical analysis of annotation results, including gene set enrichment analysis. The application supports InterPro, enzyme codes, KEGG pathways, GO direct acyclic graphs (DAGs), and GOSlim. Blast2GO is a suitable tool for plant genomics research because of its versatility, easy installation, and friendly use. Blast2GO: A Comprehensive Suite for Functional Analysis in Plant Genomics Ana Conesa Stefan Götz International Journal of Plant Genomics, Vol. 2008, No. 2008. (2008), pp. 1-12. 2008-05-14T12:13:02-00:00 2008 International Journal of Plant Genomics 2008 2008 1 12 annotation b2g blast2go my_papers http://www.citeulike.org/user/sgoetz/article/1992269 <i>Bioinformatics (16 November 2007), btm553.</i><br /><br />BiNoM (BIological NetwOrk Manager) is a new bioinformatics software which significantly facilitates the usage and the analysis of biological networks in standard systems biology formats (SBML, SBGN, BioPAX), BiNoM implements a fullfeatured BioPax editor and a method of "interfaces" for accessing BioPAX content. BiNoM is able to work with huge BioPAX files such as whole pathway databases. In addition, BiNoM allows the analysis of networks created with CellDesigner software and their conversion into BioPAX format. BiNoM comes as a library and as a Cytoscape plugin which adds a rich set of operations to Cytoscape such as path and cycle analysis, clustering sub-networks, decomposition of network into modules,cllipboard operations and others. Availability: Last version of BiNoM together with documentation, source code and API is available at http://bioinfo.curie.fr/projects/binom Contact: andrei.zinoyev@curie.fr, binom@curie.fr 10.1093/bioinformatics/btm553 BiNoM: a Cytoscape plugin for manipulating and analyzing biological networks Andrei Zinovyev Eric Viara Laurence Calzone Emmanuel Barillot doi:10.1093/bioinformatics/btm553 Bioinformatics (16 November 2007), btm553. 2007-11-27T10:46:49-00:00 2007 Bioinformatics btm553 interactome http://www.citeulike.org/user/sgoetz/article/1958928 <i>Bioinformatics (19 November 2007), btm565.</i><br /><br />Motivation: Proteins work together to drive biological processes in cellular machines. Summarizing global and local properties of the set of protein interactions, the interactome, is necessary for describing cellular systems. We consider a relatively simple per-protein feature of the interactome: the number of interaction partners for a protein, which in graph terminology is the degree of the protein. Results: Using data subject to both stochastic and systematic sources of false positive and false negative observations, we develop an explicit probability model and resultant likelihood method to estimate node degree on portions of the interactome assayed by bait-prey technologies. This approach yields substantial improvement in degree estimation over the current practice which naively sums observed edges. Accurate modeling of observed data in relation to true but unknown parameters of interest gives a formal point of reference from which to draw conclusions about the system under study. Availability: All analyses discussed in this text can be performed using the ppiStats and ppiData packages available through the Bioconductor project (http://www.bioconductor.org). Contact: dscholtens@northwestern.edu Supplementary Information: Included with the manuscript for review purposes. 10.1093/bioinformatics/btm565 Estimating node degree in bait-prey graphs Denise Scholtens Tony Chiang Wolfgang Huber Robert Gentleman doi:10.1093/bioinformatics/btm565 Bioinformatics (19 November 2007), btm565. 2007-11-22T12:54:35-00:00 2007 Bioinformatics btm565 no-tag http://www.citeulike.org/user/sgoetz/article/1930696 <i>Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE (2006), pp. 5141-5144.</i><br /><br />One of the basic needs for any healthcare professional is to be able to access to clinical information of patients in an understandable and normalized way. The lifelong clinical information of any person supported by electronic means configures his/her Electronic Health Record (EHR). This information is usually distributed among several independent and heterogeneous systems that may be syntactically or semantically incompatible. The Dual Model architecture has appeared as a new proposal for maintaining a homogeneous representation of the EHR with a clear separation between information and knowledge. Information is represented by a Reference Model which describes common data structures with minimal semantics. Knowledge is specified by archetypes, which are formal representations of clinical concepts built upon a particular Reference Model. This kind of architecture is originally thought for implantation of new clinical information systems, but archetypes can be also used for integrating data of existing and not normalized systems, adding at the same time a semantic meaning to the integrated data. In this paper we explain the possible use of a Dual Model approach for semantic integration and standardization of heterogeneous clinical data sources and present LinkEHR-Ed, a tool for developing archetypes as elements for integration purposes. LinkEHR-Ed has been designed to be easily used by the two main participants of the creation process of archetypes for clinical data integration: the Health domain expert and the Information Technologies domain expert Archetype-Based Semantic Integration and Standardization of Clinical Data David Moner Jose Maldonado Diego Bosca Jesualdo Fernandez Carlos Angulo Pere Crespo Pedro Vivancos Montserrat Robles Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE (2006), pp. 5141-5144. 2007-11-17T15:31:24-00:00 2006 Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE 5141 5144 bio-ontologies http://www.citeulike.org/user/sgoetz/article/200242 <i>Nucleic Acids Res, Vol. 32, No. Database issue. (1 January 2004)</i><br /><br />The Unified Medical Language System (http://umlsks.nlm.nih.gov) is a repository of biomedical vocabularies developed by the US National Library of Medicine. The UMLS integrates over 2 million names for some 900,000 concepts from more than 60 families of biomedical vocabularies, as well as 12 million relations among these concepts. Vocabularies integrated in the UMLS Metathesaurus include the NCBI taxonomy, Gene Ontology, the Medical Subject Headings (MeSH), OMIM and the Digital Anatomist Symbolic Knowledge Base. UMLS concepts are not only inter-related, but may also be linked to external resources such as GenBank. In addition to data, the UMLS includes tools for customizing the Metathesaurus (MetamorphoSys), for generating lexical variants of concept names (lvg) and for extracting UMLS concepts from text (MetaMap). The UMLS knowledge sources are updated quarterly. All vocabularies are available at no fee for research purposes within an institution, but UMLS users are required to sign a license agreement. The UMLS knowledge sources are distributed on CD-ROM and by FTP. The Unified Medical Language System (UMLS): integrating biomedical terminology. O Bodenreider Nucleic Acids Res, Vol. 32, No. Database issue. (1 January 2004) 2005-05-14T18:22:00-00:00 2004 Nucleic Acids Res 1362-4962 32 Database issue bio-ontologies http://www.citeulike.org/user/sgoetz/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 annotation http://www.citeulike.org/user/sgoetz/article/1198472 <i>BMC Genomics, Vol. 8 (30 March 2007), 88.</i> A meta-analysis of kidney microarray datasets: investigation of cytokine gene detection and correlation with rt-PCR and detection thresholds Walter Park Mark Stegall doi:10.1186/1471-2164-8-88 BMC Genomics, Vol. 8 (30 March 2007), 88. 2007-03-30T15:24:36-00:00 2007 BMC Genomics 1471-2164 8 88 phenomodelcite http://www.citeulike.org/user/sgoetz/article/835519 <i>Genome Res, Vol. 14, No. 6. (June 2004), pp. 1085-1094.</i><br /><br />We present a large-scale analysis of mRNA coexpression based on 60 large human data sets containing a total of 3924 microarrays. We sought pairs of genes that were reliably coexpressed (based on the correlation of their expression profiles) in multiple data sets, establishing a high-confidence network of 8805 genes connected by 220,649 "coexpression links" that are observed in at least three data sets. Confirmed positive correlations between genes were much more common than confirmed negative correlations. We show that confirmation of coexpression in multiple data sets is correlated with functional relatedness, and show how cluster analysis of the network can reveal functionally coherent groups of genes. Our findings demonstrate how the large body of accumulated microarray data can be exploited to increase the reliability of inferences about gene function. Coexpression analysis of human genes across many microarray data sets. HK Lee AK Hsu J Sajdak J Qin P Pavlidis doi:10.1101/gr.1910904 Genome Res, Vol. 14, No. 6. (June 2004), pp. 1085-1094. 2006-09-08T15:43:38-00:00 2004 Genome Res 1088-9051 14 6 1085 1094 phenomodelcite http://www.citeulike.org/user/sgoetz/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 bio-ontologies phenomodelcite http://www.citeulike.org/user/sgoetz/article/809030 <i>Nucleic Acids Res, Vol. 34, No. Web Server issue. (1 July 2006)</i><br /><br />The Gene Expression Profile Analysis Suite (GEPAS) has been running for more than four years. During this time it has evolved to keep pace with the new interests and trends in the still changing world of microarray data analysis. GEPAS has been designed to provide an intuitive although powerful web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-colour microarray experiments and other preprocessing options), to the final step of the functional annotation of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), and include different possibilities for clustering, gene selection, class prediction and array-comparative genomic hybridization management. GEPAS is extensively used by researchers of many countries and its records indicate an average usage rate of 400 experiments per day. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org. Next station in microarray data analysis: GEPAS. D Montaner J Tárraga J Huerta-Cepas J Burguet JM Vaquerizas L Conde P Minguez J Vera S Mukherjee J Valls MA Pujana E Alloza J Herrero F Al-Shahrour J Dopazo Nucleic Acids Res, Vol. 34, No. Web Server issue. (1 July 2006) 2006-08-21T11:17:44-00:00 2006 Nucleic Acids Res 1362-4962 34 Web Server issue microarray http://www.citeulike.org/user/sgoetz/article/1882392 <i>Nat Biotech, Vol. 25, No. 11. (November 2007), pp. 1251-1255.</i> The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration Barry Smith Michael Ashburner Cornelius Rosse Jonathan Bard William Bug Werner Ceusters Louis Goldberg Karen Eilbeck Amelia Ireland Christopher Mungall Neocles Leontis Philippe Rocca-Serra Alan Ruttenberg Susanna-Assunta Sansone Richard Scheuermann Nigam Shah Patricia Whetzel Suzanna Lewis doi:10.1038/nbt1346 Nat Biotech, Vol. 25, No. 11. (November 2007), pp. 1251-1255. 2007-11-08T02:21:16-00:00 2007 Nat Biotech 25 11 1251 1255 Nature Publishing Group bio-ontologies phenomodelcite http://www.citeulike.org/user/sgoetz/article/1914037 <i>(June 2005)</i> Current Official Version of the CIDOC CRM version 4.2 of the reference document. Definition of the CIDOC Conceptual Reference Model N Crofts M Doerr T Gill S Stead M Stiff (June 2005) 2007-11-14T12:53:41-00:00 2005 phenomodelcite http://www.citeulike.org/user/sgoetz/article/138953 <i>Brief Bioinform, Vol. 3, No. 2. (June 2002), pp. 166-180.</i><br /><br />Current research in the biosciences depends heavily on the effective exploitation of huge amounts of data. These are in disparate formats, remotely dispersed, and based on the different vocabularies of various disciplines. Furthermore, data are often stored or distributed using formats that leave implicit many important features relating to the structure and semantics of the data. Conceptual data modelling involves the development of implementation-independent models that capture and make explicit the principal structural properties of data. Entities such as a biopolymer or a reaction, and their relations, eg catalyses, can be formalised using a conceptual data model. Conceptual models are implementation-independent and can be transformed in systematic ways for implementation using different platforms, eg traditional database management systems. This paper describes the basics of the most widely used conceptual modelling notations, the ER (entity-relationship) model and the class diagrams of the UML (unified modelling language), and illustrates their use through several examples from bioinformatics. In particular, models are presented for protein structures and motifs, and for genomic sequences. Conceptual data modelling for bioinformatics. E Bornberg-Bauer NW Paton doi:10.1093/bib/3.2.166 Brief Bioinform, Vol. 3, No. 2. (June 2002), pp. 166-180. 2005-03-24T11:35:11-00:00 2002 Brief Bioinform 1467-5463 3 2 166 180 bio-ontologies phenomodelcite http://www.citeulike.org/user/sgoetz/article/1914007 <i>J Am Med Inform Assoc, Vol. 6, No. 6. (c 1999), pp. 478-493.</i><br /><br />Entity-attribute-value (EAV) representation is a means of organizing highly heterogeneous data using a relatively simple physical database schema. EAV representation is widely used in the medical domain, most notably in the storage of data related to clinical patient records. Its potential strengths suggest its use in other biomedical areas, in particular research databases whose schemas are complex as well as constantly changing to reflect evolving knowledge in rapidly advancing scientific domains. When deployed for such purposes, the basic EAV representation needs to be augmented significantly to handle the modeling of complex objects (classes) as well as to manage interobject relationships. The authors refer to their modification of the basic EAV paradigm as EAV/CR (EAV with classes and relationships). They describe EAV/CR representation with examples from two biomedical databases that use it. Organization of heterogeneous scientific data using the EAV/CR representation. PM Nadkarni L Marenco R Chen E Skoufos G Shepherd P Miller J Am Med Inform Assoc, Vol. 6, No. 6. (c 1999), pp. 478-493. 2007-11-14T12:46:02-00:00 1999 J Am Med Inform Assoc 1067-5027 6 6 478 493 bio-ontologies phenomodelcite http://www.citeulike.org/user/sgoetz/article/1036090 <i>Nucleic Acids Research, Vol. 35, No. Supplement 1. (January 2007), pp. D696-D699.</i> PhenomicDB: a new cross-species genotype/phenotype resource Groth Philip Pavlova Nadia Kalev Ivan Tonov Spas Georgiev Georgi Pohlenz Hans-Dieter Weiss Bertram doi:10.1093/nar/gkl662 Nucleic Acids Research, Vol. 35, No. Supplement 1. (January 2007), pp. D696-D699. 2007-01-11T09:15:53-00:00 2007 Nucleic Acids Research 0305-1048 35 Supplement 1 D696 D699 Oxford University Press bio-ontologies phenomodelcite http://www.citeulike.org/user/sgoetz/article/964344 <i>Pac Symp Biocomput (2006), pp. 64-75.</i><br /><br />Natural language processing (NLP) is a high throughput technology because it can process vast quantities of text within a reasonable time period. It has the potential to substantially facilitate biomedical research by extracting, linking, and organizing massive amounts of information that occur in biomedical journal articles as well as in textual fields of biological databases. Until recently, much of the work in biological NLP and text mining has revolved around recognizing the occurrence of biomolecular entities in articles, and in extracting particular relationships among the entities. Now, researchers have recognized a need to link the extracted information to ontologies or knowledge bases, which is a more difficult task. One such knowledge base is Gene Ontology annotations (GOA), which significantly increases semantic computations over the function, cellular components and processes of genes. For multicellular organisms, these annotations can be refined with phenotypic context, such as the cell type, tissue, and organ because establishing phenotypic contexts in which a gene is expressed is a crucial step for understanding the development and the molecular underpinning of the pathophysiology of diseases. In this paper, we propose a system, PhenoGO, which automatically augments annotations in GOA with additional context. PhenoGO utilizes an existing NLP system, called BioMedLEE, an existing knowledge-based phenotype organizer system (PhenOS) in conjunction with MeSH indexing and established biomedical ontologies. More specifically, PhenoGO adds phenotypic contextual information to existing associations between gene products and GO terms as specified in GOA. The system also maps the context to identifiers that are associated with different biomedical ontologies, including the UMLS, Cell Ontology, Mouse Anatomy, NCBI taxonomy, GO, and Mammalian Phenotype Ontology. In addition, PhenoGO was evaluated for coding of anatomical and cellular information and assigning the coded phenotypes to the correct GOA; results obtained show that PhenoGO has a precision of 91% and recall of 92%, demonstrating that the PhenoGO NLP system can accurately encode a large number of anatomical and cellular ontologies to GO annotations. The PhenoGO Database may be accessed at the following URL: http://www.phenoGO.org Phenogo: assigning phenotypic context to gene ontology annotations with natural language processing. Y Lussier T Borlawsky D Rappaport Y Liu C Friedman Pac Symp Biocomput (2006), pp. 64-75. 2006-11-28T00:04:01-00:00 2006 Pac Symp Biocomput 64 75 bio-ontologies phenomodelcite http://www.citeulike.org/user/sgoetz/article/1236494 <i>Nucleic Acids Res, Vol. 35, No. Database issue. (January 2007)</i><br /><br />The Rat Genome Database (RGD, http://rgd.mcw.edu) is one of the core resources for rat genomics and recent developments have focused on providing support for disease-based research using the rat model. Recognizing the importance of the rat as a disease model we have employed targeted curation strategies to curate genes, QTL and strain data for neurological and cardiovascular disease areas. This work has centered on rat but also includes data for mouse and human to create 'disease portals' that provide a unified view of the genes, QTL and strain models for these diseases across the three species. The disease curation efforts combined with normal curation activities have served to greatly increase the content of the database, particularly for biological information, including gene ontology, disease, pathway and phenotype ontology annotations. In addition to improving the features and database content, community outreach has been expanded to demonstrate how investigators can leverage the resources at RGD to facilitate their research and to elicit suggestions and needs for future developments. We have published a number of papers that provide additional information on the ontology annotations and the tools at RGD for data mining and analysis to better enable researchers to fully utilize the database. The Rat Genome Database, update 2007--easing the path from disease to data and back again. SN Twigger M Shimoyama S Bromberg AE Kwitek HJ Jacob Nucleic Acids Res, Vol. 35, No. Database issue. (January 2007) 2007-04-19T08:39:31-00:00 2007 Nucleic Acids Res 1362-4962 35 Database issue bio-ontologies phenomodelcite http://www.citeulike.org/user/sgoetz/article/1239726 <i>Genome Biol, Vol. 6, No. 1. (2005)</i><br /><br />The Mammalian Phenotype (MP) Ontology enables robust annotation of mammalian phenotypes in the context of mutations, quantitative trait loci and strains that are used as models of human biology and disease. The MP Ontology supports different levels and richness of phenotypic knowledge and flexible annotations to individual genotypes. It continues to develop dynamically via collaborative input from research groups, mutagenesis consortia, and biological domain experts. The MP Ontology is currently used by the Mouse Genome Database and Rat Genome Database to represent phenotypic data. The Mammalian Phenotype Ontology as a tool for annotating, analyzing and comparing phenotypic information. CL Smith CA Goldsmith JT Eppig doi:10.1186/gb-2004-6-1-r7 Genome Biol, Vol. 6, No. 1. (2005) 2007-04-20T13:30:11-00:00 2005 Genome Biol 1465-6914 6 1 bio-ontologies phenomodelcite http://www.citeulike.org/user/sgoetz/article/566073 <i>Genome Biol, Vol. 6, No. 1. (2005)</i><br /><br />The mouse is an important model of human genetic disease. Describing phenotypes of mutant mice in a standard, structured manner that will facilitate data mining is a major challenge for bioinformatics. Here we describe a novel, compositional approach to this problem which combines core ontologies from a variety of sources. This produces a framework with greater flexibility, power and economy than previous approaches. We discuss some of the issues this approach raises. Using ontologies to describe mouse phenotypes. GV Gkoutos EC Green AM Mallon JM Hancock D Davidson doi:10.1186/gb-2004-6-1-r8 Genome Biol, Vol. 6, No. 1. (2005) 2006-03-27T16:50:17-00:00 2005 Genome Biol 1465-6914 6 1 bio-ontologies phenomodelcite http://www.citeulike.org/user/sgoetz/article/763177 <i>Nature Reviews Genetics, Vol. 7, No. 8. (2006), pp. 593-605.</i> Standards for systems biology Alvis Brazma Maria Krestyaninova Ugis Sarkans doi:10.1038/nrg1922 Nature Reviews Genetics, Vol. 7, No. 8. (2006), pp. 593-605. 2006-07-18T15:21:12-00:00 2006 Nature Reviews Genetics 1471-0056 7 8 593 605 Nature Publishing Group bio-ontologies phenomodelcite http://www.citeulike.org/user/sgoetz/article/213955 <i>BMC Bioinformatics, Vol. 6, No. 1. (10 May 2005)</i><br /><br />BACKGROUND: To standardize gene product descriptions, a formal vocabulary defined as the Gene Ontology (GO) has been developed. GO terms have been categorized into biological processes, molecular functions, and cellular components. However, there is no single representation that integrates all the terms into one cohesive model. Furthermore, GO definitions have little information explaining the underlying architecture that forms these terms, such as the dynamic and static events occurring in a process. In contrast, object-oriented models have been developed to show dynamic and static events. A portion of the TGF-beta signaling pathway, which is involved in numerous cellular events including cancer, differentiation and development, was used to demonstrate the feasibility of integrating the Gene Ontology into an object-oriented model. RESULTS: Using object-oriented models we have captured the static and dynamic events that occur during a representative GO process, transforming growth factor-beta (TGF-beta) receptor complex assembly (GO:0007181). CONCLUSIONS: We demonstrate that the utility of GO terms can be enhanced by object-oriented technology, and that the GO terms can be integrated into an object-oriented model by serving as a basis for the generation of object functions and attributes. Integration of the Gene Ontology into an object-oriented architecture. Daniel Shegogue W Zheng doi:10.1186/1471-2105-6-113 BMC Bioinformatics, Vol. 6, No. 1. (10 May 2005) 2005-05-29T09:05:20-00:00 2005 BMC Bioinformatics 1471-2105 6 1 bio-ontologies http://www.citeulike.org/user/sgoetz/article/907398 <i>Brief Bioinform, Vol. 7, No. 3. (September 2006), pp. 256-274.</i><br /><br />In recent years, as a knowledge-based discipline, bioinformatics has been made more computationally amenable. After its beginnings as a technology advocated by computer scientists to overcome problems of heterogeneity, ontology has been taken up by biologists themselves as a means to consistently annotate features from genotype to phenotype. In medical informatics, artifacts called ontologies have been used for a longer period of time to produce controlled lexicons for coding schemes. In this article, we review the current position in ontologies and how they have become institutionalized within biomedicine. As the field has matured, the much older philosophical aspects of ontology have come into play. With this and the institutionalization of ontology has come greater formality. We review this trend and what benefits it might bring to ontologies and their use within biomedicine. Bio-ontologies: current trends and future directions. O Bodenreider R Stevens doi:10.1093/bib/bbl027 Brief Bioinform, Vol. 7, No. 3. (September 2006), pp. 256-274. 2006-10-20T03:43:49-00:00 2006 Brief Bioinform 1467-5463 7 3 256 274 phenomodelcite http://www.citeulike.org/user/sgoetz/article/151946 <i>Nat Genet, Vol. 29, No. 4. (December 2001), pp. 365-371.</i><br /><br />Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. A Brazma P Hingamp J Quackenbush G Sherlock P Spellman C Stoeckert J Aach W Ansorge CA Ball HC Causton T Gaasterland P Glenisson FC Holstege IF Kim V Markowitz JC Matese H Parkinson A Robinson U Sarkans S Schulze-Kremer J Stewart R Taylor J Vilo M Vingron doi:10.1038/ng1201-365 Nat Genet, Vol. 29, No. 4. (December 2001), pp. 365-371. 2005-04-07T11:33:49-00:00 2001 Nat Genet 1061-4036 29 4 365 371 phenomodelcite http://www.citeulike.org/user/sgoetz/article/1324276 <i>BMC Bioinformatics, Vol. 8 (22 May 2007), 166.</i> GOSim - An R-package for computation of information theoretic GO similarities between terms and gene products Holger Froehlich Nora Speer Annemarie Poustka Tim Beissbarth doi:10.1186/1471-2105-8-166 BMC Bioinformatics, Vol. 8 (22 May 2007), 166. 2007-05-24T04:38:16-00:00 2007 BMC Bioinformatics 1471-2105 8 166 semantic similarity http://www.citeulike.org/user/sgoetz/article/934180 <i>Bioinformatics, Vol. 19, No. 10. (1 July 2003), pp. 1275-1283.</i><br /><br />Motivation: Many bioinformatics data resources not only hold data in the form of sequences, but also as annotation. In the majority of cases, annotation is written as scientific natural language: this is suitable for humans, but not particularly useful for machine processing. Ontologies offer a mechanism by which knowledge can be represented in a form capable of such processing. In this paper we investigate the use of ontological annotation to measure the similarities in knowledge content or semantic similarity' between entries in a data resource. These allow a bioinformatician to perform a similarity measure over annotation in an analogous manner to those performed over sequences. A measure of semantic similarity for the knowledge component of bioinformatics resources should afford a biologist a new tool in their repetoire of analyses. Results: We present the results from experiments that investigate the validity of using semantic similarity by comparison with sequence similarity. We show a simple extension that enables a semantic search of the knowledge held within sequence databases. Availability: Software available from http://www.russet.org.uk Contact: p.lord@russet.org.uk 10.1093/bioinformatics/btg153 Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation PW Lord RD Stevens A Brass CA Goble doi:10.1093/bioinformatics/btg153 Bioinformatics, Vol. 19, No. 10. (1 July 2003), pp. 1275-1283. 2006-11-07T10:11:45-00:00 2003 Bioinformatics 19 10 1275 1283 semantic similarity http://www.citeulike.org/user/sgoetz/article/1153351 <i>Bioinformatics (7 March 2007)</i><br /><br />MOTIVATION: Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources. RESULTS: To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the Saccharomyces Genome Database (SGD) and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery. AVAILABILITY: The online tools are available at: http://bioinformatics.clemson.edu/G-SESAME. Supplement information: http://bioinformatics.clemson.edu/Publication/Supplement/gsp.htm. A New Method to Measure the Semantic Similarity of GO Terms. James Z Wang Zhidian Du Rapeeporn Payattakool Philip S Yu Chin-Fu Chen doi:10.1093/bioinformatics/btm087 Bioinformatics (7 March 2007) 2007-03-11T03:41:15-00:00 2007 Bioinformatics 1460-2059 semantic similarity http://www.citeulike.org/user/sgoetz/article/1889254 <i>Nucl. Acids Res. (11 October 2007), gkm806.</i><br /><br />Functional similarity based on Gene Ontology (GO) annotation is used in diverse applications like gene clustering, gene expression data analysis, protein interaction prediction and evaluation. However, there exists no comprehensive resource of functional similarity values although such a database would facilitate the use of functional similarity measures in different applications. Here, we describe FunSimMat (Functional Similarity Matrix, http://funsimmat.bioinf.mpi-inf.mpg.de/), a large new database that provides several different semantic similarity measures for GO terms. It offers various precomputed functional similarity values for proteins contained in UniProtKB and for protein families in Pfam and SMART. The web interface allows users to efficiently perform both semantic similarity searches with GO terms and functional similarity searches with proteins or protein families. All results can be downloaded in tab-delimited files for use with other tools. An additional XMLRPC interface gives automatic online access to FunSimMat for programs and remote services. 10.1093/nar/gkm806 FunSimMat: a comprehensive functional similarity database Andreas Schlicker Mario Albrecht doi:10.1093/nar/gkm806 Nucl. Acids Res. (11 October 2007), gkm806. 2007-11-09T12:50:30-00:00 2007 Nucl. Acids Res. gkm806 semantic similarity http://www.citeulike.org/user/sgoetz/article/1880124 <i>Nucleic Acids Res, Vol. 31, No. 13. (1 July 2003), pp. 3712-3715.</i><br /><br />Gene Ontology (GO) is the most widely accepted attempt to construct a unified and structured vocabulary for the description of genes and their products in any organism. Annotation by GO terms is performed in most of the current genome projects, which besides generality has the advantage of being very convenient for computer based classification methods. However, direct use of GO in small sequencing projects is not easy, especially for species not commonly represented in public databases. We present a software package (GOblet), which performs annotation based on GO terms for anonymous cDNA or protein sequences. It uses the species independent GO structure and vocabulary together with a series of protein databases collected from various sites, to perform a detailed GO annotation by sequence similarity searches. The sensitivity and the reference protein sets can be selected by the user. GOblet runs automatically and is available as a public service on our web server. The paper also addresses the reliability of automated GO annotations by using a reference set of more than 6000 human proteins. The GOblet server is accessible at http://goblet.molgen.mpg.de. Automated Gene Ontology annotation for anonymous sequence data. S Hennig D Groth H Lehrach Nucleic Acids Res, Vol. 31, No. 13. (1 July 2003), pp. 3712-3715. 2007-11-07T17:42:07-00:00 2003 Nucleic Acids Res 1362-4962 31 13 3712 3715 b2gpaper2