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Search Results: 1 - 10 of 169897 matches for " Suzanna E Lewis "
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Gene Ontology: looking backwards and forwards
Suzanna E Lewis
Genome Biology , 2004, DOI: 10.1186/gb-2004-6-1-103
Abstract: Long ago, in the pre-genome era, biological databases had to come to terms with a formidable amount of work. After Crick and Watson elucidated the structure of DNA, the field of molecular biology exploded and an ever-increasing amount of information needed to be carefully managed and organized. This was particularly true after the invention of methods to sequence DNA in the late 1970s [1,2] and, consequently, the initiation of the genome sequencing programs in the late 1980s, all of which led to an even faster acceleration of work in this field. Keeping pace with molecular developments were biological data-management efforts. These first began emerging in the 1960s when Margaret Dayhoff [3] published the Atlas of Protein Sequence and Structure [4], which later went online as the Protein Identification Resource (PIR [5]). More than 30 years ago, in the 1970s, the first protein-structure database, Protein Data Bank (PDB [6]), was founded [7] and the Jackson Laboratory developed the first mammalian genetics database [8]. A few years later the first depositories for nucleotide sequences were established - with the EMBL 'Data Library' [9] beginning in 1981 [10] at Heidelberg, Germany and GenBank [11] in 1982 [12] at Los Alamos, New Mexico - followed soon afterwards by the formal establishment of the PIR in 1984 [13] for proteins. By the late 1980s and 1990s biological databases were popping up everywhere: in 1986 SwissProt [14]; in 1989 Caenorhabditis elegans AceDB [15]; in 1991 Arabidopsis AtDB [16]; in 1992 [17] The Institute for Genomic Research (TIGR) [18]; in 1993 FlyBase [19]; and in 1994 [20], Saccharomyces Genome Database (SGD) [21]. These groups all took advantage of concurrent technological advances and pioneered the use of the internet, the worldwide web, and relational database management systems (RDBMSs) and standard query language (SQL), when these technologies first became available during the 1980s and 1990s [22-24]. Thus, many biological databases bloome
Sequence Ontology Annotation Guide
Karen Eilbeck,Suzanna E. Lewis
Comparative and Functional Genomics , 2004, DOI: 10.1002/cfg.446
Abstract: This Sequence Ontology (SO) [13] aims to unify the way in which we describe sequence annotations, by providing a controlled vocabulary of terms and the relationships between them. Using SO terms to label the parts of sequence annotations greatly facilitates downstream analyses of their contents, as it ensures that annotations produced by different groups conform to a single standard. This greatly facilitates analyses of annotation contents and characteristics, e.g. comparisons of UTRs, alternative splicing, etc. Because SO also specifies the relationships between features, e.g. part_of, kind_of, annotations described with SO terms are also better substrates for validation and visualization software.
Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation
Nicole L. Washington,Melissa A. Haendel,Christopher J. Mungall,Michael Ashburner,Monte Westerfield,Suzanna E. Lewis
PLOS Biology , 2012, DOI: 10.1371/journal.pbio.1000247
Abstract: Scientists and clinicians who study genetic alterations and disease have traditionally described phenotypes in natural language. The considerable variation in these free-text descriptions has posed a hindrance to the important task of identifying candidate genes and models for human diseases and indicates the need for a computationally tractable method to mine data resources for mutant phenotypes. In this study, we tested the hypothesis that ontological annotation of disease phenotypes will facilitate the discovery of new genotype-phenotype relationships within and across species. To describe phenotypes using ontologies, we used an Entity-Quality (EQ) methodology, wherein the affected entity (E) and how it is affected (Q) are recorded using terms from a variety of ontologies. Using this EQ method, we annotated the phenotypes of 11 gene-linked human diseases described in Online Mendelian Inheritance in Man (OMIM). These human annotations were loaded into our Ontology-Based Database (OBD) along with other ontology-based phenotype descriptions of mutants from various model organism databases. Phenotypes recorded with this EQ method can be computationally compared based on the hierarchy of terms in the ontologies and the frequency of annotation. We utilized four similarity metrics to compare phenotypes and developed an ontology of homologous and analogous anatomical structures to compare phenotypes between species. Using these tools, we demonstrate that we can identify, through the similarity of the recorded phenotypes, other alleles of the same gene, other members of a signaling pathway, and orthologous genes and pathway members across species. We conclude that EQ-based annotation of phenotypes, in conjunction with a cross-species ontology, and a variety of similarity metrics can identify biologically meaningful similarities between genes by comparing phenotypes alone. This annotation and search method provides a novel and efficient means to identify gene candidates and animal models of human disease, which may shorten the lengthy path to identification and understanding of the genetic basis of human disease.
Uberon, an integrative multi-species anatomy ontology
Christopher J Mungall, Carlo Torniai, Georgios V Gkoutos, Suzanna E Lewis, Melissa A Haendel
Genome Biology , 2012, DOI: 10.1186/gb-2012-13-1-r5
Abstract: Anatomy ontologies (AOs) are computable representations of the parts of an organism and the structural and developmental relationships that hold between them. These representations have proven vital for databasing and bioinformatics analyses in fields including medical informatics, genomics, systems biology, neuroscience and comparative morphology [1]. The structural relationships encoded in AOs allow computers to determine that a query for 'all mouse genes expressed in the lung' should also return genes expressed in sub-structures such as the alveoli (Figure 1). AOs have proven useful for querying individual databases, but integrative queries spanning multiple databases or multiple species is problematic because each database uses a different ontology constructed according to different principles and requirements. There is a lack of inter-ontology connections between anatomy ontologies, and a lack of connections from anatomy to other domains such as phenotype. This results in a parcellation of data into isolated silos, as illustrated in Figure 1. Users wishing to query over multiple datasets will have to make multiple queries and integrate the results. For example, a query for mouse and human genes expressed in the lung at any stage of development or in abnormal tissues may require four or more queries in different places. Furthermore, without additional integration it is impossible to automate more sophisticated analyses, such as comparing all expression patterns of orthologous genes across species.Table 1 summarizes some of the existing AOs, or ontologies that include an AO as a subset. Each of these ontologies has datasets that would benefit from integration. It may seem that the most effective approach would be for the community to standardize on a single anointed reference anatomy ontology, such as the Foundational Model of Anatomy (FMA) [2]. However, the FMA is designed primarily to represent post-embryonic human structures, and would be unsuitable for annota
The Sequence Ontology: a tool for the unification of genome annotations
Karen Eilbeck, Suzanna E Lewis, Christopher J Mungall, Mark Yandell, Lincoln Stein, Richard Durbin, Michael Ashburner
Genome Biology , 2005, DOI: 10.1186/gb-2005-6-5-r44
Abstract: Genomic annotations are the focal point of sequencing, bioinformatics analysis, and molecular biology. They are the means by which we attach what we know about a genome to its sequence. Unfortunately, biological terminology is notoriously ambiguous; the same word is often used to describe more than one thing and there are many dialects. For example, does a coding sequence (CDS) contain the stop codon or is the stop codon part of the 3'-untranslated region (3' UTR)? There really is no right or wrong answer to such questions, but consistency is crucial when attempting to compare annotations from different sources, or even when comparing annotations performed by the same group over an extended period of time.At present, GenBank [1] houses 220 viral genomes, 152 bacterial genomes, 20 eukaryotic genomes and 18 archeal genomes. Other centers such as The Institute for Genomic Research (TIGR) [2] and the Joint Genome Institute (JGI) [3] also maintain and distribute annotations, as do many model organism databases such as FlyBase [4], WormBase [5], The Arabidopsis Information Resource (TAIR) [6] and the Saccharomyces Genome Database (SGD) [7]. Each of these groups has their own databases and many use their own data model to describe their annotations. There is no single place at which all sets of genome annotations can be found, and several sets are informally mirrored in multiple locations, leading to location-specific version differences. This can make it hazardous to exchange, combine and compare annotation data. Clearly, if genomic annotations were always described using the same language, then comparative analysis of the wealth of information distributed by these institutions would be enormously simplified: Hence the Sequence Ontology (SO) project. SO began 2 years ago, when a group of scientists and developers from the model organism databases - FlyBase, WormBase, Ensembl, SGD and MGI - came together to collect and unify the terms they used in their sequence annotation
On the Use of Gene Ontology Annotations to Assess Functional Similarity among Orthologs and Paralogs: A Short Report
Paul D. Thomas ,Valerie Wood,Christopher J. Mungall,Suzanna E. Lewis,Judith A. Blake,on behalf of the Gene Ontology Consortium
PLOS Computational Biology , 2012, DOI: 10.1371/journal.pcbi.1002386
Abstract: A recent paper (Nehrt et al., PLoS Comput. Biol. 7:e1002073, 2011) has proposed a metric for the “functional similarity” between two genes that uses only the Gene Ontology (GO) annotations directly derived from published experimental results. Applying this metric, the authors concluded that paralogous genes within the mouse genome or the human genome are more functionally similar on average than orthologous genes between these genomes, an unexpected result with broad implications if true. We suggest, based on both theoretical and empirical considerations, that this proposed metric should not be interpreted as a functional similarity, and therefore cannot be used to support any conclusions about the “ortholog conjecture” (or, more properly, the “ortholog functional conservation hypothesis”). First, we reexamine the case studies presented by Nehrt et al. as examples of orthologs with divergent functions, and come to a very different conclusion: they actually exemplify how GO annotations for orthologous genes provide complementary information about conserved biological functions. We then show that there is a global ascertainment bias in the experiment-based GO annotations for human and mouse genes: particular types of experiments tend to be performed in different model organisms. We conclude that the reported statistical differences in annotations between pairs of orthologous genes do not reflect differences in biological function, but rather complementarity in experimental approaches. Our results underscore two general considerations for researchers proposing novel types of analysis based on the GO: 1) that GO annotations are often incomplete, potentially in a biased manner, and subject to an “open world assumption” (absence of an annotation does not imply absence of a function), and 2) that conclusions drawn from a novel, large-scale GO analysis should whenever possible be supported by careful, in-depth examination of examples, to help ensure the conclusions have a justifiable biological basis.
Integrating phenotype ontologies across multiple species
Christopher J Mungall, Georgios V Gkoutos, Cynthia L Smith, Melissa A Haendel, Suzanna E Lewis, Michael Ashburner
Genome Biology , 2010, DOI: 10.1186/gb-2010-11-1-r2
Abstract: The completion of the Human Genome Project [1,2] has resulted in an increase in high-throughput systematic projects aimed at elucidating the molecular basis of human disease. Accurate, precise, and comparable phenotypic information is critical for gaining an in-depth understanding of the relationship between diseases and genes, as well as shedding light upon the influence of different environments on individual genotypes. Natural language free-text descriptions allow for maximum expressivity, but the results are difficult to compute over. Structured controlled vocabularies and ontologies provide an alternative means of recording phenotypes in a way that combines a large degree of expressivity with the benefits of computability. A number of different ontologies have been developed for describing phenotypes, and whilst this is a welcome improvement over free-text descriptions, one problem is that these ontologies are developed for use within a particular project or species, and are not mutually interoperable. This means that it is difficult or extremely difficult to combine genotype-phenotype data from multiple databases - for example, if we wanted to search a mouse or zebrafish database for genes associated with a particular set of phenotypes associated with a human disease, this would require mapping between the individual phenotype ontologies.If we are to combine the results of a variety of phenotypic studies, then phenotypes need to be recorded in a structured systematic fashion. At the same time, the system must allow for a high degree of expressivity to capture the wide range of phenotypes observed across a variety of organisms and types of investigation. Here we propose a methodology that can be used to add value to existing phenotype ontologies by mapping them to a common reference framework based on existing standard ontologies. We implement this methodology for four active phenotype ontologies, focusing primarily on a phenotype ontology used for the mouse. O
Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation
Nicole L. Washington equal contributor,Melissa A. Haendel equal contributor ,Christopher J. Mungall,Michael Ashburner,Monte Westerfield,Suzanna E. Lewis
PLOS Biology , 2009, DOI: 10.1371/journal.pbio.1000247
Abstract: Scientists and clinicians who study genetic alterations and disease have traditionally described phenotypes in natural language. The considerable variation in these free-text descriptions has posed a hindrance to the important task of identifying candidate genes and models for human diseases and indicates the need for a computationally tractable method to mine data resources for mutant phenotypes. In this study, we tested the hypothesis that ontological annotation of disease phenotypes will facilitate the discovery of new genotype-phenotype relationships within and across species. To describe phenotypes using ontologies, we used an Entity-Quality (EQ) methodology, wherein the affected entity (E) and how it is affected (Q) are recorded using terms from a variety of ontologies. Using this EQ method, we annotated the phenotypes of 11 gene-linked human diseases described in Online Mendelian Inheritance in Man (OMIM). These human annotations were loaded into our Ontology-Based Database (OBD) along with other ontology-based phenotype descriptions of mutants from various model organism databases. Phenotypes recorded with this EQ method can be computationally compared based on the hierarchy of terms in the ontologies and the frequency of annotation. We utilized four similarity metrics to compare phenotypes and developed an ontology of homologous and analogous anatomical structures to compare phenotypes between species. Using these tools, we demonstrate that we can identify, through the similarity of the recorded phenotypes, other alleles of the same gene, other members of a signaling pathway, and orthologous genes and pathway members across species. We conclude that EQ-based annotation of phenotypes, in conjunction with a cross-species ontology, and a variety of similarity metrics can identify biologically meaningful similarities between genes by comparing phenotypes alone. This annotation and search method provides a novel and efficient means to identify gene candidates and animal models of human disease, which may shorten the lengthy path to identification and understanding of the genetic basis of human disease.
Systematic determination of patterns of gene expression during Drosophila embryogenesis
Pavel Tomancak, Amy Beaton, Richard Weiszmann, Elaine Kwan, ShengQiang Shu, Suzanna E Lewis, Stephen Richards, Michael Ashburner, Volker Hartenstein, Susan E Celniker, Gerald M Rubin
Genome Biology , 2002, DOI: 10.1186/gb-2002-3-12-research0088
Abstract: As a first step to creating a comprehensive atlas of gene-expression patterns during Drosophila embryogenesis, we examined 2,179 genes by in situ hybridization to fixed Drosophila embryos. Of the genes assayed, 63.7% displayed dynamic expression patterns that were documented with 25,690 digital photomicrographs of individual embryos. The photomicrographs were annotated using controlled vocabularies for anatomical structures that are organized into a developmental hierarchy. We also generated a detailed time course of gene expression during embryogenesis using microarrays to provide an independent corroboration of the in situ hybridization results. All image, annotation and microarray data are stored in publicly available database. We found that the RNA transcripts of about 1% of genes show clear subcellular localization. Nearly all the annotated expression patterns are distinct. We present an approach for organizing the data by hierarchical clustering of annotation terms that allows us to group tissues that express similar sets of genes as well as genes displaying similar expression patterns.Analyzing gene-expression patterns by in situ hybridization to whole-mount embryos provides an extremely rich dataset that can be used to identify genes involved in developmental processes that have been missed by traditional genetic analysis. Systematic analysis of rigorously annotated patterns of gene expression will complement and extend the types of analyses carried out using expression microarrays.Cell-fate changes that occur during development are almost always accompanied by changes in gene expression. Thus detailed knowledge of the spatial and temporal expression patterns for all genes will be an important step in deciphering the complex regulatory networks governing development.Two methods have been used successfully to determine gene-expression patterns on a large scale - RNA in situ hybridization [1] and DNA microarrays [2,3,4]. Whole-mount RNA in situ hybridization i
Survey-based naming conventions for use in OBO Foundry ontology development
Daniel Schober, Barry Smith, Suzanna E Lewis, Waclaw Kusnierczyk, Jane Lomax, Chris Mungall, Chris F Taylor, Philippe Rocca-Serra, Susanna-Assunta Sansone
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-125
Abstract: We summarize a review of existing naming conventions and highlight certain disadvantages with respect to general applicability in the biological domain. We also present the results of a survey carried out to establish which naming conventions are currently employed by OBO Foundry ontologies and to determine what their special requirements regarding the naming of entities might be. Lastly, we propose an initial set of typographic, syntactic and semantic conventions for labelling classes in OBO Foundry ontologies.Adherence to common naming conventions is more than just a matter of aesthetics. Such conventions provide guidance to ontology creators, help developers avoid flaws and inaccuracies when editing, and especially when interlinking, ontologies. Common naming conventions will also assist consumers of ontologies to more readily understand what meanings were intended by the authors of ontologies used in annotating bodies of data.A wide variety of ontologies, controlled vocabularies, and other terminological artifacts relevant to the biological or medical domains are available through open access portals such as the Ontology Lookup Service (OLS) [1], and the number of such artifacts is growing rapidly. One of the goals of the Open Biomedical Ontologies (OBO) Foundry initiative [2] is to facilitate integration among these diverse ontologies. However, such integration demands considerable effort and differences in format and style can only add obstacles to the execution of this task [3]. The heterogeneity within the set of existing ontologies derives from the use of diverse ontology engineering methodologies and is manifest in the adoption by different communities of Description Logic, Common Logic, or other formalisms. The spectrum of syntaxes used to express these formalisms, such as the Web Ontology Language (OWL) or the OBO format, and the commitment of individual communities to conceptualist or realism-based philosophical approaches are also contributing factors.
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