CiteULike: jyuh's Yip

YeastHub: a semantic web use case for integrating data in the life sciences domain.

KH Cheung — 2005-08-08T19:00:33-00:00

Bioinformatics, Vol. 21 Suppl 1 (1 June 2005)

MOTIVATION: As the semantic web technology is maturing and the need for life sciences data integration over the web is growing, it is important to explore how data integration needs can be addressed by the semantic web. The main problem that we face in data integration is a lack of widely-accepted standards for expressing the syntax and semantics of the data. We address this problem by exploring the use of semantic web technologies-including resource description framework (RDF), RDF site summary (RSS), relational-database-to-RDF mapping (D2RQ) and native RDF data repository-to represent, store and query both metadata and data across life sciences datasets. RESULTS: As many biological datasets are presently available in tabular format, we introduce an RDF structure into which they can be converted. Also, we develop a prototype web-based application called YeastHub that demonstrates how a life sciences data warehouse can be built using a native RDF data store (Sesame). This data warehouse allows integration of different types of yeast genome data provided by different resources in different formats including the tabular and RDF formats. Once the data are loaded into the data warehouse, RDF-based queries can be formulated to retrieve and query the data in an integrated fashion. AVAILABILITY: The YeastHub website is accessible via the following URL: http://yeasthub.gersteinlab.org CONTACT: kei.cheung@yale.edu.

Mapping proteins to disease terminologies: from UniProt to MeSH.

A Mottaz — 2008-05-11T03:23:53-00:00

BMC bioinformatics, Vol. 9 Suppl 5 (2008)

BACKGROUND: Although the UniProt KnowledgeBase is not a medical-oriented database, it contains information on more than 2,000 human proteins involved in pathologies. However, these annotations are not standardized, which impairs the interoperability between biological and clinical resources. In order to make these data easily accessible to clinical researchers, we have developed a procedure to link diseases described in the UniProtKB/Swiss-Prot entries to the MeSH disease terminology. RESULTS: We mapped disease names extracted either from the UniProtKB/Swiss-Prot entry comment lines or from the corresponding OMIM entry to the MeSH. Different methods were assessed on a benchmark set of 200 disease names manually mapped to MeSH terms. The performance of the retained procedure in term of precision and recall was 86% and 64% respectively. Using the same procedure, more than 3,000 disease names in Swiss-Prot were mapped to MeSH with comparable efficiency. CONCLUSIONS: This study is a first attempt to link proteins in UniProtKB to the medical resources. The indexing we provided will help clinicians and researchers navigate from diseases to genes and from genes to diseases in an efficient way. The mapping is available at: http://research.isb-sib.ch/unimed.

Annotating single amino acid polymorphisms in the UniProt/Swiss-Prot knowledgebase.

YL Yip — 2008-05-10T09:00:00-00:00

Human mutation, Vol. 29, No. 3. (March 2008), pp. 361-366.

UniProtKB/Swiss-Prot (http://beta.uniprot.org/uniprot; last accessed: 19 October 2007) is a manually curated knowledgebase providing information on protein sequences and functional annotation. It is part of the Universal Protein Resource (UniProt). The knowledgebase currently records a total of 32,282 single amino acid polymorphisms (SAPs) touching 6,086 human proteins (Release 53.2, 26 June 2007). Nearly all SAPs are derived from literature reports using strict inclusion criteria. For each SAP, the knowledgebase provides, apart from the position of the mutation and the resulting change in amino acid, information on the effects of SAPs on protein structure and function, as well as their potential involvement in diseases. Presently, there are 16,043 disease-related SAPs, 14,266 polymorphisms, and 1,973 unclassified variants recorded in UniProtKB/Swiss-Prot. Relevant information on SAPs can be found in various sections of a UniProtKB/Swiss-Prot entry. In addition to these, cross-references to human disease databases as well as other gene-specific databases, are being added regularly. In 2003, the Swiss-Prot variant pages were created to provide a concise view of the information related to the SAPs recorded in the knowledgebase. When compared to the information on missense variants listed in other mutation databases, UniProtKB/Swiss-Prot further records information on direct protein sequencing and characterization including posttranslational modifications (PTMs). The direct links to the Online Mendelian Inheritance in Man (OMIM) database entries further enhance the integration of phenotype information with data at protein level. In this regard, SAP information in UniProtKB/Swiss-Prot complements nicely those existing in genomic and phenotypic databases, and is valuable for the understanding of SAPs and diseases.

Covariance component models for multivariate binary traits in family data analysis.

BH Yip — 2008-03-16T00:50:23-00:00

Stat Med, Vol. 27, No. 7. (30 March 2008), pp. 1086-1105.

For family studies, there is now an established analytical framework for binary-trait outcomes within the generalized linear mixed models (GLMMs). However, the corresponding analysis of multivariate binary-trait (MBT) outcomes is still limited. Certain diseases, such as schizophrenia and bipolar disorder, have similarities in epidemiological features, risk factor patterns and intermediate phenotypes. To have a better etiological understanding, it is important to investigate the common genetic and environmental factors driving the comorbidity of the diseases. In this paper, we develop a suitable GLMM for MBT outcomes from extended families, such as nuclear, paternal- and maternal-halfsib families. We motivate our problem with real questions from psychiatric epidemiology and demonstrate how different substantive issues of comorbidity between two diseases can be put into the analytical framework. Copyright (c) 2007 John Wiley & Sons, Ltd.

Enhanced collagen synthesis in cultured skin fibroblasts from insulin- dependent diabetic patients with nephropathy

R Trevisan — 2008-01-28T12:22:00-00:00

J Am Soc Nephrol, Vol. 8, No. 7. (1 July 1997), pp. 1133-1139.

Cell++--simulating biochemical pathways.

C Sanford — 2008-01-01T03:04:27-00:00

Bioinformatics, Vol. 22, No. 23. (1 December 2006), pp. 2918-2925.

MOTIVATION: With the generation of a wealth of information, detailing cellular components, their functions and interactions, there is a growing need for the development of new computational tools capable of interpreting these data within spatial and dynamic contexts. Here, we introduce Cell++, a novel stochastic simulation environment with the capacity to study a wide variety of biochemical processes within a spatial context. RESULTS: Focusing on three case studies, we highlight the potential impact of spatial organization in the evolution and engineering of signaling and metabolic pathways. In addition to altering signaling and metabolic efficiency, simulations also demonstrated features consistent with the phenomenon of metabolic channeling. AVAILABILITY: Cell++ is licensed under the GNU general public license (GPL) and has been successfully implemented under Linux and IRIX operating systems. Source code together with a simple tutorial is available at http://www.compsysbio.org/CellSim/.

Multicomponent variance estimation for binary traits in family-based studies.

M Noh — 2007-11-04T04:58:43-00:00

Genet Epidemiol, Vol. 30, No. 1. (January 2006), pp. 37-47.

In biometrical genetic analyses of binary traits, the use of family data overcomes some limitations of twin studies, particularly in terms of sample size and types of genetic or environmental factors that can be estimated. However, because of computational problems, recent methods in the application of generalized linear mixed models for family data structure have limited the ability to handle large data sets with general covariates. In this paper, we investigate the use of the hierarchical likelihood approach to the analysis of binary traits from family data. In a simulation study, the method is shown to be highly accurate for the estimation of both the variance components and fixed regression parameters, even for small family sizes. For illustration, we analyze a real data set of familial aggregation of preeclampsia, a pregnancy-induced hypertension. When possible, the analysis is compared with the exact maximum likelihood approach.

LinkHub: a Semantic Web system that facilitates cross-database queries and information retrieval in proteomics.

AK Smith — 2007-06-07T03:04:30-00:00

BMC Bioinformatics, Vol. 8 Suppl 3 (2007)

BACKGROUND: A key abstraction in representing proteomics knowledge is the notion of unique identifiers for individual entities (e.g. proteins) and the massive graph of relationships among them. These relationships are sometimes simple (e.g. synonyms) but are often more complex (e.g. one-to-many relationships in protein family membership). RESULTS: We have built a software system called LinkHub using Semantic Web RDF that manages the graph of identifier relationships and allows exploration with a variety of interfaces. For efficiency, we also provide relational-database access and translation between the relational and RDF versions. LinkHub is practically useful in creating small, local hubs on common topics and then connecting these to major portals in a federated architecture; we have used LinkHub to establish such a relationship between UniProt and the North East Structural Genomics Consortium. LinkHub also facilitates queries and access to information and documents related to identifiers spread across multiple databases, acting as "connecting glue" between different identifier spaces. We demonstrate this with example queries discovering "interologs" of yeast protein interactions in the worm and exploring the relationship between gene essentiality and pseudogene content. We also show how "protein family based" retrieval of documents can be achieved. LinkHub is available at hub.gersteinlab.org and hub.nesg.org with supplement, database models and full-source code. CONCLUSION: LinkHub leverages Semantic Web standards-based integrated data to provide novel information retrieval to identifier-related documents through relational graph queries, simplifies and manages connections to major hubs such as UniProt, and provides useful interactive and query interfaces for exploring the integrated data.