Biomedical subjects

Kei-Hoi Cheung

Publications and source records attributed to Kei-Hoi Cheung.

17 recordsLinked to original sources

PubMed2006

SemBiosphere: a semantic web approach to recommending microarray clustering services.

Clustering is a popular method for analyzing microarray data. Given the large number of clustering algorithms being available, it is difficult to identify the most suitable ones for a particular task. It is also difficult to locate, download, install and run the algorithms. This paper describes a matchmaking system, SemBiosphere, which solves both problems. It recommends clustering algorithms based on some minimal user requirement inputs and the data properties. An ontology was developed in OWL, an expressive ontological language, for describing what the algorithms are and how they perform, in addition to how they can be invoked. This allows machines to "understand" the algorithms and make the recommendations. The algorithm can be implemented by different groups and in different languages, and run on different platforms at geographically distributed sites. Through the use of XML-based web services, they can all be invoked in the same standard way. The current clustering services were transformed from the non-semantic web services of the Biosphere system, which includes a variety of algorithms that have been applied to microarray gene expression data analysis. New algorithms can be incorporated into the system without too much effort. The SemBiosphere system and the complete clustering ontology can be accessed at http://yeasthub2.gersteinlab. org/sembiosphere/.

Algorithms↗

PubMed2005

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

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.

Biology↗

PubMed2005

YPED: a proteomics database for protein expression analysis.

We have developed the Yale Protein Expression Database (YPED) to address the storage, retrieval, and integrated analysis of proteomics data generated by Yale's Keck Protein Chemistry and Mass Spectrometry Facility. YPED is Web-accessible and currently handles sample requisition, result reporting and sample comparison for ICAT, DIGE and MUDPIT samples. Sample descriptions are compatible with the evolving MIAPE standards. Peptides and proteins identified using Sequest or Mascot are validated with the Trans-Proteomic Pipeline developed at the Institute of Systems Biology and data from the resulting XML file are stored in the database. Researchers can view, subset and download their data through a secure Web interface.

Databases, Protein↗

PubMed2004

A high productivity/low maintenance approach to high-performance computation for biomedicine: four case studies.

The rapid advances in high-throughput biotechnologies such as DNA microarrays and mass spectrometry have generated vast amounts of data ranging from gene expression to proteomics data. The large size and complexity involved in analyzing such data demand a significant amount of computing power. High-performance computation (HPC) is an attractive and increasingly affordable approach to help meet this challenge. There is a spectrum of techniques that can be used to achieve computational speedup with varying degrees of impact in terms of how drastic a change is required to allow the software to run on an HPC platform. This paper describes a high- productivity/low-maintenance (HP/LM) approach to HPC that is based on establishing a collaborative relationship between the bioinformaticist and HPC expert that respects the former's codes and minimizes the latter's efforts. The goal of this approach is to make it easy for bioinformatics researchers to continue to make iterative refinements to their programs, while still being able to take advantage of HPC. The paper describes our experience applying these HP/LM techniques in four bioinformatics case studies: (1) genome-wide sequence comparison using Blast, (2) identification of biomarkers based on statistical analysis of large mass spectrometry data sets, (3) complex genetic analysis involving ordinal phenotypes, (4) large-scale assessment of the effect of possible errors in analyzing microarray data. The case studies illustrate how the HP/LM approach can be applied to a range of representative bioinformatics applications and how the approach can lead to significant speedup of computationally intensive bioinformatics applications, while making only modest modifications to the programs themselves.

Amino Acid Sequence↗

PubMed2004

Handling multiple testing while interpreting microarrays with the Gene Ontology Database.

BACKGROUND: The development of software tools that analyze microarray data in the context of genetic knowledgebases is being pursued by multiple research groups using different methods. A common problem for many of these tools is how to correct for multiple statistical testing since simple corrections are overly conservative and more sophisticated corrections are currently impractical. A careful study of the nature of the distribution one would expect by chance, such as by a simulation study, may be able to guide the development of an appropriate correction that is not overly time consuming computationally. RESULTS: We present the results from a preliminary study of the distribution one would expect for analyzing sets of genes extracted from Drosophila, S. cerevisiae, Wormbase, and Gramene databases using the Gene Ontology Database. CONCLUSIONS: We found that the estimated distribution is not regular and is not predictable outside of a particular set of genes. Permutation-based simulations may be necessary to determine the confidence in results of such analyses.

Animals↗

PubMed2004

KARMA: a web server application for comparing and annotating heterogeneous microarray platforms.

We have developed a universal web server application (KARMA) that allows comparison and annotation of user-defined pairs of microarray platforms based on diverse types of genome annotation data (across different species) collected from multiple sources. The application is an effective tool for diverse microarray platforms, including arrays that are provided by (i) the Keck Microarray Resource at Yale, (ii) commercially available Affymetrix GeneChips and spotted arrays and (iii) custom arrays made by individual academics. The tool provides a web interface that allows users to input pairs of test files that represent diverse array platforms for either single or multiple species. The program dynamically identifies analogous DNA fragments spotted or synthesized on multiple microarray platforms based on the following types of information: (i) NCBI-Unigene identifiers, if the platforms being compared are within the same species or (ii) NCBI-Homologene data, if they are cross-species. The single-species comparison is implemented based on set operations: intersection, union and difference. Other forms of retrievable annotation data, including LocusLink, SwissProt and Gene Ontology (GO), are collected from multiple remote sites and stored in an integrated fashion using an Oracle database. The KARMA database, which is updated periodically, is available on line at the following URL: http://ymd.med.yale.edu/karma/cgi-bin/karma.pl.

Gene Expression Profiling↗

PubMed2004

A web services choreography scenario for interoperating bioinformatics applications.

BACKGROUND: Very often genome-wide data analysis requires the interoperation of multiple databases and analytic tools. A large number of genome databases and bioinformatics applications are available through the web, but it is difficult to automate interoperation because: 1) the platforms on which the applications run are heterogeneous, 2) their web interface is not machine-friendly, 3) they use a non-standard format for data input and output, 4) they do not exploit standards to define application interface and message exchange, and 5) existing protocols for remote messaging are often not firewall-friendly. To overcome these issues, web services have emerged as a standard XML-based model for message exchange between heterogeneous applications. Web services engines have been developed to manage the configuration and execution of a web services workflow. RESULTS: To demonstrate the benefit of using web services over traditional web interfaces, we compare the two implementations of HAPI, a gene expression analysis utility developed by the University of California San Diego (UCSD) that allows visual characterization of groups or clusters of genes based on the biomedical literature. This utility takes a set of microarray spot IDs as input and outputs a hierarchy of MeSH Keywords that correlates to the input and is grouped by Medical Subject Heading (MeSH) category. While the HTML output is easy for humans to visualize, it is difficult for computer applications to interpret semantically. To facilitate the capability of machine processing, we have created a workflow of three web services that replicates the HAPI functionality. These web services use document-style messages, which means that messages are encoded in an XML-based format. We compared three approaches to the implementation of an XML-based workflow: a hard coded Java application, Collaxa BPEL Server and Taverna Workbench. The Java program functions as a web services engine and interoperates with these web services using a web services choreography language (BPEL4WS). CONCLUSION: While it is relatively straightforward to implement and publish web services, the use of web services choreography engines is still in its infancy. However, industry-wide support and push for web services standards is quickly increasing the chance of success in using web services to unify heterogeneous bioinformatics applications. Due to the immaturity of currently available web services engines, it is still most practical to implement a simple, ad-hoc XML-based workflow by hard coding the workflow as a Java application. For advanced web service users the Collaxa BPEL engine facilitates a configuration and management environment that can fully handle XML-based workflow.

Computational Biology↗

PubMed2004

Identifying projected clusters from gene expression profiles.

In microarray gene expression data, clusters may hide in certain subspaces. For example, a set of co-regulated genes may have similar expression patterns in only a subset of the samples in which certain regulating factors are present. Their expression patterns could be dissimilar when measuring in the full input space. Traditional clustering algorithms that make use of such similarity measurements may fail to identify the clusters. In recent years a number of algorithms have been proposed to identify this kind of projected clusters, but many of them rely on some critical parameters whose proper values are hard for users to determine. In this paper, a new algorithm that dynamically adjusts its internal thresholds is proposed. It has a low dependency on user parameters while allowing users to input some domain knowledge should they be available. Experimental results show that the algorithm is capable of identifying some interesting projected clusters.

Algorithms↗

PubMed2004

Large-scale mutagenesis of the yeast genome using a Tn7-derived multipurpose transposon.

We present here an unbiased and extremely versatile insertional library of yeast genomic DNA generated by in vitro mutagenesis with a multipurpose element derived from the bacterial transposon Tn7. This mini-Tn7 element has been engineered such that a single insertion can be used to generate a lacZ fusion, gene disruption, and epitope-tagged gene product. Using this transposon, we generated a plasmid-based library of approximately 300,000 mutant alleles; by high-throughput screening in yeast, we identified and sequenced 9032 insertions affecting 2613 genes (45% of the genome). From analysis of 7176 insertions, we found little bias in Tn7 target-site selection in vitro. In contrast, we also sequenced 10,174 Tn3 insertions and found a markedly stronger preference for an AT-rich 5-base pair target sequence. We further screened 1327 insertion alleles in yeast for hypersensitivity to the chemotherapeutic cisplatin. Fifty-one genes were identified, including four functionally uncharacterized genes and 25 genes involved in DNA repair, replication, transcription, and chromatin structure. In total, the collection reported here constitutes the largest plasmid-based set of sequenced yeast mutant alleles to date and, as such, should be singularly useful for gene and genome-wide functional analysis.

Amino Acid Sequence↗

PubMed2004

Biosphere: the interoperation of web services in microarray cluster analysis.

UNLABELLED: The growing use of DNA microarrays in biomedical research has led to the proliferation of analysis tools. These software programs address different aspects of analysis (e.g. normalisation and clustering within and across individual arrays) as well as extended analysis methods (e.g. clustering, annotation and mining of multiple datasets). Therefore, microarray data analysis typically requires the interoperability of multiple software programs involving different analysis types and methods. Such interoperation is often hampered by the heterogeneity inherent in the software tools (which may function by implementing different interfaces and using different programming languages). To address this problem, we employed the simple object access protocol (SOAP)-based web service approach that provides a uniform programmatic interface to these heterogeneous software components. To demonstrate this approach in the microarray context, we created a web server application, Biosphere, which interoperates a number of web services that are geographically widely distributed. These web services include a clustering web service, which is a suite of different clustering algorithms for analysing microarray data; XEMBL, developed at the European Bioinformatics Institute (EBI) for retrieving EMBL Nucleotide Sequence Database sequence data; and three gene annotation web services: GetGO, GetHAPI and GetUMLS. GetGO allows retrieval of Gene Ontology (GO) annotation, and the other two web services retrieve annotation from the biomedical literature that is indexed based on the Medical Subject Headings (MeSH) terms. With these web services, Biosphere allows the users to do the following: (i) cluster gene expression data using seven different algorithms; (ii) visualise the clustering results that are grouped statistically in colour; and (iii) retrieve sequence, annotation and citation data for the genes of interest. AVAILABILITY: Biosphere and its web services described in Web Service Description Language (WSDL) can be accessed at http://rook.cecid.hku.hk:8280/BiosphereServer.

Cluster Analysis↗

PubMed2003

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis.

BACKGROUND: To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. RESULTS: We have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to (i) upload and populate microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams without building image files manually; (iv) visualize gene expressions for each pathway at enzyme, locus, and probe levels; and (v) perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways. CONCLUSION: PathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: (i) automatic generation of pathways associated with gene expression and (ii) statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiment(s).

Arabidopsis↗

PubMed2002

Subcellular localization of the yeast proteome.

Protein localization data are a valuable information resource helpful in elucidating eukaryotic protein function. Here, we report the first proteome-scale analysis of protein localization within any eukaryote. Using directed topoisomerase I-mediated cloning strategies and genome-wide transposon mutagenesis, we have epitope-tagged 60% of the Saccharomyces cerevisiae proteome. By high-throughput immunolocalization of tagged gene products, we have determined the subcellular localization of 2744 yeast proteins. Extrapolating these data through a computational algorithm employing Bayesian formalism, we define the yeast localizome (the subcellular distribution of all 6100 yeast proteins). We estimate the yeast proteome to encompass approximately 5100 soluble proteins and >1000 transmembrane proteins. Our results indicate that 47% of yeast proteins are cytoplasmic, 13% mitochondrial, 13% exocytic (including proteins of the endoplasmic reticulum and secretory vesicles), and 27% nuclear/nucleolar. A subset of nuclear proteins was further analyzed by immunolocalization using surface-spread preparations of meiotic chromosomes. Of these proteins, 38% were found associated with chromosomal DNA. As determined from phenotypic analyses of nuclear proteins, 34% are essential for spore viability--a percentage nearly twice as great as that observed for the proteome as a whole. In total, this study presents experimentally derived localization data for 955 proteins of previously unknown function: nearly half of all functionally uncharacterized proteins in yeast. To facilitate access to these data, we provide a searchable database featuring 2900 fluorescent micrographs at http://ygac.med.yale.edu.

Algorithms↗

PubMed2002

The TRIPLES database: a community resource for yeast molecular biology.

TRIPLES is a web-accessible database of TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces cerevisiae-a relational database housing nearly half a million data points generated from an ongoing study using large-scale transposon mutagenesis to characterize gene function in yeast. At present, TRIPLES contains three principal data sets (i.e. phenotypic data, protein localization data and expression data) for over 3500 annotated yeast genes as well as several hundred non-annotated open reading frames. In addition, the TRIPLES web site provides online order forms linked to each data set so that users may request any strain or reagent generated from this project free of charge. In response to user requests, the TRIPLES web site has undergone several recent modifications. Our localization data have been supplemented with approximately 500 fluorescent micrographs depicting actual staining patterns observed upon indirect immunofluorescence analysis of indicated epitope-tagged proteins. These localization data, as well as all other data sets within TRIPLES, are now available in full as tab-delimited text. To accommodate increased reagent requests, all orders are now cataloged in a separate database, and users are notified immediately of order receipt and shipment. Also, TRIPLES is one of five sites incorporated into the new functional analysis tool Function Junction provided by the Saccharomyces Genome Database. TRIPLES may be accessed from the Yale Genome Analysis Center (YGAC) homepage at http://ygac.med.yale.edu.

Computer Graphics↗

PubMed2002

ALFRED: An allele frequency database for anthropology.

The deluge of data from the human genome project (HGP) presents new opportunities for molecular anthropologists to study human variation through the promise of vast numbers of new polymorphisms (e.g., single nucleotide polymorphisms or SNPs). Collecting the resulting data into a single, easily accessible resource will be important to facilitate this research. We created a prototype Web-accessible database named ALFRED (ALelle FREquency Database, http://alfred.med.yale.edu/alfred/) to store and make publicly available allele frequency data on diverse polymorphic sites for many populations. In constructing this database, we considered many different concerns relating to the types of information needed for anthropology, population genetics, molecular genetics, and statistics, as well as issues of data integrity and ease of access to data. We also developed links to other Web-based databases as well as procedures for others to make links to the data in ALFRED. Here we present an overview of the issues considered and provisional solutions, as well as an example of data already available. It is our hope that this database will be useful for research and teaching in a wide range of fields, and that colleagues from various fields will contribute to making ALFRED an important resource for many studies as yet unforeseen.

Anthropology, Physical↗

PubMed2002

An integrated approach for finding overlooked genes in yeast.

We report here the discovery of 137 previously unappreciated genes in yeast through a widely applicable and highly scalable approach integrating methods of gene-trapping, microarray-based expression analysis, and genome-wide homology searching. Our approach is a multistep process in which expressed sequences are first trapped using a modified transposon that produces protein fusions to beta-galactosidase (beta-gal); non-annotated open reading frames (ORFs) translated as beta-gal chimeras are selected as a candidate pool of potential genes. To verify expression of these sequences, labeled RNA is hybridized against a microarray of oligonucleotides designed to detect gene transcripts in a strand-specific manner. In complement to this experimental method, novel genes are also identified in silico by homology to previously annotated proteins. As these methods are capable of identifying both short ORFs and antisense ORFs, our approach provides an effective supplement to current gene-finding schemes. In total, the genes discovered using this approach constitute 2% of the yeast genome and represent a wealth of overlooked biology.

Chromosomes↗

PubMed2002

YMD: a microarray database for large-scale gene expression analysis.

The use of microarray technology to perform parallel analysis of the expression pattern of a large number of genes in a single experiment has created a new frontier of medical research. The vast amount of gene expression data generated from multiple microarray experiments requires a robust database system that allows efficient data storage, retrieval, secure access, data dissemination, and integrated data analyses. To address the growing needs of microarray researchers at Yale and their collaborators, we have built the Yale Microarray Database (YMD). YMD is Web-accessible with the following features: (i) a Web program that tracks DNA samples between source plates and arrays, (ii) the capability of finding common genes/clones across different array platforms, (iii) an image file server, (iv) laboratory-based user management and access privileges, (v) project management, (vi) template data entry, (vii) linking gene expression data to annotation databases for functional analysis. YMD is currently being used on a pilot basis by several laboratories for different organisms and array platforms.

Databases, Nucleic Acid↗

PubMedDate not supplied

A dynamic approach to mapping coordinates between microplates and microarrays.

The retrieval of useful data from spotted microarray slides requires keeping track of which microplate wells and DNA sample corresponds to each spot on each array slide. Existing approaches are closely coupled with the type of arrayer in use and are computer operating-system-specific. To support the microarray researcher community at large who use different arrayers and computer platforms, increased flexibility, generality, and portability of these approaches are required. In this paper, we describe a general algorithm that correlates the well positions of DNA samples in each microplate to the positions of the spots on each array slide. Based on this algorithm, we have implemented a flexible and platform-independent program named MicroArray Convolutor (MAC) that provides a Web solution allowing the user to: (a) import a text file that identifies the DNA samples and their well locations, (b) select a transformation method that converts data in 96-well plate format into 384-well plate format, and (c) specify the output format of the array lists dependant on the configuration of the array platform as well as the downstream analysis software chosen for the array. MAC and its source code can be accessed via the following Web address: http://ymd.med.yale.edu/kei-cgi/kc_mac_dev8.pl.

Algorithms↗