CiteULike: jyuh's library [14765 articles]

Use and misuse of the gene ontology annotations.

Seung Yon Rhee — 2008-05-15T05:58:28-00:00

Nature reviews. Genetics (13 May 2008)

The Gene Ontology (GO) project is a collaboration among model organism databases to describe gene products from all organisms using a consistent and computable language. GO produces sets of explicitly defined, structured vocabularies that describe biological processes, molecular functions and cellular components of gene products in both a computer- and human-readable manner. Here we describe key aspects of GO, which, when overlooked, can cause erroneous results, and address how these pitfalls can be avoided.

PaLS: filtering common literature, biological terms and pathway information.

Andreu Alibés — 2008-05-12T10:49:37-00:00

Nucleic acids research (8 May 2008)

Many biological experiments and their subsequent analysis yield lists of genes or proteins that can potentially be important to the prognosis or diagnosis of certain diseases (e.g. cancer). Nowadays, information about the function of those genes or proteins may be already gathered in some databases, but it is essential to understand if some of the members of those lists have a function in common or if they belong to the same metabolic pathway. To help researchers filter those genes or proteins that have such information in common, we have developed PaLS (pathway and literature strainer, http://pals.bioinfo.cnio.es). PaLS takes a list or a set of lists of gene or protein identifiers and shows which ones share certain descriptors. Four publicly available databases have been used for this purpose: PubMed, which links genes with those articles that make reference to them; Gene Ontology, an annotated ontology of terms related to the cellular component, biological process or molecular function where those genes or proteins are involved; KEGG pathways and Reactome pathways. Those descriptors among these four sources of information that are shared by more members of the list (or lists) are highlighted by PaLS.

Gene Ontology annotations: what they mean and where they come from.

DP Hill — 2008-05-11T16:21:46-00:00

BMC bioinformatics, Vol. 9 Suppl 5 (2008)

To address the challenges of information integration and retrieval, the computational genomics community increasingly has come to rely on the methodology of creating annotations of scientific literature using terms from controlled structured vocabularies such as the Gene Ontology (GO). Here we address the question of what such annotations signify and of how they are created by working biologists. Our goal is to promote a better understanding of how the results of experiments are captured in annotations, in the hope that this will lead both to better representations of biological reality through annotation and ontology development and to more informed use of GO resources by experimental scientists.

Ontology Design Patterns for bio-ontologies: a case study on the Cell Cycle Ontology.

ME Aranguren — 2008-05-16T04:27:27-00:00

BMC bioinformatics, Vol. 9 Suppl 5 (2008)

BACKGROUND: Bio-ontologies are key elements of knowledge management in bioinformatics. Rich and rigorous bio-ontologies should represent biological knowledge with high fidelity and robustness. The richness in bio-ontologies is a prior condition for diverse and efficient reasoning, and hence querying and hypothesis validation. Rigour allows a more consistent maintenance. Modelling such bio-ontologies is, however, a difficult task for bio-ontologists, because the necessary richness and rigour is difficult to achieve without extensive training. RESULTS: Analogous to design patterns in software engineering, Ontology Design Patterns are solutions to typical modelling problems that bio-ontologists can use when building bio-ontologies. They offer a means of creating rich and rigorous bio-ontologies with reduced effort. The concept of Ontology Design Patterns is described and documentation and application methodologies for Ontology Design Patterns are presented. Some real-world use cases of Ontology Design Patterns are provided and tested in the Cell Cycle Ontology. Ontology Design Patterns, including those tested in the Cell Cycle Ontology, can be explored in the Ontology Design Patterns public catalogue that has been created based on the documentation system presented (http://odps.sourceforge.net/). CONCLUSIONS: Ontology Design Patterns provide a method for rich and rigorous modelling in bio-ontologies. They also offer advantages at different development levels (such as design, implementation and communication) enabling, if used, a more modular, well-founded and richer representation of the biological knowledge. This representation will produce a more efficient knowledge management in the long term.

Ontology-guided data preparation for discovering genotype-phenotype relationships

Adrien Coulet — 2008-04-30T15:14:25-00:00

BMC Bioinformatics, Vol. 9, No. Suppl 4. (2008)

BACKGROUND:Complexity and amount of post-genomic data constitute two major factors limiting the application of Knowledge Discovery in Databases (KDD) methods in life sciences. Bio-ontologies may nowadays play key roles in knowledge discovery in life science providing semantics to data and to extracted units, by taking advantage of the progress of Semantic Web technologies concerning the understanding and availability of tools for knowledge representation, extraction, and reasoning.RESULTS:This paper presents a method that exploits bio-ontologies for guiding data selection within the preparation step of the KDD process. We propose three scenarios in which domain knowledge and ontology elements such as subsumption, properties, class descriptions, are taken into account for data selection, before the data mining step. Each of these scenarios is illustrated within a case-study relative to the search of genotype-phenotype relationships in a familial hypercholesterolemia dataset. The guiding of data selection based on domain knowledge is analysed and shows a direct influence on the volume and significance of the data mining results.CONCLUSIONS:The method proposed in this paper is an efficient alternative to numerical methods for data selection based on domain knowledge. In turn, the results of this study may be reused in ontology modelling and data integration.

Terminologies for text-mining; an experiment in the lipoprotein metabolism domain.

D Alexopoulou — 2008-05-11T16:18:25-00:00

BMC bioinformatics, Vol. 9 Suppl 4 (2008)

BACKGROUND: The engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them. RESULTS: We share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition (ATR) methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods. CONCLUSIONS: Given high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking ATR results as input and following the guidelines we described. AVAILABILITY: The TFIDF term recognition is available as Web Service, described at http://gopubmed4.biotec.tu-dresden.de/IdavollWebService/services/CandidateTermGeneratorService?wsdl.

GenoWatch: a disease gene mining browser for association study.

Yan-Hau Chen — 2008-05-16T04:27:58-00:00

Nucleic acids research (25 April 2008)

A human gene association study often involves several genomic markers such as single nucleotide polymorphisms (SNPs) or short tandem repeat polymorphisms, and many statistically significant markers may be identified during the study. GenoWatch can efficiently extract up-to-date information about multiple markers and their associated genes in batch mode from many relevant biological databases in real-time. The comprehensive gene information retrieved includes gene ontology, function, pathway, disease, related articles in PubMed and so on. Subsequent SNP functional impact analysis and primer design of a target gene for re-sequencing can also be done in a few clicks. The presentation of results has been carefully designed to be as intuitive as possible to all users. The GenoWatch is available at the website http://genepipe.ngc.sinica.edu.tw/genowatch.

Extraction of semantic biomedical relations from text using conditional random fields

Markus Bundschus — 2008-04-23T08:38:33-00:00

BMC Bioinformatics, Vol. 9, No. 1. (2008)

BACKGROUND:The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work. In this paper we describe an approach that extracts both the existence of a relation and its type. Our work is based on Conditional Random Fields, which have been applied with much success to the task of named entity recognition.RESULTS:We benchmark our approach on two different tasks. The first task is the identification of semantic relations between diseases and treatments. The available data set consists of manually annotated PubMed abstracts. The second task is the identification of relations between genes and diseases from a set of concise phrases, so-called GeneRIF (Gene Reference Into Function) phrases. In our experimental setting, we do not assume that the entities are given, as is often the case in previous relation extraction work. Rather the extraction of the entities is solved as a subproblem. Compared with other state-of-the-art approaches, we achieve very competitive results on both data sets. To demonstrate the scalability of our solution, we apply our approach to the complete human GeneRIF database. The resulting gene-disease network contains 34758 semantic associations between 4939 genes and 1745 diseases. The gene-disease network is publicly available as a machine-readable RDF graph.CONCLUSIONS:We extend the framework of Conditional Random Fields towards the annotation of semantic relations from text and apply it to the biomedical domain. Our approach is based on a rich set of textual features and achieves a performance that is competitive to leading approaches. The model is quite general and can be extended to handle arbitrary biological entities and relation types. The resulting gene-disease network shows that the GeneRIF database provides a rich knowledge source for text mining. Current work is focused on improving the accuracy of detection of entities as well as entity boundaries, which will also greatly improve the relation extraction performance.

A perspective for biomedical data integration: design of databases for flow cytometry

John Drakos — 2008-02-15T13:47:05-00:00

BMC Bioinformatics, Vol. 9 (14 February 2008), 99.

Integration of clinical chemistry, expression, and metabolite data leads to better toxicological class separation.

JS Spicker — 2008-05-16T04:09:28-00:00

Toxicological sciences : an official journal of the Society of Toxicology, Vol. 102, No. 2. (April 2008), pp. 444-454.

A large number of databases are currently being implemented within toxicology aiming to integrate diverse biological data, such as clinical chemistry, expression, and other types of data. However, for these endeavors to be successful, tools for integration, visualization, and interpretation are needed. This paper presents a method for data integration using a hierarchical model based on either principal component analysis or partial least squares discriminant analysis of clinical chemistry, expression, and nuclear magnetic resonance data using a toxicological study as case. The study includes the three toxicants alpha-naphthyl-isothiocyanate, dimethylnitrosamine, and N-methylformamide administered to rats. Improved predictive ability of the different classes is seen, suggesting that this approach is a suitable method for data integration and visualization of biological data. Furthermore, the method allows for correlation of biological parameters between the different data types, which could lead to an improvement in biological interpretation.

GeneFisher-P: variations of GeneFisher as processes in Bio-jETI.

AL Lamprecht — 2008-05-09T13:44:24-00:00

BMC bioinformatics, Vol. 9 Suppl 4 (2008)

BACKGROUND: PCR primer design is an everyday, but not trivial task requiring state-of-the-art software. We describe the popular tool GeneFisher and explain its recent restructuring using workflow techniques. We apply a service-oriented approach to model and implement GeneFisher-P, a process-based version of the GeneFisher web application, as a part of the Bio-jETI platform for service modeling and execution. We show how to introduce a flexible process layer to meet the growing demand for improved user-friendliness and flexibility. RESULTS: Within Bio-jETI, we model the process using the jABC framework, a mature model-driven, service-oriented process definition platform. We encapsulate remote legacy tools and integrate web services using jETI, an extension of the jABC for seamless integration of remote resources as basic services, ready to be used in the process. Some of the basic services used by GeneFisher are in fact already provided as individual web services at BiBiServ and can be directly accessed. Others are legacy programs, and are made available to Bio-jETI via the jETI technology.The full power of service-based process orientation is required when more bioinformatics tools, available as web services or via jETI, lead to easy extensions or variations of the basic process. This concerns for instance variations of data retrieval or alignment tools as provided by the European Bioinformatics Institute (EBI). CONCLUSIONS: The resulting service- and process-oriented GeneFisher-P demonstrates how basic services from heterogeneous sources can be easily orchestrated in the Bio-jETI platform and lead to a flexible family of specialized processes tailored to specific tasks.

Evolution of web services in bioinformatics.

PB Neerincx — 2005-07-04T11:18:24-00:00

Brief Bioinform, Vol. 6, No. 2. (June 2005), pp. 178-188.

Bioinformaticians have developed large collections of tools to make sense of the rapidly growing pool of molecular biological data. Biological systems tend to be complex and in order to understand them, it is often necessary to link many data sets and use more than one tool. Therefore, bioinformaticians have experimented with several strategies to try to integrate data sets and tools. Owing to the lack of standards for data sets and the interfaces of the tools this is not a trivial task. Over the past few years building services with web-based interfaces has become a popular way of sharing the data and tools that have resulted from many bioinformatics projects. This paper discusses the interoperability problem and how web services are being used to try to solve it, resulting in the evolution of tools with web interfaces from HTML/web form-based tools not suited for automatic workflow generation to a dynamic network of XML-based web services that can easily be used to create pipelines.

SWS: accessing SRS sites contents through Web Services.

P Romano — 2008-04-24T12:30:16-00:00

BMC bioinformatics, Vol. 9 Suppl 2 (2008)

BACKGROUND: Web Services and Workflow Management Systems can support creation and deployment of network systems, able to automate data analysis and retrieval processes in biomedical research. Web Services have been implemented at bioinformatics centres and workflow systems have been proposed for biological data analysis.New databanks are often developed by taking into account these technologies, but many existing databases do not allow a programmatic access. Only a fraction of available databanks can thus be queried through programmatic interfaces. SRS is a well know indexing and search engine for biomedical databanks offering public access to many databanks and analysis tools. Unfortunately, these data are not easily and efficiently accessible through Web Services. RESULTS: We have developed 'SRS by WS' (SWS), a tool that makes information available in SRS sites accessible through Web Services. Information on known sites is maintained in a database, srsdb. SWS consists in a suite of WS that can query both srsdb, for information on sites and databases, and SRS sites. SWS returns results in a text-only format and can be accessed through a WSDL compliant client. SWS enables interoperability between workflow systems and SRS implementations, by also managing access to alternative sites, in order to cope with network and maintenance problems, and selecting the most up-to-date among available systems. CONCLUSIONS: Development and implementation of Web Services, allowing to make a programmatic access to an exhaustive set of biomedical databases can significantly improve automation of in-silico analysis. SWS supports this activity by making biological databanks that are managed in public SRS sites available through a programmatic interface.

BioCAD: an information fusion platform for bio-network inference and analysis.

D Lee — 2008-05-16T03:00:21-00:00

BMC bioinformatics, Vol. 8 Suppl 9 (2007)

BACKGROUND: As systems biology has begun to draw growing attention, bio-network inference and analysis have become more and more important. Though there have been many efforts for bio-network inference, they are still far from practical applications due to too many false inferences and lack of comprehensible interpretation in the biological viewpoints. In order for applying to real problems, they should provide effective inference, reliable validation, rational elucidation, and sufficient extensibility to incorporate various relevant information sources. RESULTS: We have been developing an information fusion software platform called BioCAD. It is utilizing both of local and global optimization for bio-network inference, text mining techniques for network validation and annotation, and Web services-based workflow techniques. In addition, it includes an effective technique to elucidate network edges by integrating various information sources. This paper presents the architecture of BioCAD and essential modules for bio-network inference and analysis. CONCLUSION: BioCAD provides a convenient infrastructure for network inference and network analysis. It automates series of users' processes by providing data preprocessing tools for various formats of data. It also helps inferring more accurate and reliable bio-networks by providing network inference tools which utilize information from distinct sources. And it can be used to analyze and validate the inferred bio-networks using information fusion tools.

RDFScape: Semantic Web meets systems biology.

A Splendiani — 2008-05-11T16:22:53-00:00

BMC bioinformatics, Vol. 9 Suppl 4 (2008)

BACKGROUND: The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain. New ontologies are being developed to formalize knowledge, e.g. about the functions of proteins. As the Semantic Web is being introduced into the Life Sciences, the basis for a distributed knowledge-base that can foster biological data analysis is laid. However, there still is a dichotomy, in tools and methodologies, between the use of ontologies in biological investigation, that is, in relation to experimental observations, and their use as a knowledge-base. RESULTS: RDFScape is a plugin that has been developed to extend a software oriented to biological analysis with support for reasoning on ontologies in the semantic web framework. We show with this plugin how the use of ontological knowledge in biological analysis can be extended through the use of inference. In particular, we present two examples relative to ontologies representing biological pathways: we demonstrate how these can be abstracted and visualized as interaction networks, and how reasoning on causal dependencies within elements of pathways can be implemented. CONCLUSIONS: The use of ontologies for the interpretation of high-throughput biological data can be improved through the use of inference. This allows the use of ontologies not only as annotations, but as a knowledge-base from which new information relevant for specific analysis can be derived.

Bio2RDF: Towards a mashup to build bioinformatics knowledge systems.

François Belleau — 2008-05-13T13:22:14-00:00

Journal of biomedical informatics (21 March 2008)

Presently, there are numerous bioinformatics databases available on different websites. Although RDF was proposed as a standard format for the web, these databases are still available in various formats. With the increasing popularity of the semantic web technologies and the ever growing number of databases in bioinformatics, there is a pressing need to develop mashup systems to help the process of bioinformatics knowledge integration. Bio2RDF is such a system, built from rdfizer programs written in JSP, the Sesame open source triplestore technology and an OWL ontology. With Bio2RDF, documents from public bioinformatics databases such as Kegg, PDB, MGI, HGNC and several of NCBI's databases can now be made available in RDF format through a unique URL in the form of http://bio2rdf.org/namespace:id. The Bio2RDF project has successfully applied the semantic web technology to publicly available databases by creating a knowledge space of RDF documents linked together with normalized URIs and sharing a common ontology. Bio2RDF is based on a three-step approach to build mashups of bioinformatics data. The present article details this new approach and illustrates the building of a mashup used to explore the implication of four transcription factor genes in Parkinson's disease. The Bio2RDF repository can be queried at http://bio2rdf.org.

GraphCrunch: a tool for large network analyses

Tijana Milenkovic — 2008-01-31T10:05:23-00:00

BMC Bioinformatics, Vol. 9, No. 1. (2008)

BACKGROUND:The recent explosion in biological and other real-world network data has created the need for improved tools for large network analyses. In addition to well established global network properties, several new mathematical techniques for analyzing local structural properties of large networks have been developed. Small over-represented subgraphs, called network motifs, have been introduced to identify simple building blocks of complex networks. Small induced subgraphs, called graphlets, have been used to develop "network signatures" that summarize network topologies. Based on these network signatures, two new highly sensitive measures of network local structural similarities were designed: the relative graphlet frequency distance (RGF-distance) and the graphlet degree distribution agreement (GDD-agreement). Finding adequate null-models for biological networks is important in many research domains. Network properties are used to assess the fit of network models to the data. Various network models have been proposed. To date, there does not exist a software tool that measures the above mentioned local network properties. Moreover, none of the existing tools compare real-world networks against a series of network models with respect to these local as well as a multitude of global network properties.RESULTS:Thus, we introduce GraphCrunch, a software tool that finds well-fitting network models by comparing large real-world networks against random graph models according to various network structural similarity measures. It has unique capabilities of finding computationally expensive RGF-distance and GDD-agreement measures. In addition, it computes several standard global network measures and thus supports the largest variety of network measures thus far. Also, it is the first software tool that compares real-world networks against a series of network models and that has built-in parallel computing capabilities allowing for a user specified list of machines on which to perform compute intensive searches for local network properties. Furthermore, GraphCrunch is easily extendible to include additional network measures and models.CONCLUSIONS:GraphCrunch is a software tool that implements the latest research on biological network models and properties: it compares real-world networks against a series of random graph models with respect to a multitude of local and global network properties. We present GraphCrunch as a comprehensive, parallelizable, and easily extendible software tool for analyzing and modeling large biological networks. The software is open-source and freely available at http://www.ics.uci.edu/~bio-nets/graphcrunch/. It runs under Linux, MacOS, and Windows Cygwin. In addition, it has an easy to use on-line web user interface that is available from the above web page.

Promoting synergistic research and education in genomics and bioinformatics.

JY Yang — 2008-05-16T03:06:42-00:00

BMC genomics, Vol. 9 Suppl 1 (2008)

Bioinformatics and Genomics are closely related disciplines that hold great promises for the advancement of research and development in complex biomedical systems, as well as public health, drug design, comparative genomics, personalized medicine and so on. Research and development in these two important areas are impacting the science and technology.High throughput sequencing and molecular imaging technologies marked the beginning of a new era for modern translational medicine and personalized healthcare. The impact of having the human sequence and personalized digital images in hand has also created tremendous demands of developing powerful supercomputing, statistical learning and artificial intelligence approaches to handle the massive bioinformatics and personalized healthcare data, which will obviously have a profound effect on how biomedical research will be conducted toward the improvement of human health and prolonging of human life in the future. The International Society of Intelligent Biological Medicine (http://www.isibm.org) and its official journals, the International Journal of Functional Informatics and Personalized Medicine (http://www.inderscience.com/ijfipm) and the International Journal of Computational Biology and Drug Design (http://www.inderscience.com/ijcbdd) in collaboration with International Conference on Bioinformatics and Computational Biology (Biocomp), touch tomorrow's bioinformatics and personalized medicine throughout today's efforts in promoting the research, education and awareness of the upcoming integrated inter/multidisciplinary field. The 2007 international conference on Bioinformatics and Computational Biology (BIOCOMP07) was held in Las Vegas, the United States of American on June 25-28, 2007. The conference attracted over 400 papers, covering broad research areas in the genomics, biomedicine and bioinformatics. The Biocomp 2007 provides a common platform for the cross fertilization of ideas, and to help shape knowledge and scientific achievements by bridging these two very important disciplines into an interactive and attractive forum. Keeping this objective in mind, Biocomp 2007 aims to promote interdisciplinary and multidisciplinary education and research. 25 high quality peer-reviewed papers were selected from 400+ submissions for this supplementary issue of BMC Genomics. Those papers contributed to a wide-range of important research fields including gene expression data analysis and applications, high-throughput genome mapping, sequence analysis, gene regulation, protein structure prediction, disease prediction by machine learning techniques, systems biology, database and biological software development. We always encourage participants submitting proposals for genomics sessions, special interest research sessions, workshops and tutorials to Professor Hamid R. Arabnia (hra@cs.uga.edu) in order to ensure that Biocomp continuously plays the leadership role in promoting inter/multidisciplinary research and education in the fields. Biocomp received top conference ranking with a high score of 0.95/1.00. Biocomp is academically co-sponsored by the International Society of Intelligent Biological Medicine and the Research Laboratories and Centers of Harvard University--Massachusetts Institute of Technology, Indiana University--Purdue University, Georgia Tech--Emory University, UIUC, UCLA, Columbia University, University of Texas at Austin and University of Iowa etc. Biocomp--Worldcomp brings leading scientists together across the nation and all over the world and aims to promote synergistic components such as keynote lectures, special interest sessions, workshops and tutorials in response to the advances of cutting-edge research.

BioManager: the use of a bioinformatics web application as a teaching tool in undergraduate bioinformatics training

Sonia Cattley — 2007-11-23T17:24:50-00:00

Brief Bioinform, Vol. 8, No. 6. (1 November 2007), pp. 457-465.

The completion of the human genome project, and other genome sequencing projects, has spearheaded the emergence of the field of bioinformatics. Using computer programs to analyse DNA and protein information has become an important area of life science research and development. While it is not necessary for most life science researchers to develop specialist bioinformatic skills (including software development), basic skills in the application of common bioinformatics software and the effective interpretation of results are increasingly required by all life science researchers. Training in bioinformatics is increasingly occurring within the university system as part of existing undergraduate science and specialist degrees. One difficulty in bioinformatics education is the sheer number of software programs required in order to provide a thorough grounding in the subject to the student. Teaching requires either a well-maintained internal server with all the required software, properly interfacing with student terminals, and with sufficient capacity to handle multiple simultaneous requests, or it requires the individual installation and maintenance of every piece of software on each computer. In both cases, there are difficult issues regarding site maintenance and accessibility. In this article, we discuss the use of BioManager, a web-based bioinformatics application integrating a variety of common bioinformatics tools, for teaching, including its role as the main bioinformatics training tool in some Australian and international universities. We discuss some of the issues with using a bioinformatics resource primarily created for research in an undergraduate teaching environment. 10.1093/bib/bbm039

Bio-jETI: a service integration, design, and provisioning platform for orchestrated bioinformatics processes.

T Margaria — 2008-05-16T02:59:18-00:00

BMC bioinformatics, Vol. 9 Suppl 4 (2008)

BACKGROUND: With Bio-jETI, we introduce a service platform for interdisciplinary work on biological application domains and illustrate its use in a concrete application concerning statistical data processing in R and xcms for an LC/MS analysis of FAAH gene knockout. METHODS: Bio-jETI uses the jABC environment for service-oriented modeling and design as a graphical process modeling tool and the jETI service integration technology for remote tool execution. CONCLUSIONS: As a service definition and provisioning platform, Bio-jETI has the potential to become a core technology in interdisciplinary service orchestration and technology transfer. Domain experts, like biologists not trained in computer science, directly define complex service orchestrations as process models and use efficient and complex bioinformatics tools in a simple and intuitive way.

A Semantic Web for bioinformatics: goals, tools, systems, applications.

N Cannata — 2008-05-11T16:16:58-00:00

BMC bioinformatics, Vol. 9 Suppl 4 (2008)

GraphFind: enhancing graph searching by low support data mining techniques.

A Ferro — 2008-05-12T12:07:35-00:00

BMC bioinformatics, Vol. 9 Suppl 4 (2008)

BACKGROUND: Biomedical and chemical databases are large and rapidly growing in size. Graphs naturally model such kinds of data. To fully exploit the wealth of information in these graph databases, a key role is played by systems that search for all exact or approximate occurrences of a query graph. To deal efficiently with graph searching, advanced methods for indexing, representation and matching of graphs have been proposed. RESULTS: This paper presents GraphFind. The system implements efficient graph searching algorithms together with advanced filtering techniques that allow approximate search. It allows users to select candidate subgraphs rather than entire graphs. It implements an effective data storage based also on low-support data mining. CONCLUSIONS: GraphFind is compared with Frowns, GraphGrep and gIndex. Experiments show that GraphFind outperforms the compared systems on a very large collection of small graphs. The proposed low-support mining technique which applies to any searching system also allows a significant index space reduction.

VariVis: a visualisation toolkit for variation databases

Timothy Smith — 2008-04-23T07:16:11-00:00

BMC Bioinformatics, Vol. 9 (23 April 2008), 206.

PatMaN: rapid alignment of short sequences to large databases.

Kay Prüfer — 2008-05-16T02:41:30-00:00

Bioinformatics (Oxford, England) (8 May 2008)

SUMMARY: We present a tool suited for searching for many short nucleotide sequences in large databases, allowing for a pre-defined number of gaps and mismatches. The commandline-driven program implements a nondeterministic automata matching-algorithm on a keyword tree of the search strings. Both queries with and without ambiguity codes can be searched. Search time is short for perfect matches, and retrieval time rises exponentially with the number of edits allowed. AVAILABILITY: The C++ source code for PatMaN is distributed under the GNU General Public License and has been tested on the GNU/Linux operating system. It is available from http://bioinf.eva.mpg.de/patman. CONTACT: pruefer@eva.mpg.de.

Automating dChip: toward reproducible sharing of microarray data analysis

Cheng Li — 2008-05-10T00:57:17-00:00

BMC Bioinformatics, Vol. 9 (08 May 2008), 231.

CSCDB: The cAMP and cGMP signaling components database.

Jianxin Lu — 2008-05-16T02:41:44-00:00

Genomics (7 May 2008)

Adenylate cyclases, guanylate cyclases, cyclic nucleotide phosphodiesterases, and cyclic nucleotide-binding proteins constitute the core of cAMP and cGMP signaling components. Using a combination of BLAST and profile search methods, we found that cyclic nucleotide-binding proteins exhibited diverse domain architectures. In addition to the domain architectures involved in the characterized functional groups, a cyclic nucleotide-binding domain was also fused to various domains involved in pyridine nucleotide-disulfide oxidoreductase, acetyltransferase, thioredoxin reductase, glutaminase, rhodanese, ferredoxin, and diguanylate cyclase, implying the versatile functions of cyclic nucleotide-binding proteins. We constructed the CSCDB database to accumulate the components of cAMP and cGMP signaling pathways in the complete genomes. User-friendly interfaces were created for easier browsing, searching, and downloading the data. Besides harboring the sequence itself, each entry provided detailed annotation information, such as sequence features, chromosomal localization, functional domains, transmembrane region, and sequence similarity against several major databases. Currently, CSCDB contains 4234 entries covering 466 organisms, including 35 eukaryotes, 382 bacteria, and 29 archaea. CSCDB can be freely accessible on the web at http://cscdb.com.cn.

Proteomics FASTA archive and reference resource.

JA Falkner — 2008-05-16T02:41:56-00:00

Proteomics, Vol. 8, No. 9. (May 2008), pp. 1756-1757.

A FASTA file archive and reference resource has been added to ProteomeCommons.org. Motivation for this new functionality derives from two primary sources. The first is the recent FASTA standardization work done by the Human Proteome Organization's Proteomics Standards Initiative (HUPO-PSI). Second is the general lack of a uniform mechanism to properly cite FASTA files used in a study, and to publicly access such FASTA files post-publication. An extension to the Tranche data sharing network has been developed that includes web-pages, documentation, and tools for facilitating the use of FASTA files. These include conversion to the new HUPO-PSI format, and provisions for both citing and publicly archiving FASTA files. This new resource is available immediately, free of charge, and can be accessed at http://www.proteomecommons.org/data/fasta/. Source-code for related tools is also freely available under the BSD license.

Cytoscape ESP: simple search of complex biological networks.

Maital Ashkenazi — 2008-05-04T12:03:46-00:00

Bioinformatics (Oxford, England) (28 April 2008)

SUMMARY: Cytoscape ESP enables searching complex biological networks on multiple attribute fields using logical operators and wildcards. Queries use an intuitive syntax and simple search line interface. ESP is implemented as a Cytoscape plugin and complements existing search functions in the Cytoscape network visualization and analysis software, allowing users to easily identify nodes, edges and subgraphs of interest, even for very large networks. AVAILABILITY: http://conklinwolf.ucsf.edu/genmappwiki/Google_Summer_of_Code_2007/Maital CONTACT: ashkenaz@agri.huji.ac.il.

A simple and rapid technique for detecting protein phosphorylation using one-dimensional isoelectric focusing gels and immunoblot analysis.

Jeffrey C Anderson — 2008-05-16T02:15:43-00:00

The Plant journal : for cell and molecular biology (9 May 2008)

We report a technique for detecting protein phosphorylation that involves isoelectric focusing in a vertical mini-gel format followed by immunoblot detection of the target protein. This method uses standard protein gel equipment, allows sensitive detection of protein phosphorylation when phosphospecific antibodies are not available, and provides a stoichiometric measure of phosphorylation. We demonstrate the application of this method for observing phosphorylation of an Arabidopsis thaliana protein in response to biotic stress.

Glycogen synthase kinase-3 protects estrogen receptor alpha from proteasomal degradation and is required for full transcriptional activity of the receptor.

J Grisouard — 2008-05-16T02:11:57-00:00

Molecular endocrinology (Baltimore, Md.), Vol. 21, No. 10. (October 2007), pp. 2427-2439.

Glycogen synthase kinase-3 (GSK-3) plays a key role in the regulation of transcription factors including steroid receptors. Having identified estrogen receptor-alpha (ERalpha) as substrate for GSK-3, the impact of GSK-3 on ERalpha function and activity upon 17beta-estradiol (E2)-dependent activation remains to be clarified. Here we show by using small interfering technology in combination with immunoblot, gene expression analysis, and luciferase reporter assays that silencing of GSK-3alpha or GSK-3beta results in the reduction of ERalpha levels and transcriptional activity in ERalpha-positive breast cancer cells. Using MCF-7 cells we demonstrate that reduction of ERalpha levels upon GSK-3 silencing was due to increased proteasomal degradation of ERalpha rather than inhibition of ERalpha protein synthesis. Indeed, under this condition, ERalpha protein was rescued using the proteasome inhibitor MG132 in presence of the protein synthesis inhibitor cycloheximide. In addition, strong accumulation of ubiquitinated ERalpha was obtained after GSK-3 silencing in the presence of MG132. We conclude that GSK-3 protects ERalpha from proteasomal degradation and plays a crucial role in ERalpha protein stabilization and turnover. Furthermore, in vitro kinase assay depicted that GSK-3beta phosphorylates ERalpha at Ser-118. GSK-3 silencing resulted in decrease of E2-induced nuclear ERalpha phosphorylation at Ser-118 and E2-induced estrogen response element-dependent luciferase reporter gene expression. Neither Ser-118 phosphorylation nor luciferase activity was restored by use of MG132. Moreover, the expression of estrogen-responsive genes (pS2 and progesterone receptor) was decreased upon GSK-3 silencing. These findings demonstrated that GSK-3 is required for E2-induced ERalpha phosphorylation at Ser-118 and full transcriptional activity of the receptor upon E2 stimulation.

Krüppel-like factor 8 induces epithelial to mesenchymal transition and epithelial cell invasion.

X Wang — 2008-05-16T02:12:05-00:00

Cancer research, Vol. 67, No. 15. (1 August 2007), pp. 7184-7193.

Tumor invasion and metastasis are the main causes of death from cancer. Epithelial to mesenchymal transition (EMT) is a determining step for a cancer cell to progress from a noninvasive to invasive state. Krüppel-like factor 8 (KLF8) plays a key role in oncogenic transformation and is highly overexpressed in several types of invasive human cancer, including breast cancer. To understand the role of KLF8 in regulating the progression of human breast cancer, we first established stable expression of KLF8 in an immortalized normal human breast epithelial cell line. We found that KLF8 strongly induced EMT and enhanced motility and invasiveness in the cells, by analyzing changes in cell morphology and epithelial and mesenchymal marker proteins, and using cell migration and Matrigel invasion assays. Chromatin immunoprecipitations (ChIP), oligonucleotide precipitations, and promoter-reporter assays showed that KLF8 directly bound and repressed the promoter of E-cadherin independent of E boxes in the promoter and Snail expression. Aberrant elevation of KLF8 expression is highly correlated with the decrease in E-cadherin expression in the invasive human breast cancer. Blocking KLF8 expression by RNA interference restored E-cadherin expression in the cancer cells and strongly inhibited the cell invasiveness. This work identifies KLF8 as a novel EMT-regulating transcription factor that opens a new avenue in EMT research and suggests an important role for KLF8 in human breast cancer invasion and metastasis.

Multivariate data analysis of two-dimensional gel electrophoresis protein patterns from few samples.

KN Jensen — 2008-05-15T09:13:57-00:00

Journal of proteome research, Vol. 7, No. 3. (March 2008), pp. 1288-1296.

One application of 2D gel electrophoresis is to reveal differences in protein pattern between two or more groups of individuals, attributable to their group membership. Multivariate data analytical methods are useful in pinpointing the spots relevant for discrimination by focusing not only on single spot differences, but on the covariance structure between proteins. However, their outcome is dependent on data scaling, and they may fail in producing valid multivariate models due to the much higher number of "irrelevant" spots present in the gels. The case where only few gels are available and where the aim is to find as many as possible of the group-dependent proteins seems particularly difficult to handle. The present paper investigates such a case regarding the effect of scaling and of prefiltering by univariate nonparametric statistics on the selection of spots. Besides, a modified 'autoscaling' of the full data set based on within-group standard deviations is introduced and shown to be advantageous in revealing potential group-dependent proteins additional to those found by prefiltering.

An improved pixel-based approach for analyzing images in two-dimensional gel electrophoresis.

MB Rye — 2008-05-15T09:13:37-00:00

Electrophoresis, Vol. 29, No. 6. (March 2008), pp. 1382-1393.

An improved pixel-based approach for analyzing 2-DE images is presented. The key feature of the method is to create a mask based on all gels in the experiment using image morphology, followed by multivariate analysis on the pixel level. The method reduces the impact of noise and background by identifying regions in the image where protein spots are present, but make no assumption on individual spot boundaries for isolated spots. This makes it possible to detect significant changes in complex regions, and visualize these changes over multiple gels in an easy way. False missing values and spot volumes caused by imposing erroneous spot boundaries are thus circumvented. The approach presented gives improved pixel-based information from the gels, and is also an alternative to existing methods for data-reduction, significance testing and visualization of 2-DE data. Results are compared with software using a common spot boundary approach on an experiment consisting of 35 full size gel images. Gel alignment is required before analysis.

A multivariate spot filtering model for two-dimensional gel electrophoresis

Morten Rye — 2008-03-20T07:52:21-00:00

ELECTROPHORESIS, Vol. 29, No. 6. (2008), pp. 1369-1381.

Image segmentation plays an important role in the automatic analysis of protein spots in 2-DE. Using image segments representing protein spots, the amount of protein in each segment can be quantified, and corresponding segments can be matched and compared for multiple gels. However, the common presence of image segments caused by noise and unwanted artefacts highly disturb the analysis and comparison of the gels. Common sources of such artefacts are cracks in the gel surface, fingerprints, dust and other pollutions. It would be advantageous to remove these unwanted artefacts during or after the segmentation procedure. To achieve this task a multivariate spot filtering model is developed using image segments from a gel segmentation. Parameters in the model are based on texture, shape and intensity measurements of the image segments. The model successfully managed to separate segments caused by noise, artefacts and cracks from image segments representing true protein spots. The classification method used is discriminant partial least squares regression.

A new method for assigning common spot boundaries for multiple gels in two-dimensional gel electrophoresis

Morten Rye — 2008-03-20T07:54:45-00:00

ELECTROPHORESIS, Vol. 29, No. 6. (2008), pp. 1359-1368.

The benefits of defining common spot boundaries when several gels from 2-DE are compared and analyzed have lately been stressed by both commercial software producers and users of this software. Though the importance of common spot boundaries is clearly stated, few reports exist that target this issue explicitly. In this study a method for defining common spots boundaries is developed, called the spot density method. The method consists of the following steps: segmentation and spot identification on each individual gel, transferring the spot-center coordinates for all gels onto a single new gel, collecting spot centers clustered together in the new gel and finally assigning pixels and new spot boundaries based on the spots in each cluster. The method is compared to a synthetic gel approach, and validated by visual inspection of three representative areas in the gels. The gel images need to be aligned prior to segmentation and spot identification, but the method can be used regardless of the choice of segmentation procedure. This makes the method an easy extension to existing methods for spot identification and matching. Conclusions based on the visual inspection are that the spot density method identifies partly overlapping spots and low-intensity spots better than the synthetic gel approach.

The myth of automated, high-throughput two-dimensional gel analysis.

BN Clark — 2008-05-15T09:14:01-00:00

Proteomics, Vol. 8, No. 6. (March 2008), pp. 1197-1203.

Many software packages have been developed to process and analyze 2-D gel images. Some programs have been touted as automated, high-throughput solutions. We tested five commercially available programs using 18 replicate gels of a rat brain protein extract. We determined computer processing time, approximate spot editing time, time required to correct spot mismatches, as well as total processing time. We also determined the number of spots automatically detected, number of spots kept after manual editing, and the percentage of automatically generated correct matches. We also determined the effect of increasing the number of replicate gels on spot matching efficiency for two of the programs. We found that for all programs tested, less than 3% of the total processing time was automated. The remainder of the time was spent in manual, subjective editing of detected spots and computer generated matches. Total processing time for 18 gels varied from 22 to 84 h. The percentage of correct matches generated automatically varied from 1 to 62%. Increasing the number of gels in an experiment dramatically reduced the percentage of automatically generated correct matches. Our results demonstrate that these 2-D gel analysis programs are not automatic or rapid, and also suggest that matching accuracy decreases as experiment size increases.

Power estimation of the t test for detecting differential gene expression

Begun — 2008-05-08T15:05:41-00:00

Functional & Integrative Genomics, Vol. 8, No. 2. (May 2008), pp. 109-113.

Technical strategies to reduce the amount of "false significant" results in quantitative proteomics.

S Fuxius — 2008-05-16T01:31:35-00:00

Proteomics, Vol. 8, No. 9. (May 2008), pp. 1780-1784.

When the p-value is set at <0.05 in statistical group comparisons, a 5% rate of "false significant" results is expected. In order to test the reliability of our 2-DE method, we loaded each of 24 gels with equal-sized samples (200 mug protein from pooled rat brain, pH 4-7, stained with ruthenium fluorescent stain for visualization) and statistically compared the first 12 gels with the last 12. In numerous experiments the rate of significant differences found far exceeded 5%. Several factors were identified as causing the following rates of false significant differences in spot intensities: (i) running samples in two different 2-DE runs (42%), (ii) running second dimension gels produced in two different gel casters (16%), (iii) normalizing the entire gel instead of separately normalizing several different gel zones (11%), (iv) using IPG strips from different packages (19%), (v) dividing the whole sample into subgroups during software analysis (9%). After controlling for all these factors, the rates of "false positive" results in our experiments were regularly reduced to approximately 5%. This is an indispensable prerequisite for avoiding too high a rate of false positive results in experiments in which different subgroups are compared statistically.

Reconstructing networks of pathways via significance analysis of their intersections.

M Francesconi — 2008-05-12T12:06:10-00:00

BMC bioinformatics, Vol. 9 Suppl 4 (2008)

BACKGROUND: Significance analysis at single gene level may suffer from the limited number of samples and experimental noise that can severely limit the power of the chosen statistical test. This problem is typically approached by applying post hoc corrections to control the false discovery rate, without taking into account prior biological knowledge. Pathway or gene ontology analysis can provide an alternative way to relax the significance threshold applied to single genes and may lead to a better biological interpretation. RESULTS: Here we propose a new analysis method based on the study of networks of pathways. These networks are reconstructed considering both the significance of single pathways (network nodes) and the intersection between them (links).We apply this method for the reconstruction of networks of pathways to two gene expression datasets: the first one obtained from a c-Myc rat fibroblast cell line expressing a conditional Myc-estrogen receptor oncoprotein; the second one obtained from the comparison of Acute Myeloid Leukemia and Acute Lymphoblastic Leukemia derived from bone marrow samples. CONCLUSION: Our method extends statistical models that have been recently adopted for the significance analysis of functional groups of genes to infer links between these groups. We show that groups of genes at the interface between different pathways can be considered as relevant even if the pathways they belong to are not significant by themselves.

Quantitative 2-D gel electrophoresis-based expression proteomics of albumin and IgG immunodepleted plasma.

MD Seferovic — 2008-05-16T01:31:58-00:00

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences, Vol. 865, No. 1-2. (1 April 2008), pp. 147-152.

Proteomic analysis of plasma is challenging because of its large dynamic range, which prevents the detection of low abundance proteins. Immunodepletion of high abundance proteins, such as albumin and IgG, has emerged as a favored technology to overcome this problem; however its suitability in quantitative expression proteomics has not yet been adequately addressed. In this study, albumin and IgG immunodepletion was evaluated by ELISAs and the reproducibility of depletion was tested with 2-DGE. Depletion of plasma resulted in removal of 62+/-1.2% of the total protein, 93+/-1.4% of the albumin (0.43 microg/microL, residual), and 94+/-1.5% of the IgG (0.21 microg/microL, residual). These results were confirmed by immunoblotting. Computerized image analysis of 2-D gels using Progenesis SameSpots software revealed an enhancement in the number of visible spots (675-1325), with 10+/-6% inter-gel variability in spot density. LC-ESI-MS/MS identification of newly resolved protein spots further validated the procedure. An innovative application of the software employed led to identification of 11 proteins lost non-specifically during depletion. This study demonstrates the effectiveness of immunodepletion of albumin and IgG in quantitative 2-DGE-based differential analysis of plasma proteins.

Integrated genomic approaches implicate osteoglycin (Ogn) in the regulation of left ventricular mass

Enrico Petretto — 2008-04-29T07:16:46-00:00

Nature Genetics, Vol. 40, No. 5. (28 April 2008), pp. 546-552.

Using genetic markers to orient the edges in quantitative trait networks: the NEO software.

Jason Aten — 2008-05-16T01:54:14-00:00

BMC systems biology, Vol. 2, No. 1. (15 April 2008)

ABSTRACT: BACKGROUND: Systems genetic studies have been used to identify genetic loci that affect transcript abundances and clinical traits such as body weight. The pairwise correlations between gene expression traits and/or clinical traits can be used to define undirected trait networks. Several authors have argued that genetic markers (e.g expression quantitative trait loci, eQTLs) can serve as causal anchors for orienting the edges of a trait network. The availability of hundreds of thousands of genetic markers poses new challenges: how to relate (anchor) traits to multiple genetic markers, how to score the genetic evidence in favor of an edge orientation, and how to weigh the information from multiple markers. RESULTS: We develop and implement Network Edge Orienting (NEO) methods and software that address the challenges of inferring unconfounded and directed gene networks from microarray-derived gene expression data by integrating mRNA levels with genetic marker data and Structural Equation Model (SEM) comparisons. The NEO software implements several manual and automatic methods for incorporating genetic information to anchor traits. The networks are oriented by considering each edge separately, thus reducing error propagation. To summarize the genetic evidence in favor of a given edge orientation, we propose Local SEM-based Edge Orienting (LEO) scores that compare the fit of several competing causal graphs. SEM fitting indices allow the user to assess local and overall model fit. The NEO software allows the user to carry out a robustness analysis with regard to genetic marker selection. We demonstrate the utility of NEO by recovering known causal relationships in the sterol homeostasis pathway using liver gene expression data from an F2 mouse cross. Further, we use NEO to study the relationship between a disease gene and a biologically important gene co-expression module in liver tissue. CONCLUSION: The NEO software can be used to orient the edges of gene co-expression networks or quantitative trait networks if the edges can be anchored to genetic marker data. R software tutorials, data, and supplementary material can be downloaded from: www.genetics.ucla.edu/labs/horvath/aten/NEO.

Statistical Power of Expression Quantitative Trait Loci for Mapping of Complex Trait Loci in Natural Populations

Paul Schliekelman — 2008-04-26T13:29:42-00:00

Genetics, Vol. 178, No. 4. (1 April 2008), pp. 2201-2216.

A number of recent genomewide surveys have found numerous QTL for gene expression, often with intermediate to high heritability values. As a result, there is currently a great deal of interest in genetical genomics--that is, the combination of genomewide expression data and molecular marker data to elucidate the genetics of complex traits. To date, most genetical genomics studies have focused on generating candidate genes for previously known trait loci or have otherwise leveraged existing knowledge about trait-related genes. The purpose of this study is to explore the potential for genetical genomics approaches in the context of genomewide scans for complex trait loci. I explore the expected strength of association between expression-level traits and a clinical trait, as a function of the underlying genetic model in natural populations. I give calculations of statistical power for detecting differential expression between affected and unaffected individuals. I model both reactive and causative expression-level traits with both additive and multiplicative multilocus models for the relationship between phenotype and genotype and explore a variety of assumptions about dominance, number of segregating loci, and other parameters. There are two key results. If a transcript is causative for the disease (in the sense that disease risk depends directly on transcript level), then the power to detect association between transcript and disease is quite good. Sample sizes on the order of 100 are sufficient for 80% power. On the other hand, if the transcript is reactive to a disease locus, then the correlation between expression-level traits and disease is low unless the expression-level trait shares several causative loci with the disease--that is, the expression-level trait itself is a complex trait. Thus, there is a trade-off between the power to show association between a reactive expression-level trait and the clinical trait of interest and the power to map expression-level QTL (eQTL) for that expression-level trait. Gene expression-level traits that are most strongly correlated with the clinical trait will themselves be complex traits and therefore often hard to map. Likewise, the expression-level traits that are easiest to map will tend to have a low correlation with the clinical trait. These results show some fundamental principles for understanding power in eQTL-based mapping studies. 10.1534/genetics.107.076687

From classical genetics to quantitative genetics to systems biology: modeling epistasis.

DL Aylor — 2008-04-17T11:57:23-00:00

PLoS genetics, Vol. 4, No. 3. (March 2008)

Gene expression data has been used in lieu of phenotype in both classical and quantitative genetic settings. These two disciplines have separate approaches to measuring and interpreting epistasis, which is the interaction between alleles at different loci. We propose a framework for estimating and interpreting epistasis from a classical experiment that combines the strengths of each approach. A regression analysis step accommodates the quantitative nature of expression measurements by estimating the effect of gene deletions plus any interaction. Effects are selected by significance such that a reduced model describes each expression trait. We show how the resulting models correspond to specific hierarchical relationships between two regulator genes and a target gene. These relationships are the basic units of genetic pathways and genomic system diagrams. Our approach can be extended to analyze data from a variety of experiments, multiple loci, and multiple environments.

Comparison of mixed-model approaches for association mapping.

B Stich — 2008-05-16T01:36:23-00:00

Genetics, Vol. 178, No. 3. (March 2008), pp. 1745-1754.

Association-mapping methods promise to overcome the limitations of linkage-mapping methods. The main objectives of this study were to (i) evaluate various methods for association mapping in the autogamous species wheat using an empirical data set, (ii) determine a marker-based kinship matrix using a restricted maximum-likelihood (REML) estimate of the probability of two alleles at the same locus being identical in state but not identical by descent, and (iii) compare the results of association-mapping approaches based on adjusted entry means (two-step approaches) with the results of approaches in which the phenotypic data analysis and the association analysis were performed in one step (one-step approaches). On the basis of the phenotypic and genotypic data of 303 soft winter wheat (Triticum aestivum L.) inbreds, various association-mapping methods were evaluated. Spearman's rank correlation between P-values calculated on the basis of one- and two-stage association-mapping methods ranged from 0.63 to 0.93. The mixed-model association-mapping approaches using a kinship matrix estimated by REML are more appropriate for association mapping than the recently proposed QK method with respect to (i) the adherence to the nominal alpha-level and (ii) the adjusted power for detection of quantitative trait loci. Furthermore, we showed that our data set could be analyzed by using two-step approaches of the proposed association-mapping method without substantially increasing the empirical type I error rate in comparison to the corresponding one-step approaches.

Visualizing data

2008-05-16T01:46:42-00:00

eQED: an efficient method for interpreting eQTL associations using protein networks

Silpa Suthram — 2008-03-04T20:48:50-00:00

Mol Syst Biol, Vol. 4 (4 March 2008)

Semiparametric methods for genome-wide linkage analysis of human gene expression data.

G Diao — 2008-05-16T01:34:34-00:00

BMC proceedings, Vol. 1 Suppl 1 (2007)

ABSTRACT : With the availability of high-throughput microarray technologies, investigators can simultaneously measure the expression levels of many thousands of genes in a short period. Although there are rich statistical methods for analyzing microarray data in the literature, limited work has been done in mapping expression quantitative trait loci (eQTL) that influence the variation in levels of gene expression. Most existing eQTL mapping methods assume that the expression phenotypes follow a normal distribution and violation of the normality assumption may lead to inflated type I error and reduced power. QTL analysis of expression data involves the mapping of many expression phenotypes at thousands or hundreds of thousands of marker loci across the whole genome. An appropriate procedure to adjust for multiple testing is essential for guarding against an abundance of false positive results. In this study, we applied a semiparametric quantitative trait loci (SQTL) mapping method to human gene expression data. The SQTL mapping method is rank-based and therefore robust to non-normality and outliers. Furthermore, we apply an efficient Monte Carlo procedure to account for multiple testing and assess the genome-wide significance level. Particularly, we apply the SQTL mapping method and the Monte-Carlo approach to the gene expression data provided by Genetic Analysis Workshop 15.

Mapping quantitative trait loci for expression abundance.

Z Jia — 2008-05-16T01:34:39-00:00

Genetics, Vol. 176, No. 1. (May 2007), pp. 611-623.

Mendelian loci that control the expression levels of transcripts are called expression quantitative trait loci (eQTL). When mapping eQTL, we often deal with thousands of expression traits simultaneously, which complicates the statistical model and data analysis. Two simple approaches may be taken in eQTL analysis: (1) individual transcript analysis in which a single expression trait is mapped at a time and the entire eQTL mapping involves separate analysis of thousands of traits and (2) individual marker analysis where differentially expressed transcripts are detected on the basis of their association with the segregation pattern of an individual marker and the entire analysis requires scanning markers of the entire genome. Neither approach is optimal because data are not analyzed jointly. We develop a Bayesian clustering method that analyzes all expressed transcripts and markers jointly in a single model. A transcript may be simultaneously associated with multiple markers. Additionally, a marker may simultaneously alter the expression of multiple transcripts. This is a model-based method that combines a Gaussian mixture of expression data with segregation of multiple linked marker loci. Parameter estimation for each variable is obtained via the posterior mean drawn from a Markov chain Monte Carlo sample. The method allows a regular quantitative trait to be included as an expression trait and subject to the same clustering assignment. If an expression trait links to a locus where a quantitative trait also links, the expressed transcript is considered to be associated with the quantitative trait. The method is applied to a microarray experiment with 60 F(2) mice measured for 25 different obesity-related quantitative traits. In the experiment, approximately 40,000 transcripts and 145 codominant markers are investigated for their associations. A program written in SAS/IML is available from the authors on request.

An image analysis suite for spot detection and spot matching in two-dimensional electrophoresis gels.

T Srinark — 2008-05-15T09:14:12-00:00

Electrophoresis, Vol. 29, No. 3. (February 2008), pp. 706-715.

We propose a suite of novel algorithms for image analysis of protein expression images obtained from 2-D electrophoresis. These algorithms are a segmentation algorithm for protein spot identification, and an algorithm for matching protein spots from two corresponding images for differential expression study. The proposed segmentation algorithm employs the watershed transformation, k-means analysis, and distance transform to locate the centroids and to extract the regions of the proteins spots. The proposed spot matching algorithm is an integration of the hierarchical-based and optimization-based methods. The hierarchical method is first used to find corresponding pairs of protein spots satisfying the local cross-correlation and overlapping constraints. The matching energy function based on local structure similarity, image similarity, and spatial constraints is then formulated and optimized. Our new algorithm suite has been extensively tested on synthetic and actual 2-D gel images from various biological experiments, and in quantitative comparisons with ImageMaster2D Platinum the proposed algorithms exhibit better spot detection and spot matching.