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BOSC 2007 Abstracts
- 1 BOSC 2007 Abstracts
- 1.1 [New Technology] The Galaxy Framework for Computational Biology Tool Integration
- 1.2 [New Technology] AJAX GBrowse: Community Genome Annotation Made Easy
- 1.3 [New Technology] Dasty2: A Web Client for Visualizing Protein Sequence Features
- 1.4 [New Technology] EMBRACE Web Services
- 1.5 [New Technology] CaGrid Cancer Biomedical Informatics Grid
- 1.6 [OS Software] The ONDEX Data Integration Framework
- 1.7 [OS Software] CGEMS: An Open-Source caIntegrator Application to support Whole Genome Association Studies
- 1.8 [OS Software] Modware: An Object-Oriented Perl Interface to the Chado Schema
- 1.9 [OS Software] XRATE: Scheme-y trees, phylo-HMMs and phylo-grammars
- 1.10 [OS Software] XMLPipeDB: A Reusable, Open Source Tool Chain for Building Relational Databases from XML Sources
- 1.11 [Software Design And Engineering] An Open Source Framework for Teaching Bioinformatics
- 1.12 [Software Design And Engineering] Tools to Facilitate Large Scale Comparative Genomic Analysis
BOSC 2007 Abstracts
[New Technology] The Galaxy Framework for Computational Biology Tool Integration
James Taylor, Visiting Member, Courant Institute, New York University
Modern biology is being revolutionized by high throughput data production, and an amazing number of powerful tools for analyzing biological data have been developed. However a substantial challenge remains: how to provide these tools in an integrated environment that is accessible not just to software developers, but to experimental biologists. We have developed a system -- Galaxy -- to address this challenge. Galaxy provides a framework that allows existing computational tools to easily be given modern web based interfaces, and integrated with other computational tools to facilitate complex multi-stage analysis. Galaxy simultaneously targets two audiences: for tool developers it eliminates the repetitive effort involved in creating high-quality user interfaces, while giving them the benefit of being able to provide their tools in an integrated environment. For experimental biologist it allows running complex analysis on huge datasets without needing to worry about details of installing tools, allocating computing resources, and file format conversions. This talk will discuss the Galaxy architecture, how it can help computational tool developers, the advantages of tool integration, and future directions.
[New Technology] AJAX GBrowse: Community Genome Annotation Made Easy
Mitch Skinner, Berkeley University
The current standard for genome browser software involves a user interface based on static web pages. These pages are created on a server from a pre-loaded database of genome features, then viewed re-motely on a web browser. It is desirable to improve on this general approach by adding two new kinds of feature:
- Instant responsiveness. In current browsers, whenever the user requests an action (such as moving to a new genome location, zooming in/out, changing view properties, etc.) the server must perform various database and page-rendering operations. This causes a significant lag, even if only a small part of the view is changed.
- Persistent community annotation. In a typical system, entry of new annotations is dependent on a database administrator. While some genome browsers allow user-generated annotations to be tran-siently displayed alongside 'official' tracks, there is typically no mechanism for permanently entering new records over the web. This has the potential to slow down community annotation.
While somewhat independent, both of these features are typical of a new generation of Web 2.0 applications. We have extended the GBrowse open-source genome browser to implement these ideas, using the recently-popularized AJAX (Asynchronous Java Script And XML) framework, whereby a carefully designated proportion of the computational load is shifted from the database server to the web-browsing client. The new framework enables a much smoother interface, familiar to users of websites such as Google Maps. For example, chromosome views can be dynamically dragged in a continuous motion without noticeable delay. Similarly, tracks can be instantly collapsed or expanded, with no waiting time while the new view is downloaded over the network. Our approach also empowers true community annotation, whereby users can add their own tracks to a genome browser for others to see, constituting a 'live genome wiki'. We currently support feature uploads in GFF format, and we will soon support the WIG format as well. We intend to provide our browser to the modENCODE project. We are currently porting all existing GBrowse functionality to this new system. As a demonstration we have working browsers for D.melanogaster and S.cerevisiae chromosomes viewable at http://genome.biowiki.org/.
- License: Artistic
- Web site: http://genome.biowiki.org
- Email: email@example.com
[New Technology] Dasty2: A Web Client for Visualizing Protein Sequence Features
Rafael C. Jimenez, European Bioinformatics Institute, Hinxton, Cambridge
Visually, Dasty2 provides numerous advantages over other DAS clients (8). Space is used effectively so there is no need to scroll in portions of the screen. It makes use of the standard colours employed by Uniprot and SRS for annotations. Dasty2 uses colours, borders, complimentary shades and separating lines to contrast features with the background and to make the relationships among annotations clear (9). Finally, Dasty2 allows grouping and sorting of annotations by various properties, and zooming within a protein sequence.
- Dasty, http://www.ebi.ac.uk/dasty/
- Dowell, R., Jokerst, R.M., Day, A., Eddy, S.R. & Stein, L. 2001. The Distributed Annotation. Sys-tem BMC Bioinformatics 2:7
- Biodas, http://www.biodas.org
- Jones, P., Vinod, N., Down, T., Hackmann, A., Kahari, A., Kretschmann, E., Quinn, A., Wieser, D., Hermjakob, H. & Apweiler, R. 2005. Dasty and UniProt DAS: a perfect pair for protein feature visu-alization. Bioinformatics, Oxford Univ Press 21, 3198-3199
- DAS registry, http://www.dasregistry.org
- Paulson, L. 2005. Building rich web applications with Ajax. Computer 38, 14-17
- OPEN SOURCE LICENSE All Dasty2 software is freely available to all users, academic or commercial, under the terms of the Apache License Version 2.0
- PROJECT WEBSITE http://www.ebi.ac.uk/dasty/
- CONTACT EMAIL List: firstname.lastname@example.org Personal: email@example.com
[New Technology] EMBRACE Web Services
Taavi Hupponen, CSC, the Finnish IT center for science
One of the main goals of the EU FP6 -funded EMBRACE (http://www.embracegrid.info) is to enable programmatic remote access to bioinformatics tools and databases through standard interfaces. As opposed to traditional web-based interfaces, this will allow the services to be accessed efficiently from own software or from tools like the workflow-enactment engine Taverna (http://taverna.sourceforge.net/).
The technology of choice for providing the service interfaces is SOAP based Web services. As interoperability is essential for successful integration, the use of specifications and best practices such as the WS-I Basic Profile and document literal wrapped style have been emphasized in interface implementations. Also issues like managing state and dealing with large data have received attention. EMBRACE Web services are listed at http://www.embracegrid.info/dokuwiki/ and they include for example tools for sequence and structure analysis as well as several bioinformatics databases.
EMBRACE is funded by the European Commission within its FP6 Programme, under the thematic area "Life sciences, genomics and biotechnology for health,"contract number LHSG-CT-2004-512092.
- Web site: http://www.embracegrid.info
- Contact: Taavi Hupponen, CSC, Finland, firstname.lastname@example.org
[New Technology] CaGrid Cancer Biomedical Informatics Grid
Krishnakant Shanbhag, NCI Center for Bioinformatics
The goal of cancer Biomedical Informatics Grid caBIG(tm) is to develop applications and the underlying systems architecture that connects together data, tools, scientists and organizations in an open federated environment. In meeting this goal, caBIG will necessarily bring together data from many and diverse data sources. The underlying service oriented infrastructure for caBIG is caGrid. caGrid defines two types of "grid services" that can be registered as nodes on the grid: Data Services and Analytical Services. caGrid provides a standard infrastructure for bioinformaticians to advertise their services thru common metadata defined in Unified Modeling Language (UML) domain information model. Users can access these grid services and data programmatically using locally managed access control policies and using strongly typed data objects in XML format. caGrid infrastructure also provides strong semantic specification thru binding to description logic terminology concepts that can be used by users to discover new and interesting scientific information using semantically aware searches.
caGrid is built upon the relevant community-driven standards of the World Wide Web Consortium (W3C ) http://www.w3.org/ and OASIS http://www.oasis-open.org . It is also informed by the efforts underway in the Open Grid Forum (OGF) http://ogf.org/ . As such, while the caGrid infrastructure is built upon the 4.0 version of the Globus Toolkit (GT4) http://www.globus.org/toolkit/ , it shares a Globus http://www.globus.org/toolkit goal to be programming language and toolkit agnostic by leveraging existing standards. Specifically, caGrid services are standard WSRF v1.2 http://www.oasis-open.org/specs/index.php#wsrfv1.2 services and can be accessed by any specification-compliant client. This presentation will provide an overview of caGrid infrastructure and supporting tools that are part of the project.
- Project Web Site https://cabig.nci.nih.gov/workspaces/Architecture/caGrid/
- Open Source License caGrid infrastructure uses a commercial friendly open source license.
- License information: http://ncicb.nci.nih.gov/download/cagridlicenseagreement.jsp
- Contact Information Email: email@example.com
[OS Software] The ONDEX Data Integration Framework
Jan Taubert, Rothamsted Research
Systems Biology routinely requires the combined analysis of several databases, text sources and experimental data. The open source framework ONDEX provides technical and semantic integration and analysis for large databases and free-text sources. Data integration and text mining in ONDEX consists of 3 steps: 1) Databases are converted into a form compliant with the unified ontology based data structure 2) Equivalent and related entities in the heterogeneous data sources are identified 3) Data analysis and knowledge extraction are applied to the integrated data, which is a requirement for making sense of large integrated datasets, composed of millions of linked elements ONDEX has several important advantages over "traditional" data warehouses: a) An application-independent database schema allows a large variety of different data sources to be integrated without modifications to the underlying schema. A traditional data warehouse would necessitate the addition of new tables and fields as data sources invariably change b) New data parsers can be developed independent of each other c) The ontology/graph based data structure of ONDEX enabled us to combine database integration methods with concept based text mining and graph analysis.
ONDEX is 100% pure Java code and uses the Berkeley DB Java Edition and Lucene. We provide Java interfaces and SOAP web services for querying and analysing integrated data sets. In addition, web services exist which allow the use of Taverna for configuring and running data integration workflows. Currently supported databases include ARACYC, DRASTIC, Enzyme Nomenclature, Gene Ontology, complete KEGG, complete MEDLINE, Plant Ontology, TRANSFAC and TRANSPATH. Additional databases can be easily added as plug-ins. Import parsers for PSI-MI, OBO, XGMML and ONDEX XML exist. Currently data can be exported to the SBML, XGMML, ONDEX XML, GML and GraphML formats. A graphical analysis front-end for ONDEX provides visualisation and layout methods. Several data analysis filters can be applied to support certain biological use case, e.g. microarray analysis for integrated pathway data, combination of transcriptomics and metabolomics analysis, and identification of highly connected components in pathways.
The work on the ONDEX system was started in 2002. Since the last release in 2005, the ONDEX system has been completely reengineered. The latest developments can be retrieved from SourceForge
[OS Software] CGEMS: An Open-Source caIntegrator Application to support Whole Genome Association Studies
Subhashree Madhavan, Ph.D., National Cancer Institute
Progress in finding better therapies for cancer treatment has been hampered by the lack of opportunity to integrate biomedical data from disparate sources to enable translation of critical information from bench to bedside and back. Hence, a critical factor in the advancement of biomedical research and delivery is the ease with which data can be integrated, redistributed and analyzed both within and across functional domains. The NCICB, in collaboration with NCI extramural and intramural research groups, has developed a novel translational informatics application called caIntegrator that allows physicians, researchers, and bioinformaticians to access and analyze clinical and experimental data across multiple clinical studies. We have leveraged the caIntegrator application framework to build the CGEMS (Cancer Genetic Markers of Susceptibility) data portal to support Whole Genome SNP association studies.
Background on CGEMS: A Genome wide SNP association analysis has been conducted in a large, national study in the U.S.A., the Prostate, Lung, Colorectal, and Ovary study (PLCO). Phase 1 of the analysis includes 1,177 subjects who developed prostate cancer during the observational period and 1,105 individuals who did not develop prostate cancer during the same time period. The data generated from these scans can be accessed through the CGEMS data portal (https://caintegrator.nci.nih.gov/cgems/). The datasets available through the portal include:
- Association test results for over 300,000 SNPs
- Frequency and descriptive statistics on these SNPs
- Individual phenotypic and genotypic data for the study participants and control samples. Note that these data can only be made available to eligible investigators after a registration process.
- Phase 2 of CGEMS includes data from GWAS (Genome wide association study) where 530000 SNPs were genotyped on breast samples from 1,145 cancer patients and 1,142 controls. The pre-computed SNP association results and population frequencies are made available via the CGEMS data portal.
caIntegrator technology: The heart of the caIntegrator framework is the caBIG compliant Clinical Genomic Object Model (CGOM). Objects in CGOM Study package represent the study, treatment arms, patient information, histology, and information on the biospecimen. The 'Specimen Finding' objects model the in-silico transformation and analyses performed on the raw experimental datasets. The 'Clinical Finding' objects provide clinical observations and assessments. 'Annotation' objects such as GeneBiomarker, ProteinBiomarker and SNPAnnotation help to provide context to various Findings. A new Findings package called 'GenotypeFinding' has been created in CGOM to support the CGEMS datasets.
Following are high-level caIntegrator features used in the CGEMS data portal development: A common set of interfaces (APIs) and specification objects that define the clinical genomic analysis services. In other words, they act as templates for the caIntegrator-based translational application(s), which will extend and implement these interfaces and specification objects. The application's user interface communicates with its caIntegrator-based middle-tier services via domain as well as business objects. The caIntegrator hybrid data system consists of a star schema database which contains the clinical and annotation data as dimensions, genotype, SNP association and SNP frequency data as facts, and Common Security Model (CSM) tables for user provisioning data. This caIntegrator translational framework offers a paradigm for rapid sharing of information and accelerates the process of analyzing results from various biomedical studies with the ultimate goal to rapidly change routine patient care.
- CaIntegrator website: http://caintegrator.nci.nih.gov
- CGEMS data portal: http://caintegrator.nci.nih.gov/cgems
- CGEMS information site: http://cgems.cancer.gov/
- CaIntegrator open source license: http://ncicb.nci.nih.gov/download/caintegratorlicenseagreement.jsp
- CaIntegrator-WGS source code bundle and seed data: http://gforge.nci.nih.gov/frs/?group_id=154
[OS Software] Modware: An Object-Oriented Perl Interface to the Chado Schema
Eric Just, Center for Genetic Medicine, Feinberg School of Medicine, Nortwestern University, Chicago, IL
Chado was developed as a relational database schema for storing genomic data for FlyBase. Subsequently, Chado was adopted and distributed by the Generic Model Organism Database (GMOD) Project and it is now in use by numerous genome databases. Agile, dynamic applications that use Chado as a data store require an intuitive application programming interface (API) consisting of rich data objects that semantically resemble biological entities. Such an API encourages faster development times by encapsulating core logic while allowing developers to focus on new challenges rather solving well-defined problems such as storage and retrieval of common biological data structures. Modware is an object-oriented Perl API developed to fill this need for bioinformatics scientists and developers who use the Chado database. An object-oriented API attempts to map data from relational tables to in-memory data objects. Augmenting the often-used one-table/one-class method of object-relational mapping, Modware collects data from many tables and tuples into data structures that more closely resemble biological entities. There are separate classes for chromosomes, mRNAs, and exons, among others, but a parent class to handle common data and methods. In addition, it provides a framework and methods to search, manipulate and reformat this data. For sequence features, Modware directly uses BioPerl's object-oriented interface which is already pervasive and widely understood in the bioinformatics world. The object-relational mapping layer based on Class::DBI and distributed by GMOD is used internally to provide lower-level calls to the database. Performance is optimized through the use of 'lazy evaluation'. Only when associated data is requested is it retrieved from the database into memory. In addition there are 'Search' classes which return groups of objects based on search criteria. For instance, one can call a method to retrieve genes which have names matching a wild card query or genes that fall in a particular region on a chromosome. Modware was developed as middleware for dictyBase but has been engineered to be organism and database independent. It is open-source (BSD License) and has been carefully documented on the web along with the source code and quick-start guide at:
- Open Source License: BSD
- Project website: http://gmod-ware.sourceforge.net
[OS Software] XRATE: Scheme-y trees, phylo-HMMs and phylo-grammars
Ian Holmes, Berkeley University
A big hit in the past couple of years has been the "phylo-HMM", a multi-sequence HMM employing Felsenstein's pruning algorithm to compute emission scores. (Stochastic grammar extensions to this idea include phylo-GHMMs and phylo-SCFGs.) The phylo-HMM idea was first introduced for genome analysis by Churchill and Felsenstein, and further developed e.g. for RNA structure prediction by Knudsen and Hein. The use of phylo-grammars by Siepel, Pedersen, Bejerano, Haussler et al for gene prediction, evolutionary analysis of rate variation, and other forms of genome annotation has gotten lots of attention in recent years.
Much of the appeal of phylo-grammars is the straight transfer of intuition and expertise from the areas of HMMs and SCFGs. However, the well-known EM algorithms used to train these models (Baum-Welch, Inside-Outside) are a little less straightforward to apply to phylo- grammars. In contrast to (say) Baum-Welch, the phylo-EM algorithm is pretty hairy and not something you'd really want to implement twice. In the past 4 years we have taken the theory of phylo-EM algorithms from a theoretical treatment (Holmes & Rubin, JMB, 2002) up to a full- blown open-source implementation of a general phylo-grammar prototyping, training and annotation engine (XRATE). Grammars can be specified using a Scheme-like format, "trained" on alignments using phylo-EM, and then used to annotate alignments. The phylo-EM code in our open-source C++ library can also be linked to by external applications (e.g. Jakob Pedersen's EVOFOLD program, which has been used to investigated recently-evolving human ncRNAs). Several developers have contributed full-time to the process, and there is considerable stability, including a battery of automated tests.
XRATE is an easy-to-use Unix app that brings the unrestricted power of phylo-grammars in reach of a first-year grad student or smart undergrad. In a historical aside, XRATE has its roots in a grammar compiler, TELEGRAPH, that was itself based on Ewan Birney's DYNAMITE (and is related to Guy Slater's EXONERATE). TELEGRAPH was presented at BOSC 2000. At BOSC 2007, I'll show how far XRATE has come by giving a tour of its rate-measurement and annotation abilities, accompanied by visualizations of the interesting variety of patterns (covariation, neighbor-dependence, conservation, lineage-specific acceleration, selection...) that can be observed in the mutation rates of genomic features.
Since the development of XRATE, a collection of phylo-grammars has begun to accumulate from user contributions, along with various Perl and Python scripting libraries for driving phylo-grammar development. I will give a quick outline of these, and also describe how we are using XRATE in production projects such as our (re)annotation pipeline for Drosophila (and other "genome clades").
- Project URL: http://biowiki.org/dart
- License: GPL
[OS Software] XMLPipeDB: A Reusable, Open Source Tool Chain for Building Relational Databases from XML Sources
Kam Dalquist, Department of Biology, Loyola Marymount University
XMLPipeDB is an open source suite of Java-based tools for automatically building relational databases from an XML schema (XSD). XMLPipeDB provides functionality for managing, querying, importing, and exporting information to and from XML data with minimum manual processing of the data. While its applicability is fairly general, the original motivation for XMLPipeDB was to create a solution for the management of biological data from different sources that are used to create Gene Databases for GenMAPP (Gene Map Annotator and Pathway Profiler), software for viewing and analyzing genomic-scale data on biological pathways. XMLPipeDB has the following tools for developers and database designers: the XSD-to-DB application takes a well-formed XSD or DTD file and converts it into a collection of Java source code and Hibernate mapping files that allows XML files based on that definition file to be read into a relational database. XSD-to-DB's conversion functions are based on the open source Hyperjaxb2 project, which adds Hibernate functionality to Sun Microsystemsexpected by the GenMAPP application.
Last year at BOSC, we reported that we used the XMLPipeDB software tool chain to create a relational databases for UniProt and Gene Ontology, and that, in turn, we used these databases to generate UniProt-centric GenMAPP Gene Databases for Escherichia coli K12. Although the production of the E. coli K12 Gene Database was an important proof of concept, several improvements were needed to make the system truly extensible to other species. First, import of XML files larger than 50 MB caused the software to crash because the JAXB unmarshaller was attempting to read the entire file into memory before writing anything back to the database. The import engine can now import files of any size because it pre-processes the data into smaller blocks before passing them to the unmarshaller. Second, unit tests and a tally engine that produces data integrity reports were incorporated into the import and export process. These features were added to the XMLPipeDB Utilities library and are therefore available as part of a generic library. Third, GenMAPP Builder was refactored to provide an export framework that was extensible to other species, and the speed of the export was improved by about 20 percent. Finally, as a result of these improvements, we have created a GenMAPP Gene Database for Arabidopsis thaliana.
- License: GNU Library or Lesser General Public License (LGPL) at
- Project Website: http://xmlpipedb.cs.lmu.edu or http://sourceforge.net/projects/xmlpipedb
[Software Design And Engineering] An Open Source Framework for Teaching Bioinformatics
Kam Dalquist, Department of Biology, Loyola Marymount University
Bioinformatics training can be categorized into three areas: tool use, algorithm design/theoretical foundations, and program development. Courses emphasizing tool use are usually targeted at biologists, while courses emphasizing algorithm design and theoretical foundations are usually targeted at computer scientists. However, there are few (if any) reports of courses that explicitly address how to teach the best practices of software development for scientific computing. Pedagogical practices in computer science itself are frequently disconnected from the expectations and skill sets required of computer scientists in industry or interdisciplinary research groups. Computer science undergraduates typically work alone instead of in a team, produce isolated programs from scratch instead of large modular projects, and throw away their code after the assignment has been graded instead of maintaining it over an extended period of time. Open source principles, culture, and tools can be leveraged to teach best practices of software development, including up-front project design, program and process documentation, quality control, data standards, and project management. Here we describe the implementation of an open source teaching framework for bioinformatics that grew out of the Recourse computer science curriculum development project at LMU (http://recourse.cs.lmu.edu/).
One mechanism by which the open source culture can be adapted to a bioinformatics curriculum is to give students an authentic problem to solve with software, one that is large enough to require a team effort. XMLPipeDB (http://xmlpipedb.cs.lmu.edu/) is an open source suite of Java-based tools for automatically building relational databases from an XML schema (XSD). XMLPipeDB was developed by graduate students as part of a team-taught course in bioinformatics that was then extended into a second workshop course on open source software development. Throughout this project, students were expected to uphold best practices of software development. The students were asked to perform up-front project design and program and process documentation. Quality control came in the form of code reviews and bug tracking. The project itself utilized XML data standards and was managed by the instructors with cycles of design reviews, setting of milestones, and evaluation of results. The students used open source tools throughout. Each student chose their own development environment (e.g., Eclipse, NetBeans, text editor + ant, etc.) but worked as a team from a SourceForge-hosted repository. The added benefit of this open source teaching framework is that it facilitates the long-term management of course projects beyond the current semester and class. This framework enabled the students to gain real world experience with open source software development and proficiency with tools widely used by the open source community, while making a concrete contribution to an open source software project.
[Software Design And Engineering] Tools to Facilitate Large Scale Comparative Genomic Analysis
James Taylor, Visiting Member, Courant Institute, New York University
Performing almost any interesting analysis at the scale of multiple genomes quickly becomes non-trivial. Analysis of this scale often require a compromise between performance and clarity. Working with data at this scale is even more challenging in research groups where storage for large datasets (such as multi-species whole genome alignments) is a shared resource accessed concurrently by many users. However, well designed supporting tools can address many of these problems by wrapping optimized algorithms in simple interfaces that allow rapid construction of new analyses that are both high performance and easy to understand and communicate.
The 'bx-python' packages is an open-source library and associated command line tools designed to make large scale comparative genomic analysis easy. It includes a library of reusable components focused on simplifying highly repetitive yet performance critical tasks. For example it includes support for
- A generic data structure for indexing on disk files that contain blocks of data associated with inter-vals on various sequences (used, for example, to provide random access to individual alignments in huge files). The indexes are optimized for use over network filesystems, as well as reducing network overhead by providing random access to the indexed files
- Support for indexing of and semi-random access to compressed files.
- Components for reading and working with genome-scale multiple local alignments (in MAF, AXT, and LAV formats)
- Data structures for working with intervals on sequences, including 'binned bitsets' which act just like chromosome sized bit arrays, but lazily allocate regions and allow large blocks of all set or all unset bits to be stored compactly, without the user needing to worry about sequence size.
These components and the included command line tools are also a core part of the Galaxy analysis platform (http://g2.bx.psu.edu), in particular its support for genome scale interval operations and working with alignments. Combined with Galaxy, 'bx-python' facilitates integrate analysis by making it incredibly easy to develop tools for performing large scale analysis efficiently, and making those tools available via the web to data producers such as experimental biologists.