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Keynote: Building Bioperl: lessons for Open-Source and Bioinformatics

Jason Stajich, Department Molecular Genetics and Microbiology, Duke University

Transforming Full-Text Literature to Formalized Facts

Stan Dong, Department of Genetics, Stanford University School of Medicine

Thursday June 23rd, 8:40am

Scientific research literature provides data to support and contradict hypotheses, but the data is not constrained to formats suitable for the extraction of information using an automated system. The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) and the Tetrahymena Genome Database (TGD; http://www.ciliate.org) are model organism databases (MOD) that provide gene and protein information for their respective model organisms. The bulk of this information is curated from published literature, and requires manual knowledge extraction by scientific curators. Manual literature based curation is precise but time consuming. Given the limitations of human resources, text-mining techniques can identify the most relevant literature that should be reviewed by scientific curators for inclusion in a MOD. An additional benefit is that the same textmining techniques can be used by the larger scientific community to identify literature that contains the appropriate information. SGD and TGD are building an automated pipeline for collecting full-text documents of literature relevant to their respective scientific communities. So far more than 15,000 full-text documents in either PDF or HTML format have been archived. Of these, 64% have already been reviewed by scientific curators and can serve as a training set for existing and novel text-mining algorithms. As our first attempt to incorporate full-text literature searching as a resource for scientific curators and the scientific community, SGD and TGD implemented Textpresso, a vocabulary-based information retrieval and extraction system developed by Muller and Kenny at WormBase. Textpresso allows users to perform custom queries of full-text journal articles based on keywords and/or ontology-based categories of terms. We report software modifications that increased the speed of Textpresso ontology markup. We also report database-specific expansions and modifications to the underlying ontologybased categories used in the processing of papers. Textpresso is a valuable tool that can help users and curators identify relevant information within a large body of literature. Other text mining strategies and techniques under development will also be presented. SGD is funded by the US National Human Genome Research Institute. TGD is funded by the National Institute of General Medical Sciences.

Textpresso is open-source software and is part of the Generic Model Organism Database (GMOD; http://www.gmod.org) effort.

Reference: Muller H, Kenny EE, Sternberg PW. Textpresso: An Ontology-Based Information Retrieval and Extraction System for Biological Literature. PLoS Biol. 2004 November; 2(11): e309.

SigPath: Quantitative information management for cell signaling pathways and networks

Fabien Campagne, Weill Medical College of Cornell University

Thursday June 23rd, 9:05 am.

Understanding complex protein networks within cells requires the ability to develop quantitative models and to numerically compute the properties and dynamical behavior of the networks. To carry out such computational analysis, it is necessary to use modeling tools and information management systems (IMSs) where the quantitative data, associated to its biological context, can be stored, curated, and reliably retrieved. The SigPath project focuses on the biochemical computation of cellular interactions and develops an IMS that stores quantitative information on the cellular components and their interactions. This information can be used to construct pathways and eventually large-scale networks. Yet, assembling the information to achieve this goal will require the active collaboration of many experimental and computational labs. The SigPath IMS aims to serve as a resource for these labs, where information can be stored electronically and shared across the internet, to enable collaborative data gathering and biochemical modeling studies. The talk will present the goals, approaches and road-map of the SigPath project. SigPath is distributed under the GNU General Public License. We invite and welcome others interested in joining this project.

Project URL: http://www.sigpath.org

License: GPL

References: http://www.biomedcentral.com/content/pdf/1471-2105-6-5.pdf http://www.biomedcentral.com/content/pdf/1471-2105-6-5.pdf

DAS/2: Next Generation Distributed Annotation System

Gregg Helt, Affymetrix, Inc.

Thursday June 23rd, 9:30am

The Distributed Annotation System (DAS) is a specification for sharing distributed annotations of biological sequences, first introduced in 2000. DAS allows researchers to integrate biological information from many different sources via standardized URL queries and XML responses over HTTP. Although DAS has enjoyed some success it also has a number of problems, and there have been many discussions on the DAS mailing list of how best to improve it, ranging from minor modifications to complete overhauls. It has become clear that a major revision of the DAS protocol is needed. We have integrated many of the suggestions from the DAS developer and user community to produce a new version of DAS, DAS/2.

The preliminary DAS/2 specification is now ready for review by a wider audience. Enhancements are currently centered on two major categories. First is revising retrieval protocols to support more flexible queries and responses. This includes the ability for servers to support arbitrary formats for annotation retrieval, and for clients to choose which format they prefer. Nested hierarchies of annotation are supported, and annotations are typed based on ontologies. Extensive filtering is also supported in the annotation request. The second major category of enhancement is development of a new writeback protocol to support creation of new biological data objects and editing of existing ones. Future plans include adding support for DAS/2 server registry and discovery.

In addition to the DAS/2 specification itself, we have developed preliminary implementations of a DAS/2 server, a DAS/2 client, and a DAS/2 validation suite. The server implementation has a plugin architecture to support different backends including GMOD databases. The client implementation is part of the Integrated Genome Browser (IGB), an application for genome visualization and exploratory analysis. The standalone validation suite is used to verify that client requests and server responses follow the specification.

Client, server, and validation suite are all available under open source licenses.

This work is funded in part by NIH grant R01HG003040.

mpiBLAST evolves - Success, Collaborations, and Challenges

Aaron Darling, Dept. of Computer Science, Dept. of Animal Health & Biomedical Science Univ. of Wisconsin-Madison

Thursday June 23rd, 10:20am

mpiBLAST has become a widely used and widely critiqued open-source parallelization of NCBI BLAST. Many of its users (and developers!) have a love/hate relationship with it. Early implementations of mpiBLAST's database segmentation method promised tremendous potential for super-linear speedup on compute clusters. As mpiBLAST has evolved it has gained considerable flexibility and applicability--originally providing only blastn functionality with approximate E-values whereas it now provides all of {t}blast{n,p,x}, all 12 result output formats, exact E-value statistics, and load-balancing. As mpiBLAST evolved, its efficiency and scalability met pitfalls that were overcome largely in part to feedback and contributions from the bioinformatics open-source community. The first part of this talk describes challenges during mpiBLAST's collaborative development and how they were overcome through community contributions. In the second part of the talk, I will assess the current state of mpiBLAST (its scalability and stability) and discuss future development and maintainence ideas. Audience feedback heartily encouraged.


PROJECT URL: http://mpiblast.lanl.gov

REMBRANDT: Building a Robust Translational Research Framework for Brain Tumor Studies

Himanso Sahni, National Cancer Institute Center for Bioinformatics (NCICB)

Thursday June 23rd, 10:45am

The mission of the National Cancer Institute Center for Bioinformatics (NCICB) is to provide informatics infrastructure and scientific applications that support advanced translational research in cancer biology and medicine. REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT) is a robust bioinformatics knowledgebase framework that leverages data warehousing technology to host and integrate clinical and functional genomics data from clinical trials involving patients suffering from Gliomas. The knowledge framework will provide researchers with the ability to perform ad hoc querying and reporting across multiple data domains, such as Gene Expression, Chromosomal aberrations and Clinical data. Scientists will be able to answer basic questions related to a patient or patient population and view the integrated data sets in a variety of contexts. Tools that link data to other annotations such as cellular pathways, gene ontology terms and genomic information are embedded within this system.

The Rembrandt application is designed using object oriented methodology and implemented using Java 2 Enterprise Edition, a Data Warehouse schema, and various other open source technologies. It is designed to conform to an n- tiered architecture that includes several layers: A web-based graphical user interface is built using Apache Struts application framework and runs on Apache Jakarta Tomcat, a busi