Data Visualization. A. B.
Cei Abreu-Goodger and Enrique Merino. Instituto de Biotecnologia, Universidad Autonoma Nacional de Mexico, Av. Universidad 2001, Cuernavaca, Morelos, 62210. Mexico.
Homograph is an X-windows graphic interface for visualizing genome-wide protein homology. A dot-plot is used to represent every pair of proteins that pass a certain similarity threshold. The dots can be selected and colored by user determined categories, by searching the gene descriptions, or by a similarity score. Homograph is available at http://www.ibt.unam.mx/paginas/cei/homograph.html.
Ann E. Loraine and Gregg A. Helt. Affymetrix, Inc.
The high frequency of alternative splicing in human genes requires specialized visualization tools that reveal how variations in transcript structure affect the encoded proteins. Techniques for visualizing alternative splicing are presented, including semantic zooming, visual encoding of translation frame, and display of protein domains in the context of genomic sequence.
Byong-Hyon Ju, Byungku Park, Kyungsook Han and Jong H. Park. Department of Computer Science and Engineering, Inha University, Inchon 402-751, South Korea.
We have developed a new algorithm for visualizing large-scale protein-protein interactions, and implemented it in a program called InterViewer. InterViewer provides an integrated framework for querying databases and directly visualizes the query results. InterViewer is an order of magnitude faster than other force-directed programs, yet generates aesthetically pleasing drawings.
Yanga Byun, Euna Jeong and Kyungsook Han. Department of Computer Science and Engineering, Inha University.
A common problem with many graph-drawing programs is that they become very slow when dealing with large-scale graphs such as protein interaction networks. We propose a new algorithm for efficiently visualizing large-scale protein interaction networks. It partitions nodes into three groups based on their interaction characteristics. An implementation of the algorithm is available at http://wilab.inha.ac.kr/protein.
Gopalan Vivek1, Tin Wee Tan1 and Shoba Ranganathan1,2. 1Department of Biochemistry and 2Department of Biological Sciences, National University of Singapore, Singapore 119260.
XdomView is a web-based graphical tool that maps protein structural domains and intron positions in eukaryotic homologues to the tertiary structure of a given protein. Since it visualizes the association of sequence signals to 3D structure in XdomView provides a valuable visualization environment for scientists working on eukaryotic gene organization, gene evolution, protein folding and protein structure classification. XdomView is available http://surya.bic.nus.edu.sg/xdom.
Sheng Zhong1, Ovidiu Lipan1, Kai-Florian Storch3, Charles J. Weitz3, Wing H. Wong1,2. 1 Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA., 2 Department of Statistics, Harvard University and 3 Department of Neurobiology, Harvard Medical School
GoSurfer software uses Gene Ontology (GO) structured vocabulary to perform comparative gene analysis. GoSurfer visualizes gene ontology information as a tree, with nodes and branches representing GO terms and paths. Different sets of genes can be mapped onto the tree with different colors. GoSurfer is available at http://biosun1.harvard.edu/~szhong/GoSurfer.htm.
Robert Stones, Adrian Charlton, Paul Brereton and Sarah Oehlschlager. Central Science Laboratory, Sand Hutton YO41 1LZ UK.
Metabolomics provides a powerful new tool for acquiring insight into functional biology. Snapshots of the levels of abundant small molecules within a cell, and how those levels change under different conditions, are very complementary to gene expression and proteomic studies. We are currently developing computer tools for acquisition of NMR metabolic profiling data, and utilising computational approaches to analyse this type of data.
Mikio Yoshida1, Yukari Shibagaki1, Hideaki Shimano1, Mariko Shima1, Tatsuo Kitahashi1, Yasutaro Fujita2 and Takashi Ito3. 1INTEC Web and Genome Informatics Corporation, Tokyo, Japan, 2Faculty of Engineering, Fukuyama University, Hiroshima Japan and 3Cancer Research Institute, Kanazawa University, Ishikawa, Japan.
WebGen-Net is a system for supporting construction of genetic networks. This system provides a graphical user interface to allow its users to interactively reconstruct genetic networks via referring biological relations collected from public databases and experimental results. A prototype system of WebGen-Net is freely available from http://genome.c.kanazawa-u.ac.jp/webgen.
David Outteridge. Department of Pharmacology University of Colorado Health Sciences Center.
Associating genes with ontology entries enables a reversed association from entries to genes. Extracting subsets of interesting entries, each describing many genes, is achieved by scoring. These scores are mapped to visual effects (coloured graphs) for clear identification of interesting entries.
D.F. Pinney, the Allgenes.org Development Group, the EPConDB Development Group, the Plasmodium Genome Database Collaborative and C.J. Stoeckert. Computational Biology and Informatics Laboratory, University of Pennsylvania, Philadelphia, Pennsylvania.
Allgenes.org, PlasmoDB, and EPConDB are web-based discovery tools relying on a single platform, GUS, which warehouses and integrates biological data from heterogeneous sources. Allgenes.org and PlasmoDB provide access to data for the human, mouse and Plasmodium falciparum genomes, respectively. EPConDB provides access to data for genes expressed in endocrine pancreas.
Runte K., Lopez R., Lombard V., Stoehr P. and Apweiler R. European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
The European Bioinformatics Institute (EBI) presents a standard for development and deployment of XML file formats. An internal working group, comprised of members from most major database providers at the EBI, has created not only definitions of commonly used data types, but also a set of coding and naming standards.
Thomas Tidwell, James Hatfield, Vishal Pampanwar and Kiran Rao. AGCoL.
MGOS is a web accessible database that correlates the genomic functionality of the host (oryza sativa) and the pathogen (Magnaporthe grisea). It will provide a query-able FPC-based map display of markers, clones, tiling path, EST matches, transcripts, and proteins. MGOS will support the exchange of annotation information via DAS.
Xiaoqing You, Heinz Hemken, Annie Titus, Lily Xu, Joanna Curlee, Francisco De La Vega and Gene Spier. Applied Biosystems, 850 Lincoln Centre Drive, Foster City, CA94404, USA.
At Applied Biosystems, we developed the SNPDB database system to facilitate and support the design of over 150,000 ready-to-use probe-based 5í nuclease SNP assays and the development of a linkage disequilibrium map. The system includes a centralized database, data acquisition tools and applications for data retrieval and analysis.
Ching KA, H. Lapp, C. Fletcher and MP Cooke. Genomics Institute of the Novartis Research Foundation.
Presentation of high-throughput screening data for human review requires visualization which highlights patterns and trends. MouseTRACS is a series of programs, which provide visual browsing, and data graphing to identify families of mutagenized mice harboring significant phenotypes. Mice are automatically flagged, scheduled for retesting, and confirmed by the analysis programs.
Gordon B. Hutchinson. RabbitHutch Biotechnology Corporation.
GenTerpret is a multi-platform, Java-based gene annotation tool that integrates the output of web and command line programs into a graphical user interface and provides a means to rapidly annotate DNA sequence. Bioinformatics developers can create their own interface using an open-architecture parse file format. GenTerpret is available at http://www.rabbithutch.com.
Louie N. van de Lagemaat1, Patrik Medstrand2 and Dixie L. Mager1. 1Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, B. C. and 2Dept. of Cell and Molecular Biology, Section for Developmental Biology, Lund University, Lund, Sweden.
Transposable element distributions vary by the age of the element and position in the genome. By partitioning the human genome in various ways, we can study the interactions between the parasitic and host genomes. We find potential evidence of selection for and against different retroelements near genes.
M.E. Smoot1, W.R. Pearson2 and S.A. Guerlain1. University of Virginia, 1Department of Systems and Information Engineering and 2Department of Biochemistry.
We have developed a web based Java/Perl/C++ software system to create and display near optimal alignments of protein or DNA sequences. The tool displays alignments sequentially and is designed to help investigators identify those parts of the alignments that are relatively invariant among a set of solutions.
Daniel McShan and Imran Shah. University of Colorado Health Science Center, 4200 E 9th Ave, C-245Denver, CO, 80120,USA.
The speed, power, and flexibility of Common Lisp are combined with the Parallel Virtual Machine (PVM) library to create Lisp-PVM -- a robust distributed environment for solving computationally intensive bioinformatics problems. We used Lisp-PVM for large-scale genomic, proteomic and metabolic computations with nearly linear increase in performance.
Murali Rangan and Arie Avnur. Gene Networks Inc., 560 S. Winchester Blvd., San Jose, California, 95128, USA.
A new gene regulation model enables the simulation of gene expression treatment. Affected genes are annotated to specify their expression level modification. The simulation software tool can read this formal annotation and the modelís gene regulation representation to calculate new gene expression levels, simulating the treatment.
David Kil, Murali Rangan and Arie Avnur. Gene Networks, Inc.560, S. Winchester Blvd, San Jose, California, 95128USA.
Gene-chip image analysis is generally regarded as a laborious, tedious, yet extremely important first step in understanding the intricate roles of genes. In this poster, we explore how the salient concepts in multiple disciplines can be integrated to create an efficient, yet powerful tool in gene-chip image analysis. The advantages of our approach are high-throughput processing and high-quality interpretation of gene-chip data.