Phytophthora ramorum: a pathogen with a remarkably wide host range causing sudden oak death on oaks and ramorum blight on woody ornamentals

Phytophthora ramorum is an oomycete plant pathogen classified in the kingdom Stramenopila. P. ramorum is the causal agent of sudden oak death on coast live oak and tanoak as well as ramorum blight on woody ornamental and forest understorey plants. It causes stem cankers on trees, and leaf blight or stem dieback on ornamentals and understorey forest species. This pathogen is managed in the USA and Europe by eradication where feasible, by containment elsewhere and by quarantine in many parts of the world. Genomic resources provide information on genes of interest to disease management and have improved tremendously since sequencing the genome in 2004. This review provides a current overview of the pathogenicity, population genetics, evolution and genomics of P. ramorum. Taxonomy: Phytophthora ramorum (Werres, De Cock & Man in't Veld): kingdom Stramenopila; phylum Oomycota; class Peronosporomycetidae; order Pythiales; family Pythiaceae; genus Phytophthora. Host range: The host range is very large and the list of known hosts continues to expand at the time of writing. Coast live oak and tanoak are ecologically, economically and culturally important forest hosts in the USA. Rhododendron, Viburnum, Pieris, Syringa and Camellia are key ornamental hosts on which P. ramorum has been found repeatedly, some of which have been involved in moving the pathogen via nursery shipments. Disease symptoms: P. ramorum causes two different diseases with differing symptoms: sudden oak death (bleeding lesions, stem cankers) on oaks and ramorum blight (twig dieback and/or foliar lesions) on tree and woody ornamental hosts. Useful websites: http://nature.berkeley.edu/comtf/ , http://rapra.csl.gov.uk/ , http://www.aphis.usda.gov/plant_health/plant_pest_info/pram/index.shtml , http://genome.jgi‐psf.org/Phyra1_1/Phyra1_1.home.html , http://pamgo.vbi.vt.edu/ , http://pmgn.vbi.vt.edu/ , http://vmd.vbi.vt.edu./ , http://web.science.oregonstate.edu/bpp/labs/grunwald/resources.htm , http://www.defra.gov.uk/planth/pramorum.htm , http://www.invasive.org/browse/subject.cfm?sub=4603 , http://www.forestry.gov.uk/forestry/WCAS‐4Z5JLL     

SChiSM2: creating interactive web page annotations of molecular structure models using Jmol

SChiSM2 is a web server-based program for creating web pages that include interactive molecular graphics using the freely-available applet, Jmol, for illustration. The program works with Internet Explorer and Firefox on Windows, Safari and Firefox on Mac OSX and Firefox on Linux. Availability: The program can be accessed at the following address: http://ci.vbi.vt.edu/cammer/schism2.html.     

YODA: selecting signature oligonucleotides

MOTIVATION: Selecting oligonucleotide probes for use in microarray design, and other applications requiring signature sequences, involves identifying sequences which will bind strongly to their intended target, while binding only weakly (or preferably, not at all) to non-target sequences which may be present in the hybridization reaction. While many tools to assist in selection of such sequences exist, all the ones we examined lack important oligo design and software features. RESULTS: YODA is an application for assisting biological researchers in selecting signature sequences. It incorporates a custom sequence similarity search to find potential cross-hybridizing non-target sequences. For this task, most oligo design tools rely on BLAST, which is ill suited for it due to an unacceptable risk of false negatives. YODA supports multiple probe design goals including single-genome, multiple-genome, pathogen-host and species/strain-identification. A graphical interface is provided as well as a command-line interface, both of which support many user-controlled parameters. YODA is easy to install and use and runs on Windows, Mac OS X and Linux platforms. AVAILABILITY: Freely available (LGLP) along with source code and additional documentation at http://pathport.vbi.vt.edu/YODA CONTACT: enordber@vbi.vt.edu.     

Implicit motif distribution based hybrid computational kernel for sequence classification

MOTIVATION: We designed a general computational kernel for classification problems that require specific motif extraction and search from sequences. Instead of searching for explicit motifs, our approach finds the distribution of implicit motifs and uses as a feature for classification. Implicit motif distribution approach may be used as modus operandi for bioinformatics problems that require specific motif extraction and search, which is otherwise computationally prohibitive. RESULTS: A system named P2SL that infer protein subcellular targeting was developed through this computational kernel. Targeting-signal was modeled by the distribution of subsequence occurrences (implicit motifs) using self-organizing maps. The boundaries among the classes were then determined with a set of support vector machines. P2SL hybrid computational system achieved approximately 81% of prediction accuracy rate over ER targeted, cytosolic, mitochondrial and nuclear protein localization classes. P2SL additionally offers the distribution potential of proteins among localization classes, which is particularly important for proteins, shuttle between nucleus and cytosol. AVAILABILITY: http://staff.vbi.vt.edu/volkan/p2sl and http://www.i-cancer.fen.bilkent.edu.tr/p2sl CONTACT: rengul@bilkent.edu.tr.     

The landscape of human proteins interacting with viruses and other pathogens

Infectious diseases result in millions of deaths each year. Mechanisms of infection have been studied in detail for many pathogens. However, many questions are relatively unexplored. What are the properties of human proteins that interact with pathogens? Do pathogens interact with certain functional classes of human proteins? Which infection mechanisms and pathways are commonly triggered by multiple pathogens? In this paper, to our knowledge, we provide the first study of the landscape of human proteins interacting with pathogens. We integrate human–pathogen protein–protein interactions (PPIs) for 190 pathogen strains from seven public databases. Nearly all of the 10,477 human-pathogen PPIs are for viral systems (98.3%), with the majority belonging to the human–HIV system (77.9%). We find that both viral and bacterial pathogens tend to interact with hubs (proteins with many interacting partners) and bottlenecks (proteins that are central to many paths in the network) in the human PPI network. We construct separate sets of human proteins interacting with bacterial pathogens, viral pathogens, and those interacting with multiple bacteria and with multiple viruses. Gene Ontology functions enriched in these sets reveal a number of processes, such as cell cycle regulation, nuclear transport, and immune response that participate in interactions with different pathogens. Our results provide the first global view of strategies used by pathogens to subvert human cellular processes and infect human cells. Supplementary data accompanying this paper is available at http://staff.vbi.vt.edu/dyermd/publications/dyer2008a.html.     

Interactive gene-order comparison for multiple small genomes

SUMMARY: The Genome Organization Analysis Tool (GOAT) is a program that performs comparative sequence analysis on ordered gene lists from annotated genomes, provides visual and tabular output, and provides means of accessing and analyzing related gene sequence data, for the purpose of comparing genome organization at the gene-order level. GOAT can be used to compare any two or more genomes or chromosomes on demand, or configured to provide access to precomputed comparisons of a specific group of genome sequences. AVAILABILITY: Demonstration web server and software download, subject to the Virginia Tech Noncommercial License are available at http://gaia.biotech.vt.edu/goat/. SUPPLEMENTARY INFORMATION: Updates, installation and configuration information are available at http://gaia.biotech.vt.edu/goat.     

PIML: the Pathogen Information Markup Language

MOTIVATION: A vast amount of information about human, animal and plant pathogens has been acquired, stored and displayed in varied formats through different resources, both electronically and otherwise. However, there is no community standard format for organizing this information or agreement on machine-readable format(s) for data exchange, thereby hampering interoperation efforts across information systems harboring such infectious disease data. RESULTS: The Pathogen Information Markup Language (PIML) is a free, open, XML-based format for representing pathogen information. XSLT-based visual presentations of valid PIML documents were developed and can be accessed through the PathInfo website or as part of the interoperable web services federation known as ToolBus/PathPort. Currently, detailed PIML documents are available for 21 pathogens deemed of high priority with regard to public health and national biological defense. A dynamic query system allows simple queries as well as comparisons among these pathogens. Continuing efforts are being taken to include other groups' supporting PIML and to develop more PIML documents. AVAILABILITY: All the PIML-related information is accessible from http://www.vbi.vt.edu/pathport/pathinfo/     

The Phosphorylation Site Database: A guide to the serine-, threonine-, and/or tyrosine-phosphorylated proteins in prokaryotic organisms

The Phosphorylation Site Database [http://vigen.biochem.vt.edu/xpd/xpd.htm] provides ready access to information from the primary scientific literature concerning those proteins from prokaryotic organisms, i.e., the members of the domains Archaea and Bacteria, that have been reported to undergo covalent phosphorylation on the hydroxyl side chains of serine, threonine, and/or tyrosine residues. Where known, the sequence of the site(s) of phosphorylation and the functional consequences of phosphorylation also are included. Active links enable users to quickly access further information concerning the phosphoprotein of interest from PubMed, GenBank, SWISS-PROT, and PIR.     

The RESID Database of protein structure modifications and the NRL-3D Sequence-Structure Database

The RESID Database is a comprehensive collection of annotations and structures for protein post-translational modifications including N-terminal, C-terminal and peptide chain cross-link modifications. The RESID Database includes systematic and frequently observed alternate names, Chemical Abstracts Service registry numbers, atomic formulas and weights, enzyme activities, taxonomic range, keywords, literature citations with database cross-references, structural diagrams and molecular models. The NRL-3D Sequence-Structure Database is derived from the three-dimensional structure of proteins deposited with the Research Collaboratory for Structural Bioinformatics Protein Data Bank. The NRL-3D Database includes standardized and frequently observed alternate names, sources, keywords, literature citations, experimental conditions and searchable sequences from model coordinates. These databases are freely accessible through the National Cancer Institute-Frederick Advanced Biomedical Computing Center at these web sites: http://www. ncifcrf.gov/RESID, http://www.ncifcrf.gov/NRL-3D; or at these National Biomedical Research Foundation Protein Information Resource web sites: http://pir.georgetown.edu/pirwww/dbinfo/resid .html, http://pir.georgetown.edu/pirwww/dbinfo/nrl3d .html     

BSD: the Biodegradative Strain Database

The Biodegradative Strain Database (BSD) is a freely-accessible, web-based database providing detailed information on degradative bacteria and the hazardous substances that they degrade, including corresponding literature citations, relevant patents and links to additional web-based biological and chemical data. The BSD (http://bsd.cme.msu.edu) is being developed within the phylogenetic framework of the Ribosomal Database Project II (RDPII: http://rdp.cme.msu.edu/html) to provide a biological complement to the chemical and degradative pathway data of the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD: http://umbbd.ahc.umn.edu). Data is accessible through a series of strain, chemical and reference lists or by keyword search. The web site also includes on-line data submission and user survey forms to solicit user contributions and suggestions. The current release contains information on over 250 degradative bacterial strains and 150 hazardous substances. The transformation of xenobiotics and other environmentally toxic compounds by microorganisms is central to strategies for biocatalysis and the bioremediation of contaminated environments. However, practical, comprehensive, strain-level information on biocatalytic/biodegradative microbes is not readily available and is often difficult to compile. Similarly, for any given environmental contaminant, there is no single resource that can provide comparative information on the array of identified microbes capable of degrading the chemical. A web site that consolidates and cross-references strain, chemical and reference data related to biocatalysis, biotransformation, biodegradation and bioremediation would be an invaluable tool for academic and industrial researchers and environmental engineers.     

PUGSVM: a caBIG™ analytical tool for multiclass gene selection and predictive classification

Phenotypic Up-regulated Gene Support Vector Machine (PUGSVM) is a cancer Biomedical Informatics Grid (caBIG?) analytical tool for multiclass gene selection and classification. PUGSVM addresses the problem of imbalanced class separability, small sample size and high gene space dimensionality, where multiclass gene markers are defined by the union of one-versus-everyone phenotypic upregulated genes, and used by a well-matched one-versus-rest support vector machine. PUGSVM provides a simple yet more accurate strategy to identify statistically reproducible mechanistic marker genes for characterization of heterogeneous diseases. AVAILABILITY: http://www.cbil.ece.vt.edu/caBIG-PUGSVM.htm.     

Web-based kinetic modelling using JWS Online

JWS Online is a repository of kinetic models, describing biological systems, which can be interactively run and interrogated over the Internet. It is implemented using a client-server strategy where the clients, in the form of web browser based Java applets, act as a graphical interface to the model servers, which perform the required numerical computations. AVAILABILITY: The JWS Online website is publicly accessible at http://jjj.biochem.sun.ac.za/ with mirrors at http://www.jjj.bio.vu.nl/ and http://jjj.vbi.vt.edu/     

Computational prediction of host-pathogen protein-protein interactions

MOTIVATION: Infectious diseases such as malaria result in millions of deaths each year. An important aspect of any host-pathogen system is the mechanism by which a pathogen can infect its host. One method of infection is via protein-protein interactions (PPIs) where pathogen proteins target host proteins. Developing computational methods that identify which PPIs enable a pathogen to infect a host has great implications in identifying potential targets for therapeutics. RESULTS: We present a method that integrates known intra-species PPIs with protein-domain profiles to predict PPIs between host and pathogen proteins. Given a set of intra-species PPIs, we identify the functional domains in each of the interacting proteins. For every pair of functional domains, we use Bayesian statistics to assess the probability that two proteins with that pair of domains will interact. We apply our method to the Homo sapiens-Plasmodium falciparum host-pathogen system. Our system predicts 516 PPIs between proteins from these two organisms. We show that pairs of human proteins we predict to interact with the same Plasmodium protein are close to each other in the human PPI network and that Plasmodium pairs predicted to interact with same human protein are co-expressed in DNA microarray datasets measured during various stages of the Plasmodium life cycle. Finally, we identify functionally enriched sub-networks spanned by the predicted interactions and discuss the plausibility of our predictions. AVAILABILITY: Supplementary data are available at http://staff.vbi.vt.edu/dyermd/publications/dyer2007a.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

A database for management of gene expression data in situ

MOTIVATION: To create a spatiotemporal atlas of Drosophila segmentation gene expression at cellular resolution. RESULTS: The expression of segmentation genes plays a crucial role in the establishment of the Drosophila body plan. Using the IBM DB2 Relational Database Management System we have designed and implemented the FlyEx database. FlyEx contains 2832 images of 14 segmentation gene expression patterns obtained from 954 embryos and 2,073,662 quantitative data records. The averaged data is available for most of segmentation genes at eight time points. FlyEx supports operations on images of gene expression patterns. The database can be used to examine the quality of data, analyze the dynamics of formation of segmentation gene expression domains, as well as estimate the variability of gene expression patterns. We also provide the capability to download data of interest. AVAILABILITY: http://urchin.spbcas.ru/flyex, http://flyex.ams.sunysb.edu/flyex     

DEPD: a novel database for differentially expressed proteins

SUMMARY: The Differentially Expressed Protein Database was designed to store the output of comparative proteomics studies and provides a publicly available query and analysis platform for data mining. The database contains information about more than 3000 differentially expressed proteins (DEPs) manually extracted from the published literature, including relevant biological, experimental and methodological elements. Tools for visualization and functional analysis of DEPs are provided via a user-friendly webinterface. AVAILABILITY: http://protchem.hunnu.edu.cn/depd/.     

A simple and efficient algorithm for gene selection using sparse logistic regression

MOTIVATION: This paper gives a new and efficient algorithm for the sparse logistic regression problem. The proposed algorithm is based on the Gauss-Seidel method and is asymptotically convergent. It is simple and extremely easy to implement; it neither uses any sophisticated mathematical programming software nor needs any matrix operations. It can be applied to a variety of real-world problems like identifying marker genes and building a classifier in the context of cancer diagnosis using microarray data. RESULTS: The gene selection method suggested in this paper is demonstrated on two real-world data sets and the results were found to be consistent with the literature. AVAILABILITY: The implementation of this algorithm is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml Supplementary Information: Supplementary material is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml     

PGDB: a curated and integrated database of genes related to the prostate

The Prostate Gene Database (PGDB: http://www.ucsf.edu/pgdb) is a curated and integrated database of genes or genomic loci related to the human prostate and prostatic diseases. Currently, PGDB covers genes involved in a number of molecular and genetic events of the prostate including gene amplification, mutation, gross deletion, methylation, polymorphism, linkage and over-expression, as published in the literature. Genes that are specifically expressed in prostate, as evidenced by analysis of data from expressed sequence tags (ESTs) and serial analysis of gene expression (SAGE), are also included. There are a total of 165 unique entries in the database. Users can either browse or query the PGDB through a web interface. For each gene, in addition to basic gene information and rich cross-references to other databases, inclusive and relevant literature references are provided to support the inclusion of the gene in the database. Detailed expression data calculated from the UniGene and SAGEmap databases are also presented.