A new version of the RDP (Ribosomal Database Project).

The Ribosomal Database Project (RDP-II), previously described by Maidak et al. [ Nucleic Acids Res. (1997), 25, 109-111], is now hosted by the Center for Microbial Ecology at Michigan State University. RDP-II is a curated database that offers ribosomal RNA (rRNA) nucleotide sequence data in aligned and unaligned forms, analysis services, and associated computer programs. During the past two years, data alignments have been updated and now include >9700 small subunit rRNA sequences. The recent development of an ObjectStore database will provide more rapid updating of data, better data accuracy and increased user access. RDP-II includes phylogenetically ordered alignments of rRNA sequences, derived phylogenetic trees, rRNA secondary structure diagrams, and various software programs for handling, analyzing and displaying alignments and trees. The data are available via anonymous ftp (ftp.cme.msu. edu) and WWW (http://www.cme.msu.edu/RDP). The WWW server provides ribosomal probe checking, approximate phylogenetic placement of user-submitted sequences, screening for possible chimeric rRNA sequences, automated alignment, and a suggested placement of an unknown sequence on an existing phylogenetic tree. Additional utilities also exist at RDP-II, including distance matrix, T-RFLP, and a Java-based viewer of the phylogenetic trees that can be used to create subtrees.     

The RDP-II (Ribosomal Database Project)

The Ribosomal Database Project (RDP-II), previously described by Maidak et al. [Nucleic Acids Res. (2000), 28, 173-174], continued during the past year to add new rRNA sequences to the aligned data and to improve the analysis commands. Release 8.0 (June 1, 2000) consisted of 16 277 aligned prokaryotic small subunit (SSU) rRNA sequences while the number of eukaryotic and mitochondrial SSU rRNA sequences in aligned form remained at 2055 and 1503, respectively. The number of prokaryotic SSU rRNA sequences more than doubled from the previous release 14 months earlier, and approximately 75% are longer than 899 bp. An RDP-II mirror site in Japan is now available (http://wdcm.nig.ac.jp/RDP/html/index.h tml). RDP-II provides aligned and annotated rRNA sequences, derived phylogenetic trees and taxonomic hierarchies, and analysis services through its WWW server (http://rdp.cme.msu.edu/). Analysis services include rRNA probe checking, approximate phylogenetic placement of user sequences, screening user sequences for possible chimeric rRNA sequences, automated alignment, production of similarity matrices and services to plan and analyze terminal restriction fragment polymorphism experiments. The RDP-II email address for questions and comments has been changed from curator@cme.msu.edu to rdpstaff@msu.edu.     

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.     

Chloroplast 2010: a database for large-scale phenotypic screening of Arabidopsis mutants

Large-scale phenotypic screening presents challenges and opportunities not encountered in typical forward or reverse genetics projects. We describe a modular database and laboratory information management system that was implemented in support of the Chloroplast 2010 Project, an Arabidopsis (Arabidopsis thaliana) reverse genetics phenotypic screen of more than 5,000 mutants (http://bioinfo.bch.msu.edu/2010_LIMS; www.plastid.msu.edu). The software and laboratory work environment were designed to minimize operator error and detect systematic process errors. The database uses Ruby on Rails and Flash technologies to present complex quantitative and qualitative data and pedigree information in a flexible user interface. Examples are presented where the database was used to find opportunities for process changes that improved data quality. We also describe the use of the data-analysis tools to discover mutants defective in enzymes of leucine catabolism (heteromeric mitochondrial 3-methylcrotonyl-coenzyme A carboxylase [At1g03090 and At4g34030] and putative hydroxymethylglutaryl-coenzyme A lyase [At2g26800]) based upon a syndrome of pleiotropic seed amino acid phenotypes that resembles previously described isovaleryl coenzyme A dehydrogenase (At3g45300) mutants. In vitro assay results support the computational annotation of At2g26800 as hydroxymethylglutaryl-coenzyme A lyase.     

Visualizing information across multidimensional post-genomic structured and textual databases

MOTIVATION: Visualizing relationships among biological information to facilitate understanding is crucial to biological research during the post-genomic era. Although different systems have been developed to view gene-phenotype relationships for specific databases, very few have been designed specifically as a general flexible tool for visualizing multidimensional genotypic and phenotypic information together. Our goal is to develop a method for visualizing multidimensional genotypic and phenotypic information and a model that unifies different biological databases in order to present the integrated knowledge using a uniform interface. RESULTS: We developed a novel, flexible and generalizable visualization tool, called PhenoGenesviewer (PGviewer), which in this paper was used to display gene-phenotype relationships from a human-curated database (OMIM) and from an automatic method using a Natural Language Processing tool called BioMedLEE. Data obtained from multiple databases were first integrated into a uniform structure and then organized by PGviewer. PGviewer provides a flexible query interface that allows dynamic selection and ordering of any desired dimension in the databases. Based on users' queries, results can be visualized using hierarchical expandable trees that present views specified by users according to their research interests. We believe that this method, which allows users to dynamically organize and visualize multiple dimensions, is a potentially powerful and promising tool that should substantially facilitate biological research. AVAILABILITY: PhenogenesViewer as well as its support and tutorial are available at http://www.dbmi.columbia.edu/pgviewer/ CONTACT: Lussier@dbmi.columbia.edu.     

The RDP (Ribosomal Database Project) continues

The Ribosomal Database Project (RDP-II), previously described by Maidak et al., continued during the past year to add new rRNA sequences to the aligned data and to improve the analysis commands. Release 7.1 (September 17, 1999) included more than 10 700 small subunit rRNA sequences. More than 850 type strain sequences were identified and added to the prokaryotic alignment, bringing the total number of type sequences to 3324 representing 2460 different species. Availability of an RDP-II mirror site in Japan is also near completion. RDP-II provides aligned and annotated rRNA sequences, derived phylogenetic trees and taxonomic hierarchies, and analysis services through its WWW server (http://rdp.cme.msu.edu/ ). Analysis services include rRNA probe checking, approx-i-mate phylogenetic placement of user sequences, screening user sequences for possible chimeric rRNA sequences, automated alignment, production of similarity matrices and services to plan and analyze terminal restriction fragment length polymorphism (T-RFLP) experiments.     

A phylogenomic approach to microbial evolution

To study the origin and evolution of biochemical pathways in microorganisms, we have developed methods and software for automatic, large-scale reconstructions of phylogenetic relationships. We define the complete set of phylogenetic trees derived from the proteome of an organism as the phylome and introduce the term phylogenetic connection as a concept that describes the relative relationships between taxa in a tree. A query system has been incorporated into the system so as to allow searches for defined categories of trees within the phylome. As a complement, we have developed the pyphy system for visualising the results of complex queries on phylogenetic connections, genomic locations and functional assignments in a graphical format. Our phylogenomics approach, which links phylogenetic information to the flow of biochemical pathways within and among microbial species, has been used to examine more than 8000 phylogenetic trees from seven microbial genomes. The results have revealed a rich web of phylogenetic connections. However, the separation of Bacteria and Archaea into two separate domains remains robust.     

Taxon combinations, parsimony analysis (PAUP*), and the taxonomy of the yellow-tailed woolly monkey, Lagothrix flavicauda

The classifications of primates, in general, and platyrrhine primates, in particular, have been greatly revised subsequent to the rationale for taxonomic decisions shifting from one rooted in the biological species concept to one rooted solely in phylogenetic affiliations. Given the phylogenetic justification provided for revised taxonomies, the scientific validity of taxonomic distinctions can be rightly judged by the robusticity of the phylogenetic results supporting them. In this study, we empirically investigated taxonomic-sampling effects on a cladogram previously inferred from craniodental data for the woolly monkeys (Lagothrix). We conducted the study primarily through much greater sampling of species-level taxa (OTUs) after improving some character codings and under a variety of outgroup choices. The results indicate that alternative selections of species subsets from within genera produce various tree topologies. These results stand even after adjusting the character set and considering the potential role of interobserver disagreement. We conclude that specific taxon combinations, in this case, generic or species pairings, of the primary study group has a biasing effect in parsimony analysis, and that the cladistic rationale for resurrecting the Oreonax generic distinction for the yellow-tailed woolly monkey (Lagothrix flavicauda) is based on an artifact of idiosyncratic sampling within the study group below the genus level. Some recommendations to minimize the problem, which is prevalent in all cladistic analyses, are proposed.     

DoriC: a database of oriC regions in bacterial genomes

Replication origins (oriCs) of bacterial genomes currently available in GenBank have been predicted by using a systematic method comprising the Z-curve analysis for nucleotide distribution asymmetry, DnaA box distribution, genes adjacent to candidate oriCs and phylogenetic relationships. These oriCs are organized into a MySQL database, DoriC, which provides extensive information and graphical views of the oriC regions. In addition, users can Blast a query sequence or even a whole genome against DoriC to find a homologous one. DoriC will be updated timely and the latest version is DoriC 1.8, in which oriCs of 425 genomes (468 chromosomes) are identified. AVAILABILITY: DoriC can be accessed from http://tubic.tju.edu.cn/doric/. SUPPLEMENTARY INFORMATION: Supplementary data are available at http://tubic.tju.edu.cn/doric/supplementary.htm.     

Stems,nodes, crown clades,and rank‐free lists: is Linnaeus dead?

Recent radical proposals to overhaul the methods of biological classification are reviewed. The proposals of phylogenetic nomenclature are to translate cladistic phylogenies directly into classifications, and to define taxon names in terms of clades. The method has a number of radical consequences for biologists: taxon names must depend rigidly on the particular cladogram favoured at the moment, familiar names may be reassigned to unfamiliar groupings, Linnaean category terms (e.g. phylum, order, family) are abandoned, and the Linnaean binomen (e.g. Homo sapiens) is abandoned. The tenets of phylogenetic nomenclature have gained strong support among some vocal theoreticians, and rigid principles for legislative control of clade names and definitions have been outlined in the PhyloCode. The consequences of this semantic maelstrom have not been worked out. In pratice, phylogenetic nomenclature will bc disastrous, promoting confusion and instability, and it should be abandoned. It is based on a fundamental misunderstanding of the difference between a phylogeny (which is real) and a classification (which is utilitarian). Under the new view, classifications are identical to phlylogenies, and so the proponents of phylogenetic nomenclature will end up abandoning classifications altogether.     

OrthologID: automation of genome-scale ortholog identification within a parsimony framework

MOTIVATION: The determination of gene orthology is a prerequisite for mining and utilizing the rapidly increasing amount of sequence data for genome-scale phylogenetics and comparative genomic studies. Until now, most researchers use pairwise distance comparisons algorithms, such as BLAST, COG, RBH, RSD and INPARANOID, to determine gene orthology. In contrast, orthology determination within a character-based phylogenetic framework has not been utilized on a genomic scale owing to the lack of efficiency and automation. RESULTS: We have developed OrthologID, a Web application that automates the labor-intensive procedures of gene orthology determination within a character-based phylogenetic framework, thus making character-based orthology determination on a genomic scale possible. In addition to generating gene family trees and determining orthologous gene sets for complete genomes, OrthologID can also identify diagnostic characters that define each orthologous gene set, as well as diagnostic characters that are responsible for classifying query sequences from other genomes into specific orthology groups. The OrthologID database currently includes several complete plant genomes, including Arabidopsis thaliana, Oryza sativa, Populus trichocarpa, as well as a unicellular outgroup, Chlamydomonas reinhardtii. To improve the general utility of OrthologID beyond plant species, we plan to expand our sequence database to include the fully sequenced genomes of prokaryotes and other non-plant eukaryotes. AVAILABILITY: http://nypg.bio.nyu.edu/orthologid/     

Accurate, rapid taxonomic classification of fungal large-subunit rRNA genes

Taxonomic and phylogenetic fingerprinting based on sequence analysis of gene fragments from the large-subunit rRNA (LSU) gene or the internal transcribed spacer (ITS) region is becoming an integral part of fungal classification. The lack of an accurate and robust classification tool trained by a validated sequence database for taxonomic placement of fungal LSU genes is a severe limitation in taxonomic analysis of fungal isolates or large data sets obtained from environmental surveys. Using a hand-curated set of 8,506 fungal LSU gene fragments, we determined the performance characteristics of a naïve Bayesian classifier across multiple taxonomic levels and compared the classifier performance to that of a sequence similarity-based (BLASTN) approach. The naïve Bayesian classifier was computationally more rapid (>460-fold with our system) than the BLASTN approach, and it provided equal or superior classification accuracy. Classifier accuracies were compared using sequence fragments of 100 bp and 400 bp and two different PCR primer anchor points to mimic sequence read lengths commonly obtained using current high-throughput sequencing technologies. Accuracy was higher with 400-bp sequence reads than with 100-bp reads. It was also significantly affected by sequence location across the 1,400-bp test region. The highest accuracy was obtained across either the D1 or D2 variable region. The naïve Bayesian classifier provides an effective and rapid means to classify fungal LSU sequences from large environmental surveys. The training set and tool are publicly available through the Ribosomal Database Project (http://rdp.cme.msu.edu/classifier/classifier.jsp).     

Rfam: an RNA family database

Rfam is a collection of multiple sequence alignments and covariance models representing non-coding RNA families. Rfam is available on the web in the UK at http://www.sanger.ac.uk/Software/Rfam/ and in the US at http://rfam.wustl.edu/. These websites allow the user to search a query sequence against a library of covariance models, and view multiple sequence alignments and family annotation. The database can also be downloaded in flatfile form and searched locally using the INFERNAL package (http://infernal.wustl.edu/). The first release of Rfam (1.0) contains 25 families, which annotate over 50 000 non-coding RNA genes in the taxonomic divisions of the EMBL nucleotide database.     

TargetDB: a database of peptides targeting proteins to subcellular locations.

SUMMARY: TargetDB is a relational database designed to represent data on protein targeting sequences, mutant signals, subcellular targets and source organisms. AVAILABILITY: TargetDB is accessible at http://molbio.nmsu.edu:81. The web interface supports both direct data authoring and database query functions. CONTACT: moconnel@nmsu. edu, tao_wei@hms.harvard.edu     

An Update on the Functional Molecular Immunology (FIMM) Database

Data on the major histocompatibility complex, T-cell epitopes, B-cell epitopes, antigens and diseases are heterogeneous and scattered among different databases and the literature. Since it has become increasingly difficult to obtain an integrated view of functional immune response components, we have developed and updated over several years the Functional molecular IMMunology (FIMM) database (http:// research.i2r.a-star.edu.sg/fimm/). FIMM contains integrated expert-curated data on protein antigens, and on human immunological receptors that recognise and bind them in healthy or disease states. Interfaces with multiple, intuitive query options and query reports provide immunologists with prioritised information that aids data interpretation, vaccine target discovery and immune disease research.     

DNA barcode sequence identification incorporating taxonomic hierarchy and within taxon variability

For DNA barcoding to succeed as a scientific endeavor an accurate and expeditious query sequence identification method is needed. Although a global multiple-sequence alignment can be generated for some barcoding markers (e.g. COI, rbcL), not all barcoding markers are as structurally conserved (e.g. matK). Thus, algorithms that depend on global multiple-sequence alignments are not universally applicable. Some sequence identification methods that use local pairwise alignments (e.g. BLAST) are unable to accurately differentiate between highly similar sequences and are not designed to cope with hierarchic phylogenetic relationships or within taxon variability. Here, I present a novel alignment-free sequence identification algorithm--BRONX--that accounts for observed within taxon variability and hierarchic relationships among taxa. BRONX identifies short variable segments and corresponding invariant flanking regions in reference sequences. These flanking regions are used to score variable regions in the query sequence without the production of a global multiple-sequence alignment. By incorporating observed within taxon variability into the scoring procedure, misidentifications arising from shared alleles/haplotypes are minimized. An explicit treatment of more inclusive terminals allows for separate identifications to be made for each taxonomic level and/or for user-defined terminals. BRONX performs better than all other methods when there is imperfect overlap between query and reference sequences (e.g. mini-barcode queries against a full-length barcode database). BRONX consistently produced better identifications at the genus-level for all query types.