The Ribosomal RNA Operon Copy Number Database (rrndb) is an Internet-accessible database containing annotated information on rRNA operon copy number among prokaryotes. Gene redundancy is uncommon in prokaryotic genomes, yet the rRNA genes can vary from one to as many as 15 copies. Despite the widespread use of 16S rRNA gene sequences for identification of prokaryotes, information on the number and sequence of individual rRNA genes in a genome is not readily accessible. In an attempt to understand the evolutionary implications of rRNA operon redundancy, we have created a phylogenetically arranged report on rRNA gene copy number for a diverse collection of prokaryotic microorganisms. Each entry (organism) in the rrndb contains detailed information linked directly to external websites including the Ribosomal Database Project, GenBank, PubMed and several culture collections. Data contained in the rrndb will be valuable to researchers investigating microbial ecology and evolution using 16S rRNA gene sequences. The rrndb web site is directly accessible on the WWW at http://rrndb.cme. msu.edu.
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.
Rapid analysis of microbial communities has proven to be a difficult task. This is due, in part, to both the tremendous diversity of the microbial world and the high complexity of many microbial communities. Several techniques for community analysis have emerged over the past decade, and most take advantage of the molecular phylogeny derived from 16S rRNA comparative sequence analysis. We describe a web-based research tool located at the Ribosomal Database Project web site (http://www.cme.msu.edu/RDP/html/analyses.html) that facilitates microbial community analysis using terminal restriction fragment length polymorphism of 16S ribosomal DNA. The analysis function (designated TAP T-RFLP) permits the user to perform in silico restriction digestions of the entire 16S sequence database and derive terminal restriction fragment sizes, measured in base pairs, from the 5 terminus of the user-specified primer to the 3 terminus of the restriction endonuclease target site. The output can be sorted and viewed either phylogenetically or by size. It is anticipated that the site will guide experimental design as well as provide insight into interpreting results of community analysis with terminal restriction fragment length polymorphisms.Many microbial communities have proven to be complex assemblages of different phylotypes and physiologies. For example, the number of species in soil is estimated to be more than 4,000 species/30 g of soil (17), and estimates of the number of bacterial species are enormous (6). It has been only within the last 20 years that we have begun to recognize the phylogenetic diversity of the microbial world. During this time 16S rRNA has emerged as one of the premier phylogenetic markers (7,16,19), providing a landmark for comparative analyses of isolated and uncultured strains as well as microbial communities.Comparative community analysis provides an accelerated approach to understanding community structure and function. It allows for the identification of unique or numerically dominant strains or groups under defined or controlled conditions. Thus, one can begin to dissect the trophic complexity of a community by changing nutrient patterns and observing the resulting changes in community structure. A number of rapid techniques to screen communities have been developed (2,3,8,9,13,14), and many of those described to date take advantage of the 16S rRNA phylogenetic marker and are cultureindependent approaches (1). Preliminary steps include the isolation of community DNA and the PCR amplification of a phylogenetic marker from the community DNA template. It is at this point that the various approaches differ in how the PCR products are separated. Terminal restriction fragment length polymorphism (T-RFLP) takes advantage of the high resolution and throughput of automated sequencing technologies to separate the polymorphic terminal fragments after restriction digestion. Because the polymorphism is based solely on the length of the fragment, direct reference can be made to the sequence database (9, 11).Th...
The biomedical research community relies on a diverse set of resources, both within their own institutions and at other research centers. In addition, an increasing number of shared electronic resources have been developed. Without effective means to locate and query these resources, it is challenging, if not impossible, for investigators to be aware of the myriad resources available, or to effectively perform resource discovery when the need arises. In this paper, we describe the development and use of the Biomedical Resource Ontology (BRO) to enable semantic annotation and discovery of biomedical resources. We also describe the Resource Discovery System (RDS) which is a federated, inter-institutional pilot project that uses the BRO to facilitate resource discovery on the Internet. Through the RDS framework and its associated Biositemaps infrastructure, the BRO facilitates semantic search and discovery of biomedical resources, breaking down barriers and streamlining scientific research that will improve human health.
Background: Chronic renal diseases are currently classified based on morphological similarities such as whether they produce predominantly inflammatory or non-inflammatory responses. However, such classifications do not reliably predict the course of the disease and its response to therapy. In contrast, recent studies in diseases such as breast cancer suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. This article describes how we extracted gene expression profiles from biopsies of patients with chronic renal diseases, and used network visualizations and associated quantitative measures to rapidly analyze similarities and differences between the diseases.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.