The Viral Bioinformatics Resource Center (VBRC) is an online resource providing access to a database of curated viral genomes and a variety of tools for bioinformatic genome analysis. [1] This resource was one of eight BRCs (Bioinformatics Resource Centers) funded by NIAID with the goal of promoting research against emerging and re-emerging pathogens, particularly those seen as potential bioterrorism threats. The VBRC is now supported by Dr. Chris Upton [2] at the University of Victoria.
The curated VBRC database contains all publicly available genomic sequences for poxviruses and African Swine Fever Viruses (ASFV). A unique aspect of this resource relative to other genomic databases is its grouping of all annotated genes into ortholog groups (i.e. protein families) based on pre-run BLASTP sequence similarity searches.
The curated database is accessed through VOCS (Viral Orthologous Clusters), a downloadable Java-based user interface, and acts as the central information source for other programs of the VBRC workbench. These programs serve a variety of bioinformatic analysis functions (whole- or subgenome alignments, genome display, and several types of gene/protein sequence analysis). The majority of these tools are programmed to take user-supplied input as well.
The VBRC covers the following viruses:
The VBRC database stores viral bioinformatic data on three levels:
VBRC provides researchers with a wide variety of database-linked tools. Of these, the central four programs are VOCs, VGO, BBB, and JDotter.
VBRC provides a number of additional Java-based analysis tools on its website. The tools in this category are each designed to perform a very specific task (e.g. regular expression searches, DNA skew plotting) and, though they can be accessed as stand-alone programs with user-supplied input, most have increased utility when launched from the central VOCS application with VBRC-supplied data.
These additional tools are as follows:
Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.
In bioinformatics, sequence clustering algorithms attempt to group biological sequences that are somehow related. The sequences can be either of genomic, "transcriptomic" (ESTs) or protein origin. For proteins, homologous sequences are typically grouped into families. For EST data, clustering is important to group sequences originating from the same gene before the ESTs are assembled to reconstruct the original mRNA.
BioJava is an open-source software project dedicated to provide Java tools to process biological data. BioJava is a set of library functions written in the programming language Java for manipulating sequences, protein structures, file parsers, Common Object Request Broker Architecture (CORBA) interoperability, Distributed Annotation System (DAS), access to AceDB, dynamic programming, and simple statistical routines. BioJava supports a range of data, starting from DNA and protein sequences to the level of 3D protein structures. The BioJava libraries are useful for automating many daily and mundane bioinformatics tasks such as to parsing a Protein Data Bank (PDB) file, interacting with Jmol and many more. This application programming interface (API) provides various file parsers, data models and algorithms to facilitate working with the standard data formats and enables rapid application development and analysis.
Comparative genomics is a field of biological research in which the genomic features of different organisms are compared. The genomic features may include the DNA sequence, genes, gene order, regulatory sequences, and other genomic structural landmarks. In this branch of genomics, whole or large parts of genomes resulting from genome projects are compared to study basic biological similarities and differences as well as evolutionary relationships between organisms. The major principle of comparative genomics is that common features of two organisms will often be encoded within the DNA that is evolutionarily conserved between them. Therefore, comparative genomic approaches start with making some form of alignment of genome sequences and looking for orthologous sequences in the aligned genomes and checking to what extent those sequences are conserved. Based on these, genome and molecular evolution are inferred and this may in turn be put in the context of, for example, phenotypic evolution or population genetics.
A sequence profiling tool in bioinformatics is a type of software that presents information related to a genetic sequence, gene name, or keyword input. Such tools generally take a query such as a DNA, RNA, or protein sequence or ‘keyword’ and search one or more databases for information related to that sequence. Summaries and aggregate results are provided in standardized format describing the information that would otherwise have required visits to many smaller sites or direct literature searches to compile. Many sequence profiling tools are software portals or gateways that simplify the process of finding information about a query in the large and growing number of bioinformatics databases. The access to these kinds of tools is either web based or locally downloadable executables.
Sequence homology is the biological homology between DNA, RNA, or protein sequences, defined in terms of shared ancestry in the evolutionary history of life. Two segments of DNA can have shared ancestry because of three phenomena: either a speciation event (orthologs), or a duplication event (paralogs), or else a horizontal gene transfer event (xenologs).
The European Bioinformatics Institute (EMBL-EBI) is an intergovernmental organization (IGO) which, as part of the European Molecular Biology Laboratory (EMBL) family, focuses on research and services in bioinformatics. It is located on the Wellcome Genome Campus in Hinxton near Cambridge, and employs over 600 full-time equivalent (FTE) staff. Institute leaders such as Rolf Apweiler, Alex Bateman, Ewan Birney, and Guy Cochrane, an adviser on the National Genomics Data Center Scientific Advisory Board, serve as part of the international research network of the BIG Data Center at the Beijing Institute of Genomics.
The completion of the human genome sequencing in the early 2000s was a turning point in genomics research. Scientists have conducted series of research into the activities of genes and the genome as a whole. The human genome contains around 3 billion base pairs nucleotide, and the huge quantity of data created necessitates the development of an accessible tool to explore and interpret this information in order to investigate the genetic basis of disease, evolution, and biological processes. The field of genomics has continued to grow, with new sequencing technologies and computational tool making it easier to study the genome.
The Saccharomyces Genome Database (SGD) is a scientific database of the molecular biology and genetics of the yeast Saccharomyces cerevisiae, which is commonly known as baker's or budding yeast. Further information is located at the Yeastract curated repository.
In bioinformatics a dot plot is a graphical method for comparing two biological sequences and identifying regions of close similarity after sequence alignment. It is a type of recurrence plot.
BLAT is a pairwise sequence alignment algorithm that was developed by Jim Kent at the University of California Santa Cruz (UCSC) in the early 2000s to assist in the assembly and annotation of the human genome. It was designed primarily to decrease the time needed to align millions of mouse genomic reads and expressed sequence tags against the human genome sequence. The alignment tools of the time were not capable of performing these operations in a manner that would allow a regular update of the human genome assembly. Compared to pre-existing tools, BLAT was ~500 times faster with performing mRNA/DNA alignments and ~50 times faster with protein/protein alignments.
MicrobesOnline is a publicly and freely accessible website that hosts multiple comparative genomic tools for comparing microbial species at the genomic, transcriptomic and functional levels. MicrobesOnline was developed by the Virtual Institute for Microbial Stress and Survival, which is based at the Lawrence Berkeley National Laboratory in Berkeley, California. The site was launched in 2005, with regular updates until 2011.
Pathema was one of the eight bioinformatics resource centers funded by the National Institute of Allergy and Infectious Diseases (NIAID), a component of the National Institute of Health (NIH), which is an agency of the United States Department of Health and Human Services.
The UCSC Genome Browser is an online and downloadable genome browser hosted by the University of California, Santa Cruz (UCSC). It is an interactive website offering access to genome sequence data from a variety of vertebrate and invertebrate species and major model organisms, integrated with a large collection of aligned annotations. The Browser is a graphical viewer optimized to support fast interactive performance and is an open-source, web-based tool suite built on top of a MySQL database for rapid visualization, examination, and querying of the data at many levels. The Genome Browser Database, browsing tools, downloadable data files, and documentation can all be found on the UCSC Genome Bioinformatics website.
GeneCards is a database of human genes that provides genomic, proteomic, transcriptomic, genetic and functional information on all known and predicted human genes. It is being developed and maintained by the Crown Human Genome Center at the Weizmann Institute of Science, in collaboration with LifeMap Sciences.
In molecular biology and genetics, DNA annotation or genome annotation is the process of describing the structure and function of the components of a genome, by analyzing and interpreting them in order to extract their biological significance and understand the biological processes in which they participate. Among other things, it identifies the locations of genes and all the coding regions in a genome and determines what those genes do.
OrthoDB presents a catalog of orthologous protein-coding genes across vertebrates, arthropods, fungi, plants, and bacteria. Orthology refers to the last common ancestor of the species under consideration, and thus OrthoDB explicitly delineates orthologs at each major radiation along the species phylogeny. The database of orthologs presents available protein descriptors, together with Gene Ontology and InterPro attributes, which serve to provide general descriptive annotations of the orthologous groups, and facilitate comprehensive orthology database querying. OrthoDB also provides computed evolutionary traits of orthologs, such as gene duplicability and loss profiles, divergence rates, sibling groups, and gene intron-exon architectures.
The Virus Pathogen Database and Analysis Resource (ViPR) is an integrative and comprehensive publicly available database and analysis resource to search, analyze, visualize, save and share data for viral pathogens in the U.S. National Institute of Allergy and Infectious Diseases (NIAID) Category A-C Priority Pathogen lists for biodefense research, and other viral pathogens causing emerging/reemerging infectious diseases. ViPR is one of the five Bioinformatics Resource Centers (BRC) funded by NIAID, a component of the National Institutes of Health (NIH), which is an agency of the United States Department of Health and Human Services.
In molecular phylogenetics, relationships among individuals are determined using character traits, such as DNA, RNA or protein, which may be obtained using a variety of sequencing technologies. High-throughput next-generation sequencing has become a popular technique in transcriptomics, which represent a snapshot of gene expression. In eukaryotes, making phylogenetic inferences using RNA is complicated by alternative splicing, which produces multiple transcripts from a single gene. As such, a variety of approaches may be used to improve phylogenetic inference using transcriptomic data obtained from RNA-Seq and processed using computational phylogenetics.