scispace - formally typeset
Open AccessJournal ArticleDOI

ViPR: an open bioinformatics database and analysis resource for virology research

Reads0
Chats0
TLDR
The Virus Pathogen Database and Analysis Resource (ViPR) is an integrated repository of data and analysis tools for multiple virus families supported by the National Institute of Allergy and Infectious Diseases (NIAID) Bioinformatics Resource Centers (BRC) program.
Abstract
The Virus Pathogen Database and Analysis Resource (ViPR, www.ViPRbrc.org) is an integrated repository of data and analysis tools for multiple virus families, supported by the National Institute of Allergy and Infectious Diseases (NIAID) Bioinformatics Resource Centers (BRC) program. ViPR contains information for human pathogenic viruses belonging to the Arenaviridae, Bunyaviridae, Caliciviridae, Coronaviridae, Flaviviridae, Filoviridae, Hepeviridae, Herpesviridae, Paramyxoviridae, Picornaviridae, Poxviridae, Reoviridae, Rhabdoviridae and Togaviridae families, with plans to support additional virus families in the future. ViPR captures various types of information, including sequence records, gene and protein annotations, 3D protein structures, immune epitope locations, clinical and surveillance metadata and novel data derived from comparative genomics analysis. Analytical and visualization tools for metadata-driven statistical sequence analysis, multiple sequence alignment, phylogenetic tree construction, BLAST comparison and sequence variation determination are also provided. Data filtering and analysis workflows can be combined and the results saved in personal ‘Workbenches’ for future use. ViPR tools and data are available without charge as a service to the virology research community to help facilitate the development of diagnostics, prophylactics and therapeutics for priority pathogens and other viruses.

read more

Citations
More filters
Journal ArticleDOI

Preliminary Identification of Potential Vaccine Targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies.

TL;DR: This study identified a set of B cell and T cell epitopes derived from the spike (S) and nucleocapsid (N) proteins that map identically to SARS-CoV-2 proteins, providing a screened set of epitopes that can help guide experimental efforts towards the development of vaccines against this novel virus.
Journal ArticleDOI

A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2

TL;DR: Parallel bioinformatic predictions identified a priori potential B and T cell epitopes for SARS-CoV-2 that can facilitate effective vaccine design against this virus of high priority.
Journal ArticleDOI

Molecular Evolution of Human Coronavirus Genomes

TL;DR: This review of recent findings on the molecular evolution of HCoV genomes is summarized, with special attention to recombination and adaptive events that generated new viral species and contributed to host shifts and to H coV emergence.
Journal ArticleDOI

NCBI Viral Genomes Resource

TL;DR: The NCBI Viral Genomes Resource is a reference resource designed to bring order to this sequence shockwave and improve usability of viral sequence data.
References
More filters
Journal ArticleDOI

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

TL;DR: A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original.
Journal ArticleDOI

MUSCLE: multiple sequence alignment with high accuracy and high throughput

TL;DR: MUSCLE is a new computer program for creating multiple alignments of protein sequences that includes fast distance estimation using kmer counting, progressive alignment using a new profile function the authors call the log-expectation score, and refinement using tree-dependent restricted partitioning.
Journal ArticleDOI

Gene Ontology: tool for the unification of biology

TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Journal ArticleDOI

MODELTEST: testing the model of DNA substitution.

TL;DR: The program MODELTEST uses log likelihood scores to establish the model of DNA evolution that best fits the data.
Journal ArticleDOI

A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood.

TL;DR: This work has used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches.
Related Papers (5)