Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Domain-centric database to uncover structure of minimally characterized viral genomes.

ABSTRACT: Protein domain-based approaches to analyzing sequence data are valuable tools for examining and exploring genomic architecture across genomes of different organisms. Here, we present a complete dataset of domains from the publicly available sequence data of 9,051 reference viral genomes. The data provided contain information such as sequence position and neighboring domains from 30,947 pHMM-identified domains from each reference viral genome. Domains were identified from viral whole-genome sequence using automated profile Hidden Markov Models (pHMM). This study also describes the framework for constructing "domain neighborhoods", as well as the dataset representing it. These data can be used to examine shared and differing domain architectures across viral genomes, to elucidate potential functional properties of genes, and potentially to classify viruses.

SUBMITTER: Bramley JC

PROVIDER: S-EPMC7316859 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Domain-centric database to uncover structure of minimally characterized viral genomes.

Bramley John C JC Yenkin Alex L AL Zaydman Mark A MA DiAntonio Aaron A Milbrandt Jeffrey D JD Buchser William J WJ

Scientific data 20200625 1

Protein domain-based approaches to analyzing sequence data are valuable tools for examining and exploring genomic architecture across genomes of different organisms. Here, we present a complete dataset of domains from the publicly available sequence data of 9,051 reference viral genomes. The data provided contain information such as sequence position and neighboring domains from 30,947 pHMM-identified domains from each reference viral genome. Domains were identified from viral whole-genome seque ...[more]

PMID: 32587259

Similar Datasets

Gene3D: structural assignment for whole genes and genomes using the CATH domain structure database.

Project description:We present a novel web-based resource, Gene3D, of precalculated structural assignments to gene sequences and whole genomes. This resource assigns structural domains from the CATH database to whole genes and links these to their curated functional and structural annotations within the CATH domain structure database, the functional Dictionary of Homologous Superfamilies (DHS) and PDBsum. Currently Gene3D provides annotation for 36 complete genomes (two eukaryotes, six archaea, and 28 bacteria). On average, between 30% and 40% of the genes of a given genome can be structurally annotated. Matches to structural domains are found using the profile-based method (PSI-BLAST). and a novel protocol, DRange, is used to resolve conflicts in matches involving different homologous superfamilies.

| S-EPMC155287 | biostudies-literature

DcGO: database of domain-centric ontologies on functions, phenotypes, diseases and more.

Project description:We present 'dcGO' (http://supfam.org/SUPERFAMILY/dcGO), a comprehensive ontology database for protein domains. Domains are often the functional units of proteins, thus instead of associating ontological terms only with full-length proteins, it sometimes makes more sense to associate terms with individual domains. Domain-centric GO, 'dcGO', provides associations between ontological terms and protein domains at the superfamily and family levels. Some functional units consist of more than one domain acting together or acting at an interface between domains; therefore, ontological terms associated with pairs of domains, triplets and longer supra-domains are also provided. At the time of writing the ontologies in dcGO include the Gene Ontology (GO); Enzyme Commission (EC) numbers; pathways from UniPathway; human phenotype ontology and phenotype ontologies from five model organisms, including plants; anatomy ontologies from three organisms; human disease ontology and drugs from DrugBank. All ontological terms have probabilistic scores for their associations. In addition to associations to domains and supra-domains, the ontological terms have been transferred to proteins, through homology, providing annotations of >80 million sequences covering 2414 complete genomes, hundreds of meta-genomes, thousands of viruses and so forth. The dcGO database is updated fortnightly, and its website provides downloads, search, browse, phylogenetic context and other data-mining facilities.

| S-EPMC3531119 | biostudies-literature

VnD: a structure-centric database of disease-related SNPs and drugs.

Project description:Numerous genetic variations have been found to be related to human diseases. Significant portion of those affect the drug response as well by changing the protein structure and function. Therefore, it is crucial to understand the trilateral relationship among genomic variations, diseases and drugs. We present the variations and drugs (VnD), a consolidated database containing information on diseases, related genes and genetic variations, protein structures and drug information. VnD was built in three steps. First, we integrated various resources systematically to deduce catalogs of disease-related genes, single nucleotide polymorphisms (SNPs), protein mutations and relevant drugs. VnD contains 137,195 disease-related gene records (13,940 distinct genes) and 16,586 genetic variation records (1790 distinct variations). Next, we carried out structure modeling and docking simulation for wild-type and mutant proteins to examine the structural and functional consequences of non-synonymous SNPs in the drug-related genes. Conformational changes in 590 wild-type and 4437 mutant proteins from drug-related genes were included in our database. Finally, we investigated the structural and biochemical properties relevant to drug binding such as the distribution of SNPs in proximal protein pockets, thermo-chemical stability, interactions with drugs and physico-chemical properties. The VnD database, available at http://vnd.kobic.re.kr:8080/VnD/ or vandd.org, would be a useful platform for researchers studying the underlying mechanism for association among genetic variations, diseases and drugs.

| S-EPMC3013797 | biostudies-literature

The transcript-centric mutations in human genomes.

Project description:Since the human genome is mostly transcribed, genetic variations must exhibit sequence signatures reflecting the relationship between transcription processes and chromosomal structures as we have observed in unicellular organisms. In this study, a set of 646 ubiquitous expression-invariable genes (EIGs) which are present in germline cells were defined and examined based on RNA-sequencing data from multiple high-throughput transcriptomic data. We demonstrated a relationship between gene expression level and transcript-centric mutations in the human genome based on single nucleotide polymorphism (SNP) data. A significant positive correlation was shown between gene expression and mutation, where highly-expressed genes accumulate more mutations than lowly-expressed genes. Furthermore, we found four major types of transcript-centric mutations: C?T, A?G, C?G, and G?T in human genomes and identified a negative gradient of the sequence variations aligning from the 5' end to the 3' end of the transcription units (TUs). The periodical occurrence of these genetic variations across TUs is associated with nucleosome phasing. We propose that transcript-centric mutations are one of the major driving forces for gene and genome evolution along with creation of new genes, gene/genome duplication, and horizontal gene transfer.

| S-EPMC5054492 | biostudies-literature

The dcGO Domain-Centric Ontology Database in 2023: New Website and Extended Annotations for Protein Structural Domains.

Project description:Protein structural domains have been less studied than full-length proteins in terms of ontology annotations. The dcGO database has filled this gap by providing mappings from protein domains to ontologies. The dcGO update in 2023 extends annotations for protein domains of multiple definitions (SCOP, Pfam, and InterPro) with commonly used ontologies that are categorised into functions, phenotypes, diseases, drugs, pathways, regulators, and hallmarks. This update adds new dimensions to the utility of both ontology and protein domain resources. A newly designed website at http://www.protdomainonto.pro/dcGO offers a more centralised and user-friendly way to access the dcGO database, with enhanced faceted search returning term- and domain-specific information pages. Users can navigate both ontology terms and annotated domains through improved ontology hierarchy browsing. A newly added facility enables domain-based ontology enrichment analysis.

| S-EPMC7614987 | biostudies-literature

gEVE: a genome-based endogenous viral element database provides comprehensive viral protein-coding sequences in mammalian genomes.

Project description:In mammals, approximately 10% of genome sequences correspond to endogenous viral elements (EVEs), which are derived from ancient viral infections of germ cells. Although most EVEs have been inactivated, some open reading frames (ORFs) of EVEs obtained functions in the hosts. However, EVE ORFs usually remain unannotated in the genomes, and no databases are available for EVE ORFs. To investigate the function and evolution of EVEs in mammalian genomes, we developed EVE ORF databases for 20 genomes of 19 mammalian species. A total of 736,771 non-overlapping EVE ORFs were identified and archived in a database named gEVE (http://geve.med.u-tokai.ac.jp). The gEVE database provides nucleotide and amino acid sequences, genomic loci and functional annotations of EVE ORFs for all 20 genomes. In analyzing RNA-seq data with the gEVE database, we successfully identified the expressed EVE genes, suggesting that the gEVE database facilitates studies of the genomic analyses of various mammalian species.Database URL: http://geve.med.u-tokai.ac.jp.

| S-EPMC4885607 | biostudies-literature

Metagenome-assembled genomes uncover a global brackish microbiome.

Project description:Microbes are main drivers of biogeochemical cycles in oceans and lakes. Although the genome is a foundation for understanding the metabolism, ecology and evolution of an organism, few bacterioplankton genomes have been sequenced, partly due to difficulties in cultivating them.We use automatic binning to reconstruct a large number of bacterioplankton genomes from a metagenomic time-series from the Baltic Sea, one of world's largest brackish water bodies. These genomes represent novel species within typical freshwater and marine clades, including clades not previously sequenced. The genomes' seasonal dynamics follow phylogenetic patterns, but with fine-grained lineage-specific variations, reflected in gene-content. Signs of streamlining are evident in most genomes, and estimated genome sizes correlate with abundance variation across filter size fractions. Comparing the genomes with globally distributed metagenomes reveals significant fragment recruitment at high sequence identity from brackish waters in North America, but little from lakes or oceans. This suggests the existence of a global brackish metacommunity whose populations diverged from freshwater and marine relatives over 100,000 years ago, long before the Baltic Sea was formed (8000 years ago). This markedly contrasts to most Baltic Sea multicellular organisms, which are locally adapted populations of freshwater or marine counterparts.We describe the gene content, temporal dynamics and biogeography of a large set of new bacterioplankton genomes assembled from metagenomes. We propose that brackish environments exert such strong selection that lineages adapted to them flourish globally with limited influence from surrounding aquatic communities.

| S-EPMC4699468 | biostudies-literature

SCPortalen: human and mouse single-cell centric database.

Project description:Published single-cell datasets are rich resources for investigators who want to address questions not originally asked by the creators of the datasets. The single-cell datasets might be obtained by different protocols and diverse analysis strategies. The main challenge in utilizing such single-cell data is how we can make the various large-scale datasets to be comparable and reusable in a different context. To challenge this issue, we developed the single-cell centric database 'SCPortalen' (http://single-cell.clst.riken.jp/). The current version of the database covers human and mouse single-cell transcriptomics datasets that are publicly available from the INSDC sites. The original metadata was manually curated and single-cell samples were annotated with standard ontology terms. Following that, common quality assessment procedures were conducted to check the quality of the raw sequence. Furthermore, primary data processing of the raw data followed by advanced analyses and interpretation have been performed from scratch using our pipeline. In addition to the transcriptomics data, SCPortalen provides access to single-cell image files whenever available. The target users of SCPortalen are all researchers interested in specific cell types or population heterogeneity. Through the web interface of SCPortalen users are easily able to search, explore and download the single-cell datasets of their interests.

| S-EPMC5753281 | biostudies-literature

NECTAR: a database of codon-centric missense variant annotations.

Project description:NECTAR (Non-synonymous Enriched Coding muTation ARchive; http://nectarmutation.org) is a database and web application to annotate disease-related and functionally important amino acids in human proteins. A number of tools are available to facilitate the interpretation of DNA variants identified in diagnostic or research sequencing. These typically identify previous reports of DNA variation at a given genomic location, predict its effects on transcript and protein sequence and may predict downstream functional consequences. Previous reports and functional annotations are typically linked by the genomic location of the variant observed. NECTAR collates disease-causing variants and functionally important amino acid residues from a number of sources. Importantly, rather than simply linking annotations by a shared genomic location, NECTAR annotates variants of interest with details of previously reported variation affecting the same codon. This provides a much richer data set for the interpretation of a novel DNA variant. NECTAR also identifies functionally equivalent amino acid residues in evolutionarily related proteins (paralogues) and, where appropriate, transfers annotations between them. As well as accessing these data through a web interface, users can upload batches of variants in variant call format (VCF) for annotation on-the-fly. The database is freely available to download from the ftp site: ftp://ftp.nectarmutation.org.

| S-EPMC3965063 | biostudies-literature

Ancient herpes simplex 1 genomes reveal recent viral structure in Eurasia.

Project description:Human herpes simplex virus 1 (HSV-1), a life-long infection spread by oral contact, infects a majority of adults globally. Phylogeographic clustering of sampled diversity into European, pan-Eurasian, and African groups has suggested the virus codiverged with human migrations out of Africa, although a much younger origin has also been proposed. We present three full ancient European HSV-1 genomes and one partial genome, dating from the 3rd to 17th century CE, sequenced to up to 9.5× with paired human genomes up to 10.16×. Considering a dataset of modern and ancient genomes, we apply phylogenetic methods to estimate the age of sampled modern Eurasian HSV-1 diversity to 4.68 (3.87 to 5.65) ka. Extrapolation of estimated rates to a global dataset points to the age of extant sampled HSV-1 as 5.29 (4.60 to 6.12) ka, suggesting HSV-1 lineage replacement coinciding with the late Neolithic period and following Bronze Age migrations.

| S-EPMC9328674 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data