scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation

TL;DR: The approach to utilizing available RNA-Seq and other data types in the authors' manual curation process for vertebrate, plant, and other species is summarized, and a new direction for prokaryotic genomes and protein name management is described.
Abstract: The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (http://www.ncbi.nlm.nih.gov/refseq/). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55,000 organisms (>4800 viruses, >40,000 prokaryotes and >10,000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. We summarize our approach to utilizing available RNA-Seq and other data types in our manual curation process for vertebrate, plant, and other species, and describe a new direction for prokaryotic genomes and protein name management.
Citations
More filters
Journal ArticleDOI
Minoru Kanehisa1, Miho Furumichi1, Mao Tanabe1, Yoko Sato2, Kanae Morishima1 
TL;DR: The content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases, and the newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined.
Abstract: KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.

5,741 citations


Additional excerpts

  • ...The viruses category is generated from the bi-monthly release of RefSeq, where individual viral genomes are not distinguished and the category is identified by the twoletter code ‘vg’....

    [...]

  • ...This category also contains GENOME and GENES, which are derived from RefSeq (3), Genbank (4) and NCBI Taxonomy (5) databases and given KEGG original annotations....

    [...]

  • ...The definition field of each GENES entry contains the data source name in parentheses, such as (RefSeq) and (GenBank), indicating that the Database name Subject KEGG Cancer Cancer research KEGG Pathogen Infectious diseases, pathogens and antimicrobial resistance KEGG Virus Virus research KEGG Plant Plant research KEGG Glycan Glycobiology research KEGG Annotation KO annotation of genes and proteins KEGG RModule Architecture of metabolic network definition was given by the original database as shown in Figure 1A. KEGG original annotation is given in the following KO subfield, K19188 in this case....

    [...]

  • ...The KEGG organisms category is the main part of GENES consisting of completely or almost completely sequenced genomes taken from RefSeq and GenBank databases....

    [...]

Journal ArticleDOI
TL;DR: The new version of the MPI Bioinformatics Toolkit is introduced, focusing on improved features for the comprehensive analysis of proteins, as well as on promoting teaching.

1,757 citations

Journal ArticleDOI
TL;DR: Improvements make the miRTarBase one of the more comprehensively annotated, experimentally validated miRNA-target interactions databases and motivate additional miRNA research efforts.
Abstract: MicroRNAs (miRNAs) are small non-coding RNAs of approximately 22 nucleotides, which negatively regulate the gene expression at the post-transcriptional level. This study describes an update of the miRTarBase (http://miRTarBase.mbc.nctu.edu.tw/) that provides information about experimentally validated miRNA-target interactions (MTIs). The latest update of the miRTarBase expanded it to identify systematically Argonaute-miRNA-RNA interactions from 138 crosslinking and immunoprecipitation sequencing (CLIP-seq) data sets that were generated by 21 independent studies. The database contains 4966 articles, 7439 strongly validated MTIs (using reporter assays or western blots) and 348 007 MTIs from CLIP-seq. The number of MTIs in the miRTarBase has increased around 7-fold since the 2014 miRTarBase update. The miRNA and gene expression profiles from The Cancer Genome Atlas (TCGA) are integrated to provide an effective overview of this exponential growth in the miRNA experimental data. These improvements make the miRTarBase one of the more comprehensively annotated, experimentally validated miRNA-target interactions databases and motivate additional miRNA research efforts.

1,517 citations

Journal ArticleDOI
TL;DR: An updated database containing 422 517 curated MTIs from 4076 miRNAs and 23 054 target genes collected from over 8500 articles is described, which serves as more comprehensively annotated, experimentally validated miRNA-target interactions databases in the field of miRNA related research.
Abstract: MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 22 nucleotides that are involved in negative regulation of mRNA at the post-transcriptional level. Previously, we developed miRTarBase which provides information about experimentally validated miRNA-target interactions (MTIs). Here, we describe an updated database containing 422 517 curated MTIs from 4076 miRNAs and 23 054 target genes collected from over 8500 articles. The number of MTIs curated by strong evidence has increased ∼1.4-fold since the last update in 2016. In this updated version, target sites validated by reporter assay that are available in the literature can be downloaded. The target site sequence can extract new features for analysis via a machine learning approach which can help to evaluate the performance of miRNA-target prediction tools. Furthermore, different ways of browsing enhance user browsing specific MTIs. With these improvements, miRTarBase serves as more comprehensively annotated, experimentally validated miRNA-target interactions databases in the field of miRNA related research. miRTarBase is available at http://miRTarBase.mbc.nctu.edu.tw/.

1,394 citations

Journal ArticleDOI
TL;DR: By employing an improved algorithm for miRNA target prediction, this work presents updated transcriptome-wide target prediction data in miRDB, including 3.5 million predicted targets regulated by 7000 miRNAs in five species, and implements the new prediction algorithm into a web server allowing custom target prediction with user-provided sequences.
Abstract: MicroRNAs (miRNAs) are small noncoding RNAs that act as master regulators in many biological processes. miRNAs function mainly by downregulating the expression of their gene targets. Thus, accurate prediction of miRNA targets is critical for characterization of miRNA functions. To this end, we have developed an online database, miRDB, for miRNA target prediction and functional annotations. Recently, we have performed major updates for miRDB. Specifically, by employing an improved algorithm for miRNA target prediction, we now present updated transcriptome-wide target prediction data in miRDB, including 3.5 million predicted targets regulated by 7000 miRNAs in five species. Further, we have implemented the new prediction algorithm into a web server, allowing custom target prediction with user-provided sequences. Another new database feature is the prediction of cell-specific miRNA targets. miRDB now hosts the expression profiles of over 1000 cell lines and presents target prediction data that are tailored for specific cell models. At last, a new web query interface has been added to miRDB for prediction of miRNA functions by integrative analysis of target prediction and Gene Ontology data. All data in miRDB are freely accessible at http://mirdb.org.

1,323 citations


Cites methods from "Reference sequence (RefSeq) databas..."

  • ...Specifically, the miRNA sequences were downloaded from miRBase version 22 (3); target transcript sequences were retrieved from the NCBI RefSeq database (12) and further parsed with BioPerl to extract the 3’-UTR sequences....

    [...]

References
More filters
Journal ArticleDOI
Eric S. Lander1, Lauren Linton1, Bruce W. Birren1, Chad Nusbaum1  +245 moreInstitutions (29)
15 Feb 2001-Nature
TL;DR: The results of an international collaboration to produce and make freely available a draft sequence of the human genome are reported and an initial analysis is presented, describing some of the insights that can be gleaned from the sequence.
Abstract: The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.

22,269 citations

Journal ArticleDOI
TL;DR: An update of the miRBase database is described, including the collation and use of deep sequencing data sets to assign levels of confidence to miR base entries, and a high confidence subset of miR Base entries are provided, based on the pattern of mapped reads.
Abstract: We describe an update of the miRBase database (http://www.mirbase.org/), the primary microRNA sequence repository. The latest miRBase release (v20, June 2013) contains 24 521 microRNA loci from 206 species, processed to produce 30 424 mature microRNA products. The rate of deposition of novel microRNAs and the number of researchers involved in their discovery continue to increase, driven largely by small RNA deep sequencing experiments. In the face of these increases, and a range of microRNA annotation methods and criteria, maintaining the quality of the microRNA sequence data set is a significant challenge. Here, we describe recent developments of the miRBase database to address this issue. In particular, we describe the collation and use of deep sequencing data sets to assign levels of confidence to miRBase entries. We now provide a high confidence subset of miRBase entries, based on the pattern of mapped reads. The high confidence microRNA data set is available alongside the complete microRNA collection at http://www.mirbase.org/. We also describe embedding microRNA-specific Wikipedia pages on the miRBase website to encourage the microRNA community to contribute and share textual and functional information.

4,705 citations

Journal ArticleDOI
TL;DR: The National Center for Biotechnology Information Reference Sequence (RefSeq) database provides a non-redundant collection of sequences representing genomic data, transcripts and proteins that pragmatically includes sequence data that are currently publicly available in the archival databases.
Abstract: The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database (http://www.ncbi.nlm.nih.gov/RefSeq/) provides a non-redundant collection of sequences representing genomic data, transcripts and proteins. Although the goal is to provide a comprehensive dataset representing the complete sequence information for any given species, the database pragmatically includes sequence data that are currently publicly available in the archival databases. The database incorporates data from over 2400 organisms and includes over one million proteins representing significant taxonomic diversity spanning prokaryotes, eukaryotes and viruses. Nucleotide and protein sequences are explicitly linked, and the sequences are linked to other resources including the NCBI Map Viewer and Gene. Sequences are annotated to include coding regions, conserved domains, variation, references, names, database cross-references, and other features using a combined approach of collaboration and other input from the scientific community, automated annotation, propagation from GenBank and curation by NCBI staff.

4,229 citations


"Reference sequence (RefSeq) databas..." refers background in this paper

  • ...Curators generally work from lists of genes with data conflicts identified by quality assurance (QA) tests, some of which were previously described (12)....

    [...]

Journal ArticleDOI
TL;DR: Among the regions of the ribosomal cistron, the internal transcribed spacer (ITS) region has the highest probability of successful identification for the broadest range of fungi, with the most clearly defined barcode gap between inter- and intraspecific variation.
Abstract: Six DNA regions were evaluated as potential DNA barcodes for Fungi, the second largest kingdom of eukaryotic life, by a multinational, multilaboratory consortium. The region of the mitochondrial cytochrome c oxidase subunit 1 used as the animal barcode was excluded as a potential marker, because it is difficult to amplify in fungi, often includes large introns, and can be insufficiently variable. Three subunits from the nuclear ribosomal RNA cistron were compared together with regions of three representative protein-coding genes (largest subunit of RNA polymerase II, second largest subunit of RNA polymerase II, and minichromosome maintenance protein). Although the protein-coding gene regions often had a higher percent of correct identification compared with ribosomal markers, low PCR amplification and sequencing success eliminated them as candidates for a universal fungal barcode. Among the regions of the ribosomal cistron, the internal transcribed spacer (ITS) region has the highest probability of successful identification for the broadest range of fungi, with the most clearly defined barcode gap between inter- and intraspecific variation. The nuclear ribosomal large subunit, a popular phylogenetic marker in certain groups, had superior species resolution in some taxonomic groups, such as the early diverging lineages and the ascomycete yeasts, but was otherwise slightly inferior to the ITS. The nuclear ribosomal small subunit has poor species-level resolution in fungi. ITS will be formally proposed for adoption as the primary fungal barcode marker to the Consortium for the Barcode of Life, with the possibility that supplementary barcodes may be developed for particular narrowly circumscribed taxonomic groups.

4,116 citations

Journal ArticleDOI
Alex Bateman, Maria Jesus Martin, Claire O'Donovan, Michele Magrane, Rolf Apweiler, Emanuele Alpi, Ricardo Antunes, Joanna Arganiska, Benoit Bely, Mark Bingley, Carlos Bonilla, Ramona Britto, Borisas Bursteinas, Gayatri Chavali, Elena Cibrian-Uhalte, Alan Wilter Sousa da Silva, Maurizio De Giorgi, Tunca Doğan, Francesco Fazzini, Paul Gane, Leyla Jael Garcia Castro, Penelope Garmiri, Emma Hatton-Ellis, Reija Hieta, Rachael P. Huntley, Duncan Legge, W Liu, Jie Luo, Alistair MacDougall, Prudence Mutowo, Andrew Nightingale, Sandra Orchard, Klemens Pichler, Diego Poggioli, Sangya Pundir, Luis Pureza, Guoying Qi, Steven Rosanoff, Rabie Saidi, Tony Sawford, Aleksandra Shypitsyna, Edward Turner, Vladimir Volynkin, Tony Wardell, Xavier Watkins, Hermann Zellner, Andrew Peter Cowley, Luis Figueira, Weizhong Li, Hamish McWilliam, Rodrigo Lopez, Ioannis Xenarios, Lydie Bougueleret, Alan Bridge, Sylvain Poux, Nicole Redaschi, Lucila Aimo, Ghislaine Argoud-Puy, Andrea H. Auchincloss, Kristian B. Axelsen, Parit Bansal, Delphine Baratin, Marie Claude Blatter, Brigitte Boeckmann, Jerven Bolleman, Emmanuel Boutet, Lionel Breuza, Cristina Casal-Casas, Edouard de Castro, Elisabeth Coudert, Béatrice A. Cuche, M Doche, Dolnide Dornevil, Séverine Duvaud, Anne Estreicher, L Famiglietti, Marc Feuermann, Elisabeth Gasteiger, Sebastien Gehant, Vivienne Baillie Gerritsen, Arnaud Gos, Nadine Gruaz-Gumowski, Ursula Hinz, Chantal Hulo, Florence Jungo, Guillaume Keller, Vicente Lara, P Lemercier, Damien Lieberherr, Thierry Lombardot, Xavier D. Martin, Patrick Masson, Anne Morgat, Teresa Batista Neto, Nevila Nouspikel, Salvo Paesano, Ivo Pedruzzi, Sandrine Pilbout, Monica Pozzato, Manuela Pruess, Catherine Rivoire, Bernd Roechert, Michel Schneider, Christian J. A. Sigrist, K Sonesson, S Staehli, Andre Stutz, Shyamala Sundaram, Michael Tognolli, Laure Verbregue, Anne Lise Veuthey, Cathy H. Wu, Cecilia N. Arighi, Leslie Arminski, Chuming Chen, Yongxing Chen, John S. Garavelli, Hongzhan Huang, Kati Laiho, Peter B. McGarvey, Darren A. Natale, Baris E. Suzek, C. R. Vinayaka, Qinghua Wang, Yuqi Wang, Lai-Su L. Yeh, Meher Shruti Yerramalla, Jian Zhang 
TL;DR: An annotation score for all entries in UniProt is introduced to represent the relative amount of knowledge known about each protein to help identify which proteins are the best characterized and most informative for comparative analysis.
Abstract: UniProt is an important collection of protein sequences and their annotations, which has doubled in size to 80 million sequences during the past year. This growth in sequences has prompted an extension of UniProt accession number space from 6 to 10 characters. An increasing fraction of new sequences are identical to a sequence that already exists in the database with the majority of sequences coming from genome sequencing projects. We have created a new proteome identifier that uniquely identifies a particular assembly of a species and strain or subspecies to help users track the provenance of sequences. We present a new website that has been designed using a user-experience design process. We have introduced an annotation score for all entries in UniProt to represent the relative amount of knowledge known about each protein. These scores will be helpful in identifying which proteins are the best characterized and most informative for comparative analysis. All UniProt data is provided freely and is available on the web at http://www.uniprot.org/.

4,050 citations