Uniprot.Ws: R Interface to Uniprot Web Services
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Uniprot at EMBL-EBI's Role in CTTV
Barbara P. Palka, Daniel Gonzalez, Edd Turner, Xavier Watkins, Maria J. Martin, Claire O’Donovan European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK UniProt at EMBL-EBI’s role in CTTV: contributing to improved disease knowledge Introduction The mission of UniProt is to provide the scientific community with a The Centre for Therapeutic Target Validation (CTTV) comprehensive, high quality and freely accessible resource of launched in Dec 2015 a new web platform for life- protein sequence and functional information. science researchers that helps them identify The UniProt Knowledgebase (UniProtKB) is the central hub for the collection of therapeutic targets for new and repurposed medicines. functional information on proteins, with accurate, consistent and rich CTTV is a public-private initiative to generate evidence on the annotation. As much annotation information as possible is added to each validity of therapeutic targets based on genome-scale experiments UniProtKB record and this includes widely accepted biological ontologies, and analysis. CTTV is working to create an R&D framework that classifications and cross-references, and clear indications of the quality of applies to a wide range of human diseases, and is committed to annotation in the form of evidence attribution of experimental and sharing its data openly with the scientific community. CTTV brings computational data. together expertise from four complementary institutions: GSK, Biogen, EMBL-EBI and Wellcome Trust Sanger Institute. UniProt’s disease expert curation Q5VWK5 (IL23R_HUMAN) This section provides information on the disease(s) associated with genetic variations in a given protein. The information is extracted from the scientific literature and diseases that are also described in the OMIM database are represented with a controlled vocabulary. -
Original Article Text Mining in the Biocuration Workflow: Applications for Literature Curation at Wormbase, Dictybase and TAIR
Database, Vol. 2012, Article ID bas040, doi:10.1093/database/bas040 ............................................................................................................................................................................................................................................................................................. Original article Text mining in the biocuration workflow: applications for literature curation at WormBase, dictyBase and TAIR Kimberly Van Auken1,*, Petra Fey2, Tanya Z. Berardini3, Robert Dodson2, Laurel Cooper4, Donghui Li3, Juancarlos Chan1, Yuling Li1, Siddhartha Basu2, Hans-Michael Muller1, Downloaded from Rex Chisholm2, Eva Huala3, Paul W. Sternberg1,5 and the WormBase Consortium 1Division of Biology, California Institute of Technology, 1200 E. California Boulevard, Pasadena, CA 91125, 2Northwestern University Biomedical Informatics Center and Center for Genetic Medicine, 420 E. Superior Street, Chicago, IL 60611, 3Department of Plant Biology, Carnegie Institution, 260 Panama Street, Stanford, CA 94305, 4Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331 and 5Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Boulevard, Pasadena, CA 91125, USA http://database.oxfordjournals.org/ *Corresponding author: Tel: +1 609 937 1635; Fax: +1 626 568 8012; Email: [email protected] Submitted 18 June 2012; Revised 30 September 2012; Accepted 2 October 2012 ............................................................................................................................................................................................................................................................................................ -
The ELIXIR Core Data Resources: Fundamental Infrastructure for The
Supplementary Data: The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences The “Supporting Material” referred to within this Supplementary Data can be found in the Supporting.Material.CDR.infrastructure file, DOI: 10.5281/zenodo.2625247 (https://zenodo.org/record/2625247). Figure 1. Scale of the Core Data Resources Table S1. Data from which Figure 1 is derived: Year 2013 2014 2015 2016 2017 Data entries 765881651 997794559 1726529931 1853429002 2715599247 Monthly user/IP addresses 1700660 2109586 2413724 2502617 2867265 FTEs 270 292.65 295.65 289.7 311.2 Figure 1 includes data from the following Core Data Resources: ArrayExpress, BRENDA, CATH, ChEBI, ChEMBL, EGA, ENA, Ensembl, Ensembl Genomes, EuropePMC, HPA, IntAct /MINT , InterPro, PDBe, PRIDE, SILVA, STRING, UniProt ● Note that Ensembl’s compute infrastructure physically relocated in 2016, so “Users/IP address” data are not available for that year. In this case, the 2015 numbers were rolled forward to 2016. ● Note that STRING makes only minor releases in 2014 and 2016, in that the interactions are re-computed, but the number of “Data entries” remains unchanged. The major releases that change the number of “Data entries” happened in 2013 and 2015. So, for “Data entries” , the number for 2013 was rolled forward to 2014, and the number for 2015 was rolled forward to 2016. The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences 1 Figure 2: Usage of Core Data Resources in research The following steps were taken: 1. API calls were run on open access full text articles in Europe PMC to identify articles that mention Core Data Resource by name or include specific data record accession numbers. -
Dual Proteome-Scale Networks Reveal Cell-Specific Remodeling of the Human Interactome
bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome Edward L. Huttlin1*, Raphael J. Bruckner1,3, Jose Navarrete-Perea1, Joe R. Cannon1,4, Kurt Baltier1,5, Fana Gebreab1, Melanie P. Gygi1, Alexandra Thornock1, Gabriela Zarraga1,6, Stanley Tam1,7, John Szpyt1, Alexandra Panov1, Hannah Parzen1,8, Sipei Fu1, Arvene Golbazi1, Eila Maenpaa1, Keegan Stricker1, Sanjukta Guha Thakurta1, Ramin Rad1, Joshua Pan2, David P. Nusinow1, Joao A. Paulo1, Devin K. Schweppe1, Laura Pontano Vaites1, J. Wade Harper1*, Steven P. Gygi1*# 1Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA. 2Broad Institute, Cambridge, MA, 02142, USA. 3Present address: ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, 02115, USA. 4Present address: Merck, West Point, PA, 19486, USA. 5Present address: IQ Proteomics, Cambridge, MA, 02139, USA. 6Present address: Vor Biopharma, Cambridge, MA, 02142, USA. 7Present address: Rubius Therapeutics, Cambridge, MA, 02139, USA. 8Present address: RPS North America, South Kingstown, RI, 02879, USA. *Correspondence: [email protected] (E.L.H.), [email protected] (J.W.H.), [email protected] (S.P.G.) #Lead Contact: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. -
Bioinformatics Study of Lectins: New Classification and Prediction In
Bioinformatics study of lectins : new classification and prediction in genomes François Bonnardel To cite this version: François Bonnardel. Bioinformatics study of lectins : new classification and prediction in genomes. Structural Biology [q-bio.BM]. Université Grenoble Alpes [2020-..]; Université de Genève, 2021. En- glish. NNT : 2021GRALV010. tel-03331649 HAL Id: tel-03331649 https://tel.archives-ouvertes.fr/tel-03331649 Submitted on 2 Sep 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE Pour obtenir le grade de DOCTEUR DE L’UNIVERSITE GRENOBLE ALPES préparée dans le cadre d’une cotutelle entre la Communauté Université Grenoble Alpes et l’Université de Genève Spécialités: Chimie Biologie Arrêté ministériel : le 6 janvier 2005 – 25 mai 2016 Présentée par François Bonnardel Thèse dirigée par la Dr. Anne Imberty codirigée par la Dr/Prof. Frédérique Lisacek préparée au sein du laboratoire CERMAV, CNRS et du Computer Science Department, UNIGE et de l’équipe PIG, SIB Dans les Écoles Doctorales EDCSV et UNIGE Etude bioinformatique des lectines: nouvelle classification et prédiction dans les génomes Thèse soutenue publiquement le 8 Février 2021, devant le jury composé de : Dr. Alexandre de Brevern UMR S1134, Inserm, Université Paris Diderot, Paris, France, Rapporteur Dr. -
What Remains to Be Discovered in the Eukaryotic Proteome?
bioRxiv preprint doi: https://doi.org/10.1101/469569; this version posted November 16, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Hidden in plain sight: What remains to be discovered in the eukaryotic proteome? Valerie Wood∗1,2, Antonia Lock3, Midori A. Harris1,2, Kim Rutherford1,2, J¨urgB¨ahler3, and Stephen G. Oliver1,2 1Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK 2Department of Biochemistry, University of Cambridge, Cambridge, UK 3Department of Genetics, Evolution and Environment, University College London, London, UK November 12, 2018 Abstract The first decade of genome sequencing stimulated an explosion in the charac- terization of unknown proteins. More recently, the pace of functional discovery has slowed, leaving around 20% of the proteins even in well-studied model or- ganisms without informative descriptions of their biological roles. Remarkably, many uncharacterized proteins are conserved from yeasts to human, suggesting that they contribute to fundamental biological processes. To fully understand biological systems in health and disease, we need to account for every part of the system. Unstudied proteins thus represent a collective blind spot that limits the progress of both basic and applied biosciences. We use a simple yet powerful metric based on Gene Ontology (GO) bio- logical process terms to define characterized and uncharacterized proteins for human, budding yeast, and fission yeast. We then identify a set of conserved but unstudied proteins in S. -
Sequence Motifs, Correlations and Structural Mapping of Evolutionary
Talk overview • Sequence profiles – position specific scoring matrix • Psi-blast. Automated way to create and use sequence Sequence motifs, correlations profiles in similarity searches and structural mapping of • Sequence patterns and sequence logos evolutionary data • Bioinformatic tools which employ sequence profiles: PFAM BLOCKS PROSITE PRINTS InterPro • Correlated Mutations and structural insight • Mapping sequence data on structures: March 2011 Eran Eyal Conservations Correlations PSSM – position specific scoring matrix • A position-specific scoring matrix (PSSM) is a commonly used representation of motifs (patterns) in biological sequences • PSSM enables us to represent multiple sequence alignments as mathematical entities which we can work with. • PSSMs enables the scoring of multiple alignments with sequences, or other PSSMs. PSSM – position specific scoring matrix Assuming a string S of length n S = s1s2s3...sn If we want to score this string against our PSSM of length n (with n lines): n alignment _ score = m ∑ s j , j j=1 where m is the PSSM matrix and sj are the string elements. PSSM can also be incorporated to both dynamic programming algorithms and heuristic algorithms (like Psi-Blast). Sequence space PSI-BLAST • For a query sequence use Blast to find matching sequences. • Construct a multiple sequence alignment from the hits to find the common regions (consensus). • Use the “consensus” to search again the database, and get a new set of matching sequences • Repeat the process ! Sequence space Position-Specific-Iterated-BLAST • Intuition – substitution matrices should be specific to sites and not global. – Example: penalize alanine→glycine more in a helix •Idea – Use BLAST with high stringency to get a set of closely related sequences. -
A Beginner's Guide to Eukaryotic Genome Annotation
REVIEWS STUDY DESIGNS A beginner’s guide to eukaryotic genome annotation Mark Yandell and Daniel Ence Abstract | The falling cost of genome sequencing is having a marked impact on the research community with respect to which genomes are sequenced and how and where they are annotated. Genome annotation projects have generally become small-scale affairs that are often carried out by an individual laboratory. Although annotating a eukaryotic genome assembly is now within the reach of non-experts, it remains a challenging task. Here we provide an overview of the genome annotation process and the available tools and describe some best-practice approaches. Genome annotation Sequencing costs have fallen so dramatically that a sin- with some basic UNIX skills, ‘do-it-yourself’ genome A term used to describe two gle laboratory can now afford to sequence large, even annotation projects are quite feasible using present- distinct processes. ‘Structural’ human-sized, genomes. Ironically, although sequencing day tools. Here we provide an overview of the eukary- genome annotation is the has become easy, in many ways, genome annotation has otic genome annotation process, describe the available process of identifying genes and their intron–exon become more challenging. Several factors are respon- toolsets and outline some best-practice approaches. structures. ‘Functional’ genome sible for this. First, the shorter read lengths of second- annotation is the process of generation sequencing platforms mean that current Assembly and annotation: an overview attaching meta-data such as genome assemblies rarely attain the contiguity of the Assembly. The first step towards the successful annota- gene ontology terms to classic shotgun assemblies of the Drosophila mela- tion of any genome is determining whether its assem- structural annotations. -
UC Davis UC Davis Previously Published Works
UC Davis UC Davis Previously Published Works Title Longer first introns are a general property of eukaryotic gene structure. Permalink https://escholarship.org/uc/item/9j42z8fm Journal PloS one, 3(8) ISSN 1932-6203 Authors Bradnam, Keith R Korf, Ian Publication Date 2008-08-29 DOI 10.1371/journal.pone.0003093 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Longer First Introns Are a General Property of Eukaryotic Gene Structure Keith R. Bradnam*, Ian Korf Genome Center, University of California Davis, Davis, California, United States of America Abstract While many properties of eukaryotic gene structure are well characterized, differences in the form and function of introns that occur at different positions within a transcript are less well understood. In particular, the dynamics of intron length variation with respect to intron position has received relatively little attention. This study analyzes all available data on intron lengths in GenBank and finds a significant trend of increased length in first introns throughout a wide range of species. This trend was found to be even stronger when using high-confidence gene annotation data for three model organisms (Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster) which show that the first intron in the 59 UTR is - on average - significantly longer than all downstream introns within a gene. A partial explanation for increased first intron length in A. thaliana is suggested by the increased frequency of certain motifs that are present in first introns. The phenomenon of longer first introns can potentially be used to improve gene prediction software and also to detect errors in existing gene annotations. -
NCBI Databases
Jon K. Lærdahl, Structural Bioinforma�cs NCBI databases Read this ar�cle! Nucleic Acids 43 Res. , D6 (2015) Jon K. Lærdahl, EMBL-‐EBI databases Structural Bioinforma�cs European Nucleo�de Archive (ENA) nucleo�de sequence database Ensembl -‐ automa�c and manually curated annota�on on selected eukaryo�c (vertebrate) genomes Ensembl Genomes – Ensembl for “all other organisms” UniProt – protein sequence and func�onal informa�on ChEMBL – database of bioac�ve compounds IntAct -‐ repository of molecular interac�ons, including protein-‐protein, protein-‐small molecule and protein-‐nucleic acid interac�ons CiteXplore – 25 million literature abstracts including PubMed, Agricola & patents Gene Ontology (GO) -‐ controlled vocabulary to describe gene and gene product a�ributes in any organism Gene Ontology Annota�on (GOA) – GO annota�ons for proteins in UniProt All data is publicly available Jon K. Lærdahl, Structural Bioinforma�cs GenBank a comprehensive public database of nucleo�de sequences and suppor�ng bibliographic and biological annota�on all publicly available DNA sequences submissions from authors – web-‐based BankIt – standalone program Sequin submissions from EST and other high-‐throughput sequencing projects daily exchange of data with ENA and DNA Data Bank of Japan (DDBJ) – all sequences submi�ed to DDBJ, ENA, or GenBank will end up in all 3 databases within few days Jon K. Lærdahl, Structural Bioinforma�cs INSDC Jon K. Lærdahl, Structural Bioinforma�cs Sequin – for submi�ng to GenBank BankIt is web-‐ based alterna�ve Link to “Create a submission” Jon K. Lærdahl, Structural Bioinforma�cs Entry in GenBank format Oct 2015: 202,237,081,559 bases in 188,372,017 sequence records in the tradi�onal GenBank divisions 1,222,635,267,498 bases in 309,198,943 sequence records in the WGS Jon K. -
Webnetcoffee
Hu et al. BMC Bioinformatics (2018) 19:422 https://doi.org/10.1186/s12859-018-2443-4 SOFTWARE Open Access WebNetCoffee: a web-based application to identify functionally conserved proteins from Multiple PPI networks Jialu Hu1,2, Yiqun Gao1, Junhao He1, Yan Zheng1 and Xuequn Shang1* Abstract Background: The discovery of functionally conserved proteins is a tough and important task in system biology. Global network alignment provides a systematic framework to search for these proteins from multiple protein-protein interaction (PPI) networks. Although there exist many web servers for network alignment, no one allows to perform global multiple network alignment tasks on users’ test datasets. Results: Here, we developed a web server WebNetcoffee based on the algorithm of NetCoffee to search for a global network alignment from multiple networks. To build a series of online test datasets, we manually collected 218,339 proteins, 4,009,541 interactions and many other associated protein annotations from several public databases. All these datasets and alignment results are available for download, which can support users to perform algorithm comparison and downstream analyses. Conclusion: WebNetCoffee provides a versatile, interactive and user-friendly interface for easily running alignment tasks on both online datasets and users’ test datasets, managing submitted jobs and visualizing the alignment results through a web browser. Additionally, our web server also facilitates graphical visualization of induced subnetworks for a given protein and its neighborhood. To the best of our knowledge, it is the first web server that facilitates the performing of global alignment for multiple PPI networks. Availability: http://www.nwpu-bioinformatics.com/WebNetCoffee Keywords: Multiple network alignment, Webserver, PPI networks, Protein databases, Gene ontology Background tools [7–10] have been developed to understand molec- Proteins are involved in almost all life processes. -
Downloaded from the National Center for Cide Resistance Mechanisms
Zhou et al. Parasites & Vectors (2018) 11:32 DOI 10.1186/s13071-017-2584-8 RESEARCH Open Access ASGDB: a specialised genomic resource for interpreting Anopheles sinensis insecticide resistance Dan Zhou, Yang Xu, Cheng Zhang, Meng-Xue Hu, Yun Huang, Yan Sun, Lei Ma, Bo Shen* and Chang-Liang Zhu Abstract Background: Anopheles sinensis is an important malaria vector in Southeast Asia. The widespread emergence of insecticide resistance in this mosquito species poses a serious threat to the efficacy of malaria control measures, particularly in China. Recently, the whole-genome sequencing and de novo assembly of An. sinensis (China strain) has been finished. A series of insecticide-resistant studies in An. sinensis have also been reported. There is a growing need to integrate these valuable data to provide a comprehensive database for further studies on insecticide-resistant management of An. sinensis. Results: A bioinformatics database named An. sinensis genome database (ASGDB) was built. In addition to being a searchable database of published An. sinensis genome sequences and annotation, ASGDB provides in-depth analytical platforms for further understanding of the genomic and genetic data, including visualization of genomic data, orthologous relationship analysis, GO analysis, pathway analysis, expression analysis and resistance-related gene analysis. Moreover, ASGDB provides a panoramic view of insecticide resistance studies in An. sinensis in China. In total, 551 insecticide-resistant phenotypic and genotypic reports on An. sinensis distributed in Chinese malaria- endemic areas since the mid-1980s have been collected, manually edited in the same format and integrated into OpenLayers map-based interface, which allows the international community to assess and exploit the high volume of scattered data much easier.