Web-Based Bioinformatics Applications in Proteomics
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Zebrafish Disease Models to Study the Pathogenesis of Inherited Manganese Transporter Defects and Provide A
Zebrafish disease models to study the pathogenesis of inherited manganese transporter defects and provide a route for drug discovery Dr Karin Tuschl University College London PhD Supervisors: Dr Philippa Mills & Prof Stephen Wilson A thesis submitted for the degree of Doctor of Philosophy University College London August 2016 Declaration I, Karin Tuschl, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Part of the work of this thesis has been published in the following articles for which copyright clearance has been obtained (see Appendix): - Tuschl K, et al. Manganese and the brain. Int Rev Neurobiol. 2013. 110:277- 312. - Tuschl K, et al. Mutations in SLC39A14 disrupt manganese homeostasis and cause childhood-onset parkinsonism-dystonia. Nat Comms. 2016. 7:11601. I confirm that these publications were written by me and may therefore partly overlap with my thesis. 2 Abstract Although manganese is required as an essential trace element excessive amounts are neurotoxic and lead to manganism, an extrapyramidal movement disorder associated with deposition of manganese in the basal ganglia. Recently, we have identified the first inborn error of manganese metabolism caused by mutations in SLC30A10, encoding a manganese transporter facilitating biliary manganese excretion. Treatment is limited to chelation therapy with intravenous disodium calcium edetate which is burdensome due to its route of administration and associated with high socioeconomic costs. Whole exome sequencing in patients with inherited hypermanganesaemia and early- onset parkinsonism-dystonia but absent SLC30A10 mutations identified SLC39A14 as a novel disease gene associated with manganese dyshomeostasis. -
Glycomics Goes Visual and Interactive
Glycomics & Lipidomics Extended Abstract Glycomics goes visual and interactive Alessandra Gastaldello structures attached to each of these sites. Mass spectrometry Abstract (MS) and microarray are high-throughput technologies that are commonly used in glycomics and glycoproteomics, which often result in the generation of large experimental datasets. Glycomics@ExPASy the glycomics tab of the Swiss Institute of Bioinformatics approaches play an essential role in automated Bioinformatics server (www.expasy.org/glycomics) was created analysis and interpretation of such data. This unit describes in 2016 to centralise web-based glycoinformatics resources and discusses the computational tools currently available for developed within an international network of glycoscientists. these analyses, and their glycomics and glycoproteomics The philosophy of this toolbox is to be {glycoscientist AND applications. protein scientist}???friendly with the aim of popularising (a) the use of bioinformatics in glycobiology and (b) the relation A key point in achieving accurate intact glycopeptide between glycobiology and protein-oriented bioinformatics identification is the definition of the glycan composition file resources. The scarcity of bridging data led us to design tools that is used to match experimental with theoretical masses by a as interactive as possible based on database connectivity in glycoproteomics search engine. At present, these files are order to facilitate data exploration and support hypothesis mainly built from searching the literature and/or querying building. The current set of resources is mostly built on top of data sources focused on posttranslational modifications. Most curated or experimental data relative to glycan structures, glycoproteomics search engines include a default composition glycoproteins, host-pathogen interactions and mass file that is readily used when processing MS data. -
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. -
Pathogenicity and Selective Constraint on Variation Near Splice Sites
Downloaded from genome.cshlp.org on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press 1 Pathogenicity and selective constraint on variation near 2 splice sites 3 AUTHORS 4 Jenny Lord1, Giuseppe Gallone1, Patrick J. Short1, Jeremy F. McRae1, Holly Ironfield1, Elizabeth H. 5 Wynn1, Sebastian S. Gerety1, Liu He1, Bronwyn Kerr2,3, Diana S. Johnson4, Emma McCann5, Esther 6 Kinning6, Frances Flinter7, I. Karen Temple8,9 , Jill Clayton-Smith2,3, Meriel McEntagart10, Sally Ann 7 Lynch11, Shelagh Joss12, Sofia Douzgou2,3, Tabib Dabir13, Virginia Clowes14, Vivienne P. M. 8 McConnell13, Wayne Lam15, Caroline F. Wright16, David R. FitzPatrick1,15, Helen V. Firth1,17, Jeffrey 9 C. Barrett1, Matthew E. Hurles1, on behalf of the Deciphering Developmental Disorders study 10 AFFILIATIONS 11 1 Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK 12 2Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University Hospitals NHS 13 Foundation Trust Manchester Academic Health Sciences Centre 14 3Division of Evolution and Genomic Sciences School of Biological Sciences University of Manchester 15 4Sheffield Clinical Genetics Service, Sheffield Children's Hospital, OPD2, Northern General Hospital, 16 Herries Road, Sheffield, S5 7AU 17 5Liverpool Women’s Hospital Foundation Trust, Crown Street, Liverpool, L8 7SS 18 6West of Scotland Regional Genetics Service, NHS Greater Glasgow and Clyde, Institute of Medical 19 Genetics, Yorkhill Hospital, Glasgow G3 8SJ, UK 20 7South East Thames Regional Genetics -
Impact of the Protein Data Bank Across Scientific Disciplines.Data Science Journal, 19: 25, Pp
Feng, Z, et al. 2020. Impact of the Protein Data Bank Across Scientific Disciplines. Data Science Journal, 19: 25, pp. 1–14. DOI: https://doi.org/10.5334/dsj-2020-025 RESEARCH PAPER Impact of the Protein Data Bank Across Scientific Disciplines Zukang Feng1,2, Natalie Verdiguel3, Luigi Di Costanzo1,4, David S. Goodsell1,5, John D. Westbrook1,2, Stephen K. Burley1,2,6,7,8 and Christine Zardecki1,2 1 Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ, US 2 Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, US 3 University of Central Florida, Orlando, Florida, US 4 Department of Agricultural Sciences, University of Naples Federico II, Portici, IT 5 Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, US 6 Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, US 7 Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, US 8 Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, US Corresponding author: Christine Zardecki ([email protected]) The Protein Data Bank archive (PDB) was established in 1971 as the 1st open access digital data resource for biology and medicine. Today, the PDB contains >160,000 atomic-level, experimentally-determined 3D biomolecular structures. PDB data are freely and publicly available for download, without restrictions. Each entry contains summary information about the structure and experiment, atomic coordinates, and in most cases, a citation to a corresponding scien- tific publication. -
Viroinformatics Investigation of B-Cell Epitope Conserved Region in SARS
© 2021 Journal of Pharmacy & Pharmacognosy Research, 9 (6), 766-779, 2021 ISSN 0719-4250 http://jppres.com/jppres Original Article Viroinformatics investigation of B-cell epitope conserved region in SARS- CoV-2 lineage B.1.1.7 isolates originated from Indonesia to develop vaccine candidate against COVID-19 [Investigación viroinformática de la región conservada del epítopo de células B en el linaje SARS-CoV-2 B.1.1.7 aislamientos originados en Indonesia para desarrollar una vacuna candidata contra COVID-19] Arif N. M. Ansori1,2#, Reviany V. Nidom1,3*#, Muhammad K. J. Kusala1,2, Setyarina Indrasari1,3, Irine Normalina1,4, Astria N. Nidom1,3, Balqis Afifah1,3, Kartika B. Sari1,5, Nor L. Ramadhaniyah1,5, Mohammad Y. Alamudi1,3, Umi Cahyaningsih6, Kuncoro P. Santoso1,2, Heri Kuswanto5, Chairul A. Nidom1,2,3* 1Coronavirus and Vaccine Formulation Research Group, Professor Nidom Foundation, Surabaya, Indonesia. 2Faculty of Veterinary Medicine, Universitas Airlangga, Surabaya, Indonesia. 3Riset AIRC Indonesia, Surabaya, Indonesia. 4Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia. 5Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia. 6Faculty of Veterinary Medicine, IPB University, Bogor, Indonesia. #Both authors contributed equally. *E-mail: [email protected], [email protected], [email protected] Abstract Resumen Context: SARS-CoV-2, a member of family Coronaviridae and the Contexto: SARS-CoV-2, un miembro de la familia Coronaviridae y el causative agent of COVID-19, -
Bioinformatics Exercises: Bovine Lactate Dehydrogenase (LDH)
CH/BI 421/621/527 F15 Bioinformatics Worksheet for LDH Bioinformatics Exercises: Bovine Lactate Dehydrogenase (LDH) BACKGROUND: Often primary structure (amino acid sequence) is the first piece of experimental information a biochemist wants to have about a protein s/he is interested in studying since it can be used to make several predictions about the properties and possible behavior of the protein such as: • Protein molecular weight by adding up the masses of the individual amino acid residues. • Isoelectric point. The isoelectric point is where the protein has no charge. Because of ionizable functional groups on amino acids, protein charge changes as a function of pH depending on whether or not these groups are protonated. By knowing the sequence, we know how many of each ionizable group our protein contains. If we know the pH range where these groups become protonated or deprotonated, we can estimate the charge of the whole protein as a function of pH. This will be discussed in more detail below. • Molar extinction coefficient. Tryptophan, Tyrosine and Cysteine residues absorb ultraviolet light at 280 nm. By knowing how many of these amino acids are found in our protein’s sequence, we can calculate how much we expect a solution of our protein to absorb 280 nm light as a function of its concentration. I say “expect” instead of “determine” because the amount of light absorbed by these amino acids is dependent on their local environment within the protein especially on whether they are on the surface and exposed to the solution or buried inside the protein. -
Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases
Genomics Proteomics Bioinformatics 18 (2020) 91–103 Genomics Proteomics Bioinformatics www.elsevier.com/locate/gpb www.sciencedirect.com PERSPECTIVE Quality Matters: Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases Qingyu Chen 1,*, Ramona Britto 2, Ivan Erill 3, Constance J. Jeffery 4, Arthur Liberzon 5, Michele Magrane 2, Jun-ichi Onami 6,7, Marc Robinson-Rechavi 8,9, Jana Sponarova 10, Justin Zobel 1,*, Karin Verspoor 1,* 1 School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia 2 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK 3 Department of Biological Sciences, University of Maryland, Baltimore, MD 21250, USA 4 Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607, USA 5 Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA 6 Japan Science and Technology Agency, National Bioscience Database Center, Tokyo 102-8666, Japan 7 National Institute of Health Sciences, Tokyo 158-8501, Japan 8 Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland 9 Department of Ecology and Evolution, University of Lausanne, CH-1015 Lausanne, Switzerland 10 Nebion AG, 8048 Zurich, Switzerland Received 8 December 2017; revised 24 October 2018; accepted 14 December 2018 Available online 9 July 2020 Handled by Zhang Zhang Introduction assembled, annotated, and ultimately submitted to primary nucleotide databases such as GenBank [2], European Nucleo- tide Archive (ENA) [3], and DNA Data Bank of Japan Biological databases represent an extraordinary collective vol- (DDBJ) [4] (collectively known as the International Nucleotide ume of work. -
Plant Protein Annotation in the Uniprot Knowledgebase
Article Plant protein annotation in the UniProt Knowledgebase SCHNEIDER, Michel, et al. Abstract The Swiss-Prot, TrEMBL, Protein Information Resource (PIR), and DNA Data Bank of Japan (DDBJ) protein database activities have united to form the Universal Protein Resource (UniProt) Consortium. UniProt presents three database layers: the UniProt Archive, the UniProt Knowledgebase (UniProtKB), and the UniProt Reference Clusters. The UniProtKB consists of two sections: UniProtKB/Swiss-Prot (fully manually curated entries) and UniProtKB/TrEMBL (automated annotation, classification and extensive cross-references). New releases are published fortnightly. A specific Plant Proteome Annotation Program (http://www.expasy.org/sprot/ppap/) was initiated to cope with the increasing amount of data produced by the complete sequencing of plant genomes. Through UniProt, our aim is to provide the scientific community with a single, centralized, authoritative resource for protein sequences and functional information that will allow the plant community to fully explore and utilize the wealth of information available for both plant and non-plant model organisms. Reference SCHNEIDER, Michel, et al. Plant protein annotation in the UniProt Knowledgebase. Plant Physiology, 2005, vol. 138, no. 1, p. 59-66 DOI : 10.1104/pp.104.058933 PMID : 15888679 Available at: http://archive-ouverte.unige.ch/unige:38249 Disclaimer: layout of this document may differ from the published version. 1 / 1 Bioinformatics Plant Protein Annotation in the UniProt Knowledgebase1 Michel Schneider*, -
Pdbefold Tutorial Tutorial Pdbefold Can May Be Accessed from Multiple Locations on the Pdbe Website
PDBe TUTORIAL PDBeFold (SSM: Secondary Structure Matching) http://pdbe.org/fold/ This PDBe tutorial introduces PDBeFold, an interactive service for comparing protein structures in 3D. This service provides: . Pairwise and multiple comparison and 3D alignment of protein structures . Examination of a protein structure for similarity with the whole Protein Data Bank (PDB) archive or SCOP. Best C -alignment of compared structures . Download and visualisation of best-superposed structures using various graphical packages PDBeFold structure alignment is based on identification of residues occupying “equivalent” geometrical positions. In other words, unlike sequence alignment, residue type is neglected. The PDBeFold service is a very powerful structure alignment tool which can perform both pairwise and multiple three dimensional alignment. In addition to this there are various options by which the results of the structural alignment query can be sorted. The results of the Secondary Structure Matching can be sorted based on the Q score (Cα- alignment), P score (taking into account RMSD, number of aligned residues, number of gaps, number of matched Secondary Structure Elements and the SSE match score), Z score (based on Gaussian Statistics), RMSD and % Sequence Identity. It is hoped that at the end of this tutorial users will be able to use PDbeFold for the analysis of their own uploaded structures or entries already in the PDB archive. Protein Data Bank in Europe http://pdbe.org PDBeFOLD Tutorial Tutorial PDBeFold can may be accessed from multiple locations on the PDBe website. From the PDBe home page (http://pdbe.org/), there are two access points for the program as shown below. -
Europe PMC Funders Group Author Manuscript Nature
Europe PMC Funders Group Author Manuscript Nature. Author manuscript; available in PMC 2009 July 31. Published in final edited form as: Nature. 2007 June 7; 447(7145): 661–678. doi:10.1038/nature05911. Europe PMC Funders Author Manuscripts Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls The Wellcome Trust Case Control Consortium* Abstract There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study (using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined ~2,000 individuals for each of 7 major diseases and a shared set of ~3,000 controls. Case-control comparisons identified 24 independent association signals at P<5×10-7: 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn’s disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals (including 58 loci with single-point P values between 10-5 and 5×10-7) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. -
EMBL-EBI-Overview.Pdf
EMBL-EBI Overview EMBL-EBI Overview Welcome Welcome to the European Bioinformatics Institute (EMBL-EBI), a global hub for big data in biology. We promote scientific progress by providing freely available data to the life-science research community, and by conducting exceptional research in computational biology. At EMBL-EBI, we manage public life-science data on a very large scale, offering a rich resource of carefully curated information. We make our data, tools and infrastructure openly available to an increasingly data-driven scientific community, adjusting to the changing needs of our users, researchers, trainees and industry partners. This proactive approach allows us to deliver relevant, up-to-date data and tools to the millions of scientists who depend on our services. We are a founding member of ELIXIR, the European infrastructure for biological information, and are central to global efforts to exchange information, set standards, develop new methods and curate complex information. Our core databases are produced in collaboration with other world leaders including the National Center for Biotechnology Information in the US, the National Institute of Genetics in Japan, SIB Swiss Institute of Bioinformatics and the Wellcome Trust Sanger Institute in the UK. We are also a world leader in computational biology research, and are well integrated with experimental and computational groups on all EMBL sites. Our research programme is highly collaborative and interdisciplinary, regularly producing high-impact works on sequence and structural alignment, genome analysis, basic biological breakthroughs, algorithms and methods of widespread importance. EMBL-EBI is an international treaty organisation, and we serve the global scientific community.