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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. -
Toolboxes for a Standardised and Systematic Study of Glycans
Campbell et al. BMC Bioinformatics 2014, 15(Suppl 1):S9 http://www.biomedcentral.com/1471-2105/15/S1/S9 RESEARCH Open Access Toolboxes for a standardised and systematic study of glycans Matthew P Campbell1, René Ranzinger2, Thomas Lütteke3, Julien Mariethoz4, Catherine A Hayes5, Jingyu Zhang1, Yukie Akune6, Kiyoko F Aoki-Kinoshita6, David Damerell7,11, Giorgio Carta8, Will S York2, Stuart M Haslam7, Hisashi Narimatsu9, Pauline M Rudd8, Niclas G Karlsson4, Nicolle H Packer1, Frédérique Lisacek4,10* From Integrated Bio-Search: 12th International Workshop on Network Tools and Applications in Biology (NETTAB 2012) Como, Italy. 14-16 November 2012 Abstract Background: Recent progress in method development for characterising the branched structures of complex carbohydrates has now enabled higher throughput technology. Automation of structure analysis then calls for software development since adding meaning to large data collections in reasonable time requires corresponding bioinformatics methods and tools. Current glycobioinformatics resources do cover information on the structure and function of glycans, their interaction with proteins or their enzymatic synthesis. However, this information is partial, scattered and often difficult to find to for non-glycobiologists. Methods: Following our diagnosis of the causes of the slow development of glycobioinformatics, we review the “objective” difficulties encountered in defining adequate formats for representing complex entities and developing efficient analysis software. Results: Various solutions already implemented and strategies defined to bridge glycobiology with different fields and integrate the heterogeneous glyco-related information are presented. Conclusions: Despite the initial stage of our integrative efforts, this paper highlights the rapid expansion of glycomics, the validity of existing resources and the bright future of glycobioinformatics. -
Life with 6000 Genes Author(S): A. Goffeau, B. G. Barrell, H. Bussey, R
Life with 6000 Genes Author(s): A. Goffeau, B. G. Barrell, H. Bussey, R. W. Davis, B. Dujon, H. Feldmann, F. Galibert, J. D. Hoheisel, C. Jacq, M. Johnston, E. J. Louis, H. W. Mewes, Y. Murakami, P. Philippsen, H. Tettelin and S. G. Oliver Source: Science, Vol. 274, No. 5287, Genome Issue (Oct. 25, 1996), pp. 546+563-567 Published by: American Association for the Advancement of Science Stable URL: https://www.jstor.org/stable/2899628 Accessed: 28-08-2019 13:31 UTC REFERENCES Linked references are available on JSTOR for this article: https://www.jstor.org/stable/2899628?seq=1&cid=pdf-reference#references_tab_contents You may need to log in to JSTOR to access the linked references. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at https://about.jstor.org/terms American Association for the Advancement of Science is collaborating with JSTOR to digitize, preserve and extend access to Science This content downloaded from 152.3.43.41 on Wed, 28 Aug 2019 13:31:29 UTC All use subject to https://about.jstor.org/terms by FISH across the whole genome. A subset of the 37. G. D. Billingsleyet al., Am. J. Hum. Genet. 52, 343 (1994); L. -
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 -
Comprehensive Analysis of CRISPR/Cas9-Mediated Mutagenesis in Arabidopsis Thaliana by Genome-Wide Sequencing
International Journal of Molecular Sciences Article Comprehensive Analysis of CRISPR/Cas9-Mediated Mutagenesis in Arabidopsis thaliana by Genome-Wide Sequencing Wenjie Xu 1,2 , Wei Fu 2, Pengyu Zhu 2, Zhihong Li 1, Chenguang Wang 2, Chaonan Wang 1,2, Yongjiang Zhang 2 and Shuifang Zhu 1,2,* 1 College of Plant Protection, China Agricultural University, Beijing 100193 China 2 Institute of Plant Quarantine, Chinese Academy of Inspection and Quarantine, Beijing 100176, China * Correspondence: [email protected] Received: 9 July 2019; Accepted: 21 August 2019; Published: 23 August 2019 Abstract: The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) system has been widely applied in functional genomics research and plant breeding. In contrast to the off-target studies of mammalian cells, there is little evidence for the common occurrence of off-target sites in plants and a great need exists for accurate detection of editing sites. Here, we summarized the precision of CRISPR/Cas9-mediated mutations for 281 targets and found that there is a preference for single nucleotide deletions/insertions and longer deletions starting from 40 nt upstream or ending at 30 nt downstream of the cleavage site, which suggested the candidate sequences for editing sites detection by whole-genome sequencing (WGS). We analyzed the on-/off-target sites of 6 CRISPR/Cas9-mediated Arabidopsis plants by the optimized method. The results showed that the on-target editing frequency ranged from 38.1% to 100%, and one off target at a frequency of 9.8%–97.3% cannot be prevented by increasing the specificity or reducing the expression level of the Cas9 enzyme. -
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. -
Introduction to Bioinformatics (Elective) – SBB1609
SCHOOL OF BIO AND CHEMICAL ENGINEERING DEPARTMENT OF BIOTECHNOLOGY Unit 1 – Introduction to Bioinformatics (Elective) – SBB1609 1 I HISTORY OF BIOINFORMATICS Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biologicaldata. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques. Bioinformatics derives knowledge from computer analysis of biological data. These can consist of the information stored in the genetic code, but also experimental results from various sources, patient statistics, and scientific literature. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics is a rapidly developing branch of biology and is highly interdisciplinary, using techniques and concepts from informatics, statistics, mathematics, chemistry, biochemistry, physics, and linguistics. It has many practical applications in different areas of biology and medicine. Bioinformatics: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data. Computational Biology: The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. "Classical" bioinformatics: "The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information.” The National Center for Biotechnology Information (NCBI 2001) defines bioinformatics as: "Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline. -
Embnet Conference 2020 Programme
EMBnet Conference 2020 Bioinformatics Approaches to Precision Research 23-24 September 2020 Zoom Platform - Time 3 pm to 8 pm (CEST) Registration: https://us02web.zoom.us/meeting/register/tZEvd-mupjwiE911qtAnYln4TrNwQlxDMYm4 Programme 1st Day: 23 September 9, 2020 15:00-15:10 Welcome & Programme Presentation Domenica D’Elia & Erik Bongcam-Rudloff 15:10-15:30 Exosomes in breast milk; a beneficial genetic trojan horse from mother to child Dimitrios Vlachakis, Aspasia Efthimiadou, Flora Bacopoulou, Elias Eliopoulos, George P. Chrousos 15:30-15:50 Precision dairy farming – A Phenomenal opportunity Tomas Klingström 15:50-16:10 Genome regulation by long non-coding RNAs Katerina Pierouli, George N. Goulielmos, Elias Eliopoulos, Dimitrios Vlachakis European Molecular Biology Network (EMBnet) and Global Bioinformatics Network since 1988 16:10-16:30 Precision Epidemiology of Multi-drug resistance bacteria: bioinformatics tools J.Donato, L. Lugo, H. Perez, H. Ballen, D. Talero, S. Prada, F.Brion, V. Rincon, L. Falquet, M.T. Reguero, E. Barreto-Hernandez 16:30-16:50 Mechanisms of epigenetic inheritance in children following exposure to abuse E. Damaskopoulou, G.P. Chrousos, E. Eliopoulos, D. Vlachakis 16:50-17:00 Break 17:00-17:20 Genome-wide association studies (GWAS) in an effort to provide insights into the complex interplay of nuclear receptor transcriptional networks and their contribution to the maintenance of homeostasis Thanassis Mitsis, G.P. Chrousos, E. Eliopoulos, D.Vlachakis 17:20-17:40 Computer aided drug design and pharmacophore modelling towards the discovery of novel anti-ebola agents Kalliopi Io Diakou, G.P. Chrousos, E. Eliopoulos, D. Vlachakis 17:40-18:00 3′-Tag RNA-sequencing Andreas Gisel 18:00-18:20 Expression profiling of non-coding RNA in coronaviruses provides clues for virus RNA interference with immune system response Arianna Consiglio, V.F.