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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. -
To Find Information About Arabidopsis Genes Leonore Reiser1, Shabari
UNIT 1.11 Using The Arabidopsis Information Resource (TAIR) to Find Information About Arabidopsis Genes Leonore Reiser1, Shabari Subramaniam1, Donghui Li1, and Eva Huala1 1Phoenix Bioinformatics, Redwood City, CA USA ABSTRACT The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource of Arabidopsis biology for plant scientists. TAIR curates and integrates information about genes, proteins, gene function, orthologs gene expression, mutant phenotypes, biological materials such as clones and seed stocks, genetic markers, genetic and physical maps, genome organization, images of mutant plants, protein sub-cellular localizations, publications, and the research community. The various data types are extensively interconnected and can be accessed through a variety of Web-based search and display tools. This unit primarily focuses on some basic methods for searching, browsing, visualizing, and analyzing information about Arabidopsis genes and genome, Additionally we describe how members of the community can share data using TAIR’s Online Annotation Submission Tool (TOAST), in order to make their published research more accessible and visible. Keywords: Arabidopsis ● databases ● bioinformatics ● data mining ● genomics INTRODUCTION The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource for the biology of Arabidopsis thaliana (Huala et al., 2001; Garcia-Hernandez et al., 2002; Rhee et al., 2003; Weems et al., 2004; Swarbreck et al., 2008, Lamesch, et al., 2010, Berardini et al., 2016). The TAIR database contains information about genes, proteins, gene expression, mutant phenotypes, germplasms, clones, genetic markers, genetic and physical maps, genome organization, publications, and the research community. In addition, seed and DNA stocks from the Arabidopsis Biological Resource Center (ABRC; Scholl et al., 2003) are integrated with genomic data, and can be ordered through TAIR. -
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 -
Orthofiller: Utilising Data from Multiple Species to Improve the Completeness of Genome Annotations Michael P
Dunne and Kelly BMC Genomics (2017) 18:390 DOI 10.1186/s12864-017-3771-x SOFTWARE Open Access OrthoFiller: utilising data from multiple species to improve the completeness of genome annotations Michael P. Dunne and Steven Kelly* Abstract Backround: Complete and accurate annotation of sequenced genomes is of paramount importance to their utility and analysis. Differences in gene prediction pipelines mean that genome annotations for a species can differ considerably in the quality and quantity of their predicted genes. Furthermore, genes that are present in genome sequences sometimes fail to be detected by computational gene prediction methods. Erroneously unannotated genes can lead to oversights and inaccurate assertions in biological investigations, especially for smaller-scale genome projects, which rely heavily on computational prediction. Results: Here we present OrthoFiller, a tool designed to address the problem of finding and adding such missing genes to genome annotations. OrthoFiller leverages information from multiple related species to identify those genes whose existence can be verified through comparison with known gene families, but which have not been predicted. By simulating missing gene annotations in real sequence datasets from both plants and fungi we demonstrate the accuracy and utility of OrthoFiller for finding missing genes and improving genome annotations. Furthermore, we show that applying OrthoFiller to existing “complete” genome annotations can identify and correct substantial numbers of erroneously missing genes in these two sets of species. Conclusions: We show that significant improvements in the completeness of genome annotations can be made by leveraging information from multiple species. Keywords: Genome annotation, Gene prediction, Orthology, Orthogroup Background of several effective algorithms for identifying genes in Genome sequences have become fundamental to many de novo sequenced genomes [3]. -
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. -
Infravec2 Open Research Data Management Plan
INFRAVEC2 OPEN RESEARCH DATA MANAGEMENT PLAN Authors: Andrea Crisanti, Gareth Maslen, Andy Yates, Paul Kersey, Alain Kohl, Clelia Supparo, Ken Vernick Date: 10th July 2020 Version: 3.0 Overview Infravec2 will align to Open Research Data, as follows: Data Types and Standards Infravec2 will generate a variety of data types, including molecular data types: genome sequence and assembly, structural annotation (gene models, repeats, other functional regions) and functional annotation (protein function assignment), variation data, and transcriptome data; arbovirus and malaria experimental infection data, linked to archived samples; and microbiome data (Operational Taxonomic Units), including natural virome composition. All data will be released according to the appropriate standards and formats for each data type. For example, DNA sequence will be released in FASTA format; variant calls in Variant Call Format; sequence alignments in BAM (Binary Alignment Map) and CRAM (Compressed Read Alignment Map) formats, etc. We will strongly encourage the organisation of linked data sets as Track Hubs, a mechanism for publishing a set of linked genomic data that aids data discovery, sharing, and selection for subsequent analysis. We will develop internal standards within the consortium to define minimal metadata that will accompany all data sets, following the template of the Minimal Information Standards for Biological and Biomedical Investigations (http://www.dcc.ac.uk/resources/metadata-standards/mibbi-minimum-information-biological- and-biomedical-investigations). Data Exploitation, Accessibility, Curation and Preservation All molecular data for which existing public data repositories exist will be submitted to such repositories on or before the publication of written manuscripts, with early release of data (i.e. -
Lab 5: Bioinformatics I Sanger Sequence Analysis
Lab 5: Bioinformatics I Sanger Sequence Analysis Project Guide The Wolbachia Project 1 Arthropod Identification 2 DNA Extraction 3 PCR 4 Gel Electrophoresis 5 Bioinformatics Content is made available under the Creative Commons Attribution-NonCommercial-No Derivatives International License. Contact ([email protected]) if you would like to make adaptations for distribution beyond the classroom. The Wolbachia Project: Discover the Microbes Within! was developed by a collaboration of scientists, educators, and outreach specialists. It is directed by the Bordenstein Lab at Vanderbilt University. https://www.vanderbilt.edu/wolbachiaproject 2 Activity at a Glance Goal To analyze and interpret the quality of Sanger sequences, and generate a consensus DNA sequence for bioinformatics analyses. Learning Objectives Upon completion of this activity, students will (i) understand the Sanger method of sequencing, also known as the chain-termination method; (ii) be able to interpret chromatograms; (iii) evaluate sequencing Quality Scores; and (iv) generate a consensus DNA sequence based on forward and reverse Sanger reactions. Prerequisite Skills While no computer programming skills are necessary to complete this work, prior exposure to personal computers and the Internet is assumed. Teaching Time: One class period Recommended Background Tutorials • DNA Learning Center Animation: Sanger Method of DNA Sequencing (https://www.dnalc.org/view/15479-sanger-method-of-dna-sequencing-3d-animation-with- narration.html) • YouTube video: The Sanger -
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. -
A Tool to Sanity Check and If Needed Reformat FASTA Files
bioRxiv preprint doi: https://doi.org/10.1101/024448; this version posted August 13, 2015. 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. Fasta-O-Matic: a tool to sanity check and if needed reformat FASTA files Jennifer Shelton Kansas State University August 11, 2015 Abstract As the shear volume of bioinformatic sequence data increases the only way to take advantage of this content is to more completely automate ro- bust analysis workflows. Analysis bottlenecks are often mundane and overlooked processing steps. Idiosyncrasies in reading and/or writing bioinformatics file formats can halt or impair analysis workflows by in- terfering with the transfer of data from one informatics tools to another. Fasta-O-Matic automates handling of common but minor format issues that otherwise may halt pipelines. The need for automation must be balanced by the need for manual confirmation that any formatting error is actually minor rather than indicative of a corrupt data file. To that end Fasta-O-Matic reports any issues detected to the user with optionally color coded and quiet or verbose logs. Fasta-O-Matic can be used as a general pre-processing tool in bioin- formatics workflows (e.g. to automatically wrap FASTA files so that they can be read by BioPerl). It was also developed as a sanity check for bioinformatic core facilities that tend to repeat common analysis steps on FASTA files received from disparate sources.