The ELIXIR Core Data Resources: Fundamental Infrastructure for The
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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. -
Ensembl Genomes: Extending Ensembl Across the Taxonomic Space P
Published online 1 November 2009 Nucleic Acids Research, 2010, Vol. 38, Database issue D563–D569 doi:10.1093/nar/gkp871 Ensembl Genomes: Extending Ensembl across the taxonomic space P. J. Kersey*, D. Lawson, E. Birney, P. S. Derwent, M. Haimel, J. Herrero, S. Keenan, A. Kerhornou, G. Koscielny, A. Ka¨ ha¨ ri, R. J. Kinsella, E. Kulesha, U. Maheswari, K. Megy, M. Nuhn, G. Proctor, D. Staines, F. Valentin, A. J. Vilella and A. Yates EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK Received August 14, 2009; Revised September 28, 2009; Accepted September 29, 2009 ABSTRACT nucleotide archives; numerous other genomes exist in states of partial assembly and annotation; thousands of Ensembl Genomes (http://www.ensemblgenomes viral genomes sequences have also been generated. .org) is a new portal offering integrated access to Moreover, the increasing use of high-throughput genome-scale data from non-vertebrate species sequencing technologies is rapidly reducing the cost of of scientific interest, developed using the Ensembl genome sequencing, leading to an accelerating rate of genome annotation and visualisation platform. data production. This not only makes it likely that in Ensembl Genomes consists of five sub-portals (for the near future, the genomes of all species of scientific bacteria, protists, fungi, plants and invertebrate interest will be sequenced; but also the genomes of many metazoa) designed to complement the availability individuals, with the possibility of providing accurate and of vertebrate genomes in Ensembl. Many of the sophisticated annotation through the similarly low-cost databases supporting the portal have been built in application of functional assays. -
Abstracts Genome 10K & Genome Science 29 Aug - 1 Sept 2017 Norwich Research Park, Norwich, Uk
Genome 10K c ABSTRACTS GENOME 10K & GENOME SCIENCE 29 AUG - 1 SEPT 2017 NORWICH RESEARCH PARK, NORWICH, UK Genome 10K c 48 KEYNOTE SPEAKERS ............................................................................................................................... 1 Dr Adam Phillippy: Towards the gapless assembly of complete vertebrate genomes .................... 1 Prof Kathy Belov: Saving the Tasmanian devil from extinction ......................................................... 1 Prof Peter Holland: Homeobox genes and animal evolution: from duplication to divergence ........ 2 Dr Hilary Burton: Genomics in healthcare: the challenges of complexity .......................................... 2 INVITED SPEAKERS ................................................................................................................................. 3 Vertebrate Genomics ........................................................................................................................... 3 Alex Cagan: Comparative genomics of animal domestication .......................................................... 3 Plant Genomics .................................................................................................................................... 4 Ksenia Krasileva: Evolution of plant Immune receptors ..................................................................... 4 Andrea Harper: Using Associative Transcriptomics to predict tolerance to ash dieback disease in European ash trees ............................................................................................................ -
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. -
Volume 21 Supplement a December 2015 Next
EMBnet.journal Volume 21 Supplement A December 2015 Next Generation Sequencing: a look into the future Final Conference & MC Meeting of COST Action BM1006 16-17 March 2015 Bratislava, Slovakia http://seqahead.eu/bratislava_2015 ESF provides COST is supported the Cost Office by the EU RTD through an EC Framework contract Programme EDITORIAL/CONTENT EMBnet.journal 21.A Editorial Contents The key task of COST Action BM1006, SeqAhead, Editorial ..............................................................2 Next Generation Sequencing (NGS) Data Analysis COST Action BM1006 (SeqAhead) closing Network, was, as its name suggests, networking; conference ........................................................3 but SeqAhead also emphasised the dissemina- Scientific Programme.........................................5 tion of knowledge. During the four years of the Keynote Lectures ................................................9 Action, SeqAhead surpassed every expectation: Oral Presentations ........................................... 13 with members participating from 29 European Posters.......................................................................25 countries, plus one international partner from South Africa, the Management Committee mem- bership reads like a “who’s-who” of European NGS research. This EMBnet.journal Conference Supplement clearly shows that during the four years of SeqAhead’s existence, the Action members ac- tively shared software and experiences, and col- laborated in numerous projects spanning diverse EMBnet.journal -
PLK-1 Promotes the Merger of the Parental Genome Into A
RESEARCH ARTICLE PLK-1 promotes the merger of the parental genome into a single nucleus by triggering lamina disassembly Griselda Velez-Aguilera1, Sylvia Nkombo Nkoula1, Batool Ossareh-Nazari1, Jana Link2, Dimitra Paouneskou2, Lucie Van Hove1, Nicolas Joly1, Nicolas Tavernier1, Jean-Marc Verbavatz3, Verena Jantsch2, Lionel Pintard1* 1Programme Equipe Labe´llise´e Ligue Contre le Cancer - Team Cell Cycle & Development - Universite´ de Paris, CNRS, Institut Jacques Monod, Paris, France; 2Department of Chromosome Biology, Max Perutz Laboratories, University of Vienna, Vienna Biocenter, Vienna, Austria; 3Universite´ de Paris, CNRS, Institut Jacques Monod, Paris, France Abstract Life of sexually reproducing organisms starts with the fusion of the haploid egg and sperm gametes to form the genome of a new diploid organism. Using the newly fertilized Caenorhabditis elegans zygote, we show that the mitotic Polo-like kinase PLK-1 phosphorylates the lamin LMN-1 to promote timely lamina disassembly and subsequent merging of the parental genomes into a single nucleus after mitosis. Expression of non-phosphorylatable versions of LMN- 1, which affect lamina depolymerization during mitosis, is sufficient to prevent the mixing of the parental chromosomes into a single nucleus in daughter cells. Finally, we recapitulate lamina depolymerization by PLK-1 in vitro demonstrating that LMN-1 is a direct PLK-1 target. Our findings indicate that the timely removal of lamin is essential for the merging of parental chromosomes at the beginning of life in C. elegans and possibly also in humans, where a defect in this process might be fatal for embryo development. *For correspondence: [email protected] Introduction Competing interests: The After fertilization, the haploid gametes of the egg and sperm have to come together to form the authors declare that no genome of a new diploid organism. -
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. -
Deep Profiling of Protease Substrate Specificity Enabled by Dual Random and Scanned Human Proteome Substrate Phage Libraries
Deep profiling of protease substrate specificity enabled by dual random and scanned human proteome substrate phage libraries Jie Zhoua, Shantao Lib, Kevin K. Leunga, Brian O’Donovanc, James Y. Zoub,d, Joseph L. DeRisic,d, and James A. Wellsa,d,e,1 aDepartment of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158; bDepartment of Biomedical Data Science, Stanford University, Stanford, CA 94305; cDepartment of Biochemistry and Biophysics, University of California, San Francisco, CA 94158; dChan Zuckerberg Biohub, San Francisco, CA 94158; and eDepartment of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158 Edited by Benjamin F. Cravatt, Scripps Research Institute, La Jolla, CA, and approved August 19, 2020 (received for review May 11, 2020) Proteolysis is a major posttranslational regulator of biology inside lysate and miss low abundance proteins and those simply not and outside of cells. Broad identification of optimal cleavage sites expressed in cell lines tested that typically express only half their and natural substrates of proteases is critical for drug discovery genomes (13). and to understand protease biology. Here, we present a method To potentially screen a larger and more diverse sequence that employs two genetically encoded substrate phage display space, investigators have developed genetically encoded substrate libraries coupled with next generation sequencing (SPD-NGS) that phage (14, 15) or yeast display libraries (16, 17). Degenerate DNA allows up to 10,000-fold deeper sequence coverage of the typical six- sequences (up to 107) encoding random peptides were fused to a to eight-residue protease cleavage sites compared to state-of-the-art phage or yeast coat protein gene for a catch-and-release strategy synthetic peptide libraries or proteomics. -
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.