The Chromosome-Centric Human Proteome Project for Cataloging Proteins Encoded in the Genome
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A High-Stringency Blueprint of the Human Proteome
Providence St. Joseph Health Providence St. Joseph Health Digital Commons Articles, Abstracts, and Reports 10-16-2020 A high-stringency blueprint of the human proteome. Subash Adhikari Edouard C Nice Eric W Deutsch Institute for Systems Biology, Seattle, WA, USA. Lydie Lane Gilbert S Omenn See next page for additional authors Follow this and additional works at: https://digitalcommons.psjhealth.org/publications Part of the Genetics and Genomics Commons Recommended Citation Adhikari, Subash; Nice, Edouard C; Deutsch, Eric W; Lane, Lydie; Omenn, Gilbert S; Pennington, Stephen R; Paik, Young-Ki; Overall, Christopher M; Corrales, Fernando J; Cristea, Ileana M; Van Eyk, Jennifer E; Uhlén, Mathias; Lindskog, Cecilia; Chan, Daniel W; Bairoch, Amos; Waddington, James C; Justice, Joshua L; LaBaer, Joshua; Rodriguez, Henry; He, Fuchu; Kostrzewa, Markus; Ping, Peipei; Gundry, Rebekah L; Stewart, Peter; Srivastava, Sanjeeva; Srivastava, Sudhir; Nogueira, Fabio C S; Domont, Gilberto B; Vandenbrouck, Yves; Lam, Maggie P Y; Wennersten, Sara; Vizcaino, Juan Antonio; Wilkins, Marc; Schwenk, Jochen M; Lundberg, Emma; Bandeira, Nuno; Marko-Varga, Gyorgy; Weintraub, Susan T; Pineau, Charles; Kusebauch, Ulrike; Moritz, Robert L; Ahn, Seong Beom; Palmblad, Magnus; Snyder, Michael P; Aebersold, Ruedi; and Baker, Mark S, "A high-stringency blueprint of the human proteome." (2020). Articles, Abstracts, and Reports. 3832. https://digitalcommons.psjhealth.org/publications/3832 This Article is brought to you for free and open access by Providence St. Joseph Health -
Enhanced Representation of Natural Product Metabolism in Uniprotkb
H OH metabolites OH Article Diverse Taxonomies for Diverse Chemistries: Enhanced Representation of Natural Product Metabolism in UniProtKB Marc Feuermann 1,* , Emmanuel Boutet 1,* , Anne Morgat 1 , Kristian B. Axelsen 1, Parit Bansal 1, Jerven Bolleman 1 , Edouard de Castro 1, Elisabeth Coudert 1, Elisabeth Gasteiger 1,Sébastien Géhant 1, Damien Lieberherr 1, Thierry Lombardot 1,†, Teresa B. Neto 1, Ivo Pedruzzi 1, Sylvain Poux 1, Monica Pozzato 1, Nicole Redaschi 1 , Alan Bridge 1 and on behalf of the UniProt Consortium 1,2,3,4,‡ 1 Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CMU, 1 Michel-Servet, CH-1211 Geneva 4, Switzerland; [email protected] (A.M.); [email protected] (K.B.A.); [email protected] (P.B.); [email protected] (J.B.); [email protected] (E.d.C.); [email protected] (E.C.); [email protected] (E.G.); [email protected] (S.G.); [email protected] (D.L.); [email protected] (T.L.); [email protected] (T.B.N.); [email protected] (I.P.); [email protected] (S.P.); [email protected] (M.P.); [email protected] (N.R.); [email protected] (A.B.); [email protected] (U.C.) 2 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK 3 Protein Information Resource, University of Delaware, 15 Innovation Way, Suite 205, Newark, DE 19711, USA 4 Protein Information Resource, Georgetown University Medical Center, 3300 Whitehaven Street NorthWest, Suite 1200, Washington, DC 20007, USA * Correspondence: [email protected] (M.F.); [email protected] (E.B.); Tel.: +41-22-379-58-75 (M.F.); +41-22-379-49-10 (E.B.) † Current address: Centre Informatique, Division Calcul et Soutien à la Recherche, University of Lausanne, CH-1015 Lausanne, Switzerland. -
Research Resources for Nuclear Receptor Signaling Pathways Neil J
Molecular Pharmacology Fast Forward. Published on May 23, 2016 as DOI: 10.1124/mol.116.103713 This article has not been copyedited and formatted. The final version may differ from this version. MOL #103713 Research resources for nuclear receptor signaling pathways Neil J. McKenna Department of Molecular and Cellular Biology and Nuclear Receptor Signaling Atlas (NURSA) Bioinformatics Resource, Downloaded from Baylor College of Medicine, Houston, TX, 77030, USA molpharm.aspetjournals.org at ASPET Journals on September 27, 2021 1 Molecular Pharmacology Fast Forward. Published on May 23, 2016 as DOI: 10.1124/mol.116.103713 This article has not been copyedited and formatted. The final version may differ from this version. MOL #103713 Running title: Research resources for NR signaling pathways Corresponding author: Neil J McKenna Room M620 Baylor College of Medicine One Baylor Plaza Downloaded from Houston, TX, 77030, USA t: 713-798-7490 molpharm.aspetjournals.org f: 713-798-6822 e: [email protected] Number of text pages: 21 at ASPET Journals on September 27, 2021 Number of tables: 1 Number of figures: 1 Number of references: 56 Number of words in Abstract: 124 Review: 3613 List of non-standard abbreviations: 17βE2, 17β-estradiol; AB, Allen Brain Atlas; BG, BIOGRID; BGS, BioGPS; CoR, coregulator; CTD, Comparative Toxicogenomics Database; DAV, DAVID; DB, DrugBank; EDC, endocrine disrupting chemical; EG, Entrez Gene; EM, Edinburgh Mouse; ENC, ENCODE; ENR, ENRICHR; ENS, Ensembl; EX, Expression Atlas; GC, GeneCards; GSEA, GeneSet Enrichment Analysis; GtoP, IUPHAR Guide To Pharmacology; 2 Molecular Pharmacology Fast Forward. Published on May 23, 2016 as DOI: 10.1124/mol.116.103713 This article has not been copyedited and formatted. -
Uniprot: the Universal Protein Knowledgebase in 2021 the Uniprot Consortium1,2,3,4,*
D480–D489 Nucleic Acids Research, 2021, Vol. 49, Database issue Published online 25 November 2020 doi: 10.1093/nar/gkaa1100 UniProt: the universal protein knowledgebase in 2021 The UniProt Consortium1,2,3,4,* 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK, 2Protein Information Resource, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA, 3Protein Information Resource, University of Delaware, Ammon-Pinizzotto Biopharmaceutical Innovation Building, Suite 147, 590 Avenue 1743, Newark, DE 19713, USA and 4SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, CH-1211 Geneva 4, Switzerland Received September 15, 2020; Revised October 21, 2020; Editorial Decision October 22, 2020; Accepted November 02, 2020 ABSTRACT tomated systems. The UniRef databases cluster sequence sets at various levels of sequence identity and the UniProt The aim of the UniProt Knowledgebase is to provide Archive (UniParc) delivers a complete set of known se- users with a comprehensive, high-quality and freely quences, including historical obsolete sequences. UniProt accessible set of protein sequences annotated with additionally integrates, interprets, and standardizes data functional information. In this article, we describe from multiple selected resources to add biological knowl- significant updates that we have made over the last edge and associated metadata to protein records and acts two years to the resource. The number of sequences as a central hub from which users can link out to 180 in UniProtKB has risen to approximately 190 million, other resources. In recognition of the quality of our data, despite continued work to reduce sequence redun- and the service we provide, UniProt was recognised as dancy at the proteome level. -
Microbes and Metagenomics in Human Health an Overview of Recent Publications Featuring Illumina® Technology TABLE of CONTENTS
Microbes and Metagenomics in Human Health An overview of recent publications featuring Illumina® technology TABLE OF CONTENTS 4 Introduction 5 Human Microbiome Gut Microbiome Gut Microbiome and Disease Inflammatory Bowel Disease (IBD) Metabolic Diseases: Diabetes and Obesity Obesity Oral Microbiome Other Human Biomes 25 Viromes and Human Health Viral Populations Viral Zoonotic Reservoirs DNA Viruses RNA Viruses Human Viral Pathogens Phages Virus Vaccine Development 44 Microbial Pathogenesis Important Microorganisms in Human Health Antimicrobial Resistance Bacterial Vaccines 54 Microbial Populations Amplicon Sequencing 16S: Ribosomal RNA Metagenome Sequencing: Whole-Genome Shotgun Metagenomics Eukaryotes Single-Cell Sequencing (SCS) Plasmidome Transcriptome Sequencing 63 Glossary of Terms 64 Bibliography This document highlights recent publications that demonstrate the use of Illumina technologies in immunology research. To learn more about the platforms and assays cited, visit www.illumina.com. An overview of recent publications featuring Illumina technology 3 INTRODUCTION The study of microbes in human health traditionally focused on identifying and 1. Roca I., Akova M., Baquero F., Carlet J., treating pathogens in patients, usually with antibiotics. The rise of antibiotic Cavaleri M., et al. (2015) The global threat of resistance and an increasingly dense—and mobile—global population is forcing a antimicrobial resistance: science for interven- tion. New Microbes New Infect 6: 22-29 1, 2, 3 change in that paradigm. Improvements in high-throughput sequencing, also 2. Shallcross L. J., Howard S. J., Fowler T. and called next-generation sequencing (NGS), allow a holistic approach to managing Davies S. C. (2015) Tackling the threat of anti- microbial resistance: from policy to sustainable microbes in human health. -
Bioinformatics Tools for RNA-Seq Gene and Isoform Quantification
on: Sequ ati en er c n in e g G & t x A Journal of e p Zhang, et al., Next Generat Sequenc & Applic p N l f i c o 2016, 3:3 a l t a i o n r ISSN: 2469-9853n u s DOI: 10.4172/2469-9853.1000140 o Next Generation Sequencing & Applications J Review Article Open Access Bioinformatics Tools for RNA-seq Gene and Isoform Quantification Chi Zhang1, Baohong Zhang1, Michael S Vincent2 and Shanrong Zhao1* 1Early Clinical Development, Pfizer Worldwide R&D, Cambridge, MA, USA 2Inflammation and Immunology RU, Pfizer Worldwide R&D, Cambridge, MA, USA *Corresponding author: Shanrong Zhao, Early Clinical Development, Pfizer Worldwide R&D, Cambridge, MA, 02139, USA, Tel: + 1-212-733-2323; E-mail: [email protected] Rec date: Oct 27, 2016; Acc date: Dec 15, 2016; Pub date: Dec 17, 2016 Copyright: © 2016 Zhang C, et al. This is an open-access article distributed under the terms of the creative commons attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract In recent years, RNA-seq has emerged as a powerful technology in estimation of gene or transcript expression. ‘Union-exon’ and transcript based approaches are widely used in gene quantification. The ‘Union-exon’ based approach is simple, but it does not distinguish between isoforms when multiple alternatively spliced transcripts are expressed from the same gene. Because a gene is expressed in one or more transcript isoforms, the transcript based approach is more biologically meaningful than the ‘union exon’-based approach. -
PROTEOMICS the Human Proteome Takes the Spotlight
RESEARCH HIGHLIGHTS PROTEOMICS The human proteome takes the spotlight Two papers report mass spectrometry– big data. “We then thought, include some surpris- based draft maps of the human proteome ‘What is a potentially good ing findings. For example, and provide broadly accessible resources. illustration for the utility Kuster’s team found protein For years, members of the proteomics of such a database?’” says evidence for 430 long inter- community have been trying to garner sup- Kuster. “We very quickly genic noncoding RNAs, port for a large-scale project to exhaustively got to the idea, ‘Why don’t which have been thought map the normal human proteome, including we try to put together the not to be translated into pro- identifying all post-translational modifica- human proteome?’” tein. Pandey’s team refined tions and protein-protein interactions and The two groups took the annotations of 808 genes providing targeted mass spectrometry assays slightly different strategies and also found evidence and antibodies for all human proteins. But a towards this common goal. for the translation of many Nik Spencer/Nature Publishing Group Publishing Nik Spencer/Nature lack of consensus on how to exactly define Pandey’s lab examined 30 noncoding RNAs and pseu- Two groups provide mass the proteome, how to carry out such a mis- normal tissues, including spectrometry evidence for dogenes. sion and whether the technology is ready has adult and fetal tissues, as ~90% of the human proteome. Obtaining evidence for not so far convinced any funding agencies to well as primary hematopoi- the last roughly 10% of pro- fund on such an ambitious project. -
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cells Article Transcriptome and Methylome Analysis Reveal Complex Cross-Talks between Thyroid Hormone and Glucocorticoid Signaling at Xenopus Metamorphosis Nicolas Buisine 1,† , Alexis Grimaldi 1,†, Vincent Jonchere 1,† , Muriel Rigolet 1, Corinne Blugeon 2 , Juliette Hamroune 2 and Laurent Marc Sachs 1,* 1 UMR7221 Molecular Physiology and Adaption, CNRS, Museum National d’Histoire Naturelle, 57 Rue Cuvier, CEDEX 05, 75231 Paris, France; [email protected] (N.B.); [email protected] (A.G.); [email protected] (V.J.); [email protected] (M.R.) 2 Genomics Core Facility, Département de Biologie, Institut de Biologie de l’ENS (IBENS), École Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France; [email protected] (C.B.); [email protected] (J.H.) * Correspondence: [email protected] † Co-first authors, alphabetic order. Abstract: Background: Most work in endocrinology focus on the action of a single hormone, and very little on the cross-talks between two hormones. Here we characterize the nature of interactions between thyroid hormone and glucocorticoid signaling during Xenopus tropicalis metamorphosis. Methods: We used functional genomics to derive genome wide profiles of methylated DNA and measured changes of gene expression after hormonal treatments of a highly responsive tissue, tailfin. Clustering classified the data into four types of biological responses, and biological networks were Citation: Buisine, N.; Grimaldi, A.; modeled by system biology. Results: We found that gene expression is mostly regulated by either Jonchere, V.; Rigolet, M.; Blugeon, C.; T or CORT, or their additive effect when they both regulate the same genes. A small but non- Hamroune, J.; Sachs, L.M. -
Is It Time for Cognitive Bioinformatics?
g in Geno nin m i ic M s ta & a P Lisitsa et al., J Data Mining Genomics Proteomics 2015, 6:2 D r f o Journal of o t e l DOI: 10.4172/2153-0602.1000173 o a m n r i c u s o J ISSN: 2153-0602 Data Mining in Genomics & Proteomics Review Article Open Access Is it Time for Cognitive Bioinformatics? Andrey Lisitsa1,2*, Elizabeth Stewart2,3, Eugene Kolker2-5 1The Russian Human Proteome Organization (RHUPO), Institute of Biomedical Chemistry, Moscow, Russian Federation 2Data Enabled Life Sciences Alliance (DELSA Global), Moscow, Russian Federation 3Bioinformatics and High-Throughput Data Analysis Laboratory, Seattle Children’s Research Institute, Seattle, WA, USA 4Predictive Analytics, Seattle Children’s Hospital, Seattle, WA, USA 5Departments of Biomedical Informatics & Medical Education and Pediatrics, University of Washington, Seattle, WA, USA Abstract The concept of cognitive bioinformatics has been proposed for structuring of knowledge in the field of molecular biology. While cognitive science is considered as “thinking about the process of thinking”, cognitive bioinformatics strives to capture the process of thought and analysis as applied to the challenging intersection of diverse fields such as biology, informatics, and computer science collectively known as bioinformatics. Ten years ago cognitive bioinformatics was introduced as a model of the analysis performed by scientists working with molecular biology and biomedical web resources. At present, the concept of cognitive bioinformatics can be examined in the context of the opportunities represented by the information “data deluge” of life sciences technologies. The unbalanced nature of accumulating information along with some challenges poses currently intractable problems for researchers. -
Proteomics and Metabolomics: the Final Frontier of Nutrition Research 71
SIGHT AND LIFE | VOL. 29(1) | 2015 PROTEOMICS AND METABOLOMICS: THE FINAL FRONTIER OF NUTRITION RESEARCH 71 Proteomics and Metabolomics: The Final Frontier of Nutrition Research Richard D Semba RNA editing, RNA splicing, post-translational modifications, and Wilmer Eye Institute, Johns Hopkins University School protein degradation; the proteome does not strictly reflect the of Medicine, Baltimore, Maryland, USA genome. Proteins function as enzymes, hormones, receptors, immune mediators, structure, transporters, and modulators of cell communication and signaling. The metabolome consists Introduction of amino acids, amines, peptides, sugars, oligonucleotides, ke- Revolutionary new technologies allow us to penetrate scientific tones, aldehydes, lipids, steroids, vitamins, and other molecules. frontiers and open vast new territories for discovery. In astrono- These metabolites reflect intrinsic chemical processes in cells my, the Hubble Space Telescope has facilitated an unprecedent- as well as environmental exposures such as diet and gut micro- ed view outwards, beyond our galaxy. Wherever the telescope is bial flora. The current Human Metabolome Database contains directed, scientists are making exciting new observations of the more than 40,000 entries7 – a number that is expected to grow deep universe. Another revolution is taking place in two fields quickly in the future. of “omics” research: proteomics and metabolomics. In contrast, The goals of proteomics include the detection of the diver- this view is directed inwards, towards the complexity of biologi- sity of proteins, their quantity, their isoforms, and the localiza- cal processes in living organisms. Proteomics is the study of the tion and interactions of proteins. The goals of metabolomics structure and function of proteins expressed by an organism. -
How Many Human Proteoforms Are There?
PERSPECTIVE PUBLISHED ONLINE: 14 FEBRUARY 2018 | DOI: 10.1038/NCHEMBIO.2576 How many human proteoforms are there? Ruedi Aebersold1, Jeffrey N Agar2, I Jonathan Amster3 , Mark S Baker4 , Carolyn R Bertozzi5, Emily S Boja6, Catherine E Costello7, Benjamin F Cravatt8 , Catherine Fenselau9, Benjamin A Garcia10, Ying Ge11,12, Jeremy Gunawardena13, Ronald C Hendrickson14, Paul J Hergenrother15, Christian G Huber16 , Alexander R Ivanov2, Ole N Jensen17, Michael C Jewett18, Neil L Kelleher19* , Laura L Kiessling20 , Nevan J Krogan21, Martin R Larsen17, Joseph A Loo22 , Rachel R Ogorzalek Loo22, Emma Lundberg23,24, Michael J MacCoss25, Parag Mallick5, Vamsi K Mootha13, Milan Mrksich18, Tom W Muir26, Steven M Patrie19, James J Pesavento27 , Sharon J Pitteri5 , Henry Rodriguez6, Alan Saghatelian28, Wendy Sandoval29, Hartmut Schlüter30 , Salvatore Sechi31, Sarah A Slavoff32, Lloyd M Smith12,33, Michael P Snyder24, Paul M Thomas19 , Mathias Uhlén34, Jennifer E Van Eyk35, Marc Vidal36, David R Walt37, Forest M White38, Evan R Williams39, Therese Wohlschlager16, Vicki H Wysocki40, Nathan A Yates41, Nicolas L Young42 & Bing Zhang42 Despite decades of accumulated knowledge about proteins and their post-translational modifications (PTMs), numerous ques- tions remain regarding their molecular composition and biological function. One of the most fundamental queries is the extent to which the combinations of DNA-, RNA- and PTM-level variations explode the complexity of the human proteome. Here, we outline what we know from current databases and measurement strategies including mass spectrometry–based proteomics. In doing so, we examine prevailing notions about the number of modifications displayed on human proteins and how they combine to generate the protein diversity underlying health and disease. -
Biomolecule and Bioentity Interaction Databases in Systems Biology: a Comprehensive Review
biomolecules Review Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review Fotis A. Baltoumas 1,* , Sofia Zafeiropoulou 1, Evangelos Karatzas 1 , Mikaela Koutrouli 1,2, Foteini Thanati 1, Kleanthi Voutsadaki 1 , Maria Gkonta 1, Joana Hotova 1, Ioannis Kasionis 1, Pantelis Hatzis 1,3 and Georgios A. Pavlopoulos 1,3,* 1 Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; zafeiropoulou@fleming.gr (S.Z.); karatzas@fleming.gr (E.K.); [email protected] (M.K.); [email protected] (F.T.); voutsadaki@fleming.gr (K.V.); [email protected] (M.G.); hotova@fleming.gr (J.H.); [email protected] (I.K.); hatzis@fleming.gr (P.H.) 2 Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark 3 Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece * Correspondence: baltoumas@fleming.gr (F.A.B.); pavlopoulos@fleming.gr (G.A.P.); Tel.: +30-210-965-6310 (G.A.P.) Abstract: Technological advances in high-throughput techniques have resulted in tremendous growth Citation: Baltoumas, F.A.; of complex biological datasets providing evidence regarding various biomolecular interactions. Zafeiropoulou, S.; Karatzas, E.; To cope with this data flood, computational approaches, web services, and databases have been Koutrouli, M.; Thanati, F.; Voutsadaki, implemented to deal with issues such as data integration, visualization, exploration, organization, K.; Gkonta, M.; Hotova, J.; Kasionis, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more I.; Hatzis, P.; et al. Biomolecule and and more difficult for an end user to know what the scope and focus of each repository is and how Bioentity Interaction Databases in redundant the information between them is.