Bioinformatics Master's Course Genome Analysis Lecture 1
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In-Cell Architecture of an Actively Transcribing-Translating Expressome
bioRxiv preprint doi: https://doi.org/10.1101/2020.02.28.970111; this version posted February 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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-NC-ND 4.0 International license. In-cell architecture of an actively transcribing-translating expressome Francis J. O’Reilly1, †, Liang Xue2,3, †, Andrea Graziadei1, †, Ludwig Sinn1, Swantje Lenz1, 5 Dimitry Tegunov4, Cedric Blötz5, Wim J. H. Hagen2, Patrick Cramer4, Jörg Stülke5, Julia Mahamid2,*, Juri Rappsilber1,6,* Affiliations: 1 Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, 10 Germany 2 Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstraße 1, 69117 Heidelberg, Germany. 3 Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences 15 4 Department of Molecular Biology, Max-Planck-Institute for Biophysical Chemistry, Am Faßberg 11, 37077, Göttingen, Germany 5 Department of General Microbiology, Institute of Microbiology and Genetics, GZMB, Georg- August-University Göttingen, Grisebachstraße 8, 37077 Göttingen, Germany 6 Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh, 20 EH9 3BF, UK *Correspondence to: [email protected], [email protected] †These authors contributed equally to this work. 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.28.970111; this version posted February 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. -
Downloaded to the User’S Computer in a Variety of Formats
Int. J. Mol. Sci. 2012, 13, 6561-6581; doi:10.3390/ijms13066561 OPEN ACCESS International Journal of Molecular Sciences ISSN 1422-0067 www.mdpi.com/journal/ijms Review State of the Art in Silico Tools for the Study of Signaling Pathways in Cancer Vanessa Medina Villaamil 1,*, Guadalupe Aparicio Gallego 1, Isabel Santamarina Cainzos 1, Manuel Valladares-Ayerbes 1,3 and Luis M. Antón Aparicio 2,3 1 Biomedical Research Institute (INIBIC), CHU A Coruña, Xubias de Abaixo s/n, PC 15006, A Coruña, Spain; E-Mails: [email protected] (G.A.G.); [email protected] (I.S.C.); [email protected] (M.V.-A.) 2 University of A Coruña (UDC), PC 15006, A Coruña, Spain; E-Mail: [email protected] 3 Medical Oncology Department, CHU A Coruña, PC 15006, A Coruña, Spain * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +34-981-178-272; Fax: +34-981-178-273. Received: 27 March 2012; in revised form: 3 May 2012 / Accepted: 10 May 2012 / Published: 29 May 2012 Abstract: In the last several years, researchers have exhibited an intense interest in the evolutionarily conserved signaling pathways that have crucial roles during embryonic development. Interestingly, the malfunctioning of these signaling pathways leads to several human diseases, including cancer. The chemical and biophysical events that occur during cellular signaling, as well as the number of interactions within a signaling pathway, make these systems complex to study. In silico resources are tools used to aid the understanding of cellular signaling pathways. -
Expressomal Approach for Comprehensive Analysis and Visualization of Ligand Sensitivities of Xenoestrogen Responsive Genes
Expressomal approach for comprehensive analysis and visualization of ligand sensitivities of xenoestrogen responsive genes Toshi Shiodaa,b,1, Noël F. Rosenthala, Kathryn R. Cosera, Mizuki Sutoa, Mukta Phatakc, Mario Medvedovicc, Vincent J. Careyb,d, and Kurt J. Isselbachera,b,1 aMolecular Profiling Laboratory, Massachusetts General Hospital Center for Cancer Research, Charlestown, MA 02129; bDepartment of Medicine, Harvard Medical School, Boston, MA 02115; cLaboratory for Statistical Genomics and Systems Biology, Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, OH 45267; and dChanning Laboratory, Brigham and Women’s Hospital, Boston, MA 02115 Contributed by Kurt J. Isselbacher, August 26, 2013 (sent for review June 17, 2013) Although biological effects of endocrine disrupting chemicals Evidence is accumulating that the EDCs may cause significant (EDCs) are often observed at unexpectedly low doses with occa- biological effects in humans or animals at doses far lower than sional nonmonotonic dose–response characteristics, transcriptome- the exposure limits set by regulatory agencies (8, 9). In addition wide profiles of sensitivities or dose-dependent behaviors of the to such low-dose effects, an increasing number of studies also EDC responsive genes have remained unexplored. Here, we describe support the concept of the nonmonotonic EDC effects, whose dose–response curves show U shapes or inverted-U shapes (8- expressome analysis for the comprehensive examination of dose- – dependent gene responses -
Link to the Report
DISCLAIMER This report was prepared as an account of a workshop sponsored by the U.S. Department of Energy. Neither the United States Government nor any agency thereof, nor any of their employees or officers, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of document authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Copyrights to portions of this report (including graphics) are reserved by original copyright holders or their assignees, and are used by the Government’s license and by permission. Requests to use any images must be made to the provider identified in the image credits. On the cover: Argonne National Laboratory’s IBM Blue Gene/P supercomputer Inset visualization of ALG13 courtesy of David Baker, University of Washington AUTHORS AND CONTRIBUTORS Sponsors and representatives Susan Gregurick, DOE/Office of Biological and Environmental Science Daniel Drell, DOE/Office of Biological and Environmental Science Christine Chalk, DOE/Office of Advanced Scientific -
Bioinformaticsbioinformatics Introduction to Genomics and Proteomics I
www. .uni-rostock.de BioinformaticsBioinformatics Introduction to genomics and proteomics I Ulf Schmitz [email protected] Bioinformatics and Systems Biology Group www.sbi.informatik.uni-rostock.de Ulf Schmitz, Introduction to genomics and proteomics I 1 www. .uni-rostock.de Outline Genomics/Genetics 1. The tree of life • Prokaryotic Genomes –Bacteria – Archaea • Eukaryotic Genomes – Homo sapiens 2. Genes • Expression Data Ulf Schmitz, Introduction to genomics and proteomics I 2 www. .uni-rostock.de Genomics - Definitions #Genetics: is the science of genes, heredity, and the variation of organisms. Humans began applying knowledge of genetics in prehistory with the domestication and breeding of plants and animals. In modern research, genetics provides tools in the investigation of the function of a particular gene, e.g. analysis of genetic interactions. #Genomics: attempts the study of large-scale genetic patterns across the genome for a given species. It deals with the systematic use of genome information to provide answers in biology, medicine, and industry. Genomics has the potential of offering new therapeutic methods for the treatment of some diseases, as well as new diagnostic methods. Major tools and methods related to genomics are bioinformatics, genetic analysis, measurement of gene expression, and determination of gene function. Ulf Schmitz, Introduction to genomics and proteomics I 3 Genes www. .uni-rostock.de •a gene coding for a protein corresponds to a sequence of nucleotides along one or more regions of a molecule of DNA • in species with double stranded DNA (dsDNA), genes may appear on either strand • bacterial genes are continuous regions of DNA bacterium: • a string of 3N nucleotides encodes a string of N amino acids • or a string of N nucleotides encodes a structural RNA molecule of N residues eukaryote: • a gene may appear split into separated segments in the DNA • an exon is a stretch of DNA retained in mRNA that the ribosomes translate into protein Ulf Schmitz, Introduction to genomics and proteomics I 4 www. -
Health of Physiology the Physiological Society and Are Registered in the UK and in the EU Respectively
The Physiological Society is a company limited by guarantee. Registered in England and Wales, No. 323575. Registered Office: Hodgkin Huxley House, 30 Farringdon Lane, London EC1R 3AW, UK. Registered Charity No. 211585. ‘The Physiological Society’ and the Physiological Society logo are trademarks belonging to Health of Physiology The Physiological Society and are registered in the UK and in the EU respectively. Health of Physiology Health of Physiology Contents 1. President’s Foreword 3 2. Executive summary Key findings 4 Recommendations 5 3. Introduction What is physiology? 6 The importance of physiology 7 The growth and fragmentation of physiology 7 4. UK life science policy Government actions 11 Current situation and future measures 12 5. The skills pipeline School 14 University 15 6. Academic output Published research 22 Research funding 24 Animal research 25 7. Diversity in physiology Diversity among physiologists 28 The Physiological Society 30 8. Conclusions and future direction The skills pipeline 32 Physiology research 34 Diversity in physiology 35 Visibility of physiology 36 9. Appendices Appendix I: Membership of steering group 38 Appendix II: Review methodology 39 Appendix III: Stakeholder meetings 40 Appendix IV: Universities responding to practical provision survey 42 President's Foreword This review addresses the question: How healthy is the discipline of physiology? Is physiology in 2016 now in a similar position to anatomy, which has largely run its course as a research discipline and has seen its departments closed, becoming -
Structural Basis of Mitochondrial Translation Shintaro Aibara1†, Vivek Singh1,2, Angelika Modelska3‡, Alexey Amunts1,2*
RESEARCH ARTICLE Structural basis of mitochondrial translation Shintaro Aibara1†, Vivek Singh1,2, Angelika Modelska3‡, Alexey Amunts1,2* 1Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden; 2Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden; 3Laboratory of Translational Genomics, Centre for Integrative Biology, University of Trento, Trento, Italy Abstract Translation of mitochondrial messenger RNA (mt-mRNA) is performed by distinct mitoribosomes comprising at least 36 mitochondria-specific proteins. How these mitoribosomal proteins assist in the binding of mt-mRNA and to what extent they are involved in the translocation of transfer RNA (mt-tRNA) is unclear. To visualize the process of translation in human mitochondria, we report ~3.0 A˚ resolution structure of the human mitoribosome, including the L7/L12 stalk, and eight structures of its functional complexes with mt-mRNA, mt-tRNAs, recycling factor and additional trans factors. The study reveals a transacting protein module LRPPRC-SLIRP that delivers mt-mRNA to the mitoribosomal small subunit through a dedicated platform formed by the mitochondria-specific protein mS39. Mitoribosomal proteins of the large subunit mL40, mL48, and *For correspondence: mL64 coordinate translocation of mt-tRNA. The comparison between those structures shows [email protected] dynamic interactions between the mitoribosome and its ligands, suggesting a sequential Present address: †Department mechanism of conformational changes. of Molecular Biology, Max- Planck-Institute for BiophysicalChemistry, Go¨ ttingen, Germany; ‡Aix Marseille Introduction Universite´, CNRS, INSERM, Translation in humans takes place in the cytosol and mitochondria. Mitochondrial translation is Centre d’Immunologie responsible for the maintenance of the cellular energetic balance through synthesis of proteins deMarseille-Luminy (CIML), Marseille, France involved in oxidative phosphorylation. -
Mycobacterial Held Is a Nucleic Acids-Clearing Factor for RNA
bioRxiv preprint doi: https://doi.org/10.1101/2020.07.20.211821; this version posted July 22, 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. 1 Mycobacterial HelD is a nucleic acids-clearing factor for RNA 2 polymerase 3 4 5 Tomáš Koubaa*#, Tomáš Koval’b*, Petra Sudzinovác*, Jiří Pospíšilc, Barbora Brezovskác, Jarmila 6 Hnilicovác, Hana Šanderovác, Martina Janouškovác, Michaela Šikovác, Petr Haladac, Michal 7 Sýkorad, Ivan Barvíke, Jiří Nováčekf, Mária Trundováb, Jarmila Duškováb, Tereza Skálováb, 8 URee Chong, Katsuhiko S. Murakamig, Jan Dohnálekb#, Libor Krásnýc# 9 10 a EMBL Grenoble, 71 Avenue des Martyrs, France 11 b Institute of Biotechnology of the Czech Academy of Sciences, Centre BIOCEV, Prumyslova 12 595, 25250 Vestec, Czech Republic 13 c Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic 14 d Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic 15 e Charles University, Faculty of Mathematics and Physics, Institute of Physics, Prague, Czech 16 Republic 17 f CEITEC, Masaryk University, Brno, Czech Republic 18 g Department of Biochemistry and Molecular Biology, The Center for RNA Molecular Biology, 19 Pennsylvania State University, University Park, PA 16802, USA 20 21 22 23 24 25 *Equal contribution 26 #Corresponding author: [email protected], [email protected], [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.20.211821; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. -
NASA Technology Roadmaps TA 6: Human Health, Life Support, and Habitation Systems
NASA Technology Roadmaps TA 6: Human Health, Life Support, and Habitation Systems May 2015 Draft 2015 NASA Technology Roadmaps DRAFT TA 6: Human Health, Life Support, and Habitation Systems Foreword NASA is leading the way with a balanced program of space exploration, aeronautics, and science research. Success in executing NASA’s ambitious aeronautics activities and space missions requires solutions to difficult technical challenges that build on proven capabilities and require the development of new capabilities. These new capabilities arise from the development of novel cutting-edge technologies. The promising new technology candidates that will help NASA achieve our extraordinary missions are identified in our Technology Roadmaps. The roadmaps are a set of documents that consider a wide range of needed technology candidates and development pathways for the next 20 years. The roadmaps are a foundational element of the Strategic Technology Investment Plan (STIP), an actionable plan that lays out the strategy for developing those technologies essential to the pursuit of NASA’s mission and achievement of National goals. The STIP provides prioritization of the technology candidates within the roadmaps and guiding principles for technology investment. The recommendations provided by the National Research Council heavily influence NASA’s technology prioritization. NASA’s technology investments are tracked and analyzed in TechPort, a web-based software system that serves as NASA’s integrated technology data source and decision support tool. Together, the roadmaps, the STIP, and TechPort provide NASA the ability to manage the technology portfolio in a new way, aligning mission directorate technology investments to minimize duplication, and lower cost while providing critical capabilities that support missions, commercial industry, and longer-term National needs. -
Genome Sequencing: Then & Now
“Aint you got an ‘ome to go to?” Genomics Transcriptomics Proteomics Metabolomics Physiomics toxicogenomics pharmacogenomics ecotoxicopharmacogenomics phosphatome, glycome, secretome metronome, mobilome, gardenome, etc… http://omics.org/index.php/Alphabetically_ordered_list_of_omes_and_omics --A-- Alignmentome: conceived before 2003. The whole set of Bacteriome: an organelle of bacteria. (Bacteriome.org). Also Carbome: BiO center. 2003. The whole set of carbone multiple sequence and structure alignments in the totality of baterial genes and proteins. based living organisms in the universe. (Carbome.org) bioinformatics. Alignments are the most important Bacterome: The totality of (Bacterome.org) Carbomics: BiO center. 2003. (Carbomics.org) representation in bioinformatics especially for homology and Behaviorome: The totality of (Behaviorome.org) Cardiogenomics: The omics approach research of evolution study. (Alignmentome.org) Behavioromics: The omics approach research of Cardiogenome (Cardiogenomics.org) in biology Alignmentomics: conceived before 2003. The study of Behavioromics (Behavioromics.org) in biology Cellome: concept is derived from the understanding that aligning strings and sequences especially in bioinformatics. Behaviourome: The totality of (Behaviourome.org) cells as a whole can be used for therapeutic purposes. As an (Alignmentomics.org) Behaviouromics: The omics approach research of important bioresource, cells are kept for biotechnology. As Alignome: 2003 . The whole set of string alignment Behaviouromics (Behaviouromics.org) in biology a distinct group concept, cellome refers to such cells and algorithms such as FASTA, BLAST and HMMER. Bibliome: 1999. EBI (European Bioinformatics Institute). their genetic materials. (Cellome.org) (Alignome.org) The whole of biological science and technology literature. Cellomics: The study of Cellome. Cellomics is also a Alignomics: The omics approach research of Alignomics Within bibliome, each word and context is interconnected company name Cellomics, Inc. -
Identification of the Expressome by Machine Learning on Omics Data
UC San Diego UC San Diego Previously Published Works Title Identification of the expressome by machine learning on omics data. Permalink https://escholarship.org/uc/item/93d8w5pb Journal Proceedings of the National Academy of Sciences of the United States of America, 116(36) ISSN 0027-8424 Authors Sartor, Ryan C Noshay, Jaclyn Springer, Nathan M et al. Publication Date 2019-09-01 DOI 10.1073/pnas.1813645116 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Identification of the expressome by machine learning on omics data Ryan C. Sartora, Jaclyn Noshayb, Nathan M. Springerb, and Steven P. Briggsa,1 aDivision of Biology, University of California San Diego, La Jolla, CA 92093; and bDepartment of Plant Biology, University of Minnesota, St. Paul, MN 55108 Contributed by Steven P. Briggs, July 11, 2019 (sent for review August 14, 2018; reviewed by James A. Birchler and Virginia Walbot) Accurate annotation of plant genomes remains complex due to Most researchers study the predicted genes that are derived the presence of many pseudogenes arising from whole-genome from whole-genome annotations. These annotation approaches duplication-generated redundancy or the capture and movement can be complicated by the presence of sequences with homology of gene fragments by transposable elements. Machine learning on to protein coding genes that may not be functional genes. These genome-wide epigenetic marks, informed by transcriptomic and false gene annotations can result from silenced paralogs fol- proteomic training data, could be used to improve annotations lowing either whole-genome duplications or tandem duplica- through classification of all putative protein-coding genes as either tions, or they may arise from capture of gene fragments by constitutively silent or able to be expressed. -
Genomics from the Perspective of the Laboratory Mouse
Comparative Medicine Vol 52, No 3 Copyright 2002 June 2002 by the American Association for Laboratory Animal Science Pages 206-223 Overview “Muromics”: Genomics from the Perspective of the Laboratory Mouse Stephen W. Barthold, DVM, PhD The laboratory mouse has emerged as the preeminent model though mice are genetically well characterized, they also add an system for mammalian genomics research, and it continues its additional depth of complexity because of their genetic back- importance as a valuable model system for hypothesis-driven re- ground. By various counts, there are approximately 3,000 inbred, search. As scientific enterprise moves from the genomics era to the congenic, coisogenic, consomic, recombinant inbred, mutant, and post-genomics era, a rich lexicon of “-omics” has arisen, including other strains of mice. Genetically altered mice are swelling these phenomics, physiomics, proteomics, glycomics, dramanomics, numbers to unprecedented levels. The following overview illus- metabolomics, pharmacogenomics, and toxicogenomics. What is trates how the inherent value of these incredibly powerful re- missing is “muromics,” a rhetorical term used here to emphasize search models is diluted by genetic mismanagement or naivete. the focus of this review: understanding genomics and phenomics in the context of the complex biology of the laboratory mouse (the What is a laboratory mouse? murome). A proper definition of the modern laboratory mouse requires The inbred nature of laboratory mice is their greatest quality, an understanding of its history and origins. What is known as allowing control of important genetic variables and precise in- the laboratory mouse evolved at the beginning of the past cen- vestigation of specific gene alterations or gene function.