Interview with Ewan Birney
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RDA COVID-19 Recommendations and Guidelines on Data Sharing
RDA COVID-19 Recommendations and Guidelines on Data Sharing DOI: 10.15497/RDA00052 Authors: RDA COVID-19 Working Group Published: 30th June 2020 Abstract: This is the final version of the Recommendations and Guidelines from the RDA COVID19 Working Group, and has been endorsed through the official RDA process. Keywords: RDA; Recommendations; COVID-19. Language: English License: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication RDA webpage: https://www.rd-alliance.org/group/rda-covid19-rda-covid19-omics-rda-covid19- epidemiology-rda-covid19-clinical-rda-covid19-1 Related resources: - RDA COVID-19 Guidelines and Recommendations – preliminary version, https://doi.org/10.15497/RDA00046 - Data Sharing in Epidemiology, https://doi.org/10.15497/RDA00049 - RDA COVID-19 Zotero Library, https://doi.org/10.15497/RDA00051 Citation and Download: RDA COVID-19 Working Group. Recommendations and Guidelines on data sharing. Research Data Alliance. 2020. DOI: https://doi.org/10.15497/RDA00052 RDA COVID-19 Recommendations and Guidelines on Data Sharing RDA Recommendation (FINAL Release) Produced by: RDA COVID-19 Working Group, 2020 Document Metadata Identifier DOI: https://doi.org/10.15497/rda00052 Citation To cite this document please use: RDA COVID-19 Working Group. Recommendations and Guidelines on data sharing. Research Data Alliance. 2020. DOI: https://doi.org/10.15497/rda00052 Title RDA COVID-19; Recommendations and Guidelines on Data Sharing, Final release 30 June 2020 Description This is the final version of the Recommendations and Guidelines -
Gene Prediction: the End of the Beginning Comment Colin Semple
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by PubMed Central http://genomebiology.com/2000/1/2/reports/4012.1 Meeting report Gene prediction: the end of the beginning comment Colin Semple Address: Department of Medical Sciences, Molecular Medicine Centre, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK. E-mail: [email protected] Published: 28 July 2000 reviews Genome Biology 2000, 1(2):reports4012.1–4012.3 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2000/1/2/reports/4012 © GenomeBiology.com (Print ISSN 1465-6906; Online ISSN 1465-6914) Reducing genomes to genes reports A report from the conference entitled Genome Based Gene All ab initio gene prediction programs have to balance sensi- Structure Determination, Hinxton, UK, 1-2 June, 2000, tivity against accuracy. It is often only possible to detect all organised by the European Bioinformatics Institute (EBI). the real exons present in a sequence at the expense of detect- ing many false ones. Alternatively, one may accept only pre- dictions scoring above a more stringent threshold but lose The draft sequence of the human genome will become avail- those real exons that have lower scores. The trick is to try and able later this year. For some time now it has been accepted increase accuracy without any large loss of sensitivity; this deposited research that this will mark a beginning rather than an end. A vast can be done by comparing the prediction with additional, amount of work will remain to be done, from detailing independent evidence. -
A Bivalent Chromatin Structure Marks Key Developmental Genes in Embryonic Stem Cells
A Bivalent Chromatin Structure Marks Key Developmental Genes in Embryonic Stem Cells Bradley E. Bernstein,1,2,3,* Tarjei S. Mikkelsen,3,4 Xiaohui Xie,3 Michael Kamal,3 Dana J. Huebert,1 James Cuff,3 Ben Fry,3 Alex Meissner,5 Marius Wernig,5 Kathrin Plath,5 Rudolf Jaenisch,5 Alexandre Wagschal,6 Robert Feil,6 Stuart L. Schreiber,3,7 and Eric S. Lander3,5 1 Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA 02129, USA 2 Department of Pathology, Harvard Medical School, Boston, MA 02115, USA 3 Broad Institute of Harvard and MIT, Cambridge, MA 02139, USA 4 Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA 5 Whitehead Institute for Biomedical Research, MIT, Cambridge, MA 02139, USA 6 Institute of Molecular Genetics, CNRS UMR-5535 and University of Montpellier-II, Montpellier, France 7 Howard Hughes Medical Institute at the Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA *Contact: [email protected] DOI 10.1016/j.cell.2006.02.041 SUMMARY which in turn modulate chromatin structure (Jenuwein and Allis, 2001; Margueron et al., 2005). The core histones The most highly conserved noncoding ele- H2A, H2B, H3, and H4 are subject to dozens of different ments (HCNEs) in mammalian genomes cluster modifications, including acetylation, methylation, and within regions enriched for genes encoding de- phosphorylation. Histone H3 lysine 4 (Lys4) and lysine velopmentally important transcription factors 27 (Lys27) methylation are of particular interest as these (TFs). This suggests that HCNE-rich regions modifications are catalyzed, respectively, by trithorax- may contain key regulatory controls involved and Polycomb-group proteins, which mediate mitotic in- heritance of lineage-specific gene expression programs in development. -
The EMBL-European Bioinformatics Institute the Hub for Bioinformatics in Europe
The EMBL-European Bioinformatics Institute The hub for bioinformatics in Europe Blaise T.F. Alako, PhD [email protected] www.ebi.ac.uk What is EMBL-EBI? • Part of the European Molecular Biology Laboratory • International, non-profit research institute • Europe’s hub for biological data, services and research The European Molecular Biology Laboratory Heidelberg Hamburg Hinxton, Cambridge Basic research Structural biology Bioinformatics Administration Grenoble Monterotondo, Rome EMBO EMBL staff: 1500 people Structural biology Mouse biology >60 nationalities EMBL member states Austria, Belgium, Croatia, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and the United Kingdom Associate member state: Australia Who we are ~500 members of staff ~400 work in services & support >53 nationalities ~120 focus on basic research EMBL-EBI’s mission • Provide freely available data and bioinformatics services to all facets of the scientific community in ways that promote scientific progress • Contribute to the advancement of biology through basic investigator-driven research in bioinformatics • Provide advanced bioinformatics training to scientists at all levels, from PhD students to independent investigators • Help disseminate cutting-edge technologies to industry • Coordinate biological data provision throughout Europe Services Data and tools for molecular life science www.ebi.ac.uk/services Browse our services 9 What services do we provide? Labs around the -
Cryptic Inoviruses Revealed As Pervasive in Bacteria and Archaea Across Earth’S Biomes
ARTICLES https://doi.org/10.1038/s41564-019-0510-x Corrected: Author Correction Cryptic inoviruses revealed as pervasive in bacteria and archaea across Earth’s biomes Simon Roux 1*, Mart Krupovic 2, Rebecca A. Daly3, Adair L. Borges4, Stephen Nayfach1, Frederik Schulz 1, Allison Sharrar5, Paula B. Matheus Carnevali 5, Jan-Fang Cheng1, Natalia N. Ivanova 1, Joseph Bondy-Denomy4,6, Kelly C. Wrighton3, Tanja Woyke 1, Axel Visel 1, Nikos C. Kyrpides1 and Emiley A. Eloe-Fadrosh 1* Bacteriophages from the Inoviridae family (inoviruses) are characterized by their unique morphology, genome content and infection cycle. One of the most striking features of inoviruses is their ability to establish a chronic infection whereby the viral genome resides within the cell in either an exclusively episomal state or integrated into the host chromosome and virions are continuously released without killing the host. To date, a relatively small number of inovirus isolates have been extensively studied, either for biotechnological applications, such as phage display, or because of their effect on the toxicity of known bacterial pathogens including Vibrio cholerae and Neisseria meningitidis. Here, we show that the current 56 members of the Inoviridae family represent a minute fraction of a highly diverse group of inoviruses. Using a machine learning approach lever- aging a combination of marker gene and genome features, we identified 10,295 inovirus-like sequences from microbial genomes and metagenomes. Collectively, our results call for reclassification of the current Inoviridae family into a viral order including six distinct proposed families associated with nearly all bacterial phyla across virtually every ecosystem. -
The for Report 07-08
THE CENTER FOR INTEGRATIVE GENOMICS REPORT 07-08 www.unil.ch/cig Table of Contents INTRODUCTION 2 The CIG at a glance 2 The CIG Scientific Advisory Committee 3 Message from the Director 4 RESEARCH 6 Richard Benton Chemosensory perception in Drosophila: from genes to behaviour 8 Béatrice Desvergne Networking activity of PPARs during development and in adult metabolic homeostasis 10 Christian Fankhauser The effects of light on plant growth and development 12 Paul Franken Genetics and energetics of sleep homeostasis and circadian rhythms 14 Nouria Hernandez Mechanisms of basal and regulated RNA polymerase II and III transcription of ncRNA in mammalian cells 16 Winship Herr Regulation of cell proliferation 18 Henrik Kaessmann Mammalian evolutionary genomics 20 Sophie Martin Molecular mechanisms of cell polarization 22 Liliane Michalik Transcriptional control of tissue repair and angiogenesis 24 Alexandre Reymond Genome structure and expression 26 Andrzej Stasiak Functional transitions of DNA structure 28 Mehdi Tafti Genetics of sleep and the sleep EEG 30 Bernard Thorens Molecular and physiological analysis of energy homeostasis in health and disease 32 Walter Wahli The multifaceted roles of PPARs 34 Other groups at the Génopode 37 CORE FACILITIES 40 Lausanne DNA Array Facility (DAFL) 42 Protein Analysis Facility (PAF) 44 Core facilities associated with the CIG 46 EDUCATION 48 Courses and lectures given by CIG members 50 Doing a PhD at the CIG 52 Seminars and symposia 54 The CIG annual retreat 62 The CIG and the public 63 Artist in residence at the CIG 63 PEOPLE 64 1 Introduction The Center for IntegratiVE Genomics (CIG) at A glance The Center for Integrative Genomics (CIG) is the newest depart- ment of the Faculty of Biology and Medicine of the University of Lausanne (UNIL). -
Learning Protein Constitutive Motifs from Sequence Data Je´ Roˆ Me Tubiana, Simona Cocco, Re´ Mi Monasson*
TOOLS AND RESOURCES Learning protein constitutive motifs from sequence data Je´ roˆ me Tubiana, Simona Cocco, Re´ mi Monasson* Laboratory of Physics of the Ecole Normale Supe´rieure, CNRS UMR 8023 & PSL Research, Paris, France Abstract Statistical analysis of evolutionary-related protein sequences provides information about their structure, function, and history. We show that Restricted Boltzmann Machines (RBM), designed to learn complex high-dimensional data and their statistical features, can efficiently model protein families from sequence information. We here apply RBM to 20 protein families, and present detailed results for two short protein domains (Kunitz and WW), one long chaperone protein (Hsp70), and synthetic lattice proteins for benchmarking. The features inferred by the RBM are biologically interpretable: they are related to structure (residue-residue tertiary contacts, extended secondary motifs (a-helixes and b-sheets) and intrinsically disordered regions), to function (activity and ligand specificity), or to phylogenetic identity. In addition, we use RBM to design new protein sequences with putative properties by composing and ’turning up’ or ’turning down’ the different modes at will. Our work therefore shows that RBM are versatile and practical tools that can be used to unveil and exploit the genotype–phenotype relationship for protein families. DOI: https://doi.org/10.7554/eLife.39397.001 Introduction In recent years, the sequencing of many organisms’ genomes has led to the collection of a huge number of protein sequences, which are catalogued in databases such as UniProt or PFAM Finn et al., 2014). Sequences that share a common ancestral origin, defining a family (Figure 1A), *For correspondence: are likely to code for proteins with similar functions and structures, providing a unique window into [email protected] the relationship between genotype (sequence content) and phenotype (biological features). -
Multi-Class Protein Classification Using Adaptive Codes
Journal of Machine Learning Research 8 (2007) 1557-1581 Submitted 8/06; Revised 4/07; Published 7/07 Multi-class Protein Classification Using Adaptive Codes Iain Melvin∗ [email protected] NEC Laboratories of America Princeton, NJ 08540, USA Eugene Ie∗ [email protected] Department of Computer Science and Engineering University of California San Diego, CA 92093-0404, USA Jason Weston [email protected] NEC Laboratories of America Princeton, NJ 08540, USA William Stafford Noble [email protected] Department of Genome Sciences Department of Computer Science and Engineering University of Washington Seattle, WA 98195, USA Christina Leslie [email protected] Center for Computational Learning Systems Columbia University New York, NY 10115, USA Editor: Nello Cristianini Abstract Predicting a protein’s structural class from its amino acid sequence is a fundamental problem in computational biology. Recent machine learning work in this domain has focused on develop- ing new input space representations for protein sequences, that is, string kernels, some of which give state-of-the-art performance for the binary prediction task of discriminating between one class and all the others. However, the underlying protein classification problem is in fact a huge multi- class problem, with over 1000 protein folds and even more structural subcategories organized into a hierarchy. To handle this challenging many-class problem while taking advantage of progress on the binary problem, we introduce an adaptive code approach in the output space of one-vs- the-rest prediction scores. Specifically, we use a ranking perceptron algorithm to learn a weight- ing of binary classifiers that improves multi-class prediction with respect to a fixed set of out- put codes. -
Life Will Never Be the Same Annual Genome Sequencing and Biology Meeting, Cold Spring Harbor Laboratory, USA
Yeast Yeast 2000; 17: 241±243. Meeting Review Life will never be the same Annual Genome Sequencing and Biology Meeting, Cold Spring Harbor Laboratory, USA. May 2000 M.A. Strivens* MRC Mammalian Genetics Unit and UK Mouse Genome Centre, Harwell, UK *Correspondence to: M. A. Strivens, MRC Mammalian Genetics Unit and UK Mouse Genome Centre, Harwell, Oxfordshire OX11 0RD,UK. E-mail: [email protected] It seems appropriate that the Cold Spring Harbor The crux of the whole-genome shotgun strategy is Genome Sequencing and Biology Meeting, which the assembly technique. Gene Myers (Celera Geno- witnessed the creation of the Human Genome mics) reported on how the `double-barrelled' shot- Organization (HUGO) in 1988, should this year gun2 approach had given a signi®cant advantage to present three major advances in genomic science: the computer algorithms employed in the assembly the completion of the ®nished sequence of Droso- of the ¯y genome. The assembly system employs a phila melanogaster; the announcement that 85% of bottom-up, nucleating strategy, initially assembling the genome of Homo sapiens is now in draft small islands of sequence of high con®dence sequence; and the complete, ®nished sequence of a (diverting the assembly of repeat regions to later second human chromosome, chromosome 21. Other stages) and then searching for other sequence major sessions of the meeting focused on single (including orientation data from clone end- nucleotide polymorphisms (SNPs), ethical, legal and sequencing) to join the islands together. In addition, social implications (ELSI), as well as comparative he con®rmed a less than 0.5% error rate in the and functional genomics. -
Dynamite: a Flexible Code Generating Language for Dynamic Programming Methods Used in Sequence Comaprison
From: ISMB-97 Proceedings. Copyright © 1997, AAAI (www.aaai.org). All rights reserved. Dynamite: A flexible code generating language for dynamic programming methods used in sequence comaprison. Ewan Birney and Richard Durbin The Sanger Centre, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. {birney,rd}@sanger.ac.uk Abstract Dynamite, which implements a standard set of DP routines We have developed a code generating language, called in C from an abstract definition of an underlying state Dynamite, specialised for the production and subsequent machine given in a relatively simple text file. manipulation of complex dynamic programming methods for biological sequence comparison. From a relatively Dynamite is designed for problems where one is simple text definition file Dynamite will produce a variety comparing two objects, the query and the target (figure 1), of implementations of a dynamic programming method, that both have a repetitive sequence-like structure, i.e. including database searches and linear space alignments. each can be considered as a series of units (e.g. for proteins The speed of the generated code is comparable to hand each unit would be a residue). The comparison is made in written code, and the additional flexibility has proved a dynamic programming matrix of cells, each invaluable in designing and testing new algorithms. An innovation is a flexible labelling system, which can be used corresponding to a unit of the query being compared to a to annotate the original sequences with biological unit of the target. Within a cell, there can be an arbitrary information. We illustrate the Dynamite syntax and number of states, each of which holds a numerical value flexibility by showing definitions for dynamic programming that is calculated using the DP recurrence relations from routines (i) to align two protein sequences under the state values in previous cells, combined with information assumption that they are both poly-topic transmembrane from the two units defining the current cell. -
UC Irvine UC Irvine Previously Published Works
UC Irvine UC Irvine Previously Published Works Title The capacity of feedforward neural networks. Permalink https://escholarship.org/uc/item/29h5t0hf Authors Baldi, Pierre Vershynin, Roman Publication Date 2019-08-01 DOI 10.1016/j.neunet.2019.04.009 License https://creativecommons.org/licenses/by/4.0/ 4.0 Peer reviewed eScholarship.org Powered by the California Digital Library University of California THE CAPACITY OF FEEDFORWARD NEURAL NETWORKS PIERRE BALDI AND ROMAN VERSHYNIN Abstract. A long standing open problem in the theory of neural networks is the devel- opment of quantitative methods to estimate and compare the capabilities of different ar- chitectures. Here we define the capacity of an architecture by the binary logarithm of the number of functions it can compute, as the synaptic weights are varied. The capacity provides an upperbound on the number of bits that can be extracted from the training data and stored in the architecture during learning. We study the capacity of layered, fully-connected, architectures of linear threshold neurons with L layers of size n1, n2,...,nL and show that in essence the capacity is given by a cubic polynomial in the layer sizes: L−1 C(n1,...,nL) = Pk=1 min(n1,...,nk)nknk+1, where layers that are smaller than all pre- vious layers act as bottlenecks. In proving the main result, we also develop new techniques (multiplexing, enrichment, and stacking) as well as new bounds on the capacity of finite sets. We use the main result to identify architectures with maximal or minimal capacity under a number of natural constraints. -
Establishing Incentives and Changing Cultures to Support Data Access
EXPERT ADVISORY GROUP ON DATA ACCESS ESTABLISHING INCENTIVES AND CHANGING CULTURES TO SUPPORT DATA ACCESS May 2014 ACKNOWLEDGEMENT This is a report of the Expert Advisory Group on Data Access (EAGDA). EAGDA was established by the MRC, ESRC, Cancer Research UK and the Wellcome Trust in 2012 to provide strategic advice on emerging scientific, ethical and legal issues in relation to data access for cohort and longitudinal studies. The report is based on work undertaken by the EAGDA secretariat at the Group’s request. The contributions of David Carr, Natalie Banner, Grace Gottlieb, Joanna Scott and Katherine Littler at the Wellcome Trust are gratefully acknowledged. EAGDA would also like to thank the representatives of the MRC, ESRC and Cancer Research UK for their support and input throughout the project. Most importantly, EAGDA owes a considerable debt of gratitude to the many individuals from the research community who contributed to this study through feeding in their expert views via surveys, interviews and focus groups. The Expert Advisory Group on Data Access Martin Bobrow (Chair) Bartha Maria Knoppers James Banks Mark McCarthy Paul Burton Andrew Morris George Davey Smith Onora O'Neill Rosalind Eeles Nigel Shadbolt Paul Flicek Chris Skinner Mark Guyer Melanie Wright Tim Hubbard 1 EXECUTIVE SUMMARY This project was developed as a key component of the workplan of the Expert Advisory Group on Data Access (EAGDA). EAGDA wished to understand the factors that help and hinder individual researchers in making their data (both published and unpublished) available to other researchers, and to examine the potential need for new types of incentives to enable data access and sharing.