1 Automated Sequence-Based Approaches for Identifying Domain Families 3 Liisa Holm and Andreas Heger
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Improving the Prediction of Transcription Factor Binding Sites To
Improving the prediction of transcription factor binding sites to aid the interpretation of non-coding single nucleotide variants. Narayan Jayaram Research Department of Structural and Molecular Biology University College London A thesis submitted to University College London for the degree of Doctor of Philosophy 1 Declaration I, Narayan Jayaram 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. Narayan Jayaram 2 Abstract Single nucleotide variants (SNVs) that occur in transcription factor binding sites (TFBSs) can disrupt the binding of transcription factors and alter gene expression which can cause inherited diseases and act as driver SNVs in cancer. The identification of SNVs in TFBSs has historically been challenging given the limited number of experimentally characterised TFBSs. The recent ENCODE project has resulted in the availability of ChIP-Seq data that provides genome wide sets of regions bound by transcription factors. These data have the potential to improve the identification of SNVs in TFBSs. However, as the ChIP-Seq data identify a broader range of DNA in which a transcription factor binds, computational prediction is required to identify the precise TFBS. Prediction of TFBSs involves scanning a DNA sequence with a Position Weight Matrix (PWM) using a pattern matching tool. This thesis focusses on the prediction of TFBSs by: (a) evaluating a set of locally-installable pattern-matching tools and identifying the best performing tool (FIMO), (b) using the ENCODE ChIP-Seq data to evaluate a set of de novo motif discovery tools that are used to derive PWMs which can handle large volumes of data, (c) identifying the best performing tool (rGADEM), (d) using rGADEM to generate a set of PWMs from the ENCODE ChIP-Seq data and (e) by finally checking that the selection of the best pattern matching tool is not unduly influenced by the choice of PWMs. -
Applied Category Theory for Genomics – an Initiative
Applied Category Theory for Genomics { An Initiative Yanying Wu1,2 1Centre for Neural Circuits and Behaviour, University of Oxford, UK 2Department of Physiology, Anatomy and Genetics, University of Oxford, UK 06 Sept, 2020 Abstract The ultimate secret of all lives on earth is hidden in their genomes { a totality of DNA sequences. We currently know the whole genome sequence of many organisms, while our understanding of the genome architecture on a systematic level remains rudimentary. Applied category theory opens a promising way to integrate the humongous amount of heterogeneous informations in genomics, to advance our knowledge regarding genome organization, and to provide us with a deep and holistic view of our own genomes. In this work we explain why applied category theory carries such a hope, and we move on to show how it could actually do so, albeit in baby steps. The manuscript intends to be readable to both mathematicians and biologists, therefore no prior knowledge is required from either side. arXiv:2009.02822v1 [q-bio.GN] 6 Sep 2020 1 Introduction DNA, the genetic material of all living beings on this planet, holds the secret of life. The complete set of DNA sequences in an organism constitutes its genome { the blueprint and instruction manual of that organism, be it a human or fly [1]. Therefore, genomics, which studies the contents and meaning of genomes, has been standing in the central stage of scientific research since its birth. The twentieth century witnessed three milestones of genomics research [1]. It began with the discovery of Mendel's laws of inheritance [2], sparked a climax in the middle with the reveal of DNA double helix structure [3], and ended with the accomplishment of a first draft of complete human genome sequences [4]. -
A Community Proposal to Integrate Structural
F1000Research 2020, 9(ELIXIR):278 Last updated: 11 JUN 2020 OPINION ARTICLE A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community) [version 1; peer review: 1 approved, 3 approved with reservations] Christine Orengo1, Sameer Velankar2, Shoshana Wodak3, Vincent Zoete4, Alexandre M.J.J. Bonvin 5, Arne Elofsson 6, K. Anton Feenstra 7, Dietland L. Gerloff8, Thomas Hamelryck9, John M. Hancock 10, Manuela Helmer-Citterich11, Adam Hospital12, Modesto Orozco12, Anastassis Perrakis 13, Matthias Rarey14, Claudio Soares15, Joel L. Sussman16, Janet M. Thornton17, Pierre Tuffery 18, Gabor Tusnady19, Rikkert Wierenga20, Tiina Salminen21, Bohdan Schneider 22 1Structural and Molecular Biology Department, University College, London, UK 2Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, CB10 1SD, UK 3VIB-VUB Center for Structural Biology, Brussels, Belgium 4Department of Oncology, Lausanne University, Swiss Institute of Bioinformatics, Lausanne, Switzerland 5Bijvoet Center, Faculty of Science – Chemistry, Utrecht University, Utrecht, 3584CH, The Netherlands 6Science for Life Laboratory, Stockholm University, Solna, S-17121, Sweden 7Dept. Computer Science, Center for Integrative Bioinformatics VU (IBIVU), Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands 8Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg 9Bioinformatics center, Department of Biology, University of Copenhagen, Copenhagen, DK-2200, -
SD Gross BFI0403
Janet Thornton Bioinformatician avant la lettre Michael Gross B ioinformatics is very much a buzzword of our time, with new courses and institutes dedicated to it sprouting up almost everywhere. Most significantly, the flood of genome data has raised the gen- eral awareness of the need to deve-lop new computational approaches to make sense of all the raw information collected. Professor Janet Thornton, the current director of the European Bioinformatics Institute (EBI), an EMBL outpost based at the Hinxton campus near Cambridge, has been in the field even before there was a word for it. Coming to structural biology with a physics degree from the University of Nottingham, she was already involved with computer-generated structural im- ages in the 1970s, when personal comput- ers and user-friendly programs had yet to be invented. The Early Years larities. Within 15 minutes, the software From there to the EBI, her remarkable Janet Thornton can check all 2.4 billion possible re- career appears to be organised in lationships and pick the ones relevant decades. During the 1970s, she did doc- software to compare structures to each to the question at hand. In comparison toral and post-doctoral research at other, recognise known folds and spot to publicly available bioinformatics the Molecular Biophysics Laboratory in new ones. Such work provides both packages such as Blast or Psiblast, Oxford and at the National Institute for fundamental insights into the workings Biopendium can provide an additional Medical Research in Mill Hill, near Lon- of evolution on a molecular level, and 30 % of annotation, according to Inphar- don. -
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. -
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 -
Functional Effects Detailed Research Plan
GeCIP Detailed Research Plan Form Background The Genomics England Clinical Interpretation Partnership (GeCIP) brings together researchers, clinicians and trainees from both academia and the NHS to analyse, refine and make new discoveries from the data from the 100,000 Genomes Project. The aims of the partnerships are: 1. To optimise: • clinical data and sample collection • clinical reporting • data validation and interpretation. 2. To improve understanding of the implications of genomic findings and improve the accuracy and reliability of information fed back to patients. To add to knowledge of the genetic basis of disease. 3. To provide a sustainable thriving training environment. The initial wave of GeCIP domains was announced in June 2015 following a first round of applications in January 2015. On the 18th June 2015 we invited the inaugurated GeCIP domains to develop more detailed research plans working closely with Genomics England. These will be used to ensure that the plans are complimentary and add real value across the GeCIP portfolio and address the aims and objectives of the 100,000 Genomes Project. They will be shared with the MRC, Wellcome Trust, NIHR and Cancer Research UK as existing members of the GeCIP Board to give advance warning and manage funding requests to maximise the funds available to each domain. However, formal applications will then be required to be submitted to individual funders. They will allow Genomics England to plan shared core analyses and the required research and computing infrastructure to support the proposed research. They will also form the basis of assessment by the Project’s Access Review Committee, to permit access to data. -
Hmms Representing All Proteins of Known Structure. SCOP Sequence Searches, Alignments and Genome Assignments Julian Gough* and Cyrus Chothia
268–272 Nucleic Acids Research, 2002, Vol. 30, No. 1 © 2002 Oxford University Press SUPERFAMILY: HMMs representing all proteins of known structure. SCOP sequence searches, alignments and genome assignments Julian Gough* and Cyrus Chothia MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, UK Received September 28, 2001; Revised and Accepted October 30, 2001 ABSTRACT (currently 59) covering approximately half of the The SUPERFAMILY database contains a library of soluble protein domains. The assignments, super- family breakdown and statistics on them are available hidden Markov models representing all proteins of from the server. The database is currently used by known structure. The database is based on the SCOP this group and others for genome annotation, structural ‘superfamily’ level of protein domain classification genomics, gene prediction and domain-based genomic which groups together the most distantly related studies. proteins which have a common evolutionary ancestor. There is a public server at http://supfam.org which provides three services: sequence searching, INTRODUCTION multiple alignments to sequences of known structure, The SUPERFAMILY database is based on the SCOP (1) and structural assignments to all complete genomes. classification of protein domains. SCOP is a structural domain- Given an amino acid or nucleotide query sequence based heirarchical classification with several levels including the server will return the domain architecture and the ‘superfamily’ level. Proteins grouped together at the super- SCOP classification. The server produces alignments family level are defined as having structural, functional and sequence evidence for a common evolutionary ancestor. It is at of the query sequences with sequences of known this level, as the name suggests, that SUPERFAMILY operates structure, and includes multiple alignments of because it is the level with the most distantly related protein genome and PDB sequences. -
Exploring the Structure and Function Paradigm Oliver C Redfern, Benoit Dessailly and Christine a Orengo
Available online at www.sciencedirect.com Exploring the structure and function paradigm Oliver C Redfern, Benoit Dessailly and Christine A Orengo Advances in protein structure determination, led by the Figure 1 shows that as the international genomics initiat- structural genomics initiatives have increased the proportion of ives gather pace, both the number of sequences and novel folds deposited in the Protein Data Bank. However, these protein families are still growing at an exponential rate, structures are often not accompanied by functional annotations although the rate of expansion of protein families is with experimental confirmation. In this review, we reassess the substantially less. This trend is also observed among meaning of structural novelty and examine its relevance domain families, which are 10-fold fewer (<10 000) than to the complexity of the structure-function paradigm. the number of protein families. By targeting these, the Recent advances in the prediction of protein function from structural genomics initiatives can aim to characterise the structure are discussed, as well as new sequence-based major building blocks of whole proteins and since these methods for partitioning large, diverse superfamilies into domains recur in different combination in the genomes, it biologically meaningful clusters. Obtaining structural data for will be an important step towards understanding the these functionally coherent groups of proteins will allow us to complete structural repertoire in nature. Furthermore, better understand the relationship between structure and the complement of molecular functions found within function. an organism is likely to be even fewer. For example, 97% of proteins in yeast can be assigned one or more of Address 4000 unique GO terms. -
AUTOPHY by Deepika Prasad a Thesis
ANALYZING MARKER GENE DIVERSITY USING AN AUTOMATED PHYLOGENETIC TOOL: AUTOPHY by Deepika Prasad A thesis submitted to the Faculty of the University of Delaware in partial fulfillment of the requirements for the degree of Master of Science in Bioinformatics and Computational Biology Spring 2017 © 2017 Deepika Prasad All Rights Reserved ANALYZING MARKER GENE DIVERSITY USING AN AUTOMATED PHYLOGENETIC TOOL: AUTOPHY by Deepika Prasad Approved: __________________________________________________________ Shawn Polson, Ph.D. Professor in charge of thesis on behalf of the Advisory Committee Approved: __________________________________________________________ Kathleen F.McCoy, Ph.D. Chair of the Department of Computer and Information Sciences Approved: __________________________________________________________ Babatunde A. Ogunnaike, Ph.D. Dean of the College of Engineering Approved: __________________________________________________________ Ann L. Ardis, Ph.D. Senior Vice Provost for Graduate and Professional Education ACKNOWLEDGMENTS I want to express my sincerest gratitude to Professor Shawn Polson for his patience, continuous support, and enthusiasm. I could not have imagined having a better mentor to guide me through the process of my Master’s thesis. I would like to thank my committee members, Professor Eric Wommack and Honzhan Huang for their insightful comments and guidance in the thesis. I would also like to express my gratitude to Barbra Ferrell for asking important questions throughout my thesis research and helping me with the writing process. I would like to thank my friend Sagar Doshi, and lab mates Daniel Nasko, and Prasanna Joglekar, for helping me out with data, presentations, and giving important advice, whenever necessary. Last but not the least, I would like to thank my family, for being my pillars of strength. -
Tunca Doğan , Alex Bateman , Maria J. Martin Your Choice
(—THIS SIDEBAR DOES NOT PRINT—) UniProt Domain Architecture Alignment: A New Approach for Protein Similarity QUICK START (cont.) DESIGN GUIDE Search using InterPro Domain Annotation How to change the template color theme This PowerPoint 2007 template produces a 44”x44” You can easily change the color theme of your poster by going to presentation poster. You can use it to create your research 1 1 1 the DESIGN menu, click on COLORS, and choose the color theme of poster and save valuable time placing titles, subtitles, text, Tunca Doğan , Alex Bateman , Maria J. Martin your choice. You can also create your own color theme. and graphics. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), We provide a series of online tutorials that will guide you Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK through the poster design process and answer your poster Correspondence: [email protected] production questions. To view our template tutorials, go online to PosterPresentations.com and click on HELP DESK. ABSTRACT METHODOLOGY RESULTS & DISCUSSION When you are ready to print your poster, go online to InterPro Domains, DAs and DA Alignment PosterPresentations.com Motivation: Similarity based methods have been widely used in order to Generation of the Domain Architectures: You can also manually change the color of your background by going to VIEW > SLIDE MASTER. After you finish working on the master be infer the properties of genes and gene products containing little or no 1) Collect the hits for each protein from InterPro. Domain annotation coverage Overlap domain hits problem in Need assistance? Call us at 1.510.649.3001 difference b/w domain databases: the InterPro database: sure to go to VIEW > NORMAL to continue working on your poster. -
Evolution and Function of Drososphila Melanogaster Cis-Regulatory Sequences
Evolution and Function of Drososphila melanogaster cis-regulatory Sequences By Aaron Hardin A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Molecular and Cell Biology in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Michael Eisen, Chair Professor Doris Bachtrog Professor Gary Karpen Professor Lior Pachter Fall 2013 Evolution and Function of Drososphila melanogaster cis-regulatory Sequences This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License 2013 by Aaron Hardin 1 Abstract Evolution and Function of Drososphila melanogaster cis-regulatory Sequences by Aaron Hardin Doctor of Philosophy in Molecular and Cell Biology University of California, Berkeley Professor Michael Eisen, Chair In this work, I describe my doctoral work studying the regulation of transcription with both computational and experimental methods on the natural genetic variation in a population. This works integrates an investigation of the consequences of polymorphisms at three stages of gene regulation in the developing fly embryo: the diversity at cis-regulatory modules, the integration of transcription factor binding into changes in chromatin state and the effects of these inputs on the final phenotype of embryonic gene expression. i I dedicate this dissertation to Mela Hardin who has been here for me at all times, even when we were apart. ii Contents List of Figures iv List of Tables vi Acknowledgments vii 1 Introduction1 2 Within Species Diversity in cis-Regulatory Modules6 2.1 Introduction....................................6 2.2 Results.......................................8 2.2.1 Genome wide diversity in transcription factor binding sites......8 2.2.2 Genome wide purifying selection on cis-regulatory modules......9 2.3 Discussion.....................................9 2.4 Methods for finding polymorphisms......................