Bioinformatics 2012 Stockholm June 11-14 2012
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Curriculum Vitae – Prof. Anders Krogh Personal Information
Curriculum Vitae – Prof. Anders Krogh Personal Information Date of Birth: May 2nd, 1959 Private Address: Borgmester Jensens Alle 22, st th, 2100 København Ø, Denmark Contact information: Dept. of Biology, Univ. of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen, Denmark. +45 3532 1329, [email protected] Web: https://scholar.google.com/citations?user=-vGMjmwAAAAJ Education Sept 1991 Ph.D. (Physics), Niels Bohr Institute, Univ. of Copenhagen, Denmark June 1987 Cand. Scient. [M. Sc.] (Physics and mathematics), NBI, Univ. of Copenhagen Professional / Work Experience (since 2000) 2018 – Professor of Bionformatics, Dept of Computer Science (50%) and Dept of Biology (50%), Univ. of Copenhagen 2002 – 2018 Professor of Bionformatics, Dept of Biology, Univ. of Copenhagen 2009 – 2018 Head of Section for Computational and RNA Biology, Dept. of Biology, Univ. of Copenhagen 2000–2002 Associate Prof., Technical Univ. of Denmark (DTU), Copenhagen Prices and Awards 2017 – Fellow of the International Society for Computational Biology https://www.iscb.org/iscb- fellows-program 2008 – Fellow, Royal Danish Academy of Sciences and Letters Public Activities & Appointments (since 2009) 2014 – Board member, Elixir, European Infrastructure for Life Science. 2014 – Steering committee member, Danish Elixir Node. 2012 – 2016 Board member, Bioinformatics Infrastructure for Life Sciences (BILS), Swedish Research Council 2011 – 2016 Director, Centre for Computational and Applied Transcriptomics (COAT) 2009 – Associate editor, BMC Bioinformatics Publications § Google Scholar: https://scholar.google.com/citations?user=-vGMjmwAAAAJ § ORCID: 0000-0002-5147-6282. ResearcherID: M-1541-2014 § Co-author of 130 peer-reviewed papers and 2 monographs § 63,000 citations and h-index of 74 (Google Scholar, June 2019) § H-index of 54 in Web of science (June 2019) § Publications in high-impact journals: Nature (5), Science (1), Cell (1), Nature Genetics (2), Nature Biotechnology (2), Nature Communications (4), Cell (1, to appear), Genome Res. -
Predicting Transmembrane Topology and Signal Peptides with Hidden Markov Models
i i “thesis” — 2006/3/6 — 10:55 — page i — #1 i i From the Center for Genomics and Bioinformatics, Karolinska Institutet, Stockholm, Sweden Predicting transmembrane topology and signal peptides with hidden Markov models Lukas Käll Stockholm, 2006 i i i i i i “thesis” — 2006/3/6 — 10:55 — page ii — #2 i i ©Lukas Käll, 2006 Except previously published papers which were reproduced with permission from the publisher. Paper I: ©2002 Federation of European Biochemical Societies Paper II: ©2004 Elsevier Ltd. Paper III: ©2005 Federation of European Biochemical Societies Paper IV: ©2005 Lukas Käll, Anders Krogh and Erik Sonnhammer Paper V: ©2006 ¿e Protein Society Published and printed by Larserics Digital Print, Sundbyberg ISBN 91-7140-719-7 i i i i i i “thesis” — 2006/3/6 — 10:55 — page iii — #3 i i Abstract Transmembrane proteins make up a large and important class of proteins. About 20% of all genes encode transmembrane proteins. ¿ey control both substances and information going in and out of a cell. Yet basic knowledge about membrane insertion and folding is sparse, and our ability to identify, over-express, purify, and crystallize transmembrane proteins lags far behind the eld of water-soluble proteins. It is dicult to determine the three dimensional structures of transmembrane proteins. ¿ere- fore, researchers normally attempt to determine their topology, i.e. which parts of the protein are buried in the membrane, and on what side of the membrane are the other parts located. Proteins aimed for export have an N-terminal sequence known as a signal peptide that is in- serted into the membrane and cleaved o. -
Biological Sequence Analysis Probabilistic Models of Proteins and Nucleic Acids
This page intentionally left blank Biological sequence analysis Probabilistic models of proteins and nucleic acids The face of biology has been changed by the emergence of modern molecular genetics. Among the most exciting advances are large-scale DNA sequencing efforts such as the Human Genome Project which are producing an immense amount of data. The need to understand the data is becoming ever more pressing. Demands for sophisticated analyses of biological sequences are driving forward the newly-created and explosively expanding research area of computational molecular biology, or bioinformatics. Many of the most powerful sequence analysis methods are now based on principles of probabilistic modelling. Examples of such methods include the use of probabilistically derived score matrices to determine the significance of sequence alignments, the use of hidden Markov models as the basis for profile searches to identify distant members of sequence families, and the inference of phylogenetic trees using maximum likelihood approaches. This book provides the first unified, up-to-date, and tutorial-level overview of sequence analysis methods, with particular emphasis on probabilistic modelling. Pairwise alignment, hidden Markov models, multiple alignment, profile searches, RNA secondary structure analysis, and phylogenetic inference are treated at length. Written by an interdisciplinary team of authors, the book is accessible to molecular biologists, computer scientists and mathematicians with no formal knowledge of each others’ fields. It presents the state-of-the-art in this important, new and rapidly developing discipline. Richard Durbin is Head of the Informatics Division at the Sanger Centre in Cambridge, England. Sean Eddy is Assistant Professor at Washington University’s School of Medicine and also one of the Principle Investigators at the Washington University Genome Sequencing Center. -
Tporthmm : Predicting the Substrate Class Of
TPORTHMM : PREDICTING THE SUBSTRATE CLASS OF TRANSMEMBRANE TRANSPORT PROTEINS USING PROFILE HIDDEN MARKOV MODELS Shiva Shamloo A thesis in The Department of Computer Science Presented in Partial Fulfillment of the Requirements For the Degree of Master of Computer Science Concordia University Montréal, Québec, Canada December 2020 © Shiva Shamloo, 2020 Concordia University School of Graduate Studies This is to certify that the thesis prepared By: Shiva Shamloo Entitled: TportHMM : Predicting the substrate class of transmembrane transport proteins using profile Hidden Markov Models and submitted in partial fulfillment of the requirements for the degree of Master of Computer Science complies with the regulations of this University and meets the accepted standards with respect to originality and quality. Signed by the final examining commitee: Examiner Dr. Sabine Bergler Examiner Dr. Andrew Delong Supervisor Dr. Gregory Butler Approved Dr. Lata Narayanan, Chair Department of Computer Science and Software Engineering 20 Dean Dr. Mourad Debbabi Faculty of Engineering and Computer Science Abstract TportHMM : Predicting the substrate class of transmembrane transport proteins using profile Hidden Markov Models Shiva Shamloo Transporters make up a large proportion of proteins in a cell, and play important roles in metabolism, regulation, and signal transduction by mediating movement of compounds across membranes but they are among the least characterized proteins due to their hydropho- bic surfaces and lack of conformational stability. There is a need for tools that predict the substrates which are transported at the level of substrate class and the level of specific substrate. This work develops a predictor, TportHMM, using profile Hidden Markov Model (HMM) and Multiple Sequence Alignment (MSA). -
A Systematic, Label-Free Method for Identifying RNA-Associated Proteins in Vivo Provides Insights Into Vertebrate Ciliary Beatin
bioRxiv preprint doi: https://doi.org/10.1101/2020.02.26.966754; this version posted March 2, 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 4.0 International license. 1 A systematic, label-free method for identifying RNA- associated proteins in vivo provides insights into vertebrate ciliary beating Kevin Drew*, Chanjae Lee*, Rachael M. Cox, Vy Dang, Caitlin C. Devitt, Ophelia Papoulas, Ryan L. Huizar, Edward M. Marcotte** and John B. Wallingford** Dept. of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, TX 78712 *These authors contributed equally **To whom correspondence should be addressed: John Wallingford Patterson Labs 2401 Speedway Austin, Texas 78712 [email protected] 512-232-2784 Edward Marcotte 2500 Speedway, MBB 3.148BA Austin, Texas 78712 [email protected] 512-471-5435 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.26.966754; this version posted March 2, 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 4.0 International license. 2 Abstract: Cell-type specific RNA-associated proteins (RAPs) are essential for development and homeostasis in animals. Despite a massive recent effort to systematically identify RAPs, we currently have few comprehensive rosters of cell-type specific RAPs in vertebrate tissues. Here, we demonstrate the feasibility of determining the RNA-interacting proteome of a defined vertebrate embryonic tissue using DIF-FRAC, a systematic and universal (i.e., label-free) method. -
A Jumping Profile HMM for Remote Protein Homology Detection
A Jumping Profile HMM for Remote Protein Homology Detection Anne-Kathrin Schultz and Mario Stanke Institut fur¨ Mikrobiologie und Genetik, Abteilung Bioinformatik, Universit¨at G¨ottingen, Germany contact: faschult2, [email protected] Abstract Our Generalization: Jumping Profile HMM We address the problem of finding new members of a given protein family in a database of protein sequences. We are given a MSA of k rows and a candidate sequence. At each position the candidate sequence is either Given a multiple sequence alignment (MSA) of the sequences in the protein family, we would like to score each aligned to the whole column of the MSA or to a certain reference sequence: We say that we are in the column candidate sequence in the database with respect to how likely it is that it belongs to the family. Successful mode or in a row mode of the HMM. methods for this task are profile Hidden Markov Models (HMM), like HMMER [Eddy, 1998] and SAM [Hughey • Column mode: (red part of Figure 1) and Krogh, 1996], and a so-called jumping alignment (JALI) [Spang et al., 2002]. As in a profile HMM each consensus column of the MSA is modeled by three states: match (M), insert (I) and delete (D).Match states model the distribution of residues in this column, they emit the amino acids We developed a Hidden Markov Model which can be regarded as a generalization of these two methods: At each with a probability which depends on all residues in this column. position the candidate sequence is either aligned to the whole column of the MSA or to a certain reference sequence. -
Dear Delegates,History of Productive Scientific Discussions of New Challenging Ideas and Participants Contributing from a Wide Range of Interdisciplinary fields
3rd IS CB S t u d ent Co u ncil S ymp os ium Welcome To The 3rd ISCB Student Council Symposium! Welcome to the Student Council Symposium 3 (SCS3) in Vienna. The ISCB Student Council's mis- sion is to develop the next generation of computa- tional biologists. We would like to thank and ac- knowledge our sponsors and the ISCB organisers for their crucial support. The SCS3 provides an ex- citing environment for active scientific discussions and the opportunity to learn vital soft skills for a successful scientific career. In addition, the SCS3 is the biggest international event targeted to students in the field of Computational Biology. We would like to thank our hosts and participants for making this event educative and fun at the same time. Student Council meetings have had a rich Dear Delegates,history of productive scientific discussions of new challenging ideas and participants contributing from a wide range of interdisciplinary fields. Such meet- We are very happy to welcomeings have you proved all touseful the in ISCBproviding Student students Council and postdocs Symposium innovative inputsin Vienna. and an Afterincreased the network suc- cessful symposiums at ECCBof potential 2005 collaborators. in Madrid and at ISMB 2006 in Fortaleza we are determined to con- tinue our efforts to provide an event for students and young researchers in the Computational Biology community. Like in previousWe ar yearse extremely our excitedintention to have is toyou crhereatee and an the opportunity vibrant city of Vforienna students welcomes to you meet to our their SCS3 event. peers from all over the world for exchange of ideas and networking. -
Trouble with Testosterone Test
Trouble with testosterone test Annual Meeting Special Section CONTENTS NEWS FEATURES PERSPECTIVES 2 14 36 EDITOR’S NOTE THE TROUBLE WITH PUBLIC AFFAIRS It’s time THE TESTOSTERONE TEST Are postdocs still invisible? 3 18 38 PRESIDENT’S MESSAGE MASTERS OF PHYSIOLOGY MINORITY AFFAIRS Celebrating serendipity 38 Exemplifying Sewer’s commitment to diversity 22 40 Diversifying the scientic 4 THE 2017 ANNUAL MEETING NEWS FROM THE HILL workforce with IMAGE 23 Expand your scientic horizons A lot at stake 42 Cultivating a focus on diversity 29 e spotlight is on you as a community 30 Promoting lifelong learning 5 34 Advance your careers, grad students MEMBER UPDATE 44 and postdocs! TRANSITIONS 35 Reminders for the 2017 ASBMB 7 Undergraduate Poster Competition Wrestling with life RETROSPECTIVE 7 Roscoe Owen Brady (1923–2016) 14 48 9 Roger Tsien (1952–2016) Experts are OPEN CHANNELS grappling with what constitutes 10 high testosterone 42 blood levels in elite NEWS track and eld Blind wins Tabor award women athletes. for work on nuclear lipids 11 JOURNAL NEWS 11 Blocking potato blight’s ability 22 to set up shop 12 Infant gut microbes’ thirst for milk proteins 13 How a single-cell marine organism makes fatty acids 12 44 TRANSITION STATES NOVEMBER 2016 ASBMB TODAY 1 EDITOR’S NOTE THE MEMBER MAGAZINE OF THE AMERICAN SOCIETY FOR BIOCHEMISTRY AND MOLECULAR BIOLOGY It’s time By Angela Hopp OFFICERS COUNCIL MEMBERS Natalie Ahn Squire J. Booker President Victoria J. DeRose Wayne Fairbrother. recently saw a documen- Ben Corb, in his “News Steven McKnight Karen G. Fleming tary on Netix about from the Hill” column, Past President Rachel Green uncontacted tribes in writes about the count- Jennifer DuBois Susan Marqusee I Secretary Jared Rutter the rainforest on the border down to a new American Celia A. -
MICROBIOLOGY Graduate Program
MICROBIOLOGY Graduate Program Student Handbook 2020-21 THE UNIVERSITY OF TEXAS AT AUSTIN Welcome! .............................................................................................................................................................. 4 Responsibilities of a Microbiology Graduate Student ........................................................................................ 4 The Graduate School ......................................................................................................................................... 4 The College of Natural Sciences (CNS) ............................................................................................................ 5 The Institute for Cell and Molecular Biology (ICMB) ......................................................................................... 5 The Microbiology Graduate Program (MIC) ...................................................................................................... 5 Microbiology Graduate Program Administration ................................................................................................ 6 The Microbiology Graduate Studies Committee (GSC) .................................................................................... 6 Degrees Offered ................................................................................................................................................... 7 Doctor of Philosophy (Ph.D.) ............................................................................................................................ -
I S C B N E W S L E T T
ISCB NEWSLETTER FOCUS ISSUE {contents} President’s Letter 2 Member Involvement Encouraged Register for ISMB 2002 3 Registration and Tutorial Update Host ISMB 2004 or 2005 3 David Baker 4 2002 Overton Prize Recipient Overton Endowment 4 ISMB 2002 Committees 4 ISMB 2002 Opportunities 5 Sponsor and Exhibitor Benefits Best Paper Award by SGI 5 ISMB 2002 SIGs 6 New Program for 2002 ISMB Goes Down Under 7 Planning Underway for 2003 Hot Jobs! Top Companies! 8 ISMB 2002 Job Fair ISCB Board Nominations 8 Bioinformatics Pioneers 9 ISMB 2002 Keynote Speakers Invited Editorial 10 Anna Tramontano: Bioinformatics in Europe Software Recommendations11 ISCB Software Statement volume 5. issue 2. summer 2002 Community Development 12 ISCB’s Regional Affiliates Program ISCB Staff Introduction 12 Fellowship Recipients 13 Awardees at RECOMB 2002 Events and Opportunities 14 Bioinformatics events world wide INTERNATIONAL SOCIETY FOR COMPUTATIONAL BIOLOGY A NOTE FROM ISCB PRESIDENT This newsletter is packed with information on development and dissemination of bioinfor- the ISMB2002 conference. With over 200 matics. Issues arise from recommendations paper submissions and over 500 poster submis- made by the Society’s committees, Board of sions, the conference promises to be a scientific Directors, and membership at large. Important feast. On behalf of the ISCB’s Directors, staff, issues are defined as motions and are discussed EXECUTIVE COMMITTEE and membership, I would like to thank the by the Board of Directors on a bi-monthly Philip E. Bourne, Ph.D., President organizing committee, local organizing com- teleconference. Motions that pass are enacted Michael Gribskov, Ph.D., mittee, and program committee for their hard by the Executive Committee which also serves Vice President work preparing for the conference. -
An Aging-Independent Replicative Lifespan in a Symmetrically Dividing
TOOLS AND RESOURCES An aging-independent replicative lifespan in a symmetrically dividing eukaryote Eric C Spivey1,2†, Stephen K Jones Jr1,2†, James R Rybarski1, Fatema A Saifuddin1, Ilya J Finkelstein1,2,3* 1Department of Molecular Biosciences, The University of Texas at Austin, Austin, United States; 2Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, United States; 3Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, United States Abstract The replicative lifespan (RLS) of a cell—defined as the number of cell divisions before death—has informed our understanding of the mechanisms of cellular aging. However, little is known about aging and longevity in symmetrically dividing eukaryotic cells because most prior studies have used budding yeast for RLS studies. Here, we describe a multiplexed fission yeast lifespan micro-dissector (multFYLM) and an associated image processing pipeline for performing high-throughput and automated single-cell micro-dissection. Using the multFYLM, we observe continuous replication of hundreds of individual fission yeast cells for over seventy-five generations. Surprisingly, cells die without the classic hallmarks of cellular aging, such as progressive changes in size, doubling time, or sibling health. Genetic perturbations and drugs can extend the RLS via an aging-independent mechanism. Using a quantitative model to analyze these results, we conclude that fission yeast does not age and that cellular aging and replicative lifespan can be uncoupled in a eukaryotic cell. DOI: 10.7554/eLife.20340.001 *For correspondence: [email protected] †These authors contributed Introduction equally to this work Aging is the progressive decrease of an organism’s fitness over time. -
The International Conference on Intelligent Biology
The Author(s) BMC Genomics 2017, 18(Suppl 6):703 DOI 10.1186/s12864-017-4018-6 INTRODUCTION Open Access The International Conference on Intelligent Biology and Medicine (ICIBM) 2016: summary and innovation in genomics Zhongming Zhao1,2*, Zhandong Liu3, Ken Chen4, Yan Guo5, Genevera I. Allen3,6, Jiajie Zhang7, W. Jim Zheng7 and Jianhua Ruan8* From The International Conference on Intelligent Biology and Medicine (ICIBM) 2016 Houston, TX, USA. 08-10 December 2016 Abstract In this editorial, we first summarize the 2016 International Conference on Intelligent Biology and Medicine (ICIBM 2016) that was held on December 8–10, 2016 in Houston, Texas, USA, and then briefly introduce the ten research articles included in this supplement issue. ICIBM 2016 included four workshops or tutorials, four keynote lectures, four conference invited talks, eight concurrent scientific sessions and a poster session for 53 accepted abstracts, covering current topics in bioinformatics, systems biology, intelligent computing, and biomedical informatics. Through our call for papers, a total of 77 original manuscripts were submitted to ICIBM 2016. After peer review, 11 articles were selected in this special issue, covering topics such as single cell RNA-seq analysis method, genome sequence and variation analysis, bioinformatics method for vaccine development, and cancer genomics. Introduction more than 150 scientists or trainees across the world with The 2016 International Conference on Intelligent Biology diverse backgrounds and training ranging from biology, and Medicine (ICIBM 2016) was held from December 8th medicine, computer science, bioengineering, bioinformat- to 10th, 2016 in Houston, Texas, USA. This is the fifth ics, statistics, mathematics, and genomics, among others.