Research Report 2006 Max Planck Institute for Molecular Genetics, Berlin Imprint | Research Report 2006
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Bonnie Berger Named ISCB 2019 ISCB Accomplishments by a Senior
F1000Research 2019, 8(ISCB Comm J):721 Last updated: 09 APR 2020 EDITORIAL Bonnie Berger named ISCB 2019 ISCB Accomplishments by a Senior Scientist Award recipient [version 1; peer review: not peer reviewed] Diane Kovats 1, Ron Shamir1,2, Christiana Fogg3 1International Society for Computational Biology, Leesburg, VA, USA 2Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel 3Freelance Writer, Kensington, USA First published: 23 May 2019, 8(ISCB Comm J):721 ( Not Peer Reviewed v1 https://doi.org/10.12688/f1000research.19219.1) Latest published: 23 May 2019, 8(ISCB Comm J):721 ( This article is an Editorial and has not been subject https://doi.org/10.12688/f1000research.19219.1) to external peer review. Abstract Any comments on the article can be found at the The International Society for Computational Biology (ISCB) honors a leader in the fields of computational biology and bioinformatics each year with the end of the article. Accomplishments by a Senior Scientist Award. This award is the highest honor conferred by ISCB to a scientist who is recognized for significant research, education, and service contributions. Bonnie Berger, Simons Professor of Mathematics and Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) is the 2019 recipient of the Accomplishments by a Senior Scientist Award. She is receiving her award and presenting a keynote address at the 2019 Joint International Conference on Intelligent Systems for Molecular Biology/European Conference on Computational Biology in Basel, Switzerland on July 21-25, 2019. Keywords ISCB, Bonnie Berger, Award This article is included in the International Society for Computational Biology Community Journal gateway. -
Ontology-Based Methods for Analyzing Life Science Data
Habilitation a` Diriger des Recherches pr´esent´ee par Olivier Dameron Ontology-based methods for analyzing life science data Soutenue publiquement le 11 janvier 2016 devant le jury compos´ede Anita Burgun Professeur, Universit´eRen´eDescartes Paris Examinatrice Marie-Dominique Devignes Charg´eede recherches CNRS, LORIA Nancy Examinatrice Michel Dumontier Associate professor, Stanford University USA Rapporteur Christine Froidevaux Professeur, Universit´eParis Sud Rapporteure Fabien Gandon Directeur de recherches, Inria Sophia-Antipolis Rapporteur Anne Siegel Directrice de recherches CNRS, IRISA Rennes Examinatrice Alexandre Termier Professeur, Universit´ede Rennes 1 Examinateur 2 Contents 1 Introduction 9 1.1 Context ......................................... 10 1.2 Challenges . 11 1.3 Summary of the contributions . 14 1.4 Organization of the manuscript . 18 2 Reasoning based on hierarchies 21 2.1 Principle......................................... 21 2.1.1 RDF for describing data . 21 2.1.2 RDFS for describing types . 24 2.1.3 RDFS entailments . 26 2.1.4 Typical uses of RDFS entailments in life science . 26 2.1.5 Synthesis . 30 2.2 Case study: integrating diseases and pathways . 31 2.2.1 Context . 31 2.2.2 Objective . 32 2.2.3 Linking pathways and diseases using GO, KO and SNOMED-CT . 32 2.2.4 Querying associated diseases and pathways . 33 2.3 Methodology: Web services composition . 39 2.3.1 Context . 39 2.3.2 Objective . 40 2.3.3 Semantic compatibility of services parameters . 40 2.3.4 Algorithm for pairing services parameters . 40 2.4 Application: ontology-based query expansion with GO2PUB . 43 2.4.1 Context . 43 2.4.2 Objective . -
DREAM: a Dialogue on Reverse Engineering Assessment And
DREAM:DREAM: aa DialogueDialogue onon ReverseReverse EngineeringEngineering AssessmentAssessment andand MethodsMethods Andrea Califano: MAGNet: Center for the Multiscale Analysis of Genetic and Cellular Networks C2B2: Center for Computational Biology and Bioinformatics ICRC: Irving Cancer RResearchesearch Center Columbia University 1 ReverseReverse EngineeringEngineering • Inference of a predictive (generative) model from data. E.g. argmax[P(Data|Model)] • Assumptions: – Model variables (E.g., DNA, mRNA, Proteins, cellular sub- structures) – Model variable space: At equilibrium, temporal dynamics, spatio- temporal dynamics, etc. – Model variable interactions: probabilistics (linear, non-linear), explicit kinetics, etc. – Model topology: known a-priori, inferred. • Question: – Model ~= Reality? ReverseReverse EngineeringEngineering Data Biological System Expression Proteomics > NFAT ATGATGGATG CTCGCATGAT CGACGATCAG GTGTAGCCTG High-throughput GGCTGGA Structure Sequence Biology … Biochemical Model Validation Control X-Y- Control X+Y+ Y X Z X+Y- X-Y+ Control Control Specific Prediction SomeSome ReverseReverse EngineeringEngineering MethodsMethods • Optimization: High-Dimensional objective function max corresponds to best topology – Liang S, Fuhrman S, Somogyi (REVEAL) – Gat-Viks and R. Shamir (Chain Functions) – Segal E, Shapira M, Regev A, Pe’er D, Botstein D, KolKollerler D, and Friedman N (Prob. Graphical Models) – Jing Yu, V. Anne Smith, Paul P. Wang, Alexander J. Hartemink, Erich D. Jarvis (Dynamic Bayesian Networks) – … • Regression: Create a general model of biochemical interactions and fit the parameters – Gardner TS, di Bernardo D, Lorentz D, and Collins JJ (NIR) – Alberto de la Fuente, Paul Brazhnik, Pedro Mendes – Roven C and Bussemaker H (REDUCE) – … • Probabilistic and Information Theoretic: Compute probability of interaction and filter with statistical criteria – Atul Butte et al. (Relevance Networks) – Gustavo Stolovitzky et al. (Co-Expression Networks) – Andrea CaCalifanolifano et al. -
Next Generation Machine Learning Prediction of Protein Cellular Sorting
TECHNISCHE UNIVERSITÄT MÜNCHEN Lehrstuhl für Bioinformatik Next Generation Machine Learning Prediction of Protein Cellular Sorting Tatyana Goldberg Vollständiger Abdruck der von der Fakultät für Informatik der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr. Daniel Cremers Prüfer der Dissertation: 1. Univ.-Prof. Dr. Burkhard Rost 2. Prof. Dr. Yana Bromberg, The State University of New Jersey/USA 3. Univ.-Prof. Dr. Iris Antes Die Dissertation wurde am 15.12.2015 bei der Technischen Universität München eingereicht und durch die Fakultät für Informatik am 22.03.2016 angenommen. ii Table of Contents Abstract ............................................................................................................................................. v Zusammenfassung ........................................................................................................................... vii Acknowledgments ............................................................................................................................ ix List of publications ........................................................................................................................... xi List of Figures and Tables ............................................................................................................... xiii 1. Introduction ............................................................................................................................... -
First Analysis Steps
FirstFirst analysisanalysis stepssteps o quality control and optimization o calibration and error modeling o data transformations Wolfgang Huber Dep. of Molecular Genome Analysis (A. Poustka) DKFZ Heidelberg Acknowledgements Anja von Heydebreck Günther Sawitzki Holger Sültmann, Klaus Steiner, Markus Vogt, Jörg Schneider, Frank Bergmann, Florian Haller, Katharina Finis, Stephanie Süß, Anke Schroth, Friederike Wilmer, Judith Boer, Martin Vingron, Annemarie Poustka Sandrine Dudoit, Robert Gentleman, Rafael Irizarry and Yee Hwa Yang: Bioconductor short course, summer 2002 and many others a microarray slide Slide: 25x75 mm Spot-to-spot: ca. 150-350 µm 4 x 4 or 8x4 sectors 17...38 rows and columns per sector ca. 4600…46000 probes/array sector: corresponds to one print-tip Terminology sample: RNA (cDNA) hybridized to the array, aka target, mobile substrate. probe: DNA spotted on the array, aka spot, immobile substrate. sector: rectangular matrix of spots printed using the same print-tip (or pin), aka print-tip-group plate: set of 384 (768) spots printed with DNA from the same microtitre plate of clones slide, array channel: data from one color (Cy3 = cyanine 3 = green, Cy5 = cyanine 5 = red). batch: collection of microarrays with the same probe layout. Raw data scanner signal resolution: 5 or 10 mm spatial, 16 bit (65536) dynamical per channel ca. 30-50 pixels per probe (60 µm spot size) 40 MB per array Raw data scanner signal resolution: 5 or 10 mm spatial, 16 bit (65536) dynamical per channel ca. 30-50 pixels per probe (60 µm spot size) 40 MB per array Image Analysis Raw data scanner signal resolution: 5 or 10 mm spatial, 16 bit (65536) dynamical per channel ca. -
Probabilistic Models for Gene Silencing Data
Probabilistic Models for Gene Silencing Data Florian Markowetz Dezember 2005 Dissertation zur Erlangung des Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) am Fachbereich Mathematik und Informatik der Freien Universit¨atBerlin 1. Referent: Prof. Dr. Martin Vingron 2. Referent: Prof. Dr. Klaus-Robert M¨uller Tag der Promotion: 26. April 2006 Preface Acknowledgements This work was carried out in the Computational Diagnostics group of the Department of Computational Molecular Biology at the Max Planck Institute for Molecular Genetics in Berlin. I thank all past and present colleagues for the good working atmosphere and the scientific—and sometimes maybe not so scientific—discussions. Especially, I am grateful to my supervisor Rainer Spang for suggesting the topic, his scientific support, and the opportunity to write this thesis under his guidance. I thank Michael Boutros for providing the expression data and for introducing me to the world of RNAi when I visited his lab at the DKFZ in Heidelberg. I thank Anja von Heydebreck, J¨org Schultz, and Martin Vingron for their advice and counsel as members of my PhD commitee. During the time I worked on this thesis, I enjoyed fruitful discussions with many people. In particular, I gratefully acknowledge Jacques Bloch, Steffen Grossmann, Achim Tresch, and Chen-Hsiang Yeang for their contributions. Special thanks go to Viola Gesellchen, Britta Koch, Stefanie Scheid, Stefan Bentink, Stefan Haas, Dennis Kostka, and Stefan R¨opcke, who read drafts of this thesis and greatly improved it by their comments. Publications Parts of this thesis have been published before. Chapter 2 grew out of lectures I gave in 2005 at the Instiute for Theoretical Physics and Mathematics (IPM) in Tehran, Iran, and at the German Conference on Bioinformatics (GCB), Hamburg, Germany. -
Machine Learning and Statistical Methods for Clustering Single-Cell RNA-Sequencing Data Raphael Petegrosso 1, Zhuliu Li 1 and Rui Kuang 1,∗
i i “main” — 2019/5/3 — 12:56 — page 1 — #1 i i Briefings in Bioinformatics doi.10.1093/bioinformatics/xxxxxx Advance Access Publication Date: Day Month Year Manuscript Category Subject Section Machine Learning and Statistical Methods for Clustering Single-cell RNA-sequencing Data Raphael Petegrosso 1, Zhuliu Li 1 and Rui Kuang 1,∗ 1Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA ∗To whom correspondence should be addressed. Associate Editor: XXXXXXX Received on XXXXX; revised on XXXXX; accepted on XXXXX Abstract Single-cell RNA-sequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single cell in a cell population. A core analysis of the scRNA-seq transcriptome profiles is to cluster the single cells to reveal cell subtypes and infer cell lineages based on the relations among the cells. This article reviews the machine learning and statistical methods for clustering scRNA- seq transcriptomes developed in the past few years. The review focuses on how conventional clustering techniques such as hierarchical clustering, graph-based clustering, mixture models, k-means, ensemble learning, neural networks and density-based clustering are modified or customized to tackle the unique challenges in scRNA-seq data analysis, such as the dropout of low-expression genes, low and uneven read coverage of transcripts, highly variable total mRNAs from single cells, and ambiguous cell markers in the presence of technical biases and irrelevant confounding biological variations. We review how cell-specific normalization, the imputation of dropouts and dimension reduction methods can be applied with new statistical or optimization strategies to improve the clustering of single cells. -
15Th International Symposium on Bioinformatics Research and Applications
ISBRA 2019 15th International Symposium on Bioinformatics Research and Applications June 3–6, 2019 Technical University of Catalonia Barcelona, Spain http://alan.cs.gsu.edu/isbra19/ About the Technical University of Catalonia The Technical University of Catalonia (UPC) is a public institution of Higher Education and Research, specialized in the areas of Architecture, Engineering, Science and Technology. The UPC has a wide spread presence in Catalonia, with nine campuses located in Barcelona and nearby towns. The campuses are accessible, well connected by public transport and equipped with the necessary facilities and services to contribute to learning, research and university life. Founded in 1968 by grouping together existing state technical schools of Architecture and Engineering in Barcelona, which date back from the mid-19th century, it gained university status in 1971. The UPC today has a student population of over 30,000, with more than 3,000 teaching and research staff and about 2,000 administrative and service staff. There are 64 bachelor’s degrees, 68 master’s degrees, and 46 doctoral programs offered by 20 schools. Further, there are 50 international double-degree agreements with 30 universities, and about 3,000 students in international mobility programs. Research teams generate e60 million in annual funding, with a total budget of about e300 million. About the Department of Computer Science The Department of Computer Science was created in 1987 and is one of the largest depart- ments at the UPC. It has about 90 full-time faculty and 50 Ph.D. students. The Department is responsible for teaching and research in several disciplines related to the foundations of computing and their applications. -
Lecture 10: Phylogeny 25,27/12/12 Phylogeny
גנומיקה חישובית Computational Genomics פרופ' רון שמיר ופרופ' רודד שרן Prof. Ron Shamir & Prof. Roded Sharan ביה"ס למדעי המחשב אוניברסיטת תל אביב School of Computer Science, Tel Aviv University , Lecture 10: Phylogeny 25,27/12/12 Phylogeny Slides: • Adi Akavia • Nir Friedman’s slides at HUJI (based on ALGMB 98) •Anders Gorm Pedersen,Technical University of Denmark Sources: Joe Felsenstein “Inferring Phylogenies” (2004) 1 CG © Ron Shamir Phylogeny • Phylogeny: the ancestral relationship of a set of species. • Represented by a phylogenetic tree branch ? leaf ? Internal node ? ? ? Leaves - contemporary Internal nodes - ancestral 2 CG Branch© Ron Shamir length - distance between sequences 3 CG © Ron Shamir 4 CG © Ron Shamir 5 CG © Ron Shamir 6 CG © Ron Shamir “classical” Phylogeny schools Classical vs. Modern: • Classical - morphological characters • Modern - molecular sequences. 7 CG © Ron Shamir Trees and Models • rooted / unrooted “molecular • topology / distance clock” • binary / general 8 CG © Ron Shamir To root or not to root? • Unrooted tree: phylogeny without direction. 9 CG © Ron Shamir Rooting an Unrooted Tree • We can estimate the position of the root by introducing an outgroup: – a species that is definitely most distant from all the species of interest Proposed root Falcon Aardvark Bison Chimp Dog Elephant 10 CG © Ron Shamir A Scally et al. Nature 483, 169-175 (2012) doi:10.1038/nature10842 HOW DO WE FIGURE OUT THESE TREES? TIMES? 12 CG © Ron Shamir Dangers of Paralogs • Right species distance: (1,(2,3)) Sequence Homology Caused -
Research Report 2009 Max Planck Institute for Molecular Genetics, Berlin Imprint | Research Report 2009
Research Report 2009 Max Planck Institute for Molecular Genetics, Berlin Imprint | Research Report 2009 Published by the Max Planck Institute for Molecular Genetics (MPIMG), Berlin, Germany, December 2009 Editorial Board: B.G. Herrmann, H. Lehrach, H.-H. Ropers, M. Vingron Conception & coordination: Patricia Marquardt Photography: Katrin Ullrich, MPIMG; David Ausserhofer Scientific Illustrations: MPIMG Production: Thomas Didier, Meta Data Contact: Max Planck Institute for Molecular Genetics Ihnestr. 63 – 73 14195 Berlin Germany Phone: +49 (0)30 8413-0 Fax: +49 (0)30 8413-1207 Email: [email protected] For further information about the MPIMG, please visit http://www.molgen.mpg.de MPI for Molecular Genetics Research Report 2009 Research Report 2009 1 Max Planck Institute for Molecular Genetics Berlin, December 2009 The Max Planck Institute for Molecular Genetics 2 MPI for Molecular Genetics Research Report 2009 Table of contents Organisational structure . 6 The Max Planck Institute for Molecular Genetics . 7 Mission . 7 Development of the Institute. 7 Research Concept . 8 Department of Developmental Genetics (Bernhard Herrmann) . 9 Transmission ratio distortion (H. Bauer) . 13 Regulatory Networks of Mesoderm Formation & Somitogenesis (B. Herrmann) . 17 Signal Transduction in Embryogenesis and Tumour Progression (M. Morkel) . 22 Organogenesis (H. Schrewe) . 26 General information about the whole Department . 29 Department of Vertebrate Genomics (Hans Lehrach) . 33 Molecular Embryology and Aging (J. Adjaye) . 40 Neuropsychiatric Genetics (L. Bertram) . 46 Automation (A. Dahl, W. Nietfeld, H. Seitz) . 49 Nucleic Acid-based Technologies (J. Glökler) . 55 Bioinformatics (R. Herwig) . 60 Comparative and Functional Genomics (H. Himmelbauer) . .65 Genetic Variation, Haplotypes & Genetics of Complex Diseases (M. Hoehe) . 69 3 in vitro Ligand Screening (Z. -
Annual Meeting 2016
The Genetics Society of Israel Annual Meeting 2016 The Hebrew University of Jerusalem Edmond J. Safra Campus Givat Ram January 25, 2016 FRONTIERS IN GENETICS X PROGRAM MEETING ORGANIZER Target Conferences Ltd. P.O. Box 51227 Tel Aviv 6713818, Israel Tel: +972 3 5175150, Fax: +972 3 5175155 E-mail: [email protected] 79 Honorary Membership in the Genetic Society of Israel is awarded to Prof. Eliezer Lifschitz from the Technion – Israel Institute of Technology, Israel We are indebted to you for your contributions to Genetics in Israel תואר חבר כבוד מוענק בשם החברה לגנטיקה בישראל לפרופ' אליעזר ליפשיץ הטכניון מכון טכנולוגי לישראל אנו מוקירים את תרומתך למחקר בתחום הגנטיקה בישראל 2 TABLE OF CONTENTS Page Board Members .......................................................................................................... 4 Acknowledgements .................................................................................................... 4 General Information .................................................................................................. 5 Exhibitors ................................................................................................................... 6 Scientific Program ....................................................................................................... 8 Invited Speakers ....................................................................................................... 10 Poster Presentations................................................................................................ -
Personal Data Education Current Research Interests Academic
Vitae of Ron Shamir 1 CURRICULUM VITAE Ron Shamir July 2011 Personal Data Address: Blavatnik School of Computer Science Tel Aviv University Tel Aviv, 69978 ISRAEL Telephones: Office: (03)640-5383, Assistant: (03)640-5391 Home: (08)946-6864 Facsimile: (03)640-5384 E-mail: [email protected] Web Page: http//www.cs.tau.ac.il/∼rshamir Group Web Page: http//actg.cs.tau.ac.il/ Born: Nov. 29, 1953, Jerusalem, Israel Married: Michal Oren-Shamir, September 8 1982 Children: Alon Shamir February 12 1985 Ittai Shamir April 25 1988 Yoav Shamir December 29 1993 Education University of California, Berkeley, USA (1981-1984) Ph.D., Operations Research. Advisors: Richard M. Karp and Ilan Adler Tel Aviv University (1978-1981) M.Sc. Operations Research. Advisor: Uri Yechiali (completed at U. C. Berkeley) The Hebrew University, Jerusalem (1975-1977) B.Sc. Mathematics and Physics Tel Aviv University (1973-1975) B.Sc. Mathematics and Physics (completed at the Hebrew University) Current Research Interests Bioinformatics / Computational molecular biology. Computational genomics, gene regulation, systems biology, genome rearrangements, human disease, in- tegrative analysis of heterogeneous high throughput biological data, Bioinformatics education. Design and analysis of algorithms. Algorithmic graph theory. Academic appointments Professor (2000-today) Tel Aviv University, School of Computer Science Associate professor (1995-2000) Tel Aviv University, School of Computer Science Senior Lecturer (1990-1995) Tel Aviv University, School of Mathematics, Department of Computer Science Lecturer; Alon fellow (1987-1989) Tel Aviv University, School of Mathematics, Department of Computer Science Vitae of Ron Shamir 2 Visiting appointments Visiting professor (March 2009) University of California, San Diego, Department of Computer Science and Engineering.