Planning the Future of Genomics: Foundational Research and Applications in Genomic Medicine July 6-8, 2010 Airlie Center, Warrenton, VA
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RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY
RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY Proceedings of the 5th WSEAS International Conference on CELLULAR and MOLECULAR BIOLOGY, BIOPHYSICS and BIOENGINEERING (BIO '09) Proceedings of the 3rd WSEAS International Conference on COMPUTATIONAL CHEMISTRY (COMPUCHEM '09) Puerto De La Cruz, Tenerife, Canary Islands, Spain December 14-16, 2009 Recent Advances in Biology and Biomedicine A Series of Reference Books and Textbooks Published by WSEAS Press ISSN: 1790-5125 www.wseas.org ISBN: 978-960-474-141-0 RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY Proceedings of the 5th WSEAS International Conference on CELLULAR and MOLECULAR BIOLOGY, BIOPHYSICS and BIOENGINEERING (BIO '09) Proceedings of the 3rd WSEAS International Conference on COMPUTATIONAL CHEMISTRY (COMPUCHEM '09) Puerto De La Cruz, Tenerife, Canary Islands, Spain December 14-16, 2009 Recent Advances in Biology and Biomedicine A Series of Reference Books and Textbooks Published by WSEAS Press www.wseas.org Copyright © 2009, by WSEAS Press All the copyright of the present book belongs to the World Scientific and Engineering Academy and Society Press. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the Editor of World Scientific and Engineering Academy and Society Press. All papers of the present volume were peer reviewed -
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]. -
Computational Methods Addressing Genetic Variation In
COMPUTATIONAL METHODS ADDRESSING GENETIC VARIATION IN NEXT-GENERATION SEQUENCING DATA by Charlotte A. Darby A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland June 2020 © 2020 Charlotte A. Darby All rights reserved Abstract Computational genomics involves the development and application of computational meth- ods for whole-genome-scale datasets to gain biological insight into the composition and func- tion of genomes, including how genetic variation mediates molecular phenotypes and disease. New biotechnologies such as next-generation sequencing generate genomic data on a massive scale and have transformed the field thanks to simultaneous advances in the analysis toolkit. In this thesis, I present three computational methods that use next-generation sequencing data, each of which addresses the genetic variations within and between human individuals in a different way. First, Samovar is a software tool for performing single-sample mosaic single-nucleotide variant calling on whole genome sequencing linked read data. Using haplotype assembly of heterozygous germline variants, uniquely made possible by linked reads, Samovar identifies variations in different cells that make up a bulk sequencing sample. We apply it to 13cancer samples in collaboration with researchers at Nationwide Childrens Hospital. Second, scHLAcount is a software pipeline that computes allele-specific molecule counts for the HLA genes from single-cell gene expression data. We use a personalized reference genome based on the individual’s genotypes to reveal allele-specific and cell type-specific gene expression patterns. Even given technology-specific biases of single-cell gene expression data, we can resolve allele-specific expression for these genes since the alleles are often quite different between the two haplotypes of an individual. -
Microblogging the ISMB: a New Approach to Conference Reporting
Message from ISCB Microblogging the ISMB: A New Approach to Conference Reporting Neil Saunders1*, Pedro Beltra˜o2, Lars Jensen3, Daniel Jurczak4, Roland Krause5, Michael Kuhn6, Shirley Wu7 1 School of Molecular and Microbial Sciences, University of Queensland, St. Lucia, Brisbane, Queensland, Australia, 2 Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California, United States of America, 3 Novo Nordisk Foundation Center for Protein Research, Panum Institute, Copenhagen, Denmark, 4 Department of Bioinformatics, University of Applied Sciences, Hagenberg, Freistadt, Austria, 5 Max-Planck-Institute for Molecular Genetics, Berlin, Germany, 6 European Molecular Biology Laboratory, Heidelberg, Germany, 7 Stanford Medical Informatics, Stanford University, Stanford, California, United States of America Cameron Neylon entitled FriendFeed for Claire Fraser-Liggett opened the meeting Scientists: What, Why, and How? (http:// with a review of metagenomics and an blog.openwetware.org/scienceintheopen/ introduction to the human microbiome 2008/06/12/friendfeed-for-scientists-what- project (http://friendfeed.com/search?q = why-and-how/) for an introduction. room%3Aismb-2008+microbiome+OR+ We—a group of science bloggers, most fraser). The subsequent Q&A session of whom met in person for the first time at covered many of the exciting challenges The International Conference on Intel- ISMB 2008—found FriendFeed a remark- for those working in this field. Clearly, ligent Systems for Molecular Biology -
Big Data, Moocs, and ... (PDF)
HHMI Constellation Studios for Science Education November 13-15, 2015 | HHMI Headquarters | Chevy Chase, MD Big Data, MOOCs, and Quantitative Education for Biologists Co-Chairs Pavel Pevzner, University of California- San Diego Sarah Elgin, Washington University Studio Objectives Discuss existing challenges in bioinformatics education with experts in computational biology and quantitative biology education, Evaluate best practices in teaching quantitative and computational biology, and Collaborate with scientist educators to develop instructional modules to support a biology curriculum that includes quantitative approaches. Friday | November 13 4:00 pm Arrival Registration Desk 5:30 – 6:00 pm Reception Great Hall 6:00 – 7:00 pm Dinner Dining Room 7:00 – 7:15 pm Welcome K202 David Asai, HHMI Cynthia Bauerle, HHMI Pavel Pevzner, University of California-San Diego Sarah Elgin, Washington University Alex Hartemink, Duke University 7:15 – 8:00 pm How to Maximize Interaction and Feedback During the Studio K202 Cynthia Bauerle and Sarah Simmons, HHMI 8:00 – 9:00 pm Keynote Presentation K202 "Computing + Biology = Discovery" Speakers: Ran Libeskind-Hadas, Harvey Mudd College Eliot Bush, Harvey Mudd College 9:00 – 11:00 pm Social The Pilot Saturday | November 14 7:30 – 8:15 am Breakfast Dining Room 8:30 – 10:00 am Lecture session 1 K202 Moderator: Pavel Pevzner 834a-854a “How is body fat regulated?” Laurie Heyer, Davidson College 856a-916a “How can we find mutations that cause cancer?” Ben Raphael, Brown University “How does a tumor evolve over time?” 918a-938a Russell Schwartz, Carnegie Mellon University “How fast do ribosomes move?” 940a-1000a Carl Kingsford, Carnegie Mellon University 10:05 – 10:55 am Breakout working groups Rooms: S221, (coffee available in each room) N238, N241, N140 1. -
The Basic Units of Life How Cell Atlases Can Shed Light on Disease Mechanisms with Remarkable Accuracy
Press Information, July 28, 2021 Cells: The Basic Units of Life How cell atlases can shed light on disease mechanisms with remarkable accuracy The Schering Stiftung awards the Ernst Schering Prize 2021 to Aviv Regev. A pioneer in the field of single-cell analysis, she successfully combines approaches from biology and computer science and thus revolutionizes the field of precision medicine. Aviv Regev is considered a pioneer in the field of single-cell biology and has broken new ground by combining the disciplines of biology, computation, and genetic engineering. She has uniquely succeeded in combining and refining some of the most important experimental and analytical tools in such a way that she can analyze the genome of hundreds of thousands of single cells simultaneously. This single-cell genome analysis makes it possible to map and characterize a large number of individual tissue cells. Aviv Regev was the first to apply these single-cell technologies to solid tumors and to successfully identify those cells and genes that influence tumor growth and resistance to treatment. In addition, she discovered rare cell types that are involved in cystic fibrosis and ulcerative colitis. Last but not least, together with international research groups, and her colleague Sarah Teichmann she built the Human Cell Atlas and inspired Prof. Aviv Regev, PhD scientists all over the world to use these tools to create a Photo: Casey Atkins comprehensive atlas of all cell types in the human body. These cell atlases of parts of the human body illuminate disease mechanisms with remarkable accuracy and have recently also been used successfully to study disease progression in COVID-19. -
CV Aviv Regev
AVIV REGEV Curriculum Vitae Education and training Ph.D., Computational Biology, Tel Aviv University, Tel Aviv, Israel, 1998-2002 Advisor: Prof. Eva Jablonka (Tel Aviv University) Advisor: Prof. Ehud Shapiro (Computer Science, Weizmann Institute) M.Sc. (direct, Summa cum laude) Tel Aviv University, Tel Aviv, Israel, 1992-1997 Advisor: Prof. Sara Lavi A student in the Adi Lautman Interdisciplinary Program for the Fostering of Excellence (studies mostly in Biology, Computer Science and Mathematics) Post Training Positions Executive Vice President and Global Head, Genentech Research and Early Development, Genentech/Roche, 2020 - Current HHMI Investigator, 2014-2020 Chair of the Faculty (Executive Leadership Team), Broad Institute, 2015 – 2020 Professor, Department of Biology, MIT, 2015-Current (on leave) Founding Director, Klarman Cell Observatory, Broad Institute, 2012-2020 Director, Cell Circuits Program, Broad Institute, 2013 - 2020 Associate Professor with Tenure, Department of Biology, MIT, 2011-2015 Early Career Scientist, Howard Hughes Medical Institute, 2009-2014 Core Member, Broad Institute of MIT and Harvard, 2006-Current (on leave) Assistant Professor, Department of Biology, MIT, 2006-2011 Bauer Fellow, Center for Genomics Research, Harvard University, 2003-2006 International Service Founding Co-Chair, Human Cell Atlas, 2016-Current Honors Vanderbilt Prize, 2021 AACR Academy, Elected Fellow, 2021 AACR-Irving Weinstein Foundation Distinguished Lecturer, 2021 James Prize in Science and Technology Integration, National Academy of -
Modeling and Analysis of RNA-Seq Data: a Review from a Statistical Perspective
Modeling and analysis of RNA-seq data: a review from a statistical perspective Wei Vivian Li 1 and Jingyi Jessica Li 1;2;∗ Abstract Background: Since the invention of next-generation RNA sequencing (RNA-seq) technolo- gies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies. The analysis of RNA-seq data at four different levels (samples, genes, transcripts, and exons) involve multiple statistical and computational questions, some of which remain challenging up to date. Results: We review RNA-seq analysis tools at the sample, gene, transcript, and exon levels from a statistical perspective. We also highlight the biological and statistical questions of most practical considerations. Conclusion: The development of statistical and computational methods for analyzing RNA- seq data has made significant advances in the past decade. However, methods developed to answer the same biological question often rely on diverse statical models and exhibit dif- ferent performance under different scenarios. This review discusses and compares multiple commonly used statistical models regarding their assumptions, in the hope of helping users select appropriate methods as needed, as well as assisting developers for future method development. 1 Introduction RNA sequencing (RNA-seq) uses the next generation sequencing (NGS) technologies to reveal arXiv:1804.06050v3 [q-bio.GN] 1 May 2018 the presence and quantity of RNA molecules in biological samples. Since its invention, RNA- seq has revolutionized transcriptome analysis in biological research. RNA-seq does not require any prior knowledge on RNA sequences, and its high-throughput manner allows for genome-wide profiling of transcriptome landscapes [1,2]. -
Cloud Computing and the DNA Data Race Michael Schatz
Cloud Computing and the DNA Data Race Michael Schatz April 14, 2011 Data-Intensive Analysis, Analytics, and Informatics Outline 1. Genome Assembly by Analogy 2. DNA Sequencing and Genomics 3. Large Scale Sequence Analysis 1. Mapping & Genotyping 2. Genome Assembly Shredded Book Reconstruction • Dickens accidentally shreds the first printing of A Tale of Two Cities – Text printed on 5 long spools It was theIt was best the of besttimes, of times, it was it wasthe worstthe worst of of times, times, it it was was the the ageage of of wisdom, wisdom, it itwas was the agethe ofage foolishness, of foolishness, … … It was theIt was best the bestof times, of times, it was it was the the worst of times, it was the theage ageof wisdom, of wisdom, it was it thewas age the of foolishness,age of foolishness, … It was theIt was best the bestof times, of times, it was it wasthe the worst worst of times,of times, it it was the age of wisdom, it wasit was the the age age of offoolishness, foolishness, … … It was It thewas best the ofbest times, of times, it wasit was the the worst worst of times,of times, itit waswas thethe ageage ofof wisdom,wisdom, it wasit was the the age age of foolishness,of foolishness, … … It wasIt thewas best the bestof times, of times, it wasit was the the worst worst of of times, it was the age of ofwisdom, wisdom, it wasit was the the age ofage foolishness, of foolishness, … … • How can he reconstruct the text? – 5 copies x 138, 656 words / 5 words per fragment = 138k fragments – The short fragments from every copy are mixed -
BENG181/CSE 181/BIMM 181 Molecular Sequence Analysis Instructor: Pavel Pevzner
COURSE ANNOUNCEMENT FOR WINTER 2021 BENG181/CSE 181/BIMM 181 Molecular Sequence Analysis https://sites.google.com/site/ucsdcse181 Instructor: Pavel Pevzner ● phone: (858) 822-4365 ● e.mail: [email protected] ● web site: bioalgorithms.ucsd.edu Teaching Assistants: ● Andrey Bzikadze ([email protected]) ● Hsuan-lin (Charlene) Her ([email protected]) Time: 6:30-7:50 Mon/Wed, Place: online (seminar Friday 4:00-4:50 online) Zoom link for the class: https://ucsd.zoom.us/j/99782745100 Zoom link for the seminar: https://ucsd.zoom.us/j/96805484881 Office hours: PP: (Th 3-5 online), TAs (online Tue 1-2 PM and 4-5 PM or by appointment online) PP zoom link: https://ucsd.zoom.us/j/96986851791 Andrey Bzikadze zoom link: https://ucsd.zoom.us/j/94881347266 Hsuan-lin (Charlene) Her zoom link: https://ucsd.zoom.us/j/95134947264 Prerequisites: The course assumes some prior background in biology, some algorithmic culture (CSE 101 course on algorithms as a prerequisite), and some programming skills. Flipped online class. Starting in 2014, the Innovative Learning Technology Initiative (ILTI) at University of California encourages professors to transform their classes into online offerings available across various UC campuses. Dr. Pevzner is funded by the ILTI and NIH to develop new online approaches to bioinformatics education at UCSD. Since 2014, well before the COVID-19 pandemic, all lectures in this class are available online rather than presented in the classroom. Multi-university class. This class closely follows the textbook Bioinformatics Algorithms: an Active Learning Approach that has now been adopted by 140+ instructors from 40+ countries. -
Steven L. Salzberg
Steven L. Salzberg McKusick-Nathans Institute of Genetic Medicine Johns Hopkins School of Medicine, MRB 459, 733 North Broadway, Baltimore, MD 20742 Phone: 410-614-6112 Email: [email protected] Education Ph.D. Computer Science 1989, Harvard University, Cambridge, MA M.Phil. 1984, M.S. 1982, Computer Science, Yale University, New Haven, CT B.A. cum laude English 1980, Yale University Research Areas: Genomics, bioinformatics, gene finding, genome assembly, sequence analysis. Academic and Professional Experience 2011-present Professor, Department of Medicine and the McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University. Joint appointments as Professor in the Department of Biostatistics, Bloomberg School of Public Health, and in the Department of Computer Science, Whiting School of Engineering. 2012-present Director, Center for Computational Biology, Johns Hopkins University. 2005-2011 Director, Center for Bioinformatics and Computational Biology, University of Maryland Institute for Advanced Computer Studies 2005-2011 Horvitz Professor, Department of Computer Science, University of Maryland. (On leave of absence 2011-2012.) 1997-2005 Senior Director of Bioinformatics (2000-2005), Director of Bioinformatics (1998-2000), and Investigator (1997-2005), The Institute for Genomic Research (TIGR). 1999-2006 Research Professor, Departments of Computer Science and Biology, Johns Hopkins University 1989-1999 Associate Professor (1996-1999), Assistant Professor (1989-1996), Department of Computer Science, Johns Hopkins University. On leave 1997-99. 1988-1989 Associate in Research, Graduate School of Business Administration, Harvard University. Consultant to Ford Motor Co. of Europe and to N.V. Bekaert (Kortrijk, Belgium). 1985-1987 Research Scientist and Senior Knowledge Engineer, Applied Expert Systems, Inc., Cambridge, MA. Designed expert systems for financial services companies. -
Using Bayesian Networks to Analyze Expression Data
JOURNAL OFCOMPUTATIONAL BIOLOGY Volume7, Numbers 3/4,2000 MaryAnn Liebert,Inc. Pp. 601–620 Using Bayesian Networks to Analyze Expression Data NIR FRIEDMAN, 1 MICHAL LINIAL, 2 IFTACH NACHMAN, 3 andDANA PE’ER 1 ABSTRACT DNAhybridization arrayssimultaneously measurethe expression level forthousands of genes.These measurementsprovide a “snapshot”of transcription levels within the cell. Ama- jorchallenge in computationalbiology is touncover ,fromsuch measurements,gene/ protein interactions andkey biological features ofcellular systems. In this paper,wepropose a new frameworkfor discovering interactions betweengenes based onmultiple expression mea- surements. This frameworkbuilds onthe use of Bayesian networks forrepresenting statistical dependencies. ABayesiannetwork is agraph-basedmodel of joint multivariateprobability distributions thatcaptures properties ofconditional independence betweenvariables. Such models areattractive for their ability todescribe complexstochastic processes andbecause theyprovide a clear methodologyfor learning from(noisy) observations.We start byshowing howBayesian networks can describe interactions betweengenes. W ethen describe amethod forrecovering gene interactions frommicroarray data using tools forlearning Bayesiannet- works.Finally, we demonstratethis methodon the S.cerevisiae cell-cycle measurementsof Spellman et al. (1998). Key words: geneexpression, microarrays, Bayesian methods. 1.INTRODUCTION centralgoal of molecularbiology isto understand the regulation of protein synthesis and its Areactionsto external