An Interview with David Haussler

Total Page:16

File Type:pdf, Size:1020Kb

Load more

Interview Life, the Universe, and Everything: An Interview with David Haussler Jane Gitschier* Departments of Medicine and Pediatrics and Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America Among the pantheon of computer looking at his picture, I realized he must scientists who have framed our capacity be your sibling. to interpret DNA sequences stands David Haussler: My only sibling is my Haussler of the University of California, brother. He was professor of biochemistry Santa Cruz (UCSC). Applying his prowess in University of Arizona and taught me in computer learning theory to the prob- how to do science. And he is really one of lems of protein modeling and gene the leading scientists in the world on structure prediction, Haussler emerged in vitamin D, which was the subject of that the mid-1990s as a trail-blazer in the field first paper. of computational biology. He came to Gitschier: How much older is he? wider prominence in 2000 during the Haussler: Twelve years. frenetic race to produce a draft sequence Gitschier: So he was established when of the human genome by nucleating an you were just a kid. impassioned team of coders and engineers Haussler: Right. The summer after who assembled the sequence data and my freshman year [in college], I spent time launched the UCSC Genome Browser. in his lab. Every third week, I would Fittingly, his team’s contribution made sacrifice a chick that was raised without manifest the vision of Robert Sinsheimer, vitamin D. I would take out its intestines who as Chancellor of UCSC in 1985 for receptors for the hormonal form of convened a pivotal workshop to explore vitamin D, and we used those receptors in sequencing the human genome. a radio-receptor competitive binding assay Haussler (Image 1) now plays, by my to first measure the level of the hormonal count, at least half-a-dozen leadership form of vitamin D in the human blood- roles, including co-director of the Genome Image 1. David Haussler. Photograph by stream, in both normal and diseased 10 K project, coordinating committee Ron Jones, courtesy of the Center for Biomo- humans. By the end of the summer, we member of The Cancer Genome Atlas lecular Science and Engineering, University of had a paper in Science! You know, big project, and director of the Center for California Santa Cruz. breakthrough. doi:10.1371/journal.pgen.1003282.g001 Biomolecular Science and Engineering at Then I went back the next summer, and UCSC. He is easily spotted by his nothing worked. I remember my brother predilection for Hawaiian shirts, whose a discussion of his growing up in the town saying to me, ‘‘Now, this is how science informality, he suggests, fosters inter- of North Hills in the San Fernando Valley. really is.’’ But I was undaunted. disciplinary collaboration. Indeed, Hauss- Haussler: My dad went to Caltech Gitschier: Let’s talk about your tran- ler’s ken for machine learning and his and because of the economic pressures of sition to science, because I know your first quest for the meaning of life are so having a young family, decided not to college experience was in art. expansive that I was tempted to title this pursue pure science, but to pursue a Haussler: I did visual art mostly. piece ‘‘Deep Thought’’, a nod to the professional position in structural engi- Acrylic painting and metal sculpture were fictional computer in Douglas Adams’ neering. He worked on mathematical probably my favorites, although I did The Hitchhiker’s Guide to the Galaxy, but problems as a hobbyist and had a love of stone lithography and all kinds of fabulous chose a more subdued allusion instead. pure science. Both my brother and I ended things in the San Francisco Academy of I located Haussler on the upper reaches up living out his dream to be a scientist. Art. Then, I switched schools and into of the stunning UCSC campus in the My brother is a highly accomplished psychology. engineering building, a sleek structure of biochemist. Gitschier: And that was where? glass and aluminum, tucked into a red- Gitschier: I saw that your first paper Haussler: That was actually at a crazy wood grove that was still dripping and in the early ’70s was with a Haussler and little experimental college. You have to fragrant from the morning’s rain. The had assumed it was your father, but then, understand that this was the early ’70s… anteroom to his modest office was deco- rated with handsome prints from UCSC’s Citation: Gitschier J (2013) Life, the Universe, and Everything: An Interview with David Haussler. PLoS scientific illustration program as well as Genet 9(1): e1003282. doi:10.1371/journal.pgen.1003282 books on the genome project, and a box Published January 31, 2013 labeled ‘‘for the intron lounge’’ was piled Copyright: ß 2013 Jane Gitschier. This is an open-access article distributed under the terms of the Creative high with journals. Haussler swept in via Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, bicycle, swiftly signed a few documents, provided the original author and source are credited. and downed a cold drink as we began with * E-mail: [email protected] PLOS Genetics | www.plosgenetics.org 1 January 2013 | Volume 9 | Issue 1 | e1003282 Gitschier: I do understand! [Haussler work in computer science. That seems like Gitschier: I see. It was a tradition! and I were born the same year.] a logical transition to me. Haussler: My great grandfather, my Haussler: My mother was hoping I’d Haussler: Logical is the correct word. grandfather, my father always ran their go to UCLA, but I was a rebel and said After studying pure mathematics as an own businesses. Never had a boss. It was a ‘‘No, I want to go to a crazy place,’’ undergrad, I decided that the foundation crazy, fierce, independent kind of tradi- Immaculate Heart College [IHC] in for everything was logic. And I read tion. Hollywood. extensively before I went to graduate Gitschier: So this was instilled in you Gitschier: Immaculate Heart doesn’t school, but even after getting my under- very early. I’m now seeing the fuller sound so ‘‘crazy’’ on the surface. graduate degree in mathematics and a context! Haussler: It doesn’t, not at all, but minor in physics, I still hadn’t decided to Haussler: Right. I wasn’t going to the thought leader there was Sister pursue a life of science. play along with any institutional programs! Corita Kent, and you remember from Gitschier: What were you thinking Those were the days when you could be the ’60s, those love posters? A lot of the of—art, philosophy, psychology? anti every institution and get away with it. art movement and the philosophy that Haussler: I wanted to get at the heart Well, I look back at my writings from was expressed in art and posters in that of the meaning of life. that time and there was some very creative era actually came out of Sister Corita Gitschier: Wow. [I had to swallow the stuff but isolated from the bulk of the Kent and a number of other rebels. The answer, ‘‘42’’.] intellectual mainstream, it’s very hard to sisters at IHC were essentially kicked Haussler: Still this rebel spirit, I guess. make progress. So I was thrilled to get re- out of the Catholic Church for being I wasn’t convinced that I would find that engaged, just by taking advanced math radicals, and they had an extremely at traditional institutions. I spent about classes at Cal Poly [San Luis Obispo]. experimental college. So I, being the nine months wandering around Europe Applied mathematics was my major, contrarian I was, applied there. It was and then settled in San Luis Obispo on the but I took computer science classes as well. strong in art and music and psychology. family farm, kind of between generations. I seized on the question of what is We studied Fritz Perls and Carl Rogers My grandfather was aging and my father computable. What can be formalized by and all of these self-realization psychol- was active as an engineer, so there was no mathematics? And the answer, according ogy thinkers at the time. And I was one to take care of it. to Alan Turing, was that this is the same as extremely into that. We had intensive While I was there, I wanted to keep what can be computed on a very simple encounter groups and dug very deeply touch with my intellectual side, so my kind of machine. I was tremendously taken into personal interactions. friends and I—it was almost like a by that and by the fact that Turing and Gitschier: But you didn’t stick with commune—believed in working hard on Kurt Go¨del had established that there Immaculate Heart. the ranch during the day and then reading were things that were fundamentally Haussler: I got interested in science and discussing philosophy, history, litera- uncomputable; true but unprovable. It by working with my brother. I then ture, and psychology at night. appealed to my mystical side. I was always transferred to Connecticut College back Gitschier: Who were these people that interested in the unity of the universe and east. Again, I liked very intimate, individ- you recruited to the farm? the mystery of it. ual learning. This was part of my whole Haussler: Well, important people that Gitschier: Are you still? psychology background. I view essential I met in my life and in my travels.
Recommended publications
  • UC Irvine UC Irvine Previously Published Works

    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.
  • EMT Biocomputing Workshop Report

    EMT Biocomputing Workshop Report

    FINAL WORKSHOP REPORT Sponsor: 004102 : Computing Research Association Award Number: AGMT dtd 7-2-08 Title: NSF Workshop on Emerging Models and Technologies in Computing: Bio-Inspired Computing and the Biology and Computer Science Interface. Report Prepared by: Professor Ron Weiss Departments of Electrical Engineering and Molecular Biology Princeton University Professor Laura Landweber Departments of Ecology and Evolutionary Biology and Molecular Biology Princeton University Other Members of the Organizing Committee Include: Professor Mona Singh Departments of Computer Science and Molecular Biology Princeton University Professor Erik Winfree Department of Computer Science California Institute of Technology Professor Mitra Basu Department of Computer Science Johns Hopkins University and the United States Naval Academy Workshop Website: https://www.ee.princeton.edu/Events/emt/ Draft: 09/07/2008 Table of Contents I. Executive Summary 1. Workshop Objectives 2. Summary of the Workshop Methodology and Findings 3. Recommendations II. Grand Challenges III. Breakout Group Presentations Appendices A1. Agenda A2. Participant List A3. Abstracts of Invited Presentations A4. Presentations Draft: 09/07/2008 I. Executive Summary I.1 Workshop Goals The National Science Foundation, Division of Computer and Information Science and Engineering (CISE), Computing and Communications Foundations (CCF) sponsored a Workshop on Emerging Models and Technologies in Computing (EMT): Bio- Inspired Computing. This workshop brought together distinguished leaders in the fields of synthetic biology, bio-computing, systems biology, and protein and nucleic acid engineering to share their vision for science and research and to learn about the research projects that EMT has funded. The goal was to explore and to drive the growing interface between Biology and Computer Science.
  • Research News

    Research News

    Computing Research News COMPUTING RESEARCH ASSOCIATION, CELEBRATING 40 YEARS OF SERVICE TO THE COMPUTING RESEARCH COMMUNITY JUNE 2013 Vol. 25 / No. 6 Announcements 2 Coalition for National Science Funding 2 CRA Announces Outstanding Undergraduate Researcher Award Winners 3 Computing Research in Action 5 CERP Infographic 6 NSF Funding Opportunity 6 CRA Recognizes Participants 7 CRA Board Members 16 CRA Board Officers 16 CRA Staff 16 Professional Opportunities 17 COMPUTING RESEARCH NEWS, JUNE 2013 Vol. 25 / No. 6 Announcements 2012 Taulbee Report Updated May 15, 2013 Corrected Table F6 Click here to download updated version CRA Releases Latest Research Issue Report New Technology-based Models for Postsecondary Learning: Conceptual Frameworks and Research Agendas The report details the findings of a National Science Foundation-Sponsored Computing Research Association Workshop held at MIT on January 9-11, 2013. From the report: “Advances in technology and in knowledge about expertise, learning, and assessment have the potential to reshape the many forms of education and training past matriculation from high school. In the next decade, higher education, military and workplace training, and professional development must all transform to exploit the opportunities of a new era, leveraging emerging technology-based models that can make learning more efficient and possibly improve student support, all at lower cost for a broader range of learners.” The report is now available as a pdf at http://cra.org/resources/research-issues/. Slides from the presentation at NSF on April 19, 2013 are also available. Investments in STEM Research and Education: Fueling American Innovation On May 7, at the Rayburn House Office Building in Brett Bode from the National Center for Supercomputing Washington, DC, the Coalition for National Science Funding Applications at University of Illinois Urbana-Champaign were (CNSF) held its 19th annual exhibition and reception, on hand to talk about the “Blue Waters” project.
  • Bch394p-364C

    Bch394p-364C

    Assembling Genomes BCH394P/364C Systems Biology / Bioinformatics Edward Marcotte, Univ of Texas at Austin 1 www.yourgenome.org/facts/timeline-the-human-genome-project Nature 409, 860-921(2001) 2 1 3 Beijing Genomics Institute “If it tastes good you should sequence it... you should know what's in the genes of that species” Wang Jun, Chief executive, BGI (Wikipedia) 4 2 https://www.technologyreview.com/s/615289/china-bgi-100-dollar-genome/ 5 6 3 https://www.prnewswire.com/news-releases/oxford-nanopore-sequencers-have-left-uk-for-china- to-support-rapid-near-sample-coronavirus-sequencing-for-outbreak-surveillance-300996908.html 7 http://www.triazzle.com ; The image from http://www.dangilbert.com/port_fun.html Reference: Jones NC, Pevzner PA, Introduction to Bioinformatics Algorithms, MIT press 8 4 “mapping” “shotgun” sequencing 9 (Translating the cloning jargon) 10 5 Thinking about the basic shotgun concept • Start with a very large set of random sequencing reads • How might we match up the overlapping sequences? • How can we assemble the overlapping reads together in order to derive the genome? 11 Thinking about the basic shotgun concept • At a high level, the first genomes were sequenced by comparing pairs of reads to find overlapping reads • Then, building a graph ( i.e. , a network) to represent those relationships • The genome sequence is a “walk” across that graph 12 6 The “Overlap-Layout-Consensus” method Overlap : Compare all pairs of reads (allow some low level of mismatches) Layout : Construct a graph describing the overlaps sequence overlap read Simplify the graph read Find the simplest path through the graph Consensus : Reconcile errors among reads along that path to find the consensus sequence 13 Building an overlap graph 5’ 3’ EUGENE W.
  • 2014 ISCB Accomplishment by a Senior Scientist Award: Gene Myers

    2014 ISCB Accomplishment by a Senior Scientist Award: Gene Myers

    Message from ISCB 2014 ISCB Accomplishment by a Senior Scientist Award: Gene Myers Christiana N. Fogg1, Diane E. Kovats2* 1 Freelance Science Writer, Kensington, Maryland, United States of America, 2 Executive Director, International Society for Computational Biology, La Jolla, California, United States of America The International Society for Computa- tional Biology (ISCB; http://www.iscb. org) annually recognizes a senior scientist for his or her outstanding achievements. The ISCB Accomplishment by a Senior Scientist Award honors a leader in the field of computational biology for his or her significant contributions to the com- munity through research, service, and education. Dr. Eugene ‘‘Gene’’ Myers of the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden has been selected as the 2014 ISCB Accom- plishment by a Senior Scientist Award winner. Myers (Image 1) was selected by the ISCB’s awards committee, which is chaired by Dr. Bonnie Berger of the Massachusetts Institute of Technology (MIT). Myers will receive his award and deliver a keynote address at ISCB’s 22nd Image 1. Gene Myers. Image credit: Matt Staley, HHMI. Annual Intelligent Systems for Molecular doi:10.1371/journal.pcbi.1003621.g001 Biology (ISMB) meeting. This meeting is being held in Boston, Massachusetts, on July 11–15, 2014, at the John B. Hynes guidance of his dissertation advisor, An- sequences and how to build evolutionary Memorial Convention Center (https:// drzej Ehrenfeucht, who had eclectic inter- trees. www.iscb.org/ismb2014). ests that included molecular biology. Myers landed his first faculty position in Myers was captivated by computer Myers, along with fellow graduate students the Department of Computer Science at programming as a young student.
  • David Haussler Elected to NAS IMS Member David Haussler Is One of 72 New Mem- Contents Bers Elected to the US National Academy of Sciences

    David Haussler Elected to NAS IMS Member David Haussler Is One of 72 New Mem- Contents Bers Elected to the US National Academy of Sciences

    Volume 35 Issue 5 IMS Bulletin June 2006 David Haussler elected to NAS IMS member David Haussler is one of 72 new mem- CONTENTS bers elected to the US National Academy of Sciences. 1 David Haussler, NAS Members are chosen in recognition of their distin- guished and continuing achievements in original 2 IMS Members’ News: Bin Yu, Hongyu Zhao, research. Ralph Cicerone, Academy president, says, Jianqing Fan “Election to the Academy is considered one of the highest honors in American science and engineering.” 4 LNMS Sale David Haussler is an investigator with the 5 IMS news Howard Hughes Medical Institute and a professor 6 Rio guide: eating, dancing, of biomolecular engineering at the University of having fun California, Santa Cruz, where he directs the Center David Haussler, recently elected to the for Biomolecular Science & Engineering. He serves 7 SSP report US National Academy of Sciences as scientific co-director for the California Institute 8 Terence’s Stuff: The Big for Quantitative Biomedical Research (QB3), and he is a consulting professor at Question both Stanford Medical School and UC San Francisco Biopharmaceutical Sciences 9 Meet the Members Department. David was recently elected to the American Academy of Arts and Sciences; 10 JSM News: IMS Invited Program highlights he is also a fellow of both AAAS and AAAI. He has won a number of prestigious awards, including recently the 2006 Dickson Prize for Science from Carnegie Mellon IMS Meetings 12 University. David received his BA degree in mathematics from Connecticut College 19 Other Meetings and in 1975, an MS in applied mathematics from California Polytechnic State University Announcements at San Luis Obispo in 1979, and his PhD in computer science from the University of 21 Employment Colorado at Boulder in 1982.
  • Cgt-Ga4gh-Presentation.Pdf

    Cgt-Ga4gh-Presentation.Pdf

    1993 Internet & TCP/IP Not yet dreamed of Starting a Genomic Internet Simple, real-time, global network for sharing somatic cancer data and associated clinical information Users Researchers, Clinicians, Pharma, Data Analysts, Citizen Scientists Cancer Gene Trust Public Public Public Data Data Data Patient Steward Steward Patient Steward Patient Participants GENIE UCSF Participants BRCA Participants Private Private Private Data Data Data Submissions Public/CGT Private/Steward • Somatic Tumor Variants • Germline Genome • Gene Expression Levels • Clinical History/EHR • Non-identifying Clinical Data • Signed Consent • Steward’s Identity • Participant’s Instructions • Public Random Participant ID • Internal Patient Participant ID Submissions View on web app Under the hood { "files": [ {"multihash": "QmVeX3fBSa6EELtFSjii8hYCQwV2M2rxaw6zDPWFhaZSxu", "name": "DO228_SA629.tsv.gz"} ], "fields": { "cancer_history_first_degree_relative": "nan", "cancer_type_prior_malignancy": "nan", "disease_status_last_followup": "relapse", "donor_age_at_diagnosis": "14", "donor_age_at_enrollment": "14", "donor_age_at_last_followup": "22", ... "donor_relapse_type": "progression (liquid tumours)", "donor_sex": "male", "donor_survival_time": "3060" } } A Submission Can be Cited in a Scientific Paper Materials and Methods For the bulk cancer samples, we downloaded RNA-Seq data for 4 samples from the Cancer Gene Trust steward https://cgt.singhealth.com.sg/ipfs/ QmP8ct1ZUTZfiYrQRATPAo7kLj4g97QiQCnRqBHp3nBaDY QmPGrYpAoAJqbetzq2TxefCTcCfBoeDbSVcvaRM5PLxfh1 QmPMbmhvN4B4XuVgnzyE8KpvRr1pDp13gqWCbqhcxdgCxi
  • Duplication, Deletion, and Rearrangement in the Mouse and Human Genomes

    Duplication, Deletion, and Rearrangement in the Mouse and Human Genomes

    Evolution’s cauldron: Duplication, deletion, and rearrangement in the mouse and human genomes W. James Kent*†, Robert Baertsch*, Angie Hinrichs*, Webb Miller‡, and David Haussler§ *Center for Biomolecular Science and Engineering and §Howard Hughes Medical Institute, Department of Computer Science, University of California, Santa Cruz, CA 95064; and ‡Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA 16802 Edited by Michael S. Waterman, University of Southern California, Los Angeles, CA, and approved July 11, 2003 (received for review April 9, 2003) This study examines genomic duplications, deletions, and rear- depending on details of definition and method. The length rangements that have happened at scales ranging from a single distribution of synteny blocks was found to be consistent with the base to complete chromosomes by comparing the mouse and theory of random breakage introduced by Nadeau and Taylor (8, human genomes. From whole-genome sequence alignments, 344 9) before significant gene order data became available. In recent large (>100-kb) blocks of conserved synteny are evident, but these comparisons of the human and mouse genomes, rearrangements are further fragmented by smaller-scale evolutionary events. Ex- of Ն100,000 bases were studied by comparing 558,000 highly cluding transposon insertions, on average in each megabase of conserved short sequence alignments (average length 340 bp) genomic alignment we observe two inversions, 17 duplications within 300-kb windows. An estimated 217 blocks of conserved (five tandem or nearly tandem), seven transpositions, and 200 synteny were found, formed from 342 conserved segments, with deletions of 100 bases or more. This includes 160 inversions and 75 length distribution roughly consistent with the random breakage duplications or transpositions of length >100 kb.
  • David Haussler

    David Haussler

    Curriculum Vitae David Haussler Investigator, Howard Hughes Medical Institute Distinguished Professor, Biomolecular Engineering, University of California, Santa Cruz Scientific Director, UC Santa Cruz Genomics Institute, University of California, Santa Cruz Co-organizer, Global Alliance for Genomics and Health Co-leader, Global Alliance for Genomics and Health, Data Working Group Cofounder, Genome 10K Project Affiliate, Crown College 1156 High Street Tel: (831) 459-2105 501 Engineering 2, MS: CBSE Fax: 831) 459-1809 University of California [email protected] Santa Cruz, CA 95064 www.cbse.ucsc.edu/people/haussler EMPLOYMENT HISTORY 2005- Distinguished Professor, Biomolecular Engineering, University of California, Santa Cruz 2000- Adjunct Professor, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco 2000- Consulting Professor, Stanford University School of Medicine (Medical Informatics) 2000- Investigator, Howard Hughes Medical Institute 8/1997-12/1997 Visiting Scientist and Co-Director of special scientific program, Isaac Newton Institute for Mathematical Sciences and Sanger Centre, Cambridge, England 1993-2004 Professor, Computer Science, University of California, Santa Cruz 9/1991-10/1991 Visiting Scientist, Mathematical Sciences Research Institute, Berkeley, CA 1989-1993 Associate Professor, Computer Science, University of California, Santa Cruz 1986-1989 Assistant Professor, Computer Science, University of California, Santa Cruz 4/1986-5/1986 Visiting Scientist, Université de Haute Normandie,
  • ENCODE: Understanding the Genome

    ENCODE: Understanding the Genome

    ENCODE: Understanding the Genome Michael Snyder November 6, 2012 Conflicts: Personalis, Genapsys, Illumina Slides From Ewan Birney, Marc Schaub, Alan Boyle Encyclopedia of DNA Elements (ENCODE) • NHGRI-funded consortium • Goal: delineate all functional elements in the human genome • Wide array of experimental assays • Three Phases: 1) Pilot 2) Scale Up 1.0 3) Scale up 2.0 The ENCODE Project Consortium. An Integrated Encyclopedia of DNA Elements in the Human Genome. Nature 2012 Project website: http://encodeproject.org The ENCODE Consortium Brad Bernstein (Eric Lander, Manolis Kellis, Tony Kouzarides) Ewan Birney (Jim Kent, Mark Gerstein, Bill Noble, Peter Bickel, Ross Hardison, Zhiping Weng) Greg Crawford (Ewan Birney, Jason Lieb, Terry Furey, Vishy Iyer) Jim Kent (David Haussler, Kate Rosenbloom) John Stamatoyannopoulos (Evan Eichler, George Stamatoyannopoulos, Job Dekker, Maynard Olson, Michael Dorschner, Patrick Navas, Phil Green) Mike Snyder (Kevin Struhl, Mark Gerstein, Peggy Farnham, Sherman Weissman) Rick Myers (Barbara Wold) Scott Tenenbaum (Luiz Penalva) Tim Hubbard (Alexandre Reymond, Alfonso Valencia, David Haussler, Ewan Birney, Jim Kent, Manolis Kellis, Mark Gerstein, Michael Brent, Roderic Guigo) Tom Gingeras (Alexandre Reymond, David Spector, Greg Hannon, Michael Brent, Roderic Guigo, Stylianos Antonarakis, Yijun Ruan, Yoshihide Hayashizaki) Zhiping Weng (Nathan Trinklein, Rick Myers) Additional ENCODE Participants: Elliott Marguiles, Eric Green, Job Dekker, Laura Elnitski, Len Pennachio, Jochen Wittbrodt .. and many senior
  • Global Alliance 3Rd Plenary Meeting

    Global Alliance 3Rd Plenary Meeting

    Global Alliance for Genomics and Health Summary of Third Plenary Meeting June 9-11, 2015, Leiden, the Netherlands This document serves as an interactive record of the third plenary meeting of the Global Alliance for Genomics and Health. An executive summary provides a short, high-level view of the three-day event. More detailed, modular session summaries follow, and include links (where available) to presentation videos. An appendix provides summaries of Working Group and Demonstration Projects. Executive Summary ................................................................................................................... 2 Strategic Roadmap, 2015-2016 ................................................................................................. 5 Introduction ................................................................................................................................. 6 Keynote Speaker: Challenges and opportunities, 2015-2020 ................................................ 7 Actions since 2nd Plenary meeting .......................................................................................... 9 (1) BRCA Challenge ......................................................................................................... 9 (2) Matchmaker Exchange .............................................................................................. 11 (3) Beacon Project and Data Sharing APIs .................................................................... 13 (4) Policy: Consent and Privacy & Security ...................................................................
  • BCH364C/394P Systems Biology / Bioinformatics Edward Marcotte, Univ of Texas at Austin

    BCH364C/394P Systems Biology / Bioinformatics Edward Marcotte, Univ of Texas at Austin

    Assembling Genomes BCH364C/394P Systems Biology / Bioinformatics Edward Marcotte, Univ of Texas at Austin http://www.triazzle.com ; The image from http://www.dangilbert.com/port_fun.html Reference: Jones NC, Pevzner PA, Introduction to Bioinformatics Algorithms, MIT press 1 “mapping” “shotgun” sequencing (Translating the cloning jargon) 2 Thinking about the basic shotgun concept • Start with a very large set of random sequencing reads • How might we match up the overlapping sequences? • How can we assemble the overlapping reads together in order to derive the genome? Thinking about the basic shotgun concept • At a high level, the first genomes were sequenced by comparing pairs of reads to find overlapping reads • Then, building a graph ( i.e. , a network) to represent those relationships • The genome sequence is a “walk” across that graph 3 The “Overlap-Layout-Consensus” method Overlap : Compare all pairs of reads (allow some low level of mismatches) Layout : Construct a graph describing the overlaps sequence overlap read Simplify the graph read Find the simplest path through the graph Consensus : Reconcile errors among reads along that path to find the consensus sequence Building an overlap graph 5’ 3’ EUGENE W. MYERS. Journal of Computational Biology . Summer 1995, 2(2): 275-290 4 Building an overlap graph Reads 5’ 3’ Overlap graph EUGENE W. MYERS. Journal of Computational Biology . Summer 1995, 2(2): 275-290 (more or less) Simplifying an overlap graph 1. Remove all contained nodes & edges going to them EUGENE W. MYERS. Journal of Computational Biology . Summer 1995, 2(2): 275-290 (more or less) 5 Simplifying an overlap graph 2.