Cole Trapnell, Ph.D
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BIOGRAPHICAL SKETCH NAME: Berger
BIOGRAPHICAL SKETCH NAME: Berger, Bonnie eRA COMMONS USER NAME (credential, e.g., agency login): BABERGER POSITION TITLE: Simons Professor of Mathematics and Professor of Electrical Engineering and Computer Science EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training and residency training if applicable. Add/delete rows as necessary.) EDUCATION/TRAINING DEGREE Completion (if Date FIELD OF STUDY INSTITUTION AND LOCATION applicable) MM/YYYY Brandeis University, Waltham, MA AB 06/1983 Computer Science Massachusetts Institute of Technology SM 01/1986 Computer Science Massachusetts Institute of Technology Ph.D. 06/1990 Computer Science Massachusetts Institute of Technology Postdoc 06/1992 Applied Mathematics A. Personal Statement Advances in modern biology revolve around automated data collection and sharing of the large resulting datasets. I am considered a pioneer in the area of bringing computer algorithms to the study of biological data, and a founder in this community that I have witnessed grow so profoundly over the last 26 years. I have made major contributions to many areas of computational biology and biomedicine, largely, though not exclusively through algorithmic innovations, as demonstrated by nearly twenty thousand citations to my scientific papers and widely-used software. In recognition of my success, I have just been elected to the National Academy of Sciences and in 2019 received the ISCB Senior Scientist Award, the pinnacle award in computational biology. My research group works on diverse challenges, including Computational Genomics, High-throughput Technology Analysis and Design, Biological Networks, Structural Bioinformatics, Population Genetics and Biomedical Privacy. I spearheaded research on analyzing large and complex biological data sets through topological and machine learning approaches; e.g. -
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. -
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. -
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 -
Lior Pachter Genome Informatics 2013 Keynote
Stories from the Supplement Lior Pachter! Department of Mathematics and Molecular & Cell Biology! UC Berkeley! ! November 1, 2013! Genome Informatics, CSHL The Cufflinks supplements • C. Trapnell, B.A. Williams, G. Pertea, A. Mortazavi, G. Kwan, M.J. van Baren, S.L. Salzberg, B.J. Wold and L. Pachter, Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation, Nature Biotechnology 28, (2010), p 511–515. Supplementary Material: 42 pages. • C. Trapnell, D.G. Hendrickson, M. Sauvageau, L. Goff, J.L. Rinn and L. Pachter, Differential analysis of gene regulation at transcript resolution with RNA-seq, Nature Biotechnology, 31 (2012), p 46–53. Supplementary Material: 70 pages. A supplementary arithmetic progression? • C. Trapnell, B.A. Williams, G. Pertea, A. Mortazavi, G. Kwan, M.J. van Baren, S.L. Salzberg, B.J. Wold and L. Pachter, Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation, Nature Biotechnology 28, (2010), p 511–515. Supplementary Material: 42 pages. • C. Trapnell, D.G. Hendrickson, M. Sauvageau, L. Goff, J.L. Rinn and L. Pachter, Differential analysis of gene regulation at transcript resolution with RNA- seq, Nature Biotechnology, 31 (2012), p 46–53. Supplementary Material: 70 pages. • A. Roberts and L. Pachter, Streaming algorithms for fragment assignment, Nature Methods 10 (2013), p 71—73. Supplementary Material: 98 pages? The nature methods manuscript checklist The nature methods manuscript checklist Emperor Joseph II: My dear young man, don't take it too hard. Your work is ingenious. It's quality work. And there are simply too many notes, that's all. -
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. -
Computational Biology: Plus C'est La Même Chose, Plus Ça Change
Computational biology: plus c'est la même chose, plus ça change The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Huttenhower, Curtis. 2011. Computational biology: plus c'est la même chose, plus ça change. Genome Biology 12(8): 307. Published Version doi:10.1186/gb-2011-12-8-307 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:10576037 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Huttenhower Genome Biology 2011, 12:307 http://genomebiology.com/2011/12/8/307 MEETING REPORT Computational biology: plus c’est la même chose, plus ça change Curtis Huttenhower* The data deluge: still keeping our heads above water Abstract Bioinformatics has been dealing with an exponential A report on the joint 19th Annual International growth in data since its coalescence as a field in the Conference on Intelligent Systems for Molecular 1980s, making the Senior Scientist Award keynote with Biology (ISMB)/10th Annual European Conference which Michael Ashburner closed the conference on Computational Biology (ECCB) meetings and particularly appropriate. This retrospective by the ‘father the 7th International Society for Computational of ontologies in biology’, to quote the introduction by Biology Student Council Symposium, Vienna, Austria, ISCB president Burkhard Rost, detailed the remarkable 15‑19 July 2011. expansion of computational biology since Ashburner’s start as a Cambridge undergraduate 50 years ago. -
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. -
Human and Mouse Gene Structure: Comparative Analysis and Application to Exon Prediction
Downloaded from genome.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press Letter Human and Mouse Gene Structure: Comparative Analysis and Application to Exon Prediction Serafim Batzoglou,1,4,7 Lior Pachter,2,7 Jill P. Mesirov,3 Bonnie Berger,1,4,6 and Eric S. Lander3,5,6 1Laboratory for Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 USA; 2Department of Mathematics, University of California Berkeley, Berkeley, California 94720 USA; 3Whitehead Institute for Biomedical Research, Cambridge, Massachusetts 02142 USA; 4Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 USA; 5Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 USA We describe a novel analytical approach to gene recognition based on cross-species comparison. We first undertook a comparison of orthologous genomic loci from human and mouse, studying the extent of similarity in the number, size and sequence of exons and introns. We then developed an approach for recognizing genes within such orthologous regions by first aligning the regions using an iterative global alignment system and then identifying genes based on conservation of exonic features at aligned positions in both species. The alignment and gene recognition are performed by new programs called GLASS and ROSETTA, respectively. ROSETTA performed well at exact identification of coding exons in 117 orthologous pairs tested. A fundamental task in analyzing genomes is to identify by comparison of syntenic human and mouse genomic the genes. This is relatively straightforward for organ- sequences. isms with compact genomes (such as bacteria, yeast, It is well known that cross-species sequence com- flies and worms) because exons tend to be large and the parison can help highlight important functional ele- introns are either non-existent or tend to be short. -
Eric S. Lander
Chancellor's Distinguished Fellows Program 2004-2005 Selective Bibliography UC Irvine Libraries Eric S. Lander February 25, 2005 Prepared by: John E. Sisson Biological Sciences Librarian [email protected] Journal Articles (Selected from over 220 articles he has authored or co-authored) Poirier, C., Y. J. Qin, C.P. Adams, Y. Anaya, J. B. Singer, A. E. Hill, Eric S. Lander, J.H. Nadeau, and C. E. Bishop. "A complex interaction of imprinted and maternal-effect genes modifies sex determination in odd sex (Ods) mice." Genetics 168, no. 3 (2004): 1557-1562. Skuse, D. H., S. Purcell, M. J. Daly, R. J. Dolan, J. S. Morris, K. Lawrence, Eric S. Lander, and P. Sklar. "What can studies on Turner syndrome tell us about the role of X- linked genes in social cognition?." American Journal of Medical Genetics Part B- Neuropsychiatric Genetics 130B, no. 1 (2004): 8-9. Rioux, J. D., H. Karinen, K. Kocher, S. G. McMahon, P. Karkkainen, E. Janatuinen, M. Heikkinen, R. Julkunen, J. Pihlajamaki, A. Naukkarinen, V. M. Kosma, M. J. Daly, Eric S. Lander, and M. Laakso. "Genomewide search and association studies in a Finnish celiac disease population: Identification of a novel locus and replication of the HLA and CTLA4 loci." American Journal of Medical Genetics Part A 130A, no. 4 (2004): 345- 350. Michalkiewicz, M., T. Michalkiewicz, R. A. Ettinger, E. A. Rutledge, J. M. Fuller, D. H. Moralejo, B. Van Yserloo, A. J. MacMurray, A. E. Kwitek, H. J. Jacob, Eric S. Lander, and A. Lernmark. "Transgenic rescue demonstrates involvement of the Ian5 gene in T cell development in the rat." Physiological Genomics 19, no.