Microblogging the ISMB: a New Approach to Conference Reporting
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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]. -
Proquest Dissertations
Automated learning of protein involvement in pathogenesis using integrated queries Eithon Cadag A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2009 Program Authorized to Offer Degree: Department of Medical Education and Biomedical Informatics UMI Number: 3394276 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. UMI Dissertation Publishing UMI 3394276 Copyright 2010 by ProQuest LLC. All rights reserved. This edition of the work is protected against unauthorized copying under Title 17, United States Code. uest ProQuest LLC 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, Ml 48106-1346 University of Washington Graduate School This is to certify that I have examined this copy of a doctoral dissertation by Eithon Cadag and have found that it is complete and satisfactory in all respects, and that any and all revisions required by the final examining committee have been made. Chair of the Supervisory Committee: Reading Committee: (SjLt KJ. £U*t~ Peter Tgffczy-Hornoch In presenting this dissertation in partial fulfillment of the requirements for the doctoral degree at the University of Washington, I agree that the Library shall make its copies freely available for inspection. I further agree that extensive copying of this dissertation is allowable only for scholarly purposes, consistent with "fair use" as prescribed in the U.S. -
Trancep: Predicting Transmembrane Transport Proteins Using Composition, Evolutionary, and Positional Information
bioRxiv preprint doi: https://doi.org/10.1101/293159; this version posted April 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. TranCEP: Predicting transmembrane transport proteins using composition, evolutionary, and positional information Munira Alballa1, Faizah Aplop2, Gregory Butler1,3* 1 Department of Computer Science and Software Engineering, Concordia University, Montr´eal, Qu´ebec, Canada 2 School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Malaysia 3 Centre for Structural and Functional Genomics, Concordia University, Montr´eal,Qu´ebec, Canada * [email protected] Abstract Transporters mediate the movement of compounds across the membranes that separate the cell from its environment, and across inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computation tools that predict the substrates which are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment, and restriction to important positions of the alignment that play a role in determining specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state of the art. -
Algorithms for Computational Biology 8Th International Conference, Alcob 2021 Missoula, MT, USA, June 7–11, 2021 Proceedings
Lecture Notes in Bioinformatics 12715 Subseries of Lecture Notes in Computer Science Series Editors Sorin Istrail Brown University, Providence, RI, USA Pavel Pevzner University of California, San Diego, CA, USA Michael Waterman University of Southern California, Los Angeles, CA, USA Editorial Board Members Søren Brunak Technical University of Denmark, Kongens Lyngby, Denmark Mikhail S. Gelfand IITP, Research and Training Center on Bioinformatics, Moscow, Russia Thomas Lengauer Max Planck Institute for Informatics, Saarbrücken, Germany Satoru Miyano University of Tokyo, Tokyo, Japan Eugene Myers Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Marie-France Sagot Université Lyon 1, Villeurbanne, France David Sankoff University of Ottawa, Ottawa, Canada Ron Shamir Tel Aviv University, Ramat Aviv, Tel Aviv, Israel Terry Speed Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia Martin Vingron Max Planck Institute for Molecular Genetics, Berlin, Germany W. Eric Wong University of Texas at Dallas, Richardson, TX, USA More information about this subseries at http://www.springer.com/series/5381 Carlos Martín-Vide • Miguel A. Vega-Rodríguez • Travis Wheeler (Eds.) Algorithms for Computational Biology 8th International Conference, AlCoB 2021 Missoula, MT, USA, June 7–11, 2021 Proceedings 123 Editors Carlos Martín-Vide Miguel A. Vega-Rodríguez Rovira i Virgili University University of Extremadura Tarragona, Spain Cáceres, Spain Travis Wheeler University of Montana Missoula, MT, USA ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Bioinformatics ISBN 978-3-030-74431-1 ISBN 978-3-030-74432-8 (eBook) https://doi.org/10.1007/978-3-030-74432-8 LNCS Sublibrary: SL8 – Bioinformatics © Springer Nature Switzerland AG 2021 This work is subject to copyright. -
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]. -
Complete Vertebrate Mitogenomes Reveal Widespread Gene Duplications and Repeats
bioRxiv preprint doi: https://doi.org/10.1101/2020.06.30.177956; this version posted July 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Complete vertebrate mitogenomes reveal widespread gene duplications and repeats Giulio Formenti+*1,2,3, Arang Rhie4, Jennifer Balacco1, Bettina Haase1, Jacquelyn Mountcastle1, Olivier Fedrigo1, Samara Brown2,3, Marco Capodiferro5, Farooq O. Al-Ajli6,7,8, Roberto Ambrosini9, Peter Houde10, Sergey Koren4, Karen Oliver11, Michelle Smith11, Jason Skelton11, Emma Betteridge11, Jale Dolucan11, Craig Corton11, Iliana Bista11, James Torrance11, Alan Tracey11, Jonathan Wood11, Marcela Uliano-Silva11, Kerstin Howe11, Shane McCarthy12, Sylke Winkler13, Woori Kwak14, Jonas Korlach15, Arkarachai Fungtammasan16, Daniel Fordham17, Vania Costa17, Simon Mayes17, Matteo Chiara18, David S. Horner18, Eugene Myers13, Richard Durbin12, Alessandro Achilli5, Edward L. Braun19, Adam M. Phillippy4, Erich D. Jarvis*1,2,3, and The Vertebrate Genomes Project Consortium +First author *Corresponding authors; [email protected]; [email protected] Author affiliations 1. The Vertebrate Genome Lab, Rockefeller University, New York, NY, USA 2. Laboratory of Neurogenetics of Language, Rockefeller University, New York, NY, USA 3. The Howards Hughes Medical Institute, Chevy Chase, MD, USA 4. Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA 5. Department of Biology and Biotechnology “L. Spallanzani”, University of Pavia, Pavia, Italy 6. Monash University Malaysia Genomics Facility, School of Science, Selangor Darul Ehsan, Malaysia 7. -
PROGRAM CHAIR: Eben Rosenthal, MD PROFFERED PAPERS CHAIR: Ellie Maghami, MD POSTER CHAIR: Maie St
AHNS 10TH INTERNATIONAL CONFERENCE ON HEAD & NECK CANCER “Survivorship through Quality & Innovation” JULY 22-25, 2021 • VIRTUAL CONFERENCE AHNS PRESIDENT: Cherie-Ann Nathan, MD, FACS CONFERENCE/DEVELOPMENT CHAIR: Robert Ferris, MD, PhD PROGRAM CHAIR: Eben Rosenthal, MD PROFFERED PAPERS CHAIR: Ellie Maghami, MD POSTER CHAIR: Maie St. John, MD Visit www.ahns2021.org for more information. WELCOME LETTER Dear Colleagues, The American Head and Neck Society (AHNS) is pleased to invite you to the virtual AHNS 10th International Conference on Head and Neck Cancer, which will be held July 22-25, 2021. The theme is Survivorship through Quality & Innovation and the scientific program has been thoughtfully designed to bring together all disciplines related to the treatment of head and neck cancer. Our assembled group of renowned head and neck surgeons, radiologists and oncologists have identified key areas of interest and major topics for us to explore. The entire conference will be presented LIVE online July 22-25, 2021. We encourage you to attend the live sessions in order to engage with the faculty and your colleagues. After the live meeting, all of the meeting content will be posted on the conference site and remain open for on-demand viewing through October 1, 2021. Attendees may earn up to 42.25 AMA PRA Category 1 Credit(s)TM as well as earn re- quired annual part II self-assessment credit in the American Board of Otolaryngology – Head and Neck Surgery’s Continu- ing Certification program (formerly known as MOC). At the conclusion of the activity, -
ISMB 99 August 6 – 10, 1999 Heidelberg, Germany the Seventh
______________________________________ Welcome to ISMB 99 August 6 – 10, 1999 Heidelberg, Germany The Seventh International Conference on Intelligent Systems for Molecular Biology ______________________________________ Final Program and Detailed Schedule Friday, August 6, 1999 Tutorial Day The tutorials will take place in the following rooms: 8:30 – 12:30 (Coffee break around 10:30) Tutorial #1 Trübnersaal Piere Baldi Probabilistic graphical models Tutorial #2 Robert-Schumann-Zimmer Douglas L. Brutlag Bioinformatics and Molecular Biology Tutorial #3 Ballsaal Martin Reese The challenge of annotating a complete eukaryotic genome: A case study in Drosophila melanogaster Tutorial #4 Gustav-Mahler-Zimmer Tandy Warnow Computational and statistical Junhyong Kim challenges involved in reconstructing evolutionary trees Tutorial #5 Sebastian-Münster-Saal Thomas Werner The biology and bioinformatics of regulatory regions in genomes Lunch (on this day served in "Grosser Saal" on the ground floor) 13:30 – 17:30 (Coffee break around 15:30) Tutorial #6 Sebastian-Münster-Saal Rob Miller EST Clustering Alan Christoffels Winston Hide Tutorial #7 Trübnersaal Kevin Karplus Getting the most out of hidden Markov Melissa Cline models Christian Barrett Tutorial #8 Robert-Schumann-Zimmer Arthur Lesk Sequence-structure relationships and evolutionary structure changes in proteins Tutorial #9 Gustav-Mahler-Zimmer David States PERL abstractions for databases and Brian Dunford distributed computing Shore Tutorial # 10 Ballsaal Zoltan Szallasi Genetic network analysis -
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