Bioinformatics in the Post-Sequence Era
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University of Maryland Establishes Center for Health Related Informatics and Bioimaging
University of Maryland Establishes Center for Health Related Informatics and Bioimaging News & Events UNIVERSITY OF MARYLAND ESTABLISHES CENTER FOR HEALTH CONTACT RELATED INFORMATICS AND BIOIMAGING Monday, June 10, 2013 Karen Robinson, Media Relations New Center, Led By Distinguished Bioinformatics Scientist Owen White, (410) 706-7590 Ph.D., Unites Scientists Across Disciplines to Integrate Genomics and Personalized Medicine into Research and Clinical Care [email protected] University of Maryland, Baltimore campus President Jay A. Perman, M.D., and Media Relations Team University of Maryland School of Medicine Dean E. Albert Reece, M.D., Ph.D., M.B.A., wish to announce the establishment of a new center to unite research scientists and physicians across disciplines. The center will employ these SEARCH ARTICLES interdisciplinary connections to enhance the use of cutting edge medical science such as genomics and personalized medicine to accelerate research discoveries and improve health care outcomes. Participants in the new News Archives University of Maryland Center for Health-Related Informatics and Bioimaging (CHIB) will collaborate with computer scientists, engineers, life scientists and others at a similar center at the University of Maryland, College Park campus, CHIB Co-Director Owen White, Ph.D. together forming a joint center supported by the M-Power Maryland initiative. LEARN MORE University of Maryland School of Medicine Dean E. Albert Reece, M.D., Ph.D., M.B.A., with the concurrence of President Perman, has appointed as co-director of the new center Owen White, Ph.D., Professor of Epidemiology and Public Health and Director of Bioinformatics at the University of Maryland School of Medicine Institute for Genome Sciences. -
Computational Analysis and Predictive Cheminformatics Modeling of Small Molecule Inhibitors of Epigenetic Modifiers
RESEARCH ARTICLE Computational Analysis and Predictive Cheminformatics Modeling of Small Molecule Inhibitors of Epigenetic Modifiers Salma Jamal1☯‡, Sonam Arora2☯‡, Vinod Scaria3* 1 CSIR Open Source Drug Discovery Unit (CSIR-OSDD), Anusandhan Bhawan, Delhi, India, 2 Delhi Technological University, Delhi, India, 3 GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India ☯ These authors contributed equally to this work. ‡ These authors are joint first authors on this work. * [email protected] a11111 Abstract Background The dynamic and differential regulation and expression of genes is majorly governed by the OPEN ACCESS complex interactions of a subset of biomolecules in the cell operating at multiple levels start- Citation: Jamal S, Arora S, Scaria V (2016) ing from genome organisation to protein post-translational regulation. The regulatory layer Computational Analysis and Predictive contributed by the epigenetic layer has been one of the favourite areas of interest recently. Cheminformatics Modeling of Small Molecule Inhibitors of Epigenetic Modifiers. PLoS ONE 11(9): This layer of regulation as we know today largely comprises of DNA modifications, histone e0083032. doi:10.1371/journal.pone.0083032 modifications and noncoding RNA regulation and the interplay between each of these major Editor: Wei Yan, University of Nevada School of components. Epigenetic regulation has been recently shown to be central to development Medicine, UNITED STATES of a number of disease processes. The availability of datasets of high-throughput screens Received: June 10, 2013 for molecules for biological properties offer a new opportunity to develop computational methodologies which would enable in-silico screening of large molecular libraries. -
Genome Informatics 2016 Davide Chicco1 and Michael M
Chicco and Hoffman Genome Biology (2017) 18:5 DOI 10.1186/s13059-016-1135-5 MEETINGREPORT Open Access Genome Informatics 2016 Davide Chicco1 and Michael M. Hoffman1,2,3* Abstract of sequence variants. Konrad Karczewski (Massachusetts General Hospital, USA) presented the Loss Of Func- A report on the Genome Informatics conference, held tion Transcript Effect Estimator (LOFTEE, https://github. at the Wellcome Genome Campus Conference Centre, com/konradjk/loftee). LOFTEE uses a support vector Hinxton, United Kingdom, 19–22 September 2016. machine to identify sequence variants that significantly disrupt a gene and potentially affect biological processes. We report a sampling of the advances in computational Martin Kircher (University of Washington, USA) dis- genomics presented at the most recent Genome Informat- cussed a massively parallel reporter assay (MPRA) that ics conference. As in Genome Informatics 2014 [1], speak- uses a lentivirus for genomic integration, called lentiM- ers presented research on personal and medical genomics, PRA [3]. He used lentiMPRA to predict enhancer activity, transcriptomics, epigenomics, and metagenomics, new and to more generally measure the functional effect of sequencing techniques, and new computational algo- non-coding variants. William McLaren (European Bioin- rithms to crunch ever-larger genomic datasets. Two formatics Institute, UK) presented Haplosaurus, a variant changes were notable. First, there was a marked increase effect predictor that uses haplotype-phased data (https:// in the number of projects involving single-cell analy- github.com/willmclaren/ensembl-vep). ses, especially single-cell RNA-seq (scRNA-seq). Second, Two presenters discussed genome informatics while participants continued the practice of present- approaches to the analysis of cancer immunotherapy ing unpublished results, a large number of the presen- response. -
Biological Computation
Portland State University PDXScholar Computer Science Faculty Publications and Presentations Computer Science 9-21-2010 Biological Computation Melanie Mitchell Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/compsci_fac Part of the Biology Commons, and the Computer Engineering Commons Let us know how access to this document benefits ou.y Citation Details Mitchell, Melanie, "Biological Computation" (2010). Computer Science Faculty Publications and Presentations. 2. https://pdxscholar.library.pdx.edu/compsci_fac/2 This Working Paper is brought to you for free and open access. It has been accepted for inclusion in Computer Science Faculty Publications and Presentations by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. Biological Computation Melanie Mitchell SFI WORKING PAPER: 2010-09-021 SFI Working Papers contain accounts of scientific work of the author(s) and do not necessarily represent the views of the Santa Fe Institute. We accept papers intended for publication in peer-reviewed journals or proceedings volumes, but not papers that have already appeared in print. Except for papers by our external faculty, papers must be based on work done at SFI, inspired by an invited visit to or collaboration at SFI, or funded by an SFI grant. ©NOTICE: This working paper is included by permission of the contributing author(s) as a means to ensure timely distribution of the scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the author(s). It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. -
Teaching Bioinformatics and Neuroinformatics Using Free Web-Based Tools
UCLA Behavioral Neuroscience Title Teaching Bioinformatics and Neuroinformatics Using Free Web-based Tools Permalink https://escholarship.org/uc/item/2689x2hk Author Grisham, William Publication Date 2009 Supplemental Material https://escholarship.org/uc/item/2689x2hk#supplemental Peer reviewed eScholarship.org Powered by the California Digital Library University of California – SUBMITTED – [Article; 34,852 Characters] Teaching Bioinformatics and Neuroinformatics Using Free Web-based Tools William Grisham 1, Natalie A. Schottler 1, Joanne Valli-Marill 2, Lisa Beck 3, Jackson Beatty 1 1Department of Psychology, UCLA; 2 Office of Instructional Development, UCLA; 3Department of Psychology, Bryn Mawr College Keywords: quantitative trait locus, digital teaching tools, web-based learning, genetic analysis, in silico tools DRAFT: Copyright William Grisham, 2009 Address correspondence to: William Grisham Department of Psychology, UCLA PO Box 951563 Los Angeles, CA 90095-1563 [email protected] Grisham Teaching Bioinformatics Using Web-Based Tools ABSTRACT This completely computer-based module’s purpose is to introduce students to bioinformatics resources. We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. This module integrates information gathered from websites dealing with anatomy (Mouse Brain Library), Quantitative Trait Locus analysis (WebQTL from GeneNetwork), bioinformatics and gene expression analyses (University of California, Santa Cruz Genome Browser, NCBI Entrez Gene, and the Allen Brain Atlas), and information resources (PubMed). This module provides for teaching genetics from the phenotypic level to the molecular level, some neuroanatomy, some aspects of histology, statistics, Quantitaive Trait Locus analysis, molecular biology including in situ hybridization and microarray analysis in addition to introducing bioinformatic resources. -
Gene Selection Using Gene Expression Data in a Virtual Environment
276 Genome Informatics 13: 276-277 (2002) Gene Selection Using Gene Expression Data in a Virtual Environment Kunihiro Nishimura1 Shumpei Ishikawa2 [email protected] [email protected] Shuichi Tsutsumi2 Hiroyuki Aburatani2 [email protected] [email protected] Koichi Hirota2 Michitaka Hirose2 [email protected] [email protected] 1 Graduate School of Information Science and Technology , The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan 2 Reseach Center for Advanced Science and Technology , The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan Keywords: gene selection, gene expression, virtual reality, visualization 1 Gene Selection for Drug Discovery The gene expression data of many normal and diseased tissues enables the selection of genes that satisfy the conditions for finding a new drug. In the process of selecting genes is required to determine many filtering parameters. The system of the selection of genes from all genes is required to have the following features: 1) visualization of the data to provide the users to grasp both holistic and detail views of the data, 2) interactivity to determine many filtering parameters, 3) provides spatial cue to understand intuitively because there are many filtering parameters. In order to visualize various information of large number of genes, the system also needs a wide field of view. Virtual reality technology satisfies these requirements above. 2 Virtual Reality Virtual reality technology can help improve genome data analysis by enabling interactive visualization and providing a three-dimensional virtual environment. -
Cheminformatics Education
Cheminformatics Education Introduction Despite some skepticism at the turn of the century1,2 the terms “cheminformatics” and “chemoinformatics” are now in common parlance. The term “chemical informatics” is used less. The premier journal in the field, the Journal of Chemical Information and Modeling, does not use any of these terms and its cheminformatics papers are scattered across multiple sections including “chemical Information”. Unfortunately it is not just the name of the discipline that is undefined: opinions also vary on the scope. Paris supplied the following definition1 (shortened here): “Chem(o)informatics is a generic term that encompasses the design, creation, organization, storage, management, retrieval, analysis, dissemination, visualization and use of chemical information, not only in its own right, but as a surrogate or index for other data, information and knowledge”. Brown3 says the discipline is “mixing of information resources to transform data into information, and information into knowledge, for the intended purpose of making better decisions faster in the arena of drug lead identification and optimization”. Gasteiger4 says that cheminformatics is the application of informatics methods to solve chemical problems and Bajorath5 agrees that a broad definition such as that is needed to cover all the different scientific activities that have evolved, or have been assimilated, under the cheminformatics umbrella. Varnek’s definition6,7 is rather different. He considers that cheminformatics is a part of theoretical chemistry based on its own molecular model; unlike quantum chemistry in which molecules are represented as ensemble of electrons and nuclei, or force fields molecular modeling dealing with classical “atoms” and “bonds”, cheminformatics considers molecules as objects (graphs and vectors) in multidimensional chemical space. -
Molecular and Neural Computation
CSE590b: Molecular and neural computation Georg Seelig 1 Administrative Georg Seelig Grading: Office: CSE 228 30% class participation: ask questions. It’s more [email protected] fun if it’s interactive. TA: Kevin Oishi 30% Homework: Due at the end of class one week after they are handed out. Late policy: -10% each day for the first 3 days, then not accepted 40% Class project: A small design project using one (or several) of the design and simulation tools that will be introduced in class [email protected] Books: There is no single book that covers the material in this class. Any book on molecular biology and on neural computation might be helpful to dig deeper. https://www.coursera.org/course/compneuro 2 Computation can be embedded in many substrates Computaon controls physical Alternave physical substrates substrates (output of computaon is can be used to make computers the physical substrate) The molecular programming project The history of computing has taught us two things: first, that the principles of computing can be embodied in a wide variety of physical substrates from gears and springs to transistors, and second that the mastery of a new physical substrate for computing has the potential to transform technology. Another revolution is just beginning, one that will result in new types of programmable systems based on molecules. Like the previous revolutions, this “molecular programming revolution” will take much of its theory from computer science, but will require reformulation of familiar concepts such as programming languages and compilers, data structures and algorithms, resources and complexity, concurrency and stochasticity, correctness and robustness. -
Informatics and the Human Genome Project
Invited manuscript: submitted to IEEE Engineering in Medicine and Biology for a special issue on genome informatics. INFORMATICS AND THE HUMAN GENOME PROJECT Robert J. Robbins US Department of Energy [email protected] David Benton National Center for Human Genome Research [email protected] Jay Snoddy US Department of Energy [email protected] TABLE OF CONTENTS INTRODUCTION 1 INFORMATION AND GENOME PROJECTS 3 THE NATURE OF INFORMATION TECHNOLOGY 4 MOORE’S LAW 5 INFORMATICS 6 INFORMATICS ENABLES BIG SCIENCE 6 THE INTELLECTUAL STANDING OF INFORMATICS 7 AGENCY COMMITMENTS 8 COMMUNITY RECOMMENDATIONS 8 OTA Report 9 Baltimore White Paper 9 GeSTeC Report 10 Workshop on Database Interoperability 11 Meeting on Interconnection of Molecular Biology Databases (MIMBD) 11 Interoperable Software Tools 12 SUMMARY NEEDS 12 CURRENT TRENDS 12 FUTURE SUPPORT 13 OPEN ISSUES 13 Invited manuscript: submitted to IEEE Engineering in Medicine and Biology for a special issue on genome informatics. INFORMATICS AND THE HUMAN GENOME PROJECT1 ROBERT J. ROBBINS, DAVID BENTON, AND JAY SNODDY INTRODUCTION Information technology is transforming biology and the relentless effects of Moore’s Law (discussed later) are transforming that transformation. Nowhere is this more apparent than in the international collaboration known as the Human Genome Project (HGP). Before considering the relationship of informatics to genomic research, let us take a moment to consider the science of the HGP. It has been known since antiquity that like begets like, more or less. Cats have kittens, dogs have puppies, and acorns grow into oak trees. A scientific basis for that observation was first provided with the development of the new science of genetics at the beginning of this century. -
Introduction to Bioinformatics a Complex Systems Approach
Introduction to Bioinformatics A Complex Systems Approach Luis M. Rocha Complex Systems Modeling CCS3 - Modeling, Algorithms, and Informatics Los Alamos National Laboratory, MS B256 Los Alamos, NM 87545 [email protected] or [email protected] 1 Bioinformatics: A Complex Systems Approach Course Layout 3 Monday: Overview and Background Luis Rocha 3 Tuesday: Gene Expression Arrays – Biology and Databases Tom Brettin 3 Wednesday: Data Mining and Machine Learning Luis Rocha and Deborah Stungis Rocha 3 Thursday: Gene Network Inference Patrik D'haeseleer 3 Friday: Database Technology, Information Retrieval and Distributed Knowledge Systems Luis Rocha 2 Bioinformatics: A Complex Systems Approach Overview and Background 3A Synthetic Approach to Biology Information Processes in Biology – Biosemiotics Genome, DNA, RNA, Protein, and Proteome – Information and Semiotics of the Genetic System Complexity of Real Information Proceses – RNA Editing and Post-Transcription changes Reductionism, Synthesis and Grand Challenges Technology of Post-genome informatics – Sequence Analysis: dynamic programming, simulated anealing, genetic algorithms Artificial Life 3 Information Processes in Biology Distinguishes Life from Non-Life Different Information Processing Systems (memory) 3 Genetic System Construction (expression, development, and maintenance) of cells ontogenetically: horizontal transmission Heredity (reproduction) of cells and phenotypes: vertical transmission 3 Immune System Internal response based on accumulated experience (information) 3 -
Biological Computation As the Revolution of Complex Engineered Systems
Biological Computation as the Revolution of Complex Engineered Systems Nelson Alfonso Gómez Cruz and Carlos Eduardo Maldonado Modeling and Simulation Laboratory Universidad del Rosario Calle 14 No. 6-25, Bogotá, Colombia ABSTRACT: Provided that there is no theoretical frame for complex engineered systems (CES) as yet, this paper claims that bio-inspired engineering can help provide such a frame. Within CES bio-inspired systems play a key role. The disclosure from bio-inspired systems and biological computation has not been sufficiently worked out, however. Biological computation is to be taken as the processing of information by living systems that is carried out in polynomial time, i.e., efficiently; such processing however is grasped by current science and research as an intractable problem (for instance, the protein folding problem). A remark is needed here: P versus NP problems should be well defined and delimited but biological computation problems are not. The shift from conventional engineering to bio-inspired engineering needs bring the subject (or problem) of computability to a new level. Within the frame of computation, so far, the prevailing paradigm is still the Turing-Church thesis. In other words, conventional engineering is still ruled by the Church-Turing thesis (CTt). However, CES is ruled by CTt, too. Contrarily to the above, we shall argue here that biological computation demands a more careful thinking that leads us towards hypercomputation. Bio- inspired engineering and CES thereafter, must turn its regard toward biological computation. Thus, biological computation can and should be taken as the ground for engineering complex non-linear systems. Biological systems do compute in terms of hypercomputation, indeed. -
IT 468 – Natural Computing
July 2020 IT 468 – Natural Computing Instructor Manish K Gupta (http://www.mankg.com) Room 2209 Faculty Block 2 mankg [at] daiict.ac.in Phone: +91-79-68261549 WhatsApp:+91-9898512703 Lab: http://www.guptalab.org Overview In last 60 years Information and Communication Technology (ICT) has had a great impact on our society. The most profound and accelerated impact of ICT can be seen in the last decade in the form of cell phones, connected computers and Internet. We even have a virtual currency. ICT is an interdisciplinary discipline combining IT (Information Technology) and CT (Communication Technology). IT has its root in computer science and CT has its root in theory of communication. Both the fields now can be seen as two sides of the same coin. Both deals with information, in IT we store (send information from now to then) and manipulate the information and in CT we send information from here to there (communicate). The mathematical principles of ICT lie in theoretical computer science (Turing machine) and information and coding theory (work of Shannon and Hamming). Realization of ICT is via logic gates and circuits in the area of Electronics and VLSI. If you look around the Nature around you many times you feel: What are the principles of Natural ICT? Can we use these principles to create Natural ICT engineering? We are fortunate enough that due to advancement of many fields we are now in a position to talk about Natural ICT. There are three main areas of Natural ICT: 1. ICT inspired by Nature 2.