Database Technology for Bioinformatics from Information Retrieval to Knowledge Systems
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Bioinformatics 1
Bioinformatics 1 Bioinformatics School School of Science, Engineering and Technology (http://www.stmarytx.edu/set/) School Dean Ian P. Martines, Ph.D. ([email protected]) Department Biological Science (https://www.stmarytx.edu/academics/set/undergraduate/biological-sciences/) Bioinformatics is an interdisciplinary and growing field in science for solving biological, biomedical and biochemical problems with the help of computer science, mathematics and information technology. Bioinformaticians are in high demand not only in research, but also in academia because few people have the education and skills to fill available positions. The Bioinformatics program at St. Mary’s University prepares students for graduate school, medical school or entry into the field. Bioinformatics is highly applicable to all branches of life sciences and also to fields like personalized medicine and pharmacogenomics — the study of how genes affect a person’s response to drugs. The Bachelor of Science in Bioinformatics offers three tracks that students can choose. • Bachelor of Science in Bioinformatics with a minor in Biology: 120 credit hours • Bachelor of Science in Bioinformatics with a minor in Computer Science: 120 credit hours • Bachelor of Science in Bioinformatics with a minor in Applied Mathematics: 120 credit hours Students will take 23 credit hours of core Bioinformatics classes, which included three credit hours of internship or research and three credit hours of a Bioinformatics Capstone course. BS Bioinformatics Tracks • Bachelor of Science -
13 Genomics and Bioinformatics
Enderle / Introduction to Biomedical Engineering 2nd ed. Final Proof 5.2.2005 11:58am page 799 13 GENOMICS AND BIOINFORMATICS Spencer Muse, PhD Chapter Contents 13.1 Introduction 13.1.1 The Central Dogma: DNA to RNA to Protein 13.2 Core Laboratory Technologies 13.2.1 Gene Sequencing 13.2.2 Whole Genome Sequencing 13.2.3 Gene Expression 13.2.4 Polymorphisms 13.3 Core Bioinformatics Technologies 13.3.1 Genomics Databases 13.3.2 Sequence Alignment 13.3.3 Database Searching 13.3.4 Hidden Markov Models 13.3.5 Gene Prediction 13.3.6 Functional Annotation 13.3.7 Identifying Differentially Expressed Genes 13.3.8 Clustering Genes with Shared Expression Patterns 13.4 Conclusion Exercises Suggested Reading At the conclusion of this chapter, the reader will be able to: & Discuss the basic principles of molecular biology regarding genome science. & Describe the major types of data involved in genome projects, including technologies for collecting them. 799 Enderle / Introduction to Biomedical Engineering 2nd ed. Final Proof 5.2.2005 11:58am page 800 800 CHAPTER 13 GENOMICS AND BIOINFORMATICS & Describe practical applications and uses of genomic data. & Understand the major topics in the field of bioinformatics and DNA sequence analysis. & Use key bioinformatics databases and web resources. 13.1 INTRODUCTION In April 2003, sequencing of all three billion nucleotides in the human genome was declared complete. This landmark of modern science brought with it high hopes for the understanding and treatment of human genetic disorders. There is plenty of evidence to suggest that the hopes will become reality—1631 human genetic diseases are now associated with known DNA sequences, compared to the less than 100 that were known at the initiation of the Human Genome Project (HGP) in 1990. -
Use of Bioinformatics Resources and Tools by Users of Bioinformatics Centers in India Meera Yadav University of Delhi, [email protected]
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Library Philosophy and Practice (e-journal) Libraries at University of Nebraska-Lincoln 2015 Use of Bioinformatics Resources and Tools by Users of Bioinformatics Centers in India meera yadav University of Delhi, [email protected] Manlunching Tawmbing Saha Institute of Nuclear Physisc, [email protected] Follow this and additional works at: http://digitalcommons.unl.edu/libphilprac Part of the Library and Information Science Commons yadav, meera and Tawmbing, Manlunching, "Use of Bioinformatics Resources and Tools by Users of Bioinformatics Centers in India" (2015). Library Philosophy and Practice (e-journal). 1254. http://digitalcommons.unl.edu/libphilprac/1254 Use of Bioinformatics Resources and Tools by Users of Bioinformatics Centers in India Dr Meera, Manlunching Department of Library and Information Science, University of Delhi, India [email protected], [email protected] Abstract Information plays a vital role in Bioinformatics to achieve the existing Bioinformatics information technologies. Librarians have to identify the information needs, uses and problems faced to meet the needs and requirements of the Bioinformatics users. The paper analyses the response of 315 Bioinformatics users of 15 Bioinformatics centers in India. The papers analyze the data with respect to use of different Bioinformatics databases and tools used by scholars and scientists, areas of major research in Bioinformatics, Major research project, thrust areas of research and use of different resources by the user. The study identifies the various Bioinformatics services and resources used by the Bioinformatics researchers. Keywords: Informaion services, Users, Inforamtion needs, Bioinformatics resources 1. Introduction ‘Needs’ refer to lack of self-sufficiency and also represent gaps in the present knowledge of the users. -
Challenges in Bioinformatics
Yuri Quintana, PhD, delivered a webinar in AllerGen’s Webinars for Research Success series on February 27, 2018, discussing different approaches to biomedical informatics and innovations in big-data platforms for biomedical research. His main messages and a hyperlinked index to his presentation follow. WHAT IS BIOINFORMATICS? Bioinformatics is an interdisciplinary field that develops analytical methods and software tools for understanding clinical and biological data. It combines elements from many fields, including basic sciences, biology, computer science, mathematics and engineering, among others. WHY DO WE NEED BIOINFORMATICS? Chronic diseases are rapidly expanding all over the world, and associated healthcare costs are increasing at an astronomical rate. We need to develop personalized treatments tailored to the genetics of increasingly diverse patient populations, to clinical and family histories, and to environmental factors. This requires collecting vast amounts of data, integrating it, and making it accessible and usable. CHALLENGES IN BIOINFORMATICS Data collection, coordination and archiving: These technologies have evolved to the point Many sources of biomedical data—hospitals, that we can now analyze not only at the DNA research centres and universities—do not have level, but down to the level of proteins. DNA complete data for any particular disease, due in sequencers, DNA microarrays, and mass part to patient numbers, but also to the difficulties spectrometers are generating tremendous of data collection, inter-operability -
Blockchain Database for a Cyber Security Learning System
Session ETD 475 Blockchain Database for a Cyber Security Learning System Sophia Armstrong Department of Computer Science, College of Engineering and Technology East Carolina University Te-Shun Chou Department of Technology Systems, College of Engineering and Technology East Carolina University John Jones College of Engineering and Technology East Carolina University Abstract Our cyber security learning system involves an interactive environment for students to practice executing different attack and defense techniques relating to cyber security concepts. We intend to use a blockchain database to secure data from this learning system. The data being secured are students’ scores accumulated by successful attacks or defends from the other students’ implementations. As more professionals are departing from traditional relational databases, the enthusiasm around distributed ledger databases is growing, specifically blockchain. With many available platforms applying blockchain structures, it is important to understand how this emerging technology is being used, with the goal of utilizing this technology for our learning system. In order to successfully secure the data and ensure it is tamper resistant, an investigation of blockchain technology use cases must be conducted. In addition, this paper defined the primary characteristics of the emerging distributed ledgers or blockchain technology, to ensure we effectively harness this technology to secure our data. Moreover, we explored using a blockchain database for our data. 1. Introduction New buzz words are constantly surfacing in the ever evolving field of computer science, so it is critical to distinguish the difference between temporary fads and new evolutionary technology. Blockchain is one of the newest and most developmental technologies currently drawing interest. -
An Introduction to Cloud Databases a Guide for Administrators
Compliments of An Introduction to Cloud Databases A Guide for Administrators Wendy Neu, Vlad Vlasceanu, Andy Oram & Sam Alapati REPORT Break free from old guard databases AWS provides the broadest selection of purpose-built databases allowing you to save, grow, and innovate faster Enterprise scale at 3-5x the performance 14+ database engines 1/10th the cost of vs popular alternatives - more than any other commercial databases provider Learn more: aws.amazon.com/databases An Introduction to Cloud Databases A Guide for Administrators Wendy Neu, Vlad Vlasceanu, Andy Oram, and Sam Alapati Beijing Boston Farnham Sebastopol Tokyo An Introduction to Cloud Databases by Wendy A. Neu, Vlad Vlasceanu, Andy Oram, and Sam Alapati Copyright © 2019 O’Reilly Media Inc. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more infor‐ mation, contact our corporate/institutional sales department: 800-998-9938 or [email protected]. Development Editor: Jeff Bleiel Interior Designer: David Futato Acquisitions Editor: Jonathan Hassell Cover Designer: Karen Montgomery Production Editor: Katherine Tozer Illustrator: Rebecca Demarest Copyeditor: Octal Publishing, LLC September 2019: First Edition Revision History for the First Edition 2019-08-19: First Release The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. An Introduction to Cloud Databases, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the authors, and do not represent the publisher’s views. -
Comparing Bioinformatics Software Development by Computer Scientists and Biologists: an Exploratory Study
Comparing Bioinformatics Software Development by Computer Scientists and Biologists: An Exploratory Study Parmit K. Chilana Carole L. Palmer Amy J. Ko The Information School Graduate School of Library The Information School DUB Group and Information Science DUB Group University of Washington University of Illinois at University of Washington [email protected] Urbana-Champaign [email protected] [email protected] Abstract Although research in bioinformatics has soared in the last decade, much of the focus has been on high- We present the results of a study designed to better performance computing, such as optimizing algorithms understand information-seeking activities in and large-scale data storage techniques. Only recently bioinformatics software development by computer have studies on end-user programming [5, 8] and scientists and biologists. We collected data through semi- information activities in bioinformatics [1, 6] started to structured interviews with eight participants from four emerge. Despite these efforts, there still is a gap in our different bioinformatics labs in North America. The understanding of problems in bioinformatics software research focus within these labs ranged from development, how domain knowledge among MBB computational biology to applied molecular biology and and CS experts is exchanged, and how the software biochemistry. The findings indicate that colleagues play a development process in this domain can be improved. significant role in information seeking activities, but there In our exploratory study, we used an information is need for better methods of capturing and retaining use perspective to begin understanding issues in information from them during development. Also, in bioinformatics software development. We conducted terms of online information sources, there is need for in-depth interviews with 8 participants working in 4 more centralization, improved access and organization of bioinformatics labs in North America. -
Middleware-Based Database Replication: the Gaps Between Theory and Practice
Appears in Proceedings of the ACM SIGMOD Conference, Vancouver, Canada (June 2008) Middleware-based Database Replication: The Gaps Between Theory and Practice Emmanuel Cecchet George Candea Anastasia Ailamaki EPFL EPFL & Aster Data Systems EPFL & Carnegie Mellon University Lausanne, Switzerland Lausanne, Switzerland Lausanne, Switzerland [email protected] [email protected] [email protected] ABSTRACT There exist replication “solutions” for every major DBMS, from Oracle RAC™, Streams™ and DataGuard™ to Slony-I for The need for high availability and performance in data Postgres, MySQL replication and cluster, and everything in- management systems has been fueling a long running interest in between. The naïve observer may conclude that such variety of database replication from both academia and industry. However, replication systems indicates a solved problem; the reality, academic groups often attack replication problems in isolation, however, is the exact opposite. Replication still falls short of overlooking the need for completeness in their solutions, while customer expectations, which explains the continued interest in developing new approaches, resulting in a dazzling variety of commercial teams take a holistic approach that often misses offerings. opportunities for fundamental innovation. This has created over time a gap between academic research and industrial practice. Even the “simple” cases are challenging at large scale. We deployed a replication system for a large travel ticket brokering This paper aims to characterize the gap along three axes: system at a Fortune-500 company faced with a workload where performance, availability, and administration. We build on our 95% of transactions were read-only. Still, the 5% write workload own experience developing and deploying replication systems in resulted in thousands of update requests per second, which commercial and academic settings, as well as on a large body of implied that a system using 2-phase-commit, or any other form of prior related work. -
What Is a Database? Differences Between the Internet and Library
What is a Database? Library databases are mostly full-text material (in their entirety) and summaries or descriptions of articles. They are collections of articles from newspapers, magazines and journals and electronic reference sources. Databases are selected for the quality and variety of resources they offer and are accessed using the Internet. Your library pays for you to have access to a number of relevant databases. You support this with your tuition, so get the most out of your money! You can access them from home or school via the Library Webpage or use the link below. http://www.mxcc.commnet.edu/Content/Find_Articles.asp Two short videos on the benefits of using library databases: http://www.youtube.com/watch?v=VUp1P-ubOIc http://youtu.be/Q2GMtIuaNzU Differences Between the Internet and Library Databases The Internet Library Databases Examples Google, Yahoo, Bing LexisNexis, Literary Reference Center or Health and Wellness Resource Center Review process None – anyone can add Checked for accuracy by content to the Web. publishers. Chosen by your college’s library. Includes “peer-reviewed” scholarly articles. Reliability Unknown Very No quality control mechanisms! Content Anything, from pictures of a Scholarly journal articles, Book person’s pets to personal reviews, Research papers, (usually not researched and Conference papers, and other unsubstantiated) opinions on scholarly information gun control, abortion, etc. How often updated Unknown/varies. Regularly – daily, quarterly monthly Cost “Free” but some of the info Library has paid for you to you may need for your access these databases. assignment requires a fee. Organization Very little or no organization Very organized Availability Websites come and go. -
Bioinformatics Careers
University of Pittsburgh | School of Arts and Sciences | Department of Biological Sciences BIOINFORMATICS CAREERS General Information • Bioinformatics is an interdisciplinary field that incorporates computer science and biology to research, develop, and apply computational tools and approaches to manage and process large sets of biological data. • The Bioinformatics career focuses on creating software tools to store, manage, interpret, and analyze data at the genome, proteome, transcriptome, and metabalome levels. Primary investigations consist of integrating information from DNA and protein sequences and protein structure and function. • Bioinformatics jobs exist in biomedical, molecular medicine, energy development, biotechnology, environmental restoration, homeland security, forensic investigations, agricultural, and animal science fields. • Most bioinformatics jobs require a Bachelor’s degree. Post-grad programs, University level teaching & research, and administrative positions require a graduate degree. The following list provides a brief sample of responsibilities, employers, jobs, and industries for individuals with a degree in this major. This is by no means an exhaustive listing, but is simply designed to give initial insight into a particular career field that would employ the skills and knowledge gained through this major. Areas Employers Environmental/Government • BLM • Private • Sequence genomes of bacteria useful in energy production, • DOE Contractors environmental cleanup, industrial processing, and toxic waste • EPA • National Parks reduction. • NOAA • State Wildlife • Examination of genomes dependent on carbon sources to address • USDA Agencies climate change concerns. • USFS • Army Corp of • Sequence genomes of plants and animals to produce stronger, • USFWS Engineers resistant crops and healthier livestock. • USGS • Transfer genes into plants to improve nutritional quality. Industrial • DOD • Study the genetic material of microbes and isolate the genes that give • DOE them their unique abilities to survive under extreme conditions. -
Data Quality Requirements Analysis and Modeling December 1992 TDQM-92-03 Richard Y
Published in the Ninth International Conference of Data Engineering Vienna, Austria, April 1993 Data Quality Requirements Analysis and Modeling December 1992 TDQM-92-03 Richard Y. Wang Henry B. Kon Stuart E. Madnick Total Data Quality Management (TDQM) Research Program Room E53-320 Sloan School of Management Massachusetts Institute of Technology Cambridge, MA 02139 USA 617-253-2656 Fax: 617-253-3321 Acknowledgments: Work reported herein has been supported, in part, by MITís Total Data Quality Management (TDQM) Research Program, MITís International Financial Services Research Center (IFSRC), Fujitsu Personal Systems, Inc. and Bull-HN. The authors wish to thank Gretchen Fisher for helping prepare this manuscript. To Appear in the Ninth International Conference on Data Engineering Vienna, Austria April 1993 Data Quality Requirements Analysis and Modeling Richard Y. Wang Henry B. Kon Stuart E. Madnick Sloan School of Management Massachusetts Institute of Technology Cambridge, Mass 02139 [email protected] ABSTRACT Data engineering is the modeling and structuring of data in its design, development and use. An ultimate goal of data engineering is to put quality data in the hands of users. Specifying and ensuring the quality of data, however, is an area in data engineering that has received little attention. In this paper we: (1) establish a set of premises, terms, and definitions for data quality management, and (2) develop a step-by-step methodology for defining and documenting data quality parameters important to users. These quality parameters are used to determine quality indicators, to be tagged to data items, about the data manufacturing process such as data source, creation time, and collection method. -
Problems for Structure Learning: Aggregation and Computational Complexity
(in press). In S. Das, D. Caragea, W. H. Hsu, & S. M. Welch (Eds.), Computational methodologies in gene regulatory networks. Hershey, PA: IGI Global Publishing. Problems for Structure Learning: Aggregation and Computational Complexity Frank Wimberly Carnegie Mellon University (retired), USA David Danks Carnegie Mellon University and Institute for Human & Machine Cognition, USA Clark Glymour Carnegie Mellon University and Institute for Human & Machine Cognition, USA Tianjiao Chu University of Pittsburgh, USA ABSTRACT Machine learning methods to find graphical models of genetic regulatory networks from cDNA microarray data have become increasingly popular in recent years. We provide three reasons to question the reliability of such methods: (1) a major theoretical challenge to any method using conditional independence relations; (2) a simulation study using realistic data that confirms the importance of the theoretical challenge; and (3) an analysis of the computational complexity of algorithms that avoid this theoretical challenge. We have no proof that one cannot possibly learn the structure of a genetic regulatory network from microarray data alone, nor do we think that such a proof is likely. However, the combination of (i) fundamental challenges from theory, (ii) practical evidence that those challenges arise in realistic data, and (iii) the difficulty of avoiding those challenges leads us to conclude that it is unlikely that current microarray technology will ever be successfully applied to this structure learning problem. INTRODUCTION An important goal of cell biology is to understand the network of dependencies through which genes in a tissue type regulate the synthesis and concentrations of protein species. A mediating step in such synthesis is the production of messenger RNA (mRNA).