DOCSLIB.ORG

Curriculum in Bioinformatics and Computational Biology (GRAD) 1

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Curriculum in Bioinformatics and Computational Biology (GRAD) 1 Curriculum in Bioinformatics and Computational Biology (GRAD) 1 participating Ph.D. programs. During their first year BBSP students are CURRICULUM IN part of small, interest-based groups led by several faculty members. These groups meet frequently and provide a research community BIOINFORMATICS AND for students until they join a degree-granting program. Students are encouraged to apply as early as possible, preferably before December 1. COMPUTATIONAL BIOLOGY (Applicants seeking a master's degree are not considered for admission.) (GRAD) Financial Aid Stipends for predoctoral students are available from an NIH predoctoral Contact Information training grant and from the University. Tuition, student fees, and graduate student health insurance are also covered by the training grant and the Curriculum in Bioinformatics and Computational Biology University. Visit Program Website (http://bcb.unc.edu) In addition to the dissertation requirements of The Graduate School William Valdar, Director (four full semesters of credit including at least six hours of doctoral [email protected] dissertation, a written preliminary examination, an oral examination Timothy Elston, Associate Director and a dissertation), students in the Curriculum in Bioinformatics and [email protected] Computational Biology must meet the following requirements: Jonathon Cornett, Student Services Manager • complete all five of the BCB core courses [email protected] • complete twelve hours of elective courses (as determined by student and thesis advisor); at least 9 hours must be computational Modern biology, in this postgenome age, is being greatly enriched by • participate in the BCB Colloquium as attendees during the first and an infusion of ideas from a variety of computational fields, including second years and as presenters in later years computer science, information science, mathematics, operations research, and statistics. In turn, biological problems are motivating • act as teaching assistant for one of the BCB modules in Year 2 innovations in these computational sciences. There is a high demand for • attend BCB Student Seminars in Years 2 and 3; present in BCB scientists who can bridge these disciplines. The goal of the Curriculum Student Seminars beginning in Year 3 until graduation in Bioinformatics and Computational Biology (BCB) is to train such • participate in BCB Grant Writing course in Year 2 scientists through a rigorous and balanced curriculum that transcends • attend BCB sponsored special seminars and new student symposium traditional departmental boundaries. • attend the BCB annual retreat; present posters or talks Incoming students are expected to matriculate from a broad range Students are required to rotate through at least three laboratories before of disciplines; thus, it is important to ensure that all students have a choosing a thesis advisor. The advisor or co-advisor must be from common foundation on which to build their BCB training. The first year is BCB Core Faculty list. It is strongly recommended that students attend dedicated to establishing this foundation and training all students with national meetings in order to better understand how their research fits a common set of core BCB courses. BCB students will also participate with progress in their field. in three laboratory research rotations their first year and ultimately join a laboratory at the end of those rotations. Research work is done in the laboratory facilities of the individual faculty member and is supported Professors primarily by faculty research grants. Jim Bear, Cell Biology and Physiology Kerry Bloom, Mechanisms of Chromosome Segregation in Yeast; Curriculum faculty have appointments in 18 departments in the School Chromosome and Spindle Dynamics of Medicine, School of Dentistry, Gillings School of Global Public Health, Charles Carter, Protein Crystallography, Structural Polymorphism and Eshelman School of Pharmacy, School of Information and Library Function Science, and the College of Arts and Sciences. This level of diversity Jeff Dangl, Plant Genetics and Cellular Biology, Plant Disease Resistance allows students a broad range of research opportunities. and Cell Death Control Ian Davis, Epigenomic and Transcriptomic Consequences of Genetic Requirements for Admission for Graduate Alterations in Cancer and Applications to Therapeutic Discovery Henrik Dohlman, Regulators of G Protein Signaling Work Timothy Elston, Mathematical Modeling of Biological Networks Ideal BCB candidates should have an undergraduate degree in a Gregory Forest, Mathematical Modeling of Mucociliary Transport biological, physical, mathematical, or computational science. They must Processes apply to the program through a unified application program known as the Terry Furey, Chromatin and Gene Regulation, Cancer Genomics, High- Biological and Biomedical Sciences Program (BBSP). Students apply for Throughput Sequencing graduate study in the biological or biomedical sciences at UNC–Chapel Amy Gladfelter, Cell Cycle Control, Cytokinesis, and Cell Shape Hill. Students interested in any of the BBSP research areas apply to Determination From Fungi to Mammals BBSP, and those whose application portfolio places them highest on the Shawn Gomez, Systems Biology, Mathematical Modeling of Protein admission list are asked to visit Chapel Hill for interviews. Students who Interaction Networks are ultimately admitted to UNC–Chapel Hill make no formal commitment Klaus Hahn, Spatio-Temporal Dynamics of Signaling in Living Cells to a Ph.D. program. After completing their first year of study students Corbin Jones, Evolution and Underlying Genetics of Species-Specific leave BBSP, join a thesis laboratory, and matriculate into one of 14 Adaptations 2 Curriculum in Bioinformatics and Computational Biology (GRAD) Brian Kuhlman, Protein Design/Modeling, Protein Interactions Todd Vision, Evolution of Genome Organization, Architecture of Complex Alain Laederach, RNA Folding Bioinformatics Traits Yun Li, The Development of Statistical Methods and Their Application to the Genetic Dissection of Complex Diseases and Traits Assistant Professors Yufeng Liu, Statistical Learning and Genomic Analysis Brad Dickerson, Timing Precision in Drosophil Flight Terry Magnuson, Mammalian Genetics/Genomics/Development/Mouse Daniel Dominguez, How Gene Expression is Controlled by Proteins That Models of Human Disease Bind RNA Steve Marron, Analyzing Data That Lie in Nonstandard Spaces Jill Dowen, Integrates the Areas of Transcriptional Regulation, Three- William Marzluff, Regulation of RNA Metabolism in Animal Cells Dimensional Genome Organization and Functional Genomics to Karen Mohlke, Complex Traits, Genetics of Type 2 Diabetes Understand How the Architecture of the Genome Influences Gene Peter Mucha, Networks, Complex Systems, Interacting Particle Systems, Expression During Development and Disease Computational Social Science Marty Ferris, Genetics Fernando Pardo-Manuel de Villena, Evolution, Mouse Genetics, Hector Franco, Gene Regulation, Epigenetics, Chromatin and RNA Biology, Epigenetics, Female Meiosis, Chromosome Segregation, Meiotic Drive Especially As It Pertains to Cancer Charles Perou, Genomic and Molecular Classification of Human Tumors Flavio Frohlich, Cortical Neurophysiology, Computational Neuroscience, to Guide Therapy Brain Stimulation, Epilepsy Jan Prins, High-Performance Computing, Algorithms, Programming Katherine Hoadley, Genomic Characterization and Integrative Genomic Languages, Scientific Computing Approaches to Better Understand Cancer Jack Snoeyink, Discrete and Computational Geometry Applications to Yuchao Jiang, Statistical Modeling, Method Development and Data Molecular Biology Analysis in Genetics and Genomics John Sondek, Structural Biology of Signal Transduction Wesley Legant, Microscopy, 3d Image Analysis, Biomaterials, Cell Brian Strahl, The Role That Histone Post-Translational Modifications Migration, Cancer Metastasis, Tissue Engineering Have in Chromatin Biology Mike Love, Statistical and Computational Methods For the Analysis Alex Tropsha, Computational Analysis of Protein Structure and Drug of High-Throughput Sequencing Assays to Facilitate Biomedical and Design Biological Research William Valdar, Mapping of Complex Disease Loci in Animal Models, Ehssan Nazockdast, Applied Physical Science Statistical Genetics Adam Palmer, Investigate and Develop Combinations of Cancer Therapies Kevin Weeks, Structural and Chemical Biology of the Transcriptome Using Experiments, Simulations, and Computational Analysis of Clinical Kirk Wilhelmsen, Genetic Mapping of Susceptibility Loci for Complex Data Neurological Diseases Doug Phanstiel, Experimental and Computational Approaches to Study Mark Zylka, Use of Genome-Wide Approaches to Study Transcriptional the Mechanisms Through Which Enhancers and Three-Dimensional Regulators Linked to Autism, Use of RNA Sequencing and Targeted Chromatin Structure Regulate Gene Transcription During Cellular Sequencing to Identify Chemical Risk Factors for Brain Disorders (Autism, Differentiation Brain Aging, Neurodegeneration, ADHD), Transcriptional Mechanisms Jesse Raab, Regulation and Function of Altered Chromatin Remodeling Associated with Long Genes Complex Activity Christoph Rau, the transcriptomic and epigenomic landscape underlying Associate Professors cardiovascular disorders J. Mauro Calabrese, Sequence Rules to Predict Long Noncoding RNA Alexander Rubinsteyn, Machine Learning for Adaptive Immunity, Function, Mechanisms of Transcriptional Regulation by Long Noncoding
Load more

Recommended publications
  • 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
    [Show full text]
  • Biophysics, Structural and Computational Biology (BSCB) Faculty Administers the Ph.D
    UNIVERSITY OF ROCHESTER SCHOOL OF MEDICINE AND DENTISTRY Graduate Studies in BIOPHYSICS, STRUCTURAL AND COMPUTATIONAL BIOLOGY Student Handbook August 2019 David Mathews, Program Director Joseph Wedekind, Education Committee Chair Melissa Vera, Graduate Studies Coordinator PREFACE The Biophysics, Structural and Computational Biology (BSCB) Faculty administers the Ph.D. degree program in Biophysics for the Department of Biochemistry and Biophysics. This handbook is intended to outline the major features and policies of the program. The general features of the graduate experience at the University of Rochester are summarized in the Graduate Bulletin, which is updated every two years. Students and advisors will need to consult both sources, though it is our intent to provide the salient features here. Policy, of course, continues to evolve in response to the changing needs of the graduate programs and the students in them. Thus, it is wise to verify any crucial decisions with the Graduate Studies Coordinator. i TABLE OF CONTENTS Page I. DEFINITIONS 1 II. BIOPHYSICS CURRICULUM 2 A. Courses 2 1. Core Curriculum 2. Elective Courses 3 B. Suggested Progress Toward the Ph.D. in Biophysics 4 C. Other Educational Opportunities 4 1. Departmental Seminars 4 2. BSCB Program Retreat 5 3. Bioinformatics Cluster 5 D. Exemptions from Course Requirements 5 E. Minimum Course Performance 5 F. M.D./Ph.D. Students 6 III. ADDITIONAL DETAILS OF PROCEDURES AND REQUIREMENTS 7 A. Faculty Advisors for Entering Students 7 B. Student Laboratory Rotations 7 C. Radiation Certificate 8 D. Student Research Seminar 8 E. First Year Preliminary Examination and Evaluation 9 F. Teaching Assistantship 12 G.
    [Show full text]
  • 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.
    [Show full text]
  • A Sample Workload for Bioinformatics and Computational Biology for Optimizing Next-Generation High-Performance Computer Systems
    BioSPLASH: A sample workload for bioinformatics and computational biology for optimizing next-generation high-performance computer systems David A. Bader∗ Vipin Sachdeva Department of Electrical and Computer Engineering University of New Mexico, Albuquerque, NM 87131 Virat Agarwal Gaurav Goel Abhishek N. Singh Indian Institute of Technology, New Delhi May 1, 2005 Abstract BioSPLASH is a suite of representative applications that we have assembled from the com- putational biology community, where the codes are carefully selected to span a breadth of algorithms and performance characteristics. The main results of this paper are the assembly of a scalable bioinformatics workload with impact to the DARPA High Produc- tivity Computing Systems Program to develop revolutionarily-new economically- viable high-performance computing systems,andanalyses of the performance of these codes for computationally demanding instances using the cycle-accurate IBM MAMBO simulator and real performance monitoring on an Apple G5 system. Hence, our work is novel in that it is one of the first efforts to incorporate life science application per- formance for optimizing high-end computer system architectures. 1 Algorithms in Computational Biology In the 50 years since the discovery of the structure of DNA, and with new techniques for sequencing the entire genome of organisms, biology is rapidly moving towards a data-intensive, computational science. Biologists are in search of biomolecular sequence data, for its comparison with other genomes, and because its structure determines function and leads to the understanding of bio- chemical pathways, disease prevention and cure, and the mechanisms of life itself. Computational biology has been aided by recent advances in both technology and algorithms; for instance, the ability to sequence short contiguous strings of DNA and from these reconstruct the whole genome (e.g., see [34, 2, 33]) and the proliferation of high-speed micro array, gene, and protein chips (e.g., see [27]) for the study of gene expression and function determination.
    [Show full text]
  • Biology (BA) Biology (BA)
    Biology (BA) Biology (BA) This program is offered by the College of Arts & Sciences/ • CHEM 1110 General Chemistry II (3 hours) Biological Sciences Department and is only available at the St. and CHEM 1111 General Chemistry II: Lab (1 hour) Louis home campus. • CHEM 2100 Organic Chemistry I (3 hours) and CHEM 2101 Organic Chemistry I: Lab (1 hour) Program Description • MATH 1430 College Algebra (3 hours) • MATH 2200 Statistics (3 hours) The bachelor of arts degree is designed for students who seek or STAT 3100 Inferential Statistics (3 hours) a broad education in biology. This degree is suitable preparation or PSYC 2750 Introduction to Measurement and Statistics (3 for a diverse range of careers including health science, science hours) education and ecology-related fields. • PHYS 1710 College Physics I (3 hours) Students can earn the BA in biology alone, or with one of four and PHYS 1711 College Physics I: Lab (1 hour) emphases: biodiversity, computational biology, education or • PHYS 1720 College Physics II (3 hours) health science. and PHYS 1721 College Physics II: Lab (1 hour) Learning Outcomes BA in Biology (66 hours) Students who complete any of the bachelor of arts in biology will The general degree offers the greatest flexibility, allowing students be able to: to select 12 hours of electives from any of our 2000+ level BIOL, CHEM or PHYS courses in addition to the 54 credits of core • Describe biological, chemical and physical principles as they coursework in biology listed above. (Up to 3 credit hours of BIOL relate to the natural world in writings and presentations to a 4700/CHEM 4700/PHYS 4700 can be used toward these 12 credit diverse audience.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • Computational and Systems Biology
    COMPUTATIONAL AND SYSTEMS BIOLOGY COMPUTATIONAL AND SYSTEMS BIOLOGY quantitative imaging; regulatory genomics and proteomics; single cell manipulations and measurement; stem cell and developmental systems biology; and synthetic biology and biological design. The eld of computational and systems biology represents a synthesis of ideas and approaches from the life sciences, physical The CSB PhD program is an Institute-wide program that has been sciences, computer science, and engineering. Recent advances jointly developed by the Departments of Biology, Biological in biology, including the human genome project and massively Engineering, and Electrical Engineering and Computer Science. parallel approaches to probing biological samples, have created new The program integrates biology, engineering, and computation to opportunities to understand biological problems from a systems address complex problems in biological systems, and CSB PhD perspective. Systems modeling and design are well established students have the opportunity to work with CSBi faculty from across in engineering disciplines but are newer in biology. Advances in the Institute. The curriculum has a strong emphasis on foundational computational and systems biology require multidisciplinary teams material to encourage students to become creators of future tools with skill in applying principles and tools from engineering and and technologies, rather than merely practitioners of current computer science to solve problems in biology and medicine. To approaches. Applicants must have an undergraduate degree in provide education in this emerging eld, the Computational and biology (or a related eld), bioinformatics, chemistry, computer Systems Biology (CSB) program integrates MIT's world-renowned science, mathematics, statistics, physics, or an engineering disciplines in biology, engineering, mathematics, and computer discipline, with dual-emphasis degrees encouraged.
    [Show full text]
  • Database Technology for Bioinformatics from Information Retrieval to Knowledge Systems
    Database Technology for Bioinformatics From Information Retrieval to Knowledge Systems 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 Molecular Biology Databases 3 Bibliographic databases On-line journals and bibliographic citations – MEDLINE (1971, www.nlm.nih.gov) 3 Factual databases Repositories of Experimental data associated with published articles and that can be used for computerized analysis – Nucleic acid sequences: GenBank (1982, www.ncbi.nlm.nih.gov), EMBL (1982, www.ebi.ac.uk), DDBJ (1984, www.ddbj.nig.ac.jp) – Amino acid sequences: PIR (1968, www-nbrf.georgetown.edu), PRF (1979, www.prf.op.jp), SWISS-PROT (1986, www.expasy.ch) – 3D molecular structure: PDB (1971, www.rcsb.org), CSD (1965, www.ccdc.cam.ac.uk) Lack standardization of data contents 3 Knowledge Bases Intended for automatic inference rather than simple retrieval – Motif libraries: PROSITE (1988, www.expasy.ch/sprot/prosite.html) – Molecular Classifications: SCOP (1994, www.mrc-lmb.cam.ac.uk) – Biochemical Pathways: KEGG (1995, www.genome.ad.jp/kegg) Difference between knowledge and data (semiosis and syntax)?? 2 Growth of sequence and 3D Structure databases Number of Entries 3 Database Technology and Bioinformatics 3 Databases Computerized collection of data for Information Retrieval Shared by many users Stored records are organized with a predefined set of data items (attributes) Managed by a computer program: the database
    [Show full text]
  • 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.
    [Show full text]
  • 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).
    [Show full text]