Bioinformatics
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Bioinformatics
Bioinformatics Bioinformatics is the combination of biology and information technology. The discipline encompasses any computational tools and methods used to manage, analyze and manipulate large sets of biological data. Essentially, bioinformatics has three components: The creation of databases allowing the storage and management of large biological data sets. The development of algorithms and statistics to determine relationships among members of large data sets. The use of these tools for the analysis and interpretation of various types of biological data, including DNA, RNA and protein sequences, protein structures, gene expression profiles, and biochemical pathways. The term bioinformatics first came into use in the 1990s and was originally synonymous with the management and analysis of DNA, RNA and protein sequence data. Computational tools for sequence analysis had been available since the 1960s, but this was a minority interest until advances in sequencing technology led to a rapid expansion in the number of stored sequences in databases such as GenBank. Now, the term has expanded to incorporate many other types of biological data, for example protein structures, gene expression profiles and protein interactions. Each of these areas requires its own set of databases, algorithms and statistical methods. Bioinformatics is largely, although not exclusively, a computer-based discipline. Computers are important in bioinformatics for two reasons: First, many bioinformatics problems require the same task to be repeated millions of times. For example, comparing a new sequence to every other sequence stored in a database or comparing a group of sequences systematically to determine evolutionary relationships. In such cases, the ability of computers to process information and test alternative solutions rapidly is indispensable. -
Genetically Engineered Plants: a Potential Solution to Climate Change
Genetically Engineered Plants: A Potential Solution to Climate Change Dr Charles DeLisi GENETICALLY ENGINEERED PLANTS: A POTENTIAL SOLUTION TO CLIMATE CHANGE Climate change is already having devastating effects felt across the globe. Without adequate measures to counteract the human drivers behind climate change, these negative consequences are guaranteed to increase in severity in the coming decades. Esteemed biomedical scientist, Dr Charles DeLisi of Boston University, urges that a multi-disciplinary approach to mitigating climate change is vital. Using predictive modelling, he has demonstrated the potential power of genetically engineering plants to remove excess carbon dioxide from the atmosphere, thereby mitigating climate change. ‘During the post-industrial period, the planetary temperature has increased rapidly,’ explains Dr Charles DeLisi of Boston University. ‘Ice sheet melting has accelerated, ocean levels have risen with concomitant increases in coastal flooding, and extreme weather events have increased in frequency.’ Emissions Reductions: Not the Whole Solution Thus far, measures to tackle climate change have focused on reducing our emissions of carbon dioxide and other greenhouse gases. Over the last three The Climate Emergency These changes are driven by increasing decades, over 100 nations across the levels of atmospheric ‘greenhouse globe have signed multiple agreements Climate change is, arguably, the most gases’, such as carbon dioxide. Released to voluntarily limit their emissions, significant global threat that society as a by-product of fossil fuel combustion but these measures alone are proving faces. Even small perturbations and other human activities, the level ineffective at slowing climatic in the planet’s climatic system of carbon dioxide in the atmosphere disruption. -
Cancer Informatics (A)
Cancer Open Access: Full open access to this and thousands of other papers at Informatics http://www.la-press.com. Computational Advances in Cancer Informatics (A) §§Xiaoqian Jiang §§Bairong Shen Assistant Professor of Biomedical Informatics, University Professor of Systems Biology, Soochow University, of California San Diego, La Jolla, CA, USA. Suzhou, Jiangsu, China. §§Rui Chen §§Rong Xu Research Assistant Professor of Computer Science, Hong Assistant Professor, Division of Medical Informatics, Case Kong Baptist University, Kowloon Tong, China. Western Reserve University, Cleveland, OH, USA. §§Samuel Cheng §§Song Yi Associate Professor of Electrical and Computing Research Fellow of Genetics, Harvard Medical School, Engineering, University of Oklahoma, Norman, OK, USA. Boston, MA, USA. §§Xia Jiang Assistant Professor of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. Supplement Aims and Scope C a n c e r I n f o r m a t i c s r e p r e s e nt s a h y b r i d d i s c i p l i n e e n c o m p a s s i n g §§ Gene Set Enrichment Analysis the fields of oncology, computer science, bioinformatics, sta- §§ Hybrid Computing tistics, computational biology, genomics, proteomics, metabo- §§ Efficient Cloud Storage and Retrieval lomics, pharmacology, and quantitative epidemiology. The §§ Matching of Expression Patterns common bond or challenge that unifies the various disciplines §§ Multi-Modal Analysis is the need to bring order to the massive amounts of data gen- §§ Splice Variations and Chip Seq System Algorithms erated by researchers and clinicians attempting to find the §§ Rapid High-Throughput Analysis underlying causes and effective means of treating cancer. -
Bioinformatics and Biology Insights Structural and Functional Insights
Bioinformatics and Biology Insights OPEN ACCESS Full open access to this and thousands of other papers at REVIEW http://www.la-press.com. Structural and Functional Insights on the Myosin Superfamily Divya P. Syamaladevi1,2, James A. Spudich1,3,* and R. Sowdhamini1,* 1National Centre for Biological Sciences (NCBS-TIFR), GKVK Campus, Bellary Road, Bangalore, India. 2Sugarcane Breeding Institute (SBI-ICAR), Coimbatore, Tamilnadu, India. 3Department of Biochemistry, Stanford University, Stanford, California. *Corresponding authors email: [email protected]; [email protected] Abstract: The myosin superfamily is a versatile group of molecular motors involved in the transport of specific biomolecules, vesicles and organelles in eukaryotic cells. The processivity of myosins along an actin filament and transport of intracellular ‘cargo’ are achieved by generating physical force from chemical energy of ATP followed by appropriate conformational changes. The typical myosin has a head domain, which harbors an ATP binding site, an actin binding site, and a light-chain bound ‘lever arm’, followed often by a coiled coil domain and a cargo binding domain. Evolution of myosins started at the point of evolution of eukaryotes, S. cerevisiae being the simplest one known to contain these molecular motors. The coiled coil domain of the myosin classes II, V and VI in whole genomes of several model organisms display differences in the length and the strength of interactions at the coiled coil interface. Myosin II sequences have long-length coiled coil regions that are predicted to have a highly stable dimeric interface. These are interrupted, how- ever, by regions that are predicted to be unstable, indicating possibilities of alternate conformations, associations to make thick fila- ments, and interactions with other molecules. -
Bioinformatics
Bioinformatics 1 ioinformatics is a hybrid science that links biological data with techniques for information storage, distribution, and analysis to support multiple areas of scientific Bresearch, including biomedicine. Bioinformatics is fed by high-throughput data- generating experiments, including genomic sequence determinations and measurements of gene expression patterns. Database projects curate and annotate the data and then distribute it via the World Wide Web. Mining these data leads to scientific discoveries and to the identification of new clinical applications. In the field of medicine in particular, a number of important applications for bioinformatics have been discovered. For example, it is used to identify correlations between gene sequences and diseases, to predict protein structures from amino acid sequences, to aid in the design of novel drugs, and to tailor treatments to individual patients based on their DNA sequences (pharmacogenomics). “[Helix]”, ill. under ‘’Bioinformatics’’, S [&] T: Dependable Solutions, www.stcorp.nl/step/bioinformatics 120431 Bibliotheca Alexandrina Updated by Ghada Sami 1 The goal of bioinformatics is the extension of experimental data by predictions. A fundamental goal of computational biology is the prediction of protein structure from an amino acid sequence. The spontaneous folding of proteins shows that this should be possible. Progress in the development of methods to predict protein folding is measured by biennial Critical Assessment of Structure Prediction (CASP) programs, which involve blind tests of structure prediction methods. Bioinformatics is also used to predict interactions between proteins, given individual structures of the partners. This is known as the “docking problem.” Protein-protein complexes show good complementarity in surface shape and polarity and are stabilized largely by weak interactions, such as burial of hydrophobic surface, hydrogen bonds, and van der Waals forces. -
Potential Predatory and Legitimate Biomedical Journals
Shamseer et al. BMC Medicine (2017) 15:28 DOI 10.1186/s12916-017-0785-9 RESEARCHARTICLE Open Access Potential predatory and legitimate biomedical journals: can you tell the difference? A cross-sectional comparison Larissa Shamseer1,2* , David Moher1,2, Onyi Maduekwe3, Lucy Turner4, Virginia Barbour5, Rebecca Burch6, Jocalyn Clark7, James Galipeau1, Jason Roberts8 and Beverley J. Shea9 Abstract Background: The Internet has transformed scholarly publishing, most notably, by the introduction of open access publishing. Recently, there has been a rise of online journals characterized as ‘predatory’, which actively solicit manuscripts and charge publications fees without providing robust peer review and editorial services. We carried out a cross-sectional comparison of characteristics of potential predatory, legitimate open access, and legitimate subscription-based biomedical journals. Methods: On July 10, 2014, scholarly journals from each of the following groups were identified – potential predatory journals (source: Beall’s List), presumed legitimate, fully open access journals (source: PubMed Central), and presumed legitimate subscription-based (including hybrid) journals (source: Abridged Index Medicus). MEDLINE journal inclusion criteria were used to screen and identify biomedical journals from within the potential predatory journals group. One hundred journals from each group were randomly selected. Journal characteristics (e.g., website integrity, look and feel, editors and staff, editorial/peer review process, instructions to authors, -
Boston University Graduate Program in Bioinformatics Handbook 2012
1 Boston University Graduate Program in Bioinformatics Handbook 2012-2013 http://www.bu.edu/bioinformatics Department of Biochemistry (School of Medicine) Department of Biology (College of Arts and Sciences) Biomedical Engineering Department (College of Engineering) Department of Biostatistics (School of Public Health) Department of Chemistry (College of Arts and Sciences) Department of Computational Biomedicine (School of Medicine) Department of Computer Science (College of Arts and Sciences) Electrical and Computer Engineering Department (College of Engineering) Genetics and Genomics Department (School of Medicine) Department of Mathematics and Statistics (College of Arts and Sciences) Department of Medicine (School of Medicine) Department of Microbiology (School of Medicine) Department of Mechanical Engineering (College of Engineering) Molecular and Cell Biology (Goldman School of Dental Health) Department of Neurology (School of Medicine) Periodontology & Oral Biology (Goldman School of Dental Health) Department of Physics (College of Arts and Sciences) Pulmonary Medicine (School of Medicine) Systems Engineering Division (College of Engineering) Center for Computational Science Center for Advanced Biotechnology Center for Advanced Genomic Technology Last Updated: 10/11/12 2 Bioinformatics Faculty and Staff Faculty Director Thomas Tullius Director of Bioinformatics Associate Directors Gary Benson* Associate Director of IGERT Scott Mohr Administrative Director, Director of Graduate Studies Department of Biochemistry (BUSM) Joseph Zaia Professor Associate Director, Center for Biomedical Mass Spectrometry Department of Biology (CAS) Cynthia Bradham Assistant Professor Geoffrey M. Cooper Professor, Associate Dean of the Faculty, Natural Sciences John Finnerty Associate Professor Ulla Hansen Professor Edward Loechler Professor Kimberly McCall Associate Professor # Daniel Segre* Associate Professor Dean Tolan Professor David Waxman Professor Department of Biomedical Engineering (ENG) Charles Cantor* Professor, Director of Center for Advanced Biotechnology James J. -
Molecular Biology, Cell Biology & Biochemistry (MCBB) Graduate
2016-2017 Molecular Biology, Cell Biology & Biochemistry (MCBB) Graduate Program Guide Table of Contents MCBB Graduate Program Administration.................................................................................................................................... 5 Participating Staff by Department.................................................................................................................................................. 6 Participating Faculty by Department............................................................................................................................................ 7 Facilities.................................................................................................................................................................................................... 10 MCBB Program Requirements.......................................................................................................................................................... 12 • Seminars................................................................................................................................................................................... 12 • Ph.D. Course Requirements............................................................................................................................................... 13 • M.A. Course Requirements............................................................................................................................................... -
Human Genome Project
Human Genome Project The Human Genome Project (HGP) was an international scientific research project with the goal of determining the sequence of nucleotide base pairs that make up human DNA, and of identifying and mapping all of the genes of the human genome from both a physical and a functional standpoint.[1] It remains the world's largest collaborative biological project.[2] After the idea was picked up in 1984 by the US government when the planning started, the project formally launched in 1990 and was declared complete in 2003[3]. Funding came from the US government through the National Institutes of Health (NIH) as well as numerous other groups from around the world. A parallel project was conducted outside government by the Celera Corporation, or Celera Genomics, which was formally launched in 1998. Most of the government-sponsored sequencing was performed in twenty universities and research centers in the United States, the United Kingdom, Japan, France, Logo HGP; Vitruvian Man, Leonardo Germany, Spain and China.[4] da Vinci The Human Genome Project originally aimed to map the nucleotides contained in a human haploid reference genome (more than three billion). The "genome" of any given individual is unique; mapping the "human genome" involved sequencing a small number of individuals and then assembling these together to get a complete sequence for each chromosome. Therefore, the finished human genome is a mosaic, not representing any one individual. Contents Human Genome Project History State of completion Applications and proposed -
Evolutionary Bioinformatics Phylotempo
CORE Metadata, citation and similar papers at core.ac.uk Provided by PubMed Central Evolutionary Bioinformatics OPEN ACCESS Full open access to this and thousands of other papers at SHORT REPORT http://www.la-press.com. PhyloTempo: A Set of R Scripts for Assessing and Visualizing Temporal Clustering in Genealogies Inferred from Serially Sampled Viral Sequences Melissa M. Norström1,*, Mattia C.F. Prosperi2,*, Rebecca R. Gray3, Annika C. Karlsson1 and Marco Salemi2 1Department of Laboratory Medicine, Division of Clinical Microbiology, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden. 2University of Florida, College of Medicine, Department of Pathology, Immunology and Laboratory Medicine and Emerging Pathogens Institute, Gainesville, Florida, USA. 3Department of Zoology, University of Oxford, Oxford, UK. *These authors contributed equally. Corresponding author email: [email protected] Abstract: Serially-sampled nucleotide sequences can be used to infer demographic history of evolving viral populations. The shape of a phylogenetic tree often reflects the interplay between evolutionary and ecological processes. Several approaches exist to analyze the topology and traits of a phylogenetic tree, by means of tree balance, branching patterns and comparative properties. The temporal clustering (TC) statistic is a new topological measure, based on ancestral character reconstruction, which characterizes the temporal structure of a phylogeny. Here, PhyloTempo is the first implementation of the TC in the R language, integrating several other topological measures in a user-friendly graphical framework. The comparison of the TC statistic with other measures provides multifaceted insights on the dynamic processes shaping the evolution of pathogenic viruses. The features and applicability of PhyloTempo were tested on serially-sampled intra-host human and simian immunodeficiency virus population data sets. -
The High Road to the Human Genome
A ccomplishing S e q u e n c in g t h e H u m a n G e n o m e Andrew Bartlett Submitted to Cardiff University in fulfilment of the requirements for the degree of Doctor of Philosophy September 2008 i UMI Number: U584600 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. Dissertation Publishing UMI U584600 Published by ProQuest LLC 2013. Copyright in the Dissertation held by the Author. Microform Edition © ProQuest LLC. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code. ProQuest LLC 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, Ml 48106-1346 D e c l a r a t io n This work has not previously been accepted in substance for any degree and is not concurrently submitted in candidature for any degree. Signed ..... y . .........(candidate) Date .............. STATEMENT 1 This thesis is being submitted in partial fulfilment of the requirements for the degree of PhD. S igned /. JjTU..../...... .J.........(cand idate) Date........... STATEMENT 2 This thesis is the result of my own independent work/investigation, except where otherwise stated. Other sources are acknowledged by explicit references. A bibliography is appended. Signed KJ. ) .... (candidate) Date............ .?. .4?.. ■ STATEMENT 3 I hereby give consent for my thesis, if accepted, to be available for photocopying and for inter-library loan, and for the title and summary to be made available to outside organisations. -
Human Genome Program in 1986.”
extracted from BER Exceptional Service Awards 1997 EXCEPTIONAL SERVICE AWARD for Exploring Genomes Charles DeLisi .................................................................. 20 Betty Mansfield ................................................................ 22 J. Craig Venter .................................................................. 24 Exploring Genomes Exploring OE initiated the world’s first development of biological resources; cost- genome program in 1986 after effective, automated technologies for mapping Dconcluding that the most useful approach for and sequencing; and tools for genome-data detecting inherited mutations—an important analysis. The project currently is on track to DOE health mission—is to obtain a complete deliver the sequence of 3 billion human base DNA reference sequence. In addition, the pairs by 2005. analytical power developed in pursuit of that Vital to the project’s continued suc- goal will lead to myriad applications in widely cess is DOE’s consistent and focused com- disparate fields including bioremediation, mitment to disseminating information about medicine, agriculture, and renewable energy. the progress, resources, and other results Many are surprised to learn that the generated in the Human Genome Project. longest-running federally funded genome These communication efforts also inform research effort is the 12-year-old DOE Human researchers across the broader scientific Genome Program. Its goal is to analyze the community, who are beginning to apply the genetic material—the genome—that