Introduction to Bioinformatics (Databases)
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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. -
A Call for Public Archives for Biological Image Data Public Data Archives Are the Backbone of Modern Biological Research
comment A call for public archives for biological image data Public data archives are the backbone of modern biological research. Biomolecular archives are well established, but bioimaging resources lag behind them. The technology required for imaging archives is now available, thus enabling the creation of the frst public bioimage datasets. We present the rationale for the construction of bioimage archives and their associated databases to underpin the next revolution in bioinformatics discovery. Jan Ellenberg, Jason R. Swedlow, Mary Barlow, Charles E. Cook, Ugis Sarkans, Ardan Patwardhan, Alvis Brazma and Ewan Birney ince the mid-1970s it has been possible image data to encourage both reuse and in automation and throughput allow this to analyze the molecular composition extraction of knowledge. to be done in a systematic manner. We Sof living organisms, from the individual Despite the long history of biological expect that, for example, super-resolution units (nucleotides, amino acids, and imaging, the tools and resources for imaging will be applied across biological metabolites) to the macromolecules they collecting, managing, and sharing image and biomedical imaging; examples in are part of (DNA, RNA, and proteins), data are immature compared with those multidimensional imaging8 and high- as well as the interactions among available for sequence and 3D structure content screening9 have recently been these components1–3. The cost of such data. In the following sections we reported. It is very likely that imaging data measurements has decreased remarkably outline the current barriers to progress, volume and complexity will continue to over time, and technological development the technological developments that grow. Second, the emergence of powerful has widened their scope from investigations could provide solutions, and how those computer-based approaches for image of gene and transcript sequences (genomics/ infrastructure solutions can meet the interpretation, quantification, and modeling transcriptomics) to proteins (proteomics) outlined need. -
3 on the Nature of Biological Data
ON THE NATURE OF BIOLOGICAL DATA 35 3 On the Nature of Biological Data Twenty-first century biology will be a data-intensive enterprise. Laboratory data will continue to underpin biology’s tradition of being empirical and descriptive. In addition, they will provide confirming or disconfirming evidence for the various theories and models of biological phenomena that researchers build. Also, because 21st century biology will be a collective effort, it is critical that data be widely shareable and interoperable among diverse laboratories and computer systems. This chapter describes the nature of biological data and the requirements that scientists place on data so that they are useful. 3.1 DATA HETEROGENEITY An immense challenge—one of the most central facing 21st century biology—is that of managing the variety and complexity of data types, the hierarchy of biology, and the inevitable need to acquire data by a wide variety of modalities. Biological data come in many types. For instance, biological data may consist of the following:1 • Sequences. Sequence data, such as those associated with the DNA of various species, have grown enormously with the development of automated sequencing technology. In addition to the human genome, a variety of other genomes have been collected, covering organisms including bacteria, yeast, chicken, fruit flies, and mice.2 Other projects seek to characterize the genomes of all of the organisms living in a given ecosystem even without knowing all of them beforehand.3 Sequence data generally 1This discussion of data types draws heavily on H.V. Jagadish and F. Olken, eds., Data Management for the Biosciences, Report of the NSF/NLM Workshop of Data Management for Molecular and Cell Biology, February 2-3, 2003, Available at http:// www.eecs.umich.edu/~jag/wdmbio/wdmb_rpt.pdf. -
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 -
Bioinformatics Study of Lectins: New Classification and Prediction In
Bioinformatics study of lectins : new classification and prediction in genomes François Bonnardel To cite this version: François Bonnardel. Bioinformatics study of lectins : new classification and prediction in genomes. Structural Biology [q-bio.BM]. Université Grenoble Alpes [2020-..]; Université de Genève, 2021. En- glish. NNT : 2021GRALV010. tel-03331649 HAL Id: tel-03331649 https://tel.archives-ouvertes.fr/tel-03331649 Submitted on 2 Sep 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE Pour obtenir le grade de DOCTEUR DE L’UNIVERSITE GRENOBLE ALPES préparée dans le cadre d’une cotutelle entre la Communauté Université Grenoble Alpes et l’Université de Genève Spécialités: Chimie Biologie Arrêté ministériel : le 6 janvier 2005 – 25 mai 2016 Présentée par François Bonnardel Thèse dirigée par la Dr. Anne Imberty codirigée par la Dr/Prof. Frédérique Lisacek préparée au sein du laboratoire CERMAV, CNRS et du Computer Science Department, UNIGE et de l’équipe PIG, SIB Dans les Écoles Doctorales EDCSV et UNIGE Etude bioinformatique des lectines: nouvelle classification et prédiction dans les génomes Thèse soutenue publiquement le 8 Février 2021, devant le jury composé de : Dr. Alexandre de Brevern UMR S1134, Inserm, Université Paris Diderot, Paris, France, Rapporteur Dr. -
Maternally Inherited Pirnas Direct Transient Heterochromatin Formation
RESEARCH ARTICLE Maternally inherited piRNAs direct transient heterochromatin formation at active transposons during early Drosophila embryogenesis Martin H Fabry, Federica A Falconio, Fadwa Joud, Emily K Lythgoe, Benjamin Czech*, Gregory J Hannon* CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, United Kingdom Abstract The PIWI-interacting RNA (piRNA) pathway controls transposon expression in animal germ cells, thereby ensuring genome stability over generations. In Drosophila, piRNAs are intergenerationally inherited through the maternal lineage, and this has demonstrated importance in the specification of piRNA source loci and in silencing of I- and P-elements in the germ cells of daughters. Maternally inherited Piwi protein enters somatic nuclei in early embryos prior to zygotic genome activation and persists therein for roughly half of the time required to complete embryonic development. To investigate the role of the piRNA pathway in the embryonic soma, we created a conditionally unstable Piwi protein. This enabled maternally deposited Piwi to be cleared from newly laid embryos within 30 min and well ahead of the activation of zygotic transcription. Examination of RNA and protein profiles over time, and correlation with patterns of H3K9me3 deposition, suggests a role for maternally deposited Piwi in attenuating zygotic transposon expression in somatic cells of the developing embryo. In particular, robust deposition of piRNAs targeting roo, an element whose expression is mainly restricted to embryonic development, results in the deposition of transient heterochromatic marks at active roo insertions. We hypothesize that *For correspondence: roo, an extremely successful mobile element, may have adopted a lifestyle of expression in the [email protected] embryonic soma to evade silencing in germ cells. -
A SARS-Cov-2 Sequence Submission Tool for the European Nucleotide
Databases and ontologies Downloaded from https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btab421/6294398 by guest on 25 June 2021 A SARS-CoV-2 sequence submission tool for the European Nucleotide Archive Miguel Roncoroni 1,2,∗, Bert Droesbeke 1,2, Ignacio Eguinoa 1,2, Kim De Ruyck 1,2, Flora D’Anna 1,2, Dilmurat Yusuf 3, Björn Grüning 3, Rolf Backofen 3 and Frederik Coppens 1,2 1Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium, 1VIB Center for Plant Systems Biology, 9052 Ghent, Belgium and 2University of Freiburg, Department of Computer Science, Freiburg im Breisgau, Baden-Württemberg, Germany ∗To whom correspondence should be addressed. Associate Editor: XXXXXXX Received on XXXXX; revised on XXXXX; accepted on XXXXX Abstract Summary: Many aspects of the global response to the COVID-19 pandemic are enabled by the fast and open publication of SARS-CoV-2 genetic sequence data. The European Nucleotide Archive (ENA) is the European recommended open repository for genetic sequences. In this work, we present a tool for submitting raw sequencing reads of SARS-CoV-2 to ENA. The tool features a single-step submission process, a graphical user interface, tabular-formatted metadata and the possibility to remove human reads prior to submission. A Galaxy wrap of the tool allows users with little or no bioinformatic knowledge to do bulk sequencing read submissions. The tool is also packed in a Docker container to ease deployment. Availability: CLI ENA upload tool is available at github.com/usegalaxy- eu/ena-upload-cli (DOI 10.5281/zenodo.4537621); Galaxy ENA upload tool at toolshed.g2.bx.psu.edu/view/iuc/ena_upload/382518f24d6d and https://github.com/galaxyproject/tools- iuc/tree/master/tools/ena_upload (development) and; ENA upload Galaxy container at github.com/ELIXIR- Belgium/ena-upload-container (DOI 10.5281/zenodo.4730785) Contact: [email protected] 1 Introduction Nucleotide Archive (ENA). -
In Silico Analysis of Single Nucleotide Polymorphisms
Central JSM Bioinformatics, Genomics and Proteomics Original Research *Corresponding author Anfal Ibrahim Bahereldeen, Department of medical laboratory sciences, Sudan University of Science and In silico analysis of Single Technology, Khartoum, Sudan; Tel: 249916060120; Email: [email protected] Submitted: 14 October 2019 Nucleotide Polymorphisms Accepted: 23 January 2020 Published: 27 January 2020 (SNPs) in Human HFE Gene ISSN: 2576-1102 Copyright coding region © 2020 Bahereldeen AI, et ail. OPEN ACCESS Anfal Ibrahim Bahereldeen1*, Reel Gamal Alfadil2, Hala Mohammed Ali2, Randa Musa Nasser2, Nada Tagelsir Eisa2, Keywords Wesal Hassan Mahmoud2, and Shaymaa Mohammedazeem • In silico • HFE gene, Polyphen-2 2 Osman • I mutant 1Department of Medical laboratory sciences, Sudan University of science and • Project hope Technology, Sudan 2Department of Medical laboratory sciences, Al-Neelain University, Sudan Abstract Background: HFE gene is a HLA class1-like molecule, expressed in the different cells and tissues, mutations on this gene reported to cause about 80-90% of Hereditary haemochromatosis (HHC) and it is increasing the risk of different diseases. In this study; we aimed to analysis the SNPs in HFE gene by using different computational methods. Methodology: We obtained HFE gene nsSNPs from dbSNP/NCBI database, Deleterious nsSNPs predicted by different bioinformatics servers including; SIFT, polyphen-2, I-mutant and SNPs & GO servers. Protein structure analysis done by using Project hope and RaptorX tools then visualized by Chimera software and function, interaction and network of HFE gene analysis done by Gene MANIA program. Results: SIFT and Polyphen-2 servers predicted 75 deleterious nsSNPs and nine polymorphisms from them predicted as highly damaging and disease associated. -
Visualization of Biomedical Data
Visualization of Biomedical Data Corresponding author: Seán I. O’Donoghue; email: [email protected] • Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Eveleigh NSW 2015, Australia • Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney NSW 2010, Australia • School of Biotechnology and Biomolecular Sciences, UNSW, Kensington NSW 2033, Australia Benedetta Frida Baldi; email: [email protected] • Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney NSW 2010, Australia Susan J Clark; email: [email protected] • Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney NSW 2010, Australia Aaron E. Darling; email: [email protected] • The ithree institute, University of Technology Sydney, Ultimo NSW 2007, Australia James M. Hogan; email: [email protected] • School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD, 4000, Australia Sandeep Kaur; email: [email protected] • School of Computer Science and Engineering, UNSW, Kensington NSW 2033, Australia Lena Maier-Hein; email: [email protected] • Div. Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany Davis J. McCarthy; email: [email protected] • European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD, Hinxton, Cambridge, UK • St. Vincent’s Institute of Medical Research, Fitzroy VIC 3065, Australia William -
Six-Fold Speed-Up of Smith-Waterman Sequence Database Searches Using Parallel Processing on Common Microprocessors
Six-fold speed-up of Smith-Waterman sequence database searches using parallel processing on common microprocessors Running head: Six-fold speed-up of Smith-Waterman searches Torbjørn Rognes* and Erling Seeberg Institute of Medical Microbiology, University of Oslo, The National Hospital, NO-0027 Oslo, Norway Abstract Motivation: Sequence database searching is among the most important and challenging tasks in bioinformatics. The ultimate choice of sequence search algorithm is that of Smith- Waterman. However, because of the computationally demanding nature of this method, heuristic programs or special-purpose hardware alternatives have been developed. Increased speed has been obtained at the cost of reduced sensitivity or very expensive hardware. Results: A fast implementation of the Smith-Waterman sequence alignment algorithm using SIMD (Single-Instruction, Multiple-Data) technology is presented. This implementation is based on the MMX (MultiMedia eXtensions) and SSE (Streaming SIMD Extensions) technology that is embedded in Intel’s latest microprocessors. Similar technology exists also in other modern microprocessors. Six-fold speed-up relative to the fastest previously known Smith-Waterman implementation on the same hardware was achieved by an optimised 8-way parallel processing approach. A speed of more than 150 million cell updates per second was obtained on a single Intel Pentium III 500MHz microprocessor. This is probably the fastest implementation of this algorithm on a single general-purpose microprocessor described to date. Availability: Online searches with the software are available at http://dna.uio.no/search/ Contact: [email protected] Published in Bioinformatics (2000) 16 (8), 699-706. Copyright © (2000) Oxford University Press. *) To whom correspondence should be addressed.