Biological Data and Database
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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. -
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 -
Biological Databases
Biological Databases Biology Is A Data Science ● Hundreds of thousand of species ● Million of articles in scientific literature ● Genetic Information ○ Gene names ○ Phenotype of mutants ○ Location of genes/mutations on chromosomes ○ Linkage (relationships between genes) 2 Data and Metadata ● Data are “concrete” objects ○ e.g. number, tweet, nucleotide sequence ● Metadata describes properties of data ○ e.g. object is a number, each tweet has an author ● Database structure may contain metadata ○ Type of object (integer, float, string, etc) ○ Size of object (strings at most 4 characters long) ○ Relationships between data (chromosomes have zero or more genes) 3 What is a Database? ● A data collection that needs to be : ○ Organized ○ Searchable ○ Up-to-date ● Challenge: ○ change “meaningless” data into useful, accessible information 4 A spreadsheet can be a Database ● Rectangular data ● Structured ● No metadata ● Search tools: ○ Excel ○ grep ○ python/R 5 A filesystem can be a Database ● Hierarchical data ● Some metadata ○ File, symlink, etc ● Unstructured ● Search tools: ○ ls ○ find ○ locate 6 Organization and Types of Databases ● Every database has tools that: ○ Store ○ Extract ○ Modify ● Flat file databases (flat DBMS) ○ Simple, restrictive, table ● Hierarchical databases ○ Simple, restrictive, tables ● Relational databases (RDBMS) ○ Complex, versatile, tables ● Object-oriented databases (ODBMS) ● Data warehouses and distributed databases ● Unstructured databases (object store DBs) 7 Where do the data come from ? 8 Types of Biological Data -
Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases
Genomics Proteomics Bioinformatics 18 (2020) 91–103 Genomics Proteomics Bioinformatics www.elsevier.com/locate/gpb www.sciencedirect.com PERSPECTIVE Quality Matters: Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases Qingyu Chen 1,*, Ramona Britto 2, Ivan Erill 3, Constance J. Jeffery 4, Arthur Liberzon 5, Michele Magrane 2, Jun-ichi Onami 6,7, Marc Robinson-Rechavi 8,9, Jana Sponarova 10, Justin Zobel 1,*, Karin Verspoor 1,* 1 School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia 2 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK 3 Department of Biological Sciences, University of Maryland, Baltimore, MD 21250, USA 4 Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607, USA 5 Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA 6 Japan Science and Technology Agency, National Bioscience Database Center, Tokyo 102-8666, Japan 7 National Institute of Health Sciences, Tokyo 158-8501, Japan 8 Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland 9 Department of Ecology and Evolution, University of Lausanne, CH-1015 Lausanne, Switzerland 10 Nebion AG, 8048 Zurich, Switzerland Received 8 December 2017; revised 24 October 2018; accepted 14 December 2018 Available online 9 July 2020 Handled by Zhang Zhang Introduction assembled, annotated, and ultimately submitted to primary nucleotide databases such as GenBank [2], European Nucleo- tide Archive (ENA) [3], and DNA Data Bank of Japan Biological databases represent an extraordinary collective vol- (DDBJ) [4] (collectively known as the International Nucleotide ume of work. -
Bioinformatics: a Practical Guide to the Analysis of Genes and Proteins, Second Edition Andreas D
BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto BIOINFORMATICS SECOND EDITION METHODS OF BIOCHEMICAL ANALYSIS Volume 43 BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto Designations used by companies to distinguish their products are often claimed as trademarks. In all instances where John Wiley & Sons, Inc., is aware of a claim, the product names appear in initial capital or ALL CAPITAL LETTERS. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. Copyright ᭧ 2001 by John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic or mechanical, including uploading, downloading, printing, decompiling, recording or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher. -
When Biology Gets Personal: Hidden Challenges of Privacy and Ethics in Biological Big Data
Pacific Symposium on Biocomputing 2019 When Biology Gets Personal: Hidden Challenges of Privacy and Ethics in Biological Big Data Gamze Gürsoy* Computational Biology and Bioinformatics Program, Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, 06511, USA Email: [email protected] Arif Harmanci Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA Email: [email protected] Haixu Tang† School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47405, USA Email: [email protected] Erman Ayday Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, 44106, USA Email: [email protected] Steven E. Brenner# University of California Berkeley, CA, 94720-3012, USA Email: [email protected] High-throughput technologies for biological data acquisition are advancing at an increasing pace. Most prominently, the decreasing cost of DNA sequencing has led to an exponential growth of sequence information, including individual human genomes. This session of the 2019 Pacific Symposium on Biocomputing presents the distinctive privacy and ethical challenges related to the generation, storage, processing, study, and sharing of individuals’ biological data generated by multitude of technologies including but not limited to genomics, proteomics, metagenomics, bioimaging, biosensors, and personal health trackers. The mission is to bring together computational biologists, experimental biologists, computer scientists, ethicists, and policy and lawmakers to share ideas, discuss the challenges related to biological data and privacy. Keywords: biological data privacy, genomics, genetic testing * This work is partially supported by NIH grant U01EB023686. † This work is partially supported by NIH grant U01EB023685 and NSF grant CNS-1408874. -
Will a Biological Database Be Different from a Biological Journal? Philip Bourne
Perspectives Will a Biological Database Be Different from a Biological Journal? Philip Bourne he differences, or otherwise, between biological checked for format compliance and reviewed internally by databases and journals is an important question to experienced annotators. There are even parallel T consider as we ponder the future dissemination and presubmission steps in journals and databases. For example, impact of science. If databases and journals remain discrete, potential authors in PLoS Computational Biology may make our methods of assimilating information will change presubmission inquiries to confirm the suitability of their relatively little in the years to come. On the other hand, if paper, and depositors to the PDB may run their entries databases and journals become more integrated, the way we against a validation server to determine whether the data are do science could change significantly. As both Editor-in-Chief in compliance, prior to having the same tests run by a PDB of PLoS Computational Biology and Codirector of the Protein annotator. Data Bank (PDB), one of the oldest and widely used data Once registered with the corresponding online submission resources in molecular biology, the question is particularly system, a journal manuscript receives a permanent pertinent. Here, I give my perspective on what could and, I manuscript number, while a database entry receives a unique believe, should happen in the future. identifier. Subsequent revisions can be mapped to these My vision is that a traditional biological journal will respective numbers, so that both journals and databases can become just one part of various biological data resources as provide an accurate audit trail of journal manuscripts and the scientific knowledge in published papers is stored and database entries, respectively. -
COMBINED STUDY on DIGITAL SEQUENCE INFORMATION in PUBLIC and PRIVATE DATABASES and TRACEABILITY Note by the Executive Secretary 1
CBD Distr. GENERAL CBD/DSI/AHTEG/2020/1/4 31 January 2020 ENGLISH ONLY AD HOC TECHNICAL EXPERT GROUP ON DIGITAL SEQUENCE INFORMATION ON GENETIC RESOURCES Montreal, Canada, 17-20 March 2020 COMBINED STUDY ON DIGITAL SEQUENCE INFORMATION IN PUBLIC AND PRIVATE DATABASES AND TRACEABILITY Note by the Executive Secretary 1. At its fourteenth meeting, the Conference of the Parties to the Convention on Biological Diversity requested the Executive Secretary “to commission a peer-reviewed study on ongoing developments in the field of traceability of digital information, including how traceability is addressed by databases, and how these could inform discussions on digital sequence information on genetic resources” (decision 14/20, para. 11 (c)), and “to commission a peer-reviewed study on public and, to the extent possible, private databases of digital sequence information on genetic resources, including the terms and conditions on which access is granted or controlled, the biological scope and the size of the databases, numbers of accessions and their origin, governing policies, and the providers and users of the digital sequence information on genetic resources and encourages the owners of private databases to provide the necessary information;” (decision 14/20, para. (d)). 2. Accordingly, and with financial support from Norway and the European Union, the Executive Secretary commissioned a research team to carry out the studies in a combined manner, taking into account the conceptual linkages between the two studies, and also partly for practical reasons. 3. A draft of the combined study was made available online for peer review from 22 October to 22 November 2019.1 The comments received in response have been made available online.2 The research team revised the study in the light of the comments received and prepared, in consultation with the Secretariat, the final version as presented herein. -
Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering
Hindawi Publishing Corporation e Scientific World Journal Volume 2014, Article ID 523634, 6 pages http://dx.doi.org/10.1155/2014/523634 Research Article Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering Lei Yang and Xianglong Tang School of Computer Science and Technology, Harbin Institute of Technology, Mailbox 352, 92 West Dazhi Street, Nan Gang District, Harbin 150001, China Correspondence should be addressed to Lei Yang; [email protected] Received 24 August 2013; Accepted 5 November 2013; Published 22 January 2014 Academic Editors: C. Angelini and Y. Lai Copyright © 2014 L. Yang and X. Tang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false- negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. -
From Big Data Analysis to Personalized Medicine for All: Challenges and Opportunities Akram Alyass1, Michelle Turcotte1 and David Meyre1,2*
Alyass et al. BMC Medical Genomics (2015) 8:33 DOI 10.1186/s12920-015-0108-y REVIEW Open Access From big data analysis to personalized medicine for all: challenges and opportunities Akram Alyass1, Michelle Turcotte1 and David Meyre1,2* Abstract Recent advances in high-throughput technologies have led to the emergence of systems biology as a holistic science to achieve more precise modeling of complex diseases. Many predict the emergence of personalized medicine in the near future. We are, however, moving from two-tiered health systems to a two-tiered personalized medicine. Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries. This is mirrored by an increasing lag between our ability to generate and analyze big data. Several bottlenecks slow-down the transition from conventional to personalized medicine: generation of cost-effective high-throughput data; hybrid education and multidisciplinary teams; data storage and processing; data integration and interpretation; and individual and global economic relevance. This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine. Keywords: Big data, Omics, Personalized medicine, High-throughput technologies, Cloud computing, Integrative methods, High-dimensionality Introduction of biology and medicine [5, 6]. The use of deterministic Access to large omics (genomics, transcriptomics, proteo- networks for normal and abnormal phenotypes are mics, epigenomic, metagenomics, metabolomics, nutrio- thought to allow for the proactive maintenance of wellness mics, etc.) data has revolutionized biology and has led to specific to the individual, that is predictive, preventive, the emergence of systems biology for a better understand- personalized, and participatory medicine (P4, or more ing of biological mechanisms.