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Introduction to Bioinformatics Software and Computing Infrastructures
I have all this data – now what? Introduction to Bioinformatics Software and Computing Infrastructures Timothy Stockwell (JCVI) Vivien Dugan (NIAID/NIH) Bioinformatics • Bioinformatics Deals with methods for managing and analyzing biological data. Focus on using software to generate useful biological knowledge. • Bioinformatics for Sequencing and Genomics Sample tracking, LIMS (lab process tracking). Sequencing and assembly Genome annotation (structural and functional) Cross-genome comparisons My NGS run finished….. DATA OVERLOAD!!! DATA OVERLOAD!!! DATA OVERLOAD!!! Genomics Resources • General resources • Genomics resources • Bioinformatics resources • Pathogen-specific resources USA NIH National Center for Biomedical Information (NCBI) • NCBI – home page, http://www.ncbi.nlm.nih.gov • GenBank – genetic sequence database http://www.ncbi.nlm.nih.gov/genbank • PubMed – database of citations and links to over 22 million biomedical articles http://www.ncbi.nlm.nih.gov/pubmed • BLAST – Basic Local Alignment Search Tool – to search for similar biological sequences http://blast.ncbi.nlm.nih.gov/Blast.cgi Bioinformatics Resource Centers (BRCs) https://vectorbase.org/ http://eupathdb.org/ http://patric.vbi.vt.edu/ http://www.pathogenportal.org/ http://www.viprbrc.org/ http://www.fludb.org/ NIAID Bioinformatics Resource Centers Bioinformatic Services • Community-based Database & Bioinformatics Resource Centers • Partnerships with Infectious Diseases Research and Public Health communities • Genomic, omics, experimental, & clinical metadata -
Benchmarking of Bioperl, Perl, Biojava, Java, Biopython, and Python for Primitive Bioinformatics Tasks 6 and Choosing a Suitable Language
Taewan Ryu : Benchmarking of BioPerl, Perl, BioJava, Java, BioPython, and Python for Primitive Bioinformatics Tasks 6 and Choosing a Suitable Language Benchmarking of BioPerl, Perl, BioJava, Java, BioPython, and Python for Primitive Bioinformatics Tasks and Choosing a Suitable Language Taewan Ryu Dept of Computer Science, California State University, Fullerton, CA 92834, USA ABSTRACT Recently many different programming languages have emerged for the development of bioinformatics applications. In addition to the traditional languages, languages from open source projects such as BioPerl, BioPython, and BioJava have become popular because they provide special tools for biological data processing and are easy to use. However, it is not well-studied which of these programming languages will be most suitable for a given bioinformatics task and which factors should be considered in choosing a language for a project. Like many other application projects, bioinformatics projects also require various types of tasks. Accordingly, it will be a challenge to characterize all the aspects of a project in order to choose a language. However, most projects require some common and primitive tasks such as file I/O, text processing, and basic computation for counting, translation, statistics, etc. This paper presents the benchmarking results of six popular languages, Perl, BioPerl, Python, BioPython, Java, and BioJava, for several common and simple bioinformatics tasks. The experimental results of each language are compared through quantitative evaluation metrics such as execution time, memory usage, and size of the source code. Other qualitative factors, including writeability, readability, portability, scalability, and maintainability, that affect the success of a project are also discussed. The results of this research can be useful for developers in choosing an appropriate language for the development of bioinformatics applications. -
Lie Therory and Hermite Polynomials
International Journal For Research In Advanced Computer Science And Engineering ISSN: 2208-2107 A Computational Method for Sequence analyzing Rekardo Fosalli, Teriso Joloobi School of Computer Science, University of De Cyprus, Cyprus Abstract: To study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures. Common uses of bioinformatics include the identification of candidate genes and nucleotides (SNPs). Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. In a less formal way, bioinformatics also tries to understand the organisational principles within nucleic acid and protein sequences. Keywords: Clustering, RNA sequences, RNA-Seq, Data Mining, Bioinformatics, Graph Mining. 1. INTRODUCTION Bioinformatics has become an important part of many areas of biology. In experimental molecular biology, bioinformatics techniques such as image and signal processing allow extraction of useful results from large amounts of raw data. In the field of genetics and genomics, it aids in sequencing and annotating genomes and their observed mutations. It plays a role in the text mining of biological literature and the development of biological and gene ontologies to organize and query biological data. It also plays a role in the analysis of gene and protein expression and regulation. -
Biolib: Sharing High Performance Code Between Bioperl, Biopython, Bioruby, R/Bioconductor and Biojava by Pjotr Prins
BioLib: Sharing high performance code between BioPerl, BioPython, BioRuby, R/Bioconductor and BioJAVA by Pjotr Prins Dept. of Nematology, Wageningen University, The Netherlands ([email protected]) website: http://biolib.open-bio.org/ git repository: http://github.com/pjotrp/biolib/ LICENSE: BioLib defaults to the BSD license, embedded libraries may override BioLib provides the infrastructure for mapping existing and novel C/C++ libraries against the major high level languages using a combination of SWIG and cmake. This allows writing code once and sharing it with all bioinformaticians - who, as it happens, are a diverse lot with diverse computing platforms and programming language preferences. Bioinformatics is facing some major challenges handling IO data from microarrays, sequencers etc., where every innovation comes with new data formats and data size increases rapidly. Writing so- lutions in every language domain usually entails duplication of effort. At the same time developers are a scarce resource in every Bio* project. In practice this often means missing or incomplete func- tionality. For example microarray support is only satisfactory in R/Bioconductor, but lacking in all other domains. By mapping libraries using SWIG we can support all languages in one strike, thereby concentrating the effort of supporting IO in one place. BioLib also provides an alternative to webservices. Webservices are often used to cross language barriers using a ’slow’ network interface. BioLib provides an alternative when fast low overhead communication is desired, like for high throughput, high performance computing. BioLib has mapped libraries for Affymetrix microarray IO (the Affyio implementation by Ben Bol- stad) and the Staden IO lib (James Bonfield) for 454-sequencer trace files, amongst others, and made these available for the major languages on the major computing platforms (thanks to CMake). -
The Bioperl Toolkit: Perl Modules for the Life Sciences
Downloaded from genome.cshlp.org on January 25, 2012 - Published by Cold Spring Harbor Laboratory Press The Bioperl Toolkit: Perl Modules for the Life Sciences Jason E. Stajich, David Block, Kris Boulez, et al. Genome Res. 2002 12: 1611-1618 Access the most recent version at doi:10.1101/gr.361602 Supplemental http://genome.cshlp.org/content/suppl/2002/10/20/12.10.1611.DC1.html Material References This article cites 14 articles, 9 of which can be accessed free at: http://genome.cshlp.org/content/12/10/1611.full.html#ref-list-1 Article cited in: http://genome.cshlp.org/content/12/10/1611.full.html#related-urls Email alerting Receive free email alerts when new articles cite this article - sign up in the box at the service top right corner of the article or click here To subscribe to Genome Research go to: http://genome.cshlp.org/subscriptions Cold Spring Harbor Laboratory Press Downloaded from genome.cshlp.org on January 25, 2012 - Published by Cold Spring Harbor Laboratory Press Resource The Bioperl Toolkit: Perl Modules for the Life Sciences Jason E. Stajich,1,18,19 David Block,2,18 Kris Boulez,3 Steven E. Brenner,4 Stephen A. Chervitz,5 Chris Dagdigian,6 Georg Fuellen,7 James G.R. Gilbert,8 Ian Korf,9 Hilmar Lapp,10 Heikki Lehva¨slaiho,11 Chad Matsalla,12 Chris J. Mungall,13 Brian I. Osborne,14 Matthew R. Pocock,8 Peter Schattner,15 Martin Senger,11 Lincoln D. Stein,16 Elia Stupka,17 Mark D. Wilkinson,2 and Ewan Birney11 1University Program in Genetics, Duke University, Durham, North Carolina 27710, USA; 2National Research Council of -
Sophie Dumont
Sophie Dumont University of California, San Francisco 513 Parnassus Ave, HSW-613, San Francisco, CA 94143-0512, USA Office: (415) 502-1229 / Cell: (510) 229-9846 [email protected] ; http://dumontlab.ucsf.edu/ EDUCATION 9/2000-12/2005 Ph.D., Biophysics, University of California, Berkeley, CA 10/1999-8/2000 Candidate for D.Phil., Theoretical Physics, University of Oxford, UK 9/1995-6/1999 B.A., Physics, magna cum laude, Princeton University, Princeton, NJ RESEARCH POSITIONS & TRAINING 7/2012-present Assistant Professor, University of California, San Francisco Dept of Cell & Tissue Biology and Dept of Cellular & Molecular Pharmacology Member, QB3 California Institute for Quantitative Biosciences Member, NSF Center for Cellular Construction Affiliate Member, UCSF Cancer Center Graduate program member: Bioengineering, Biomedical Sciences, Biophysics, Tetrad 7/2006-5/2012 Postdoctoral Fellow, Harvard Medical School Mentor: Prof. Timothy Mitchison (Systems Biology) 7/2006-6/2009 Junior Fellow, Harvard Society of Fellows 9/2000-12/2005 Graduate Student, University of California, Berkeley Advisor: Prof. Carlos Bustamante (Physics and Molecular & Cell Biology) 10/1999-8/2000 Graduate Student, University of Oxford Advisor: Prof. Douglas Abraham (Theoretical Physics) 6/1997-5/1999 Undergraduate Student, Princeton University Advisor: Prof. Stanislas Leibler (Physics) AWARDS 2016-2021 NSF CAREER Award 2016 Margaret Oakley Dayhoff Award of the Biophysical Society 2015-2020 NIH New Innovator Award (DP2) 2013-2018 Rita Allen Foundation and Milton -
Review of Java
Review of Java z Classes are object factories ¾ Encapsulate state/data and behavior/methods ¾ Ask not what you can do to an object, but what … z A program is created by using classes in libraries provided and combining these with classes you design/implement ¾ Design classes, write methods, classes communicate ¾ Communication is via method call z We've concentrated on control within and between methods ¾ Data types: primitive, array, String ¾ Control: if, for-loop, while-loop, return Genome Revolution: COMPSCI 006G 3.1 Smallest of 2, 3, …,n z We want to print the lesser of two elements, e.g., comparing the lengths of two DNA strands int small = Math.min(s1.length(),s2.length()); z Where does min function live? How do we access it? ¾ Could we write this ourselves? Why use library method? public class Math { public static int min(int x, int y) { if (x < y) return x; else return y; } } Genome Revolution: COMPSCI 006G 3.2 Generalize from two to three z Find the smallest of three strand lengths: s1, s2, s3 int small = … z Choices in writing code? ¾ Write sequence of if statements ¾ Call library method ¾ Advantages? Disadvantages? Genome Revolution: COMPSCI 006G 3.3 Generalize from three to N z Find the smallest strand length of N (any number) in array public int smallest(String[] dnaCollection) { // return shortest length in dnaCollection } z How do we write this code? Where do we start? ¾ ¾ ¾ Genome Revolution: COMPSCI 006G 3.4 Static methods analyzed z Typically a method invokes behavior on an object ¾ Returns property of object, e.g., s.length(); -
Plat: a Web Based Protein Local Alignment Tool
University of Rhode Island DigitalCommons@URI Open Access Master's Theses 2017 Plat: A Web Based Protein Local Alignment Tool Stephen H. Jaegle University of Rhode Island, [email protected] Follow this and additional works at: https://digitalcommons.uri.edu/theses Recommended Citation Jaegle, Stephen H., "Plat: A Web Based Protein Local Alignment Tool" (2017). Open Access Master's Theses. Paper 1080. https://digitalcommons.uri.edu/theses/1080 This Thesis is brought to you for free and open access by DigitalCommons@URI. It has been accepted for inclusion in Open Access Master's Theses by an authorized administrator of DigitalCommons@URI. For more information, please contact [email protected]. PLAT: A WEB BASED PROTEIN LOCAL ALIGNMENT TOOL BY STEPHEN H. JAEGLE A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE UNIVERSITY OF RHODE ISLAND 2017 MASTER OF SCIENCE THESIS OF STEPHEN H. JAEGLE APPROVED: Thesis Committee: Major Professor Lutz Hamel Victor Fay-Wolfe Ying Zhang Nasser H. Zawia DEAN OF THE GRADUATE SCHOOL UNIVERSITY OF RHODE ISLAND 2017 ABSTRACT Protein structure largely determines functionality; three-dimensional struc- tural alignment is thus important to analysis and prediction of protein function. Protein Local Alignment Tool (PLAT) is an implementation of a web-based tool with a graphic interface that performs local protein structure alignment based on user-selected amino acids. Global alignment compares entire structures; local alignment compares parts of structures. Given input from the user and the RCSB Protein Data Bank, PLAT determines an optimal translation and rotation that minimizes the distance between the structures defined by the selected inputs. -
The Bioperl Toolkit: Perl Modules for the Life Sciences
Downloaded from genome.cshlp.org on October 30, 2013 - Published by Cold Spring Harbor Laboratory Press View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Cold Spring Harbor Laboratory Institutional Repository The Bioperl Toolkit: Perl Modules for the Life Sciences Jason E. Stajich, David Block, Kris Boulez, et al. Genome Res. 2002 12: 1611-1618 Access the most recent version at doi:10.1101/gr.361602 Supplemental http://genome.cshlp.org/content/suppl/2002/10/20/12.10.1611.DC1.html Material References This article cites 14 articles, 9 of which can be accessed free at: http://genome.cshlp.org/content/12/10/1611.full.html#ref-list-1 Creative This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the Commons first six months after the full-issue publication date (see License http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as described at http://creativecommons.org/licenses/by-nc/3.0/. Email Alerting Receive free email alerts when new articles cite this article - sign up in the box at the Service top right corner of the article or click here. To subscribe to Genome Research go to: http://genome.cshlp.org/subscriptions Cold Spring Harbor Laboratory Press Resource The Bioperl Toolkit: Perl Modules for the Life Sciences Jason E. Stajich,1,18,19 David Block,2,18 Kris Boulez,3 Steven E. Brenner,4 Stephen A. Chervitz,5 Chris Dagdigian,6 Georg Fuellen,7 James G.R. -
The Biojava Tutorial the Biojava Tutorial
The BioJava Tutorial The BioJava Tutorial BioJava is a library of open source classes intended as a framework for applications which analyse or present biological sequence data. This tutorial illustrates the core sequence-handling interfaces available to the application programmer, and explains how BioJava differs from other sequence-handling libraries. For more comprehensive descriptions of the BioJava API, please consult the JavaDoc documentation. 1. Symbols and SymbolLists 2. Sequences and Features 3. Sequence I/O basics 4. ChangeEvent overview 5. ChangeEvent example using Distribution objects 6. Implementing Changeable 7. Blast-like parsing (NCBI Blast, WU-Blast, HMMER) 8. walkthrough of one of the dynamic programming examples 9. Installing BioSQL The BioJava tutorial, like BioJava itself, is a work in progress, and all suggestions (and offers to write extra chapters ;) are welcome. If you see any glaring errors, or would like to contribute some documentation, please contact Thomas Down or the biojava-l mailing list. http://www.biojava.org/tutorials/index.html [02/04/2003 13.39.36] BioJava.org - Main Page BioJava.org Open Bio sites About BioJava bioperl.org The BioJava Project is an open-source project dedicated to providing Java tools biopython.org for processing biological data. This will include objects for manipulating bioxml.org sequences, file parsers, CORBA interoperability, DAS, access to ACeDB, biodas.org dynamic programming, and simple statistical routines to name just a few things. biocorba.org The BioJava library is useful for automating those daily and mundane bioinformatics tasks. As the library matures, the BioJava libraries will provide a Documentation foundation upon which both free software and commercial packages can be Overview developed. -
Gestalt of Bioinformatics
DeepaliSanjay,Bhatnagar,Gurpreet Kaur 6 Gestalt ofBioinformatics DeepaliSanjay,Bhatnagar,Gurpreet Kaur Computer Applications Deptt.,Computer ApplicationsDeptt.,Computer Applications Deptt. GianiZailSingh,PTU, GianiZailSingh,PTU,GianiZailSingh,PTU Campus,BathindaCampus,BathindaCampus,Bathinda [email protected] Abstract-Bioinformatics is a new and rapid evolving To study biological data different concepts of computer discipline that is emerging from the fields of experimental science, statistics, mathematics and engineering are molecular biology and biochemistry. It integrates many core combined together. subjects likeBiology, computer science, biochemistry, statistics, mathematics and others. Bioinformatics is an Biological data + Computer Calculations= application of computer technology that is skyrocketing in the Bioinformatics.Bioinformatics is new science to manage recent years.Bioinformatics develops methods and software of biological information. To gather, store, analyse and tools for understanding the concepts of biology .There is integrate biological and genetic information, computers growing interest in the application of artificial intelligence are used. (AI) techniques in bioinformatics. Bioinformatics, the subject of current review, is defined as the application of computational techniques to understand and organise the information associated with biological macromolecules. This paper will give a clear glance overview of bioinformatics, its definition, aims and applications in Artificial Intelligence in computer science. -
Visual Programming for Next-Generation Sequencing Data Analytics Franco Milicchio1, Rebecca Rose2, Jiang Bian3, Jae Min4 and Mattia Prosperi4*
Milicchio et al. BioData Mining (2016) 9:16 DOI 10.1186/s13040-016-0095-3 REVIEW Open Access Visual programming for next-generation sequencing data analytics Franco Milicchio1, Rebecca Rose2, Jiang Bian3, Jae Min4 and Mattia Prosperi4* * Correspondence: [email protected] 4Department of Epidemiology, Abstract College of Public Health and Health Professions & College of Medicine, Background: High-throughput or next-generation sequencing (NGS) technologies University of Florida, 2004 Mowry have become an established and affordable experimental framework in biological Road, Gainesville 32610-0231, FL, and medical sciences for all basic and translational research. Processing and USA Full list of author information is analyzing NGS data is challenging. NGS data are big, heterogeneous, sparse, and available at the end of the article error prone. Although a plethora of tools for NGS data analysis has emerged in the past decade, (i) software development is still lagging behind data generation capabilities, and (ii) there is a ‘cultural’ gap between the end user and the developer. Text: Generic software template libraries specifically developed for NGS can help in dealing with the former problem, whilst coupling template libraries with visual programming may help with the latter. Here we scrutinize the state-of-the-art low-level software libraries implemented specifically for NGS and graphical tools for NGS analytics. An ideal developing environment for NGS should be modular (with a native library interface), scalable in computational methods (i.e. serial, multithread, distributed), transparent (platform-independent), interoperable (with external software interface), and usable (via an intuitive graphical user interface). These characteristics should facilitate both the run of standardized NGS pipelines and the development of new workflows based on technological advancements or users’ needs.