Biopython: Freely Available Python Tools for Computational Molecular Biology and Bioinformatics Peter J
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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 -
Python Data Analytics Open a File for Reading: Infile = Open("Input.Txt", "R")
DATA 301: Data Analytics (2) Python File Input/Output DATA 301 Many data processing tasks require reading and writing to files. Introduction to Data Analytics I/O Type Python Data Analytics Open a file for reading: infile = open("input.txt", "r") Dr. Ramon Lawrence Open a file for writing: University of British Columbia Okanagan outfile = open("output.txt", "w") [email protected] Open a file for read/write: myfile = open("data.txt", "r+") DATA 301: Data Analytics (3) DATA 301: Data Analytics (4) Reading from a Text File (as one String) Reading from a Text File (line by line) infile = open("input.txt", "r") infile = open("input.txt", "r") for line in infile: print(line.strip('\n')) val = infile.read() Read all file as one string infile.close() print(val) infile.close() Close file # Alternate syntax - will auto-close file with open("input.txt", "r") as infile: for line in infile: print(line.strip('\n')) DATA 301: Data Analytics (5) DATA 301: Data Analytics (6) Writing to a Text File Other File Methods outfile = open("output.txt", "w") infile = open("input.txt", "r") for n in range(1,11): # Check if a file is closed outfile.write(str(n) + "\n") print(infile.closed)# False outfile.close() # Read all lines in the file into a list lines = infile.readlines() infile.close() print(infile.closed)# True DATA 301: Data Analytics (7) DATA 301: Data Analytics (8) Use Split to Process a CSV File Using csv Module to Process a CSV File with open("data.csv", "r") as infile: import csv for line in infile: line = line.strip(" \n") with open("data.csv", "r") as infile: fields = line.split(",") csvfile = csv.reader(infile) for i in range(0,len(fields)): for row in csvfile: fields[i] = fields[i].strip() if int(row[0]) > 1: print(fields) print(row) DATA 301: Data Analytics (9) DATA 301: Data Analytics (10) List all Files in a Directory Python File I/O Question Question: How many of the following statements are TRUE? import os print(os.listdir(".")) 1) A Python file is automatically closed for you. -
Biopython BOSC 2007
The 8th annual Bioinformatics Open Source Conference (BOSC 2007) 18th July, Vienna, Austria Biopython Project Update Peter Cock, MOAC Doctoral Training Centre, University of Warwick, UK Talk Outline What is python? What is Biopython? Short history Project organisation What can you do with it? How can you contribute? Acknowledgements The 8th annual Bioinformatics Open Source Conference Biopython Project Update @ BOSC 2007, Vienna, Austria What is Python? High level programming language Object orientated Open Source, free ($$$) Cross platform: Linux, Windows, Mac OS X, … Extensible in C, C++, … The 8th annual Bioinformatics Open Source Conference Biopython Project Update @ BOSC 2007, Vienna, Austria What is Biopython? Set of libraries for computational biology Open Source, free ($$$) Cross platform: Linux, Windows, Mac OS X, … Sibling project to BioPerl, BioRuby, BioJava, … The 8th annual Bioinformatics Open Source Conference Biopython Project Update @ BOSC 2007, Vienna, Austria Popularity by Google Hits Python 98 million Biopython 252,000 Perl 101 million BioPerlBioPerl 610,000 Ruby 101 million BioRuby 122,000 Java 289 million BioJava 185,000 Both Perl and Python are strong at text Python may have the edge for numerical work (with the Numerical python libraries) The 8th annual Bioinformatics Open Source Conference Biopython Project Update @ BOSC 2007, Vienna, Austria Biopython history 1999 : Started by Jeff Chang & Andrew Dalke 2000 : Biopython 0.90, first release 2001 : Biopython 1.00, “semi-complete” 2002 -
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 -
Bioinformatics and Computational Biology with Biopython
Biopython 1 Bioinformatics and Computational Biology with Biopython Michiel J.L. de Hoon1 Brad Chapman2 Iddo Friedberg3 [email protected] [email protected] [email protected] 1 Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo 108-8639, Japan 2 Plant Genome Mapping Laboratory, University of Georgia, Athens, GA 30602, USA 3 The Burnham Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA Keywords: Python, scripting language, open source 1 Introduction In recent years, high-level scripting languages such as Python, Perl, and Ruby have gained widespread use in bioinformatics. Python [3] is particularly useful for bioinformatics as well as computational biology because of its numerical capabilities through the Numerical Python project [1], in addition to the features typically found in scripting languages. Because of its clear syntax, Python is remarkably easy to learn, making it suitable for occasional as well as experienced programmers. The open-source Biopython project [2] is an international collaboration that develops libraries for Python to facilitate common tasks in bioinformatics. 2 Summary of current features of Biopython Biopython contains parsers for a large number of file formats such as BLAST, FASTA, Swiss-Prot, PubMed, KEGG, GenBank, AlignACE, Prosite, LocusLink, and PDB. Sequences are described by a standard object-oriented representation, creating an integrated framework for manipulating and ana- lyzing such sequences. Biopython enables users to -
Biopython Project Update 2013
Biopython Project Update 2013 Peter Cock & the Biopython Developers, BOSC 2013, Berlin, Germany Twitter: @pjacock & @biopython Introduction 2 My Employer After PhD joined Scottish Crop Research Institute In 2011, SCRI (Dundee) & MLURI (Aberdeen) merged as The James Hutton Institute Government funded research institute I work mainly on the genomics of Plant Pathogens I use Biopython in my day to day work More about this in tomorrow’s panel discussion, “Strategies for Funding and Maintaining Open Source Software” 3 Biopython Open source bioinformatics library for Python Sister project to: BioPerl BioRuby BioJava EMBOSS etc (see OBF Project BOF meeting tonight) Long running! 4 Brief History of Biopython 1999 - Started by Andrew Dalke & Jef Chang 2000 - First release, announcement publication Chapman & Chang (2000). ACM SIGBIO Newsletter 20, 15-19 2001 - Biopython 1.00 2009 - Application note publication Cock et al. (2009) DOI:10.1093/bioinformatics/btp163 2011 - Biopython 1.57 and 1.58 2012 - Biopython 1.59 and 1.60 2013 - Biopython 1.61 and 1.62 beta 5 Recap from last BOSC 2012 Eric Talevich presented in Boston Biopython 1.58, 1.59 and 1.60 Visualization enhancements for chromosome and genome diagrams, and phylogenetic trees More file format parsers BGZF compression Google Summer of Code students ... Bio.Phylo paper submitted and in review ... Biopython working nicely under PyPy 1.9 ... 6 Publications 7 Bio.Phylo paper published Talevich et al (2012) DOI:10.1186/1471-2105-13-209 Talevich et al. BMC Bioinformatics 2012, 13:209 http://www.biomedcentral.com/1471-2105/13/209 SOFTWARE OpenAccess Bio.Phylo: A unified toolkit for processing, analyzing and visualizing phylogenetic trees in Biopython Eric Talevich1*, Brandon M Invergo2,PeterJACock3 and Brad A Chapman4 Abstract Background: Ongoing innovation in phylogenetics and evolutionary biology has been accompanied by a proliferation of software tools, data formats, analytical techniques and web servers. -
Biopython Tutorial and Cookbook
Biopython Tutorial and Cookbook Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck Last Update{15 June 2003 Contents 1 Introduction 4 1.1 What is Biopython?.........................................4 1.1.1 What can I find in the biopython package.........................4 1.2 Installing Biopython.........................................5 1.3 FAQ..................................................5 2 Quick Start { What can you do with Biopython?6 2.1 General overview of what Biopython provides...........................6 2.2 Working with sequences.......................................6 2.3 A usage example........................................... 10 2.4 Parsing biological file formats.................................... 10 2.4.1 General parser design.................................... 10 2.4.2 Writing your own consumer................................. 11 2.4.3 Making it easier....................................... 13 2.4.4 FASTA files as Dictionaries................................. 14 2.4.5 I love parsing { please don't stop talking about it!.................... 16 2.5 Connecting with biological databases................................ 16 2.6 What to do next........................................... 17 3 Cookbook { Cool things to do with it 18 3.1 BLAST................................................ 18 3.1.1 Running BLAST over the internet............................. 18 3.1.2 Parsing the output from the WWW version of BLAST.................. 19 3.1.3 The BLAST record class................................... 21 3.1.4 Running BLAST -
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. -
Bioruby 2010 Updates: Moving to Agile Bioinformatics
BioRuby 2010 updates: moving to agile bioinformatics Raoul J.P. Bonnal1, Naohisa Goto2, Mitsuteru Nakao3, Jan Aerts4, Pjotr Prins5 ,Toshiaki Katayama6 1 Fondazione INGM, Milan, Italy [email protected]; 2 Research Institute for Microbial Diseases, Osaka University, Japan; 3 Database Center for Life Science, Tokyo, Japan; 4 Wellcome Trust Sanger Institute, Cambridge, UK; 5 Wageningen University and Groningen Bioinformatics Center, Netherlands; 6 Institute of Medical Science, University of Tokyo, Japan URL: http://bioruby.org Code: http://github.com/bioruby/bioruby License: The Ruby License BioRuby provides tools and libraries for the Ruby programming language with the aim of providing an integrated environment for bioinformatics. The Ruby programming language was incepted around 1993 and is a reflective, general purpose object-oriented programming language that combines syntax inspired by Perl, with Smalltalk-like features (http://www.ruby-lang.org). Ruby has gained in popularity by virtue of its clean object oriented programming (OOP) design and its functional programming characteristics. These characteristics allow programming with less source code, thereby improving programmer productivity (Aerts and Law, 2009). The BioRuby project is started in 2000 and after the initial release in 2001, we made a steady development to its functionality. The project is supported by the Open Bioinformatics Foundation (OBF) initiative, which hosts the website and mailing list. The current software development tree is hosted on GitHub (https://github.com/), a public distributed source control system, which allows any developer to start contributing code to the BioRuby project. Here we report recent developments in BioRuby. Since the last BOSC presentation in 2007 we had two major releases, 1.3.0 and 1.4.0. -
Open Source Tools and Toolkits for Bioinformatics: Significance, and Where Are We? Jasone.Stajichandhilmarlapp
BRIEFINGS IN BIOINFORMATICS. VOL 7. NO 3. 287^296 doi:10.1093/bib/bbl026 Open source tools and toolkits for bioinformatics: significance, and where are we? JasonE.StajichandHilmarLapp Submitted: 11th October 2005; Received (in revised form): 28th June 2006 Abstract This review summarizes important work in open-source bioinformatics software that has occurred over the past couple of years. The survey is intended to illustrate how programs and toolkits whose source code has been developed or released under an Open Source license have changed informatics-heavy areas of life science research. Rather than creating a comprehensive list of all tools developed over the last 2^3 years, we use a few selected projects encompassing toolkit libraries, analysis tools, data analysis environments and interoperability standards to show how freely available and modifiable open-source software can serve as the foundation for building important applications, analysis workflows and resources. Keywords: bioinformatics; open source; software; genomics INTRODUCTION This picture is one of a multitude of projects, Compared to several years ago, there are numerous which at the surface seem to have commonalities, open source projects active in the life science arena, but upon closer inspection have many differences each offering freely available source code that and lack an overall organization or direction that promises to address a specific problem or problem would tie them together by more than just the domain of biological sciences in a reusable way. shared principle of open source. This scenario of Bioinformatics.org (http://www.bioinformatics.org) each project offering ‘stuff’ the utility or application alone is host to 275 projects, which address a of which is often not immediately clear, is much bioinformatics need by definition.