DOCSLIB.ORG

Biopython Tutorial and Cookbook

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Biopython Tutorial and Cookbook Biopython Tutorial and Cookbook Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck, Michiel de Hoon, Peter Cock, Tiago Ant˜ao Last Update – 2 April 2010 (Biopython 1.54 beta) Contents 1 Introduction 7 1.1 What is Biopython? ......................................... 7 1.2 What can I find in the Biopython package ............................. 7 1.3 Installing Biopython ......................................... 8 1.4 Frequently Asked Questions (FAQ) ................................. 8 2 Quick Start – What can you do with Biopython? 12 2.1 General overview of what Biopython provides ........................... 12 2.2 Working with sequences ....................................... 12 2.3 A usage example ........................................... 13 2.4 Parsing sequence file formats .................................... 14 2.4.1 Simple FASTA parsing example ............................... 14 2.4.2 Simple GenBank parsing example ............................. 15 2.4.3 I love parsing – please don’t stop talking about it! .................... 15 2.5 Connecting with biological databases ................................ 15 2.6 What to do next ........................................... 16 3 Sequence objects 17 3.1 Sequences and Alphabets ...................................... 17 3.2 Sequences act like strings ...................................... 18 3.3 Slicing a sequence .......................................... 19 3.4 Turning Seq objects into strings ................................... 20 3.5 Concatenating or adding sequences ................................. 20 3.6 Changing case ............................................. 21 3.7 Nucleotide sequences and (reverse) complements ......................... 21 3.8 Transcription ............................................. 22 3.9 Translation .............................................. 23 3.10 Translation Tables .......................................... 25 3.11 Comparing Seq objects ........................................ 27 3.12 MutableSeq objects .......................................... 28 3.13 UnknownSeq objects ......................................... 29 3.14 Working with directly strings .................................... 30 4 Sequence Record objects 31 4.1 The SeqRecord object ........................................ 31 4.2 Creating a SeqRecord ........................................ 32 4.2.1 SeqRecord objects from scratch ............................... 32 4.2.2 SeqRecord objects from FASTA files ............................ 33 4.2.3 SeqRecord objects from GenBank files ........................... 34 4.3 SeqFeature objects .......................................... 35 1 4.3.1 SeqFeatures themselves ................................... 35 4.3.2 Locations ........................................... 37 4.4 References ............................................... 38 4.5 The format method .......................................... 39 4.6 Slicing a SeqRecord ......................................... 39 4.7 Adding SeqRecord objects ...................................... 42 5 Sequence Input/Output 44 5.1 Parsing or Reading Sequences .................................... 44 5.1.1 Reading Sequence Files ................................... 44 5.1.2 Iterating over the records in a sequence file ........................ 45 5.1.3 Getting a list of the records in a sequence file ....................... 46 5.1.4 Extracting data ........................................ 47 5.2 Parsing sequences from the net ................................... 49 5.2.1 Parsing GenBank records from the net ........................... 49 5.2.2 Parsing SwissProt sequences from the net ......................... 50 5.3 Sequence files as Dictionaries – In memory ............................. 50 5.3.1 Specifying the dictionary keys ................................ 51 5.3.2 Indexing a dictionary using the SEGUID checksum .................... 52 5.4 Sequence files as Dictionaries – Indexed files ............................ 53 5.4.1 Specifying the dictionary keys ................................ 53 5.4.2 Getting the raw data for a record .............................. 54 5.4.3 Discussion ........................................... 54 5.5 Writing Sequence Files ........................................ 54 5.5.1 Round trips .......................................... 56 5.5.2 Converting between sequence file formats ......................... 56 5.5.3 Converting a file of sequences to their reverse complements ............... 57 5.5.4 Getting your SeqRecord objects as formatted strings ................... 58 6 Multiple Sequence Alignment objects 59 6.1 Parsing or Reading Sequence Alignments ............................. 59 6.1.1 Single Alignments ...................................... 60 6.1.2 Multiple Alignments ..................................... 62 6.1.3 Ambiguous Alignments ................................... 64 6.2 Writing Alignments .......................................... 66 6.2.1 Converting between sequence alignment file formats ................... 67 6.2.2 Getting your alignment objects as formatted strings ................... 69 6.3 Manipulating Alignments ...................................... 69 6.4 Alignment Tools ........................................... 72 6.4.1 ClustalW ........................................... 72 6.4.2 MUSCLE ........................................... 75 6.4.3 MUSCLE using stdout .................................... 76 6.4.4 MUSCLE using stdin and stdout .............................. 76 6.4.5 EMBOSS needle and water ................................. 77 7 BLAST 80 7.1 Running BLAST over the Internet ................................. 80 7.2 Running BLAST locally ....................................... 82 7.2.1 Introduction ......................................... 82 7.2.2 Standalone NCBI “legacy” BLAST ............................. 82 7.2.3 Standalone NCBI BLAST+ ................................. 82 7.2.4 WU-BLAST and AB-BLAST ................................ 83 2 7.3 Parsing BLAST output ....................................... 83 7.4 The BLAST record class ....................................... 85 7.5 Deprecated BLAST parsers ..................................... 86 7.5.1 Parsing plain-text BLAST output ............................. 86 7.5.2 Parsing a plain-text BLAST file full of BLAST runs ................... 89 7.5.3 Finding a bad record somewhere in a huge plain-text BLAST file ............ 90 7.6 Dealing with PSI-BLAST ...................................... 91 7.7 Dealing with RPS-BLAST ...................................... 91 8 Accessing NCBI’s Entrez databases 92 8.1 Entrez Guidelines ........................................... 93 8.2 EInfo: Obtaining information about the Entrez databases .................... 93 8.3 ESearch: Searching the Entrez databases .............................. 95 8.4 EPost: Uploading a list of identifiers ................................ 96 8.5 ESummary: Retrieving summaries from primary IDs ....................... 97 8.6 EFetch: Downloading full records from Entrez ........................... 97 8.7 ELink: Searching for related items in NCBI Entrez ........................ 100 8.8 EGQuery: Global Query - counts for search terms ........................ 101 8.9 ESpell: Obtaining spelling suggestions ............................... 102 8.10 Parsing huge Entrez XML files ................................... 102 8.11 Specialized parsers .......................................... 103 8.11.1 Parsing Medline records ................................... 103 8.11.2 Parsing GEO records ..................................... 105 8.11.3 Parsing UniGene records ................................... 106 8.12 Using a proxy ............................................. 107 8.13 Examples ............................................... 108 8.13.1 PubMed and Medline .................................... 108 8.13.2 Searching, downloading, and parsing Entrez Nucleotide records ............. 109 8.13.3 Searching, downloading, and parsing GenBank records .................. 111 8.13.4 Finding the lineage of an organism ............................. 112 8.14 Using the history and WebEnv ................................... 113 8.14.1 Searching for and downloading sequences using the history ............... 113 8.14.2 Searching for and downloading abstracts using the history ................ 114 8.14.3 Searching for citations .................................... 114 9 Swiss-Prot and ExPASy 116 9.1 Parsing Swiss-Prot files ....................................... 116 9.1.1 Parsing Swiss-Prot records ................................. 116 9.1.2 Parsing the Swiss-Prot keyword and category list ..................... 118 9.2 Parsing Prosite records ........................................ 119 9.3 Parsing Prosite documentation records ............................... 120 9.4 Parsing Enzyme records ....................................... 120 9.5 Accessing the ExPASy server .................................... 122 9.5.1 Retrieving a Swiss-Prot record ............................... 122 9.5.2 Searching Swiss-Prot ..................................... 123 9.5.3 Retrieving Prosite and Prosite documentation records .................. 123 9.6 Scanning the Prosite database .................................... 124 3 10 Going 3D: The PDB module 126 10.1 Structure representation ....................................... 126 10.1.1 Structure ........................................... 128 10.1.2 Model ............................................. 129 10.1.3 Chain ............................................
Load more

Recommended publications
  • Treebase: an R Package for Discovery, Access and Manipulation of Online Phylogenies
    Methods in Ecology and Evolution 2012, 3, 1060–1066 doi: 10.1111/j.2041-210X.2012.00247.x APPLICATION Treebase: an R package for discovery, access and manipulation of online phylogenies Carl Boettiger1* and Duncan Temple Lang2 1Center for Population Biology, University of California, Davis, CA,95616,USA; and 2Department of Statistics, University of California, Davis, CA,95616,USA Summary 1. The TreeBASE portal is an important and rapidly growing repository of phylogenetic data. The R statistical environment has also become a primary tool for applied phylogenetic analyses across a range of questions, from comparative evolution to community ecology to conservation planning. 2. We have developed treebase, an open-source software package (freely available from http://cran.r-project. org/web/packages/treebase) for the R programming environment, providing simplified, programmatic and inter- active access to phylogenetic data in the TreeBASE repository. 3. We illustrate how this package creates a bridge between the TreeBASE repository and the rapidly growing collection of R packages for phylogenetics that can reduce barriers to discovery and integration across phyloge- netic research. 4. We show how the treebase package can be used to facilitate replication of previous studies and testing of methods and hypotheses across a large sample of phylogenies, which may help make such important reproduc- ibility practices more common. Key-words: application programming interface, database, programmatic, R, software, TreeBASE, workflow include, but are not limited to, ancestral state reconstruction Introduction (Paradis 2004; Butler & King 2004), diversification analysis Applications that use phylogenetic information as part of their (Paradis 2004; Rabosky 2006; Harmon et al.
    [Show full text]
  • NASP: an Accurate, Rapid Method for the Identification of Snps in WGS Datasets That Supports Flexible Input and Output Formats Jason Sahl
    Himmelfarb Health Sciences Library, The George Washington University Health Sciences Research Commons Environmental and Occupational Health Faculty Environmental and Occupational Health Publications 8-2016 NASP: an accurate, rapid method for the identification of SNPs in WGS datasets that supports flexible input and output formats Jason Sahl Darrin Lemmer Jason Travis James Schupp John Gillece See next page for additional authors Follow this and additional works at: http://hsrc.himmelfarb.gwu.edu/sphhs_enviro_facpubs Part of the Bioinformatics Commons, Integrative Biology Commons, Research Methods in Life Sciences Commons, and the Systems Biology Commons APA Citation Sahl, J., Lemmer, D., Travis, J., Schupp, J., Gillece, J., Aziz, M., & +several additional authors (2016). NASP: an accurate, rapid method for the identification of SNPs in WGS datasets that supports flexible input and output formats. Microbial Genomics, 2 (8). http://dx.doi.org/10.1099/mgen.0.000074 This Journal Article is brought to you for free and open access by the Environmental and Occupational Health at Health Sciences Research Commons. It has been accepted for inclusion in Environmental and Occupational Health Faculty Publications by an authorized administrator of Health Sciences Research Commons. For more information, please contact [email protected]. Authors Jason Sahl, Darrin Lemmer, Jason Travis, James Schupp, John Gillece, Maliha Aziz, and +several additional authors This journal article is available at Health Sciences Research Commons: http://hsrc.himmelfarb.gwu.edu/sphhs_enviro_facpubs/212 Methods Paper NASP: an accurate, rapid method for the identification of SNPs in WGS datasets that supports flexible input and output formats Jason W. Sahl,1,2† Darrin Lemmer,1† Jason Travis,1 James M.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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
    [Show full text]
  • 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
    [Show full text]
  • Introduction to Bioinformatics (Elective) – SBB1609
    SCHOOL OF BIO AND CHEMICAL ENGINEERING DEPARTMENT OF BIOTECHNOLOGY Unit 1 – Introduction to Bioinformatics (Elective) – SBB1609 1 I HISTORY OF BIOINFORMATICS Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biologicaldata. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques. Bioinformatics derives knowledge from computer analysis of biological data. These can consist of the information stored in the genetic code, but also experimental results from various sources, patient statistics, and scientific literature. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics is a rapidly developing branch of biology and is highly interdisciplinary, using techniques and concepts from informatics, statistics, mathematics, chemistry, biochemistry, physics, and linguistics. It has many practical applications in different areas of biology and medicine. Bioinformatics: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data. Computational Biology: The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. "Classical" bioinformatics: "The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information.” The National Center for Biotechnology Information (NCBI 2001) defines bioinformatics as: "Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • Can Hybridization Be Detected Between African Wolf and Sympatric Canids?
    Can hybridization be detected between African wolf and sympatric canids? Sunniva Helene Bahlk Master of Science Thesis 2015 Center for Ecological and Evolutionary Synthesis Department of Bioscience Faculty of Mathematics and Natural Science University of Oslo, Norway © Sunniva Helene Bahlk 2015 Can hybridization be detected between African wolf and sympatric canids? Sunniva Helene Bahlk http://www.duo.uio.no/ Print: Reprosentralen, University of Oslo II Table of contents Acknowledgments ...................................................................................................................... 1 Abstract ...................................................................................................................................... 3 Introduction ................................................................................................................................ 5 The species in the genus Canis are closely related and widely distributed ........................... 5 Next-generation sequencing and bioinformatics .................................................................. 6 The concepts of hybridization and introgression ................................................................... 8 The aim of my study ............................................................................................................... 9 Materials and Methods ............................................................................................................ 11 Origin of the samples and laboratory protocols .................................................................
    [Show full text]
  • A Comprehensive SARS-Cov-2 Genomic Analysis Identifies Potential Targets for Drug Repurposing
    PLOS ONE RESEARCH ARTICLE A comprehensive SARS-CoV-2 genomic analysis identifies potential targets for drug repurposing 1☯ 1☯ 2,3 Nithishwer Mouroug Anand , Devang Haresh Liya , Arpit Kumar PradhanID *, Nitish Tayal4, Abhinav Bansal5, Sainitin Donakonda6, Ashwin Kumar Jainarayanan7,8* 1 Department of Physical Sciences, Indian Institute of Science Education and Research, Mohali, India, 2 Graduate School of Systemic Neuroscience, Ludwig Maximilian University of Munich, Munich, Germany, 3 Klinikum rechts der Isar, Technische UniversitaÈt MuÈnchen, MuÈnchen, Germany, 4 Department of Biological Sciences, Indian Institute of Science Education and Research, Mohali, India, 5 Department of Chemical a1111111111 Sciences, Indian Institute of Science Education and Research, Mohali, India, 6 Institute of Molecular a1111111111 Immunology and Experimental Oncology, Klinikum rechts der Isar, Technische UniversitaÈt MuÈnchen, a1111111111 MuÈnchen, Germany, 7 The Kennedy Institute of Rheumatology, University of Oxford, Oxford, United a1111111111 Kingdom, 8 Interdisciplinary Bioscience DTP, University of Oxford, Oxford, United Kingdom a1111111111 ☯ These authors contributed equally to this work. * [email protected] (AKP); [email protected] (AKJ) OPEN ACCESS Abstract Citation: Anand NM, Liya DH, Pradhan AK, Tayal N, The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is a novel Bansal A, Donakonda S, et al. (2021) A comprehensive SARS-CoV-2 genomic analysis human coronavirus strain (HCoV) was initially reported in December 2019 in Wuhan City, identifies potential targets for drug repurposing. China. This acute infection caused pneumonia-like symptoms and other respiratory tract ill- PLoS ONE 16(3): e0248553. https://doi.org/ ness. Its higher transmission and infection rate has successfully enabled it to have a global 10.1371/journal.pone.0248553 spread over a matter of small time.
    [Show full text]
  • Computational Biology and Bioinformatics
    DEPARTMENT OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS UNIVERSITY OF KERALA MSc. PROGRAMME IN COMPUTATIONAL BIOLOGY SYLLABUS Under Credit and Semester System w. e. f. 2017 Admissions DEPARTMENT OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS UNIVERSITY OF KERALA MSc. PROGRAMME IN COMPUTATIONAL BIOLOGY SYLLABUS PROGRAMME OBJECTIVES Aims to equip students with basic computational and mathematical skill To impart basic life science knowledge To acquire advanced computational and modelling skills required to address problems of life sciences for computational perspectives MSc. Syllabus of the Programme Number of Semester Course Code Name of the course Credits Core Courses BIN-C-411 Introduction to Life Sciences & Bioinformatics 4 BIN-C-412 Applied Mathematics 4 BIN-C-413 Web programming and Databases 4 I BIN-C-414 Bioinformatics Lab I 3 Internal Electives BIN-E-415 (i) Python Programming (E) 2 BIN-E-415 (ii) Seminar I (E) 2 BIN-E-415 (iii) Programming in R (E) 2 BIN-E-415 (iv) Communication skill in English (Elective skill course) 2 Core Courses BIN-C-421 Creativity, Research & Knowledge Management 4 BIN-C-422 Fundamentals of Molecular Biology 4 BIN-C-423 Computational Genomics 4 BIN-C-424 Bioinformatics Lab II 3 II Internal Electives BIN-E-425 (i) Perl and BioPerl (E) 4 BIN-E-425 (ii) Case Study (E) 2 BIN-E-425 (iii) Android App Development for Bioinformatics(E) 2 BIN-E-425 (iv) Negotiated Studies(E) 2 BIN-E-425 (v) Communication skill in English (Elective skill course) 2 Core Courses BIN-C-431 Proteomics and CADD 4 BIN-C-432 Phylogenetics
    [Show full text]