The Bioperl Toolkit: Perl Modules for the Life Sciences
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A Bivalent Chromatin Structure Marks Key Developmental Genes in Embryonic Stem Cells
A Bivalent Chromatin Structure Marks Key Developmental Genes in Embryonic Stem Cells Bradley E. Bernstein,1,2,3,* Tarjei S. Mikkelsen,3,4 Xiaohui Xie,3 Michael Kamal,3 Dana J. Huebert,1 James Cuff,3 Ben Fry,3 Alex Meissner,5 Marius Wernig,5 Kathrin Plath,5 Rudolf Jaenisch,5 Alexandre Wagschal,6 Robert Feil,6 Stuart L. Schreiber,3,7 and Eric S. Lander3,5 1 Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA 02129, USA 2 Department of Pathology, Harvard Medical School, Boston, MA 02115, USA 3 Broad Institute of Harvard and MIT, Cambridge, MA 02139, USA 4 Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA 5 Whitehead Institute for Biomedical Research, MIT, Cambridge, MA 02139, USA 6 Institute of Molecular Genetics, CNRS UMR-5535 and University of Montpellier-II, Montpellier, France 7 Howard Hughes Medical Institute at the Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA *Contact: [email protected] DOI 10.1016/j.cell.2006.02.041 SUMMARY which in turn modulate chromatin structure (Jenuwein and Allis, 2001; Margueron et al., 2005). The core histones The most highly conserved noncoding ele- H2A, H2B, H3, and H4 are subject to dozens of different ments (HCNEs) in mammalian genomes cluster modifications, including acetylation, methylation, and within regions enriched for genes encoding de- phosphorylation. Histone H3 lysine 4 (Lys4) and lysine velopmentally important transcription factors 27 (Lys27) methylation are of particular interest as these (TFs). This suggests that HCNE-rich regions modifications are catalyzed, respectively, by trithorax- may contain key regulatory controls involved and Polycomb-group proteins, which mediate mitotic in- heritance of lineage-specific gene expression programs in development. -
Easybuild Documentation Release 20210907.0
EasyBuild Documentation Release 20210907.0 Ghent University Tue, 07 Sep 2021 08:55:41 Contents 1 What is EasyBuild? 3 2 Concepts and terminology 5 2.1 EasyBuild framework..........................................5 2.2 Easyblocks................................................6 2.3 Toolchains................................................7 2.3.1 system toolchain.......................................7 2.3.2 dummy toolchain (DEPRECATED) ..............................7 2.3.3 Common toolchains.......................................7 2.4 Easyconfig files..............................................7 2.5 Extensions................................................8 3 Typical workflow example: building and installing WRF9 3.1 Searching for available easyconfigs files.................................9 3.2 Getting an overview of planned installations.............................. 10 3.3 Installing a software stack........................................ 11 4 Getting started 13 4.1 Installing EasyBuild........................................... 13 4.1.1 Requirements.......................................... 14 4.1.2 Using pip to Install EasyBuild................................. 14 4.1.3 Installing EasyBuild with EasyBuild.............................. 17 4.1.4 Dependencies.......................................... 19 4.1.5 Sources............................................. 21 4.1.6 In case of installation issues. .................................. 22 4.2 Configuring EasyBuild.......................................... 22 4.2.1 Supported configuration -
Bbedit 13.5 User Manual
User Manual BBEdit™ Professional Code and Text Editor for the Macintosh Bare Bones Software, Inc. ™ BBEdit 13.5 Product Design Jim Correia, Rich Siegel, Steve Kalkwarf, Patrick Woolsey Product Engineering Jim Correia, Seth Dillingham, Matt Henderson, Jon Hueras, Steve Kalkwarf, Rich Siegel, Steve Sisak Engineers Emeritus Chris Borton, Tom Emerson, Pete Gontier, Jamie McCarthy, John Norstad, Jon Pugh, Mark Romano, Eric Slosser, Rob Vaterlaus Documentation Fritz Anderson, Philip Borenstein, Stephen Chernicoff, John Gruber, Jeff Mattson, Jerry Kindall, Caroline Rose, Allan Rouselle, Rich Siegel, Vicky Wong, Patrick Woolsey Additional Engineering Polaschek Computing Icon Design Bryan Bell Factory Color Schemes Luke Andrews Additional Color Schemes Toothpaste by Cat Noon, and Xcode Dark by Andrew Carter. Used by permission. Additional Icons By icons8. Used under license Additional Artwork By Jonathan Hunt PHP keyword lists Contributed by Ted Stresen-Reuter. Previous versions by Carsten Blüm Published by: Bare Bones Software, Inc. 73 Princeton Street, Suite 206 North Chelmsford, MA 01863 USA (978) 251-0500 main (978) 251-0525 fax https://www.barebones.com/ Sales & customer service: [email protected] Technical support: [email protected] BBEdit and the BBEdit User Manual are copyright ©1992-2020 Bare Bones Software, Inc. All rights reserved. Produced/published in USA. Copyrights, Licenses & Trademarks cmark ©2014 by John MacFarlane. Used under license; part of the CommonMark project LibNcFTP Used under license from and copyright © 1996-2010 Mike Gleason & NcFTP Software Exuberant ctags ©1996-2004 Darren Hiebert (source code here) PCRE2 Library Written by Philip Hazel and Zoltán Herczeg ©1997-2018 University of Cambridge, England Info-ZIP Library ©1990-2009 Info-ZIP. -
Intermediate Perl – Session 7
1.1.2.8 – Intermediate Perl 1.1.2.8.7 Intermediate Perl – Session 7 · POD – plain old documentation · processing command line parameters · processing configuration files 9/23/2008 1.1.2.8.7 - Intermediate Perl - POD, parameters and configuration 1 1.1.2.8 – Intermediate Perl POD – plain old documentation ·embed documentation in your scripts with POD ·POD is very simple because it stands for Plain Old Documentation · it is meant to be easy to use – and it is! · POD is a simple markup language · write documentation once and export it to multiple formats · man, html, text · POD formatting codes are embedded in your script ·Pod::Usage module displays documentation for the script when the script is executed · how handy is that? 9/23/2008 1.1.2.8.7 - Intermediate Perl - POD, parameters and configuration 2 1.1.2.8 – Intermediate Perl POD structure – sections start and end pod with =pod =pod and =cut =head1 NAME script – take over the world in one line of Perl separate paragraphs by =head1 SYNOPSIS new lines script –mode EVIL|GOOD [-debug] use =head1 and =head2 =head1 DESCRIPTION for headings You can take over the world as an EVIL doer or a GOOD doer. Pick one. =head2 EVIL indent code Evil is more fun. =head2 GOOD =over and =back to =over indent text =item * advantages =item * for bullet lists none =item * disadvantages no fun =back =cut 9/23/2008 1.1.2.8.7 - Intermediate Perl - POD, parameters and configuration 3 1.1.2.8 – Intermediate Perl POD structure – ordinary paragraphs ordinary paragraphs # contents of podexample =pod representing text that =head1 EXAMPLE you'd like wrapped and justified have no This is an ordinary paragraph that will be indented, wrapped and maybe even justified. -
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. -
"This Book Was a Joy to Read. It Covered All Sorts of Techniques for Debugging, Including 'Defensive' Paradigms That Will Eliminate Bugs in the First Place
Perl Debugged By Peter Scott, Ed Wright Publisher : Addison Wesley Pub Date : March 01, 2001 ISBN : 0-201-70054-9 Table of • Pages : 288 Contents "This book was a joy to read. It covered all sorts of techniques for debugging, including 'defensive' paradigms that will eliminate bugs in the first place. As coach of the USA Programming Team, I find the most difficult thing to teach is debugging. This is the first text I've even heard of that attacks the problem. It does a fine job. Please encourage these guys to write more." -Rob Kolstad Perl Debugged provides the expertise and solutions developers require for coding better, faster, and more reliably in Perl. Focusing on debugging, the most vexing aspect of programming in Perl, this example-rich reference and how-to guide minimizes development, troubleshooting, and maintenance time resulting in the creation of elegant and error-free Perl code. Designed for the novice to intermediate software developer, Perl Debugged will save the programmer time and frustration in debugging Perl programs. Based on the authors' extensive experience with the language, this book guides developers through the entire programming process, tackling the benefits, plights, and pitfalls of Perl programming. Beginning with a guided tour of the Perl documentation, the book progresses to debugging, testing, and performance issues, and also devotes a chapter to CGI programming in Perl. Throughout the book, the authors espouse defensible paradigms for improving the accuracy and performance of Perl code. In addition, Perl Debugged includes Scott and Wright's "Perls of Wisdom" which summarize key ideas from each of the chapters, and an appendix containing a comprehensive listing of Perl debugger commands. -
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 -
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. -
Name Description
Perl version 5.10.0 documentation - perlnewmod NAME perlnewmod - preparing a new module for distribution DESCRIPTION This document gives you some suggestions about how to go about writingPerl modules, preparing them for distribution, and making them availablevia CPAN. One of the things that makes Perl really powerful is the fact that Perlhackers tend to want to share the solutions to problems they've faced,so you and I don't have to battle with the same problem again. The main way they do this is by abstracting the solution into a Perlmodule. If you don't know what one of these is, the rest of thisdocument isn't going to be much use to you. You're also missing out onan awful lot of useful code; consider having a look at perlmod, perlmodlib and perlmodinstall before coming back here. When you've found that there isn't a module available for what you'retrying to do, and you've had to write the code yourself, considerpackaging up the solution into a module and uploading it to CPAN so thatothers can benefit. Warning We're going to primarily concentrate on Perl-only modules here, ratherthan XS modules. XS modules serve a rather different purpose, andyou should consider different things before distributing them - thepopularity of the library you are gluing, the portability to otheroperating systems, and so on. However, the notes on preparing the Perlside of the module and packaging and distributing it will apply equallywell to an XS module as a pure-Perl one. What should I make into a module? You should make a module out of any code that you think is going to beuseful to others. -
Bioperl What’S Bioperl?
Bioperl What’s Bioperl? Bioperl is not a new language It is a collection of Perl modules that facilitate the development of Perl scripts for bioinformatics applications. Bioperl and perl Bioperl Modules Perl Modules Perls script input Perl Interpreter output Bioperl and Perl Why bioperl for bioinformatics? Perl is good at file manipulation and text processing, which make up a large part of the routine tasks in bioinformatics. Perl language, documentation and many Perl packages are freely available. Perl is easy to get started in, to write small and medium-sized programs. Where to get help Type perldoc <modulename> in terminal Search for particular module in https://metacpan.org Bioperl Document Object-oriented and Process-oriented programming Process-oriented: Yuan Hao eats chicken Name object: $name Action method: eat Food object: $food Object-oriented: $name->eat($food) Modularize the program Platform and Related Software Required Perl 5.6.1 or higher Version 5.8 or higher is highly recommended make for Mac OS X, this requires installing the Xcode Developer Tools Installation On Linux or Max OS X Install from cpanminus: perlbrew install-cpanm cpanm Bio::Perl Install from source code: git clone https://github.com/bioperl/bioperl-live.git cd bioperl-live perl Build.PL ./Build test (optional) ./Build install Installation On Windows Install MinGW (MinGW is incorporated in Strawberry Perl, but must it be installed through PPM for ActivePerl) : ppm install MinGW Install Module::Build, Test::Harness and Test::Most through CPAN: Type cpan to enter the CPAN shell. At the cpan> prompt, type install CPAN Quit (by typing ‘q’) and reload CPAN. -
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 -
Research Computing Facility an Update from Dr
Research Computing Facility An Update from Dr. Francesca Dominici June 20, 2013 Dear all, We are very excited to provide you some important updates regarding the research computing facility at the Faculty of Arts and Science (FASRC) http://rc.fas.harvard.edu. Please note that we are phasing out the HSPH cluster, and if you are currently leasing nodes on the HSPH cluster we will be working with you to migrate to FASRC. We have developed a FAQ document, which is available at the web link https://rc.fas.harvard.edu/hsph-at-fas-rc-frequently- asked-questions/ and also included in this message. Updates: 1. 158 HSPH accounts have been opened on FASRC, enabling users to run computing jobs on the FAS High Performance Computing Cluster (HPCC), also known as Odyssey 2. Several HSPH faculty have worked with the FASRC team to purchase data storage equipment and hardware that have been deployed at FASRC in Cambridge and linked to Odyssey via a secured network 3. FASRC has developed personalized solutions for our faculty to transfer secure data from HSPH to FAS in accordance with data user agreements. 4. FASRC has been mentioned as a key strength in training and research grant applications from HSPH, and high impact papers have been published that previously were delayed for lack of computing power 5. Please bookmark the web site http://rc.fas.harvard.edu/hsph- overview/ for additional and up to date information To access FASRC you will be charged approximately $3000 per year per account. Access for PhD and ScD students is free.