Lesson 3 Advanced Unix File Handling and Job Control Commands
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State Notation Language and Sequencer Users' Guide
State Notation Language and Sequencer Users’ Guide Release 2.0.99 William Lupton ([email protected]) Benjamin Franksen ([email protected]) June 16, 2010 CONTENTS 1 Introduction 3 1.1 About.................................................3 1.2 Acknowledgements.........................................3 1.3 Copyright...............................................3 1.4 Note on Versions...........................................4 1.5 Notes on Release 2.1.........................................4 1.6 Notes on Releases 2.0.0 to 2.0.12..................................7 1.7 Notes on Release 2.0.........................................9 1.8 Notes on Release 1.9......................................... 12 2 Installation 13 2.1 Prerequisites............................................. 13 2.2 Download............................................... 13 2.3 Unpack................................................ 14 2.4 Configure and Build......................................... 14 2.5 Building the Manual......................................... 14 2.6 Test.................................................. 15 2.7 Use.................................................. 16 2.8 Report Bugs............................................. 16 2.9 Contribute.............................................. 16 3 Tutorial 19 3.1 The State Transition Diagram.................................... 19 3.2 Elements of the State Notation Language.............................. 19 3.3 A Complete State Program...................................... 20 3.4 Adding a -
Job Scheduling for SAP® Contents at a Glance
Kees Verruijt, Arnoud Roebers, Anjo de Heus Job Scheduling for SAP® Contents at a Glance Foreword ............................................................................ 13 Preface ............................................................................... 15 1 General Job Scheduling ...................................................... 19 2 Decentralized SAP Job Scheduling .................................... 61 3 SAP Job Scheduling Interfaces .......................................... 111 4 Centralized SAP Job Scheduling ........................................ 125 5 Introduction to SAP Central Job Scheduling by Redwood ... 163 6Installation......................................................................... 183 7 Principles and Processes .................................................... 199 8Operation........................................................................... 237 9Customer Cases................................................................. 281 The Authors ........................................................................ 295 Index .................................................................................. 297 Contents Foreword ............................................................................................... 13 Preface ................................................................................................... 15 1 General Job Scheduling ...................................................... 19 1.1 Organizational Uses of Job Scheduling .................................. -
A Rapid, Sensitive, Scalable Method for Precision Run-On Sequencing
bioRxiv preprint doi: https://doi.org/10.1101/2020.05.18.102277; this version posted May 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 A rapid, sensitive, scalable method for 2 Precision Run-On sequencing (PRO-seq) 3 4 Julius Judd1*, Luke A. Wojenski2*, Lauren M. Wainman2*, Nathaniel D. Tippens1, Edward J. 5 Rice3, Alexis Dziubek1,2, Geno J. Villafano2, Erin M. Wissink1, Philip Versluis1, Lina Bagepalli1, 6 Sagar R. Shah1, Dig B. Mahat1#a, Jacob M. Tome1#b, Charles G. Danko3,4, John T. Lis1†, 7 Leighton J. Core2† 8 9 *Equal contribution 10 1Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA 11 2Department of Molecular and Cell Biology, Institute of Systems Genomics, University of 12 Connecticut, Storrs, CT 06269, USA 13 3Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, 14 NY 14853, USA 15 4Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, 16 Ithaca, NY 14853, USA 17 #aPresent address: Koch Institute for Integrative Cancer Research, Massachusetts Institute of 18 Technology, Cambridge, MA 02139, USA 19 #bPresent address: Department of Genome Sciences, University of Washington, Seattle, WA, 20 USA 21 †Correspondence to: [email protected] or [email protected] 22 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.18.102277; this version posted May 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. -
Specification of the "Sequ" Command
1 Specification of the "sequ" command 2 Copyright © 2013 Bart Massey 3 Revision 0, 1 October 2013 4 This specification describes the "universal sequence" command sequ. The sequ command is a 5 backward-compatible set of extensions to the UNIX [seq] 6 (http://www.gnu.org/software/coreutils/manual/html_node/seq-invo 7 cation.html) command. There are many implementations of seq out there: this 8 specification is built on the seq supplied with GNU Coreutils version 8.21. 9 The seq command emits a monotonically increasing sequence of numbers. It is most commonly 10 used in shell scripting: 11 TOTAL=0 12 for i in `seq 1 10` 13 do 14 TOTAL=`expr $i + $TOTAL` 15 done 16 echo $TOTAL 17 prints 55 on standard output. The full sequ command does this basic counting operation, plus 18 much more. 19 This specification of sequ is in several stages, known as compliance levels. Each compliance 20 level adds required functionality to the sequ specification. Level 1 compliance is equivalent to 21 the Coreutils seq command. 22 The usual specification language applies to this document: MAY, SHOULD, MUST (and their 23 negations) are used in the standard fashion. 24 Wherever the specification indicates an error, a conforming sequ implementation MUST 25 immediately issue appropriate error message specific to the problem. The implementation then 26 MUST exit, with a status indicating failure to the invoking process or system. On UNIX systems, 27 the error MUST be indicated by exiting with status code 1. 28 When a conforming sequ implementation successfully completes its output, it MUST 29 immediately exit, with a status indicating success to the invoking process or systems. -
Transcriptome-Wide M a Detection with DART-Seq
Transcriptome-wide m6A detection with DART-Seq Kate D. Meyer ( [email protected] ) Duke University School of Medicine https://orcid.org/0000-0001-7197-4054 Method Article Keywords: RNA, epitranscriptome, RNA methylation, m6A, methyladenosine, DART-Seq Posted Date: September 23rd, 2019 DOI: https://doi.org/10.21203/rs.2.12189/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/7 Abstract m6A is the most abundant internal mRNA modication and plays diverse roles in gene expression regulation. Much of our current knowledge about m6A has been driven by recent advances in the ability to detect this mark transcriptome-wide. Antibody-based approaches have been the method of choice for global m6A mapping studies. These methods rely on m6A antibodies to immunoprecipitate methylated RNAs, followed by next-generation sequencing to identify m6A-containing transcripts1,2. While these methods enabled the rst identication of m6A sites transcriptome-wide and have dramatically improved our ability to study m6A, they suffer from several limitations. These include requirements for high amounts of input RNA, costly and time-consuming library preparation, high variability across studies, and m6A antibody cross-reactivity with other modications. Here, we describe DART-Seq (deamination adjacent to RNA modication targets), an antibody-free method for global m6A detection. In DART-Seq, the C to U deaminating enzyme, APOBEC1, is fused to the m6A-binding YTH domain. This fusion protein is then introduced to cellular RNA either through overexpression in cells or with in vitro assays, and subsequent deamination of m6A-adjacent cytidines is then detected by RNA sequencing to identify m6A sites. -
Installation Guide
Installation Guide Installation Guide Schrödinger Suite 2011 Schrödinger Press Installation Guide Copyright © 2011 Schrödinger, LLC. All rights reserved. While care has been taken in the preparation of this publication, Schrödinger assumes no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein. Canvas, CombiGlide, ConfGen, Epik, Glide, Impact, Jaguar, Liaison, LigPrep, Maestro, Phase, Prime, PrimeX, QikProp, QikFit, QikSim, QSite, SiteMap, Strike, and WaterMap are trademarks of Schrödinger, LLC. Schrödinger and MacroModel are registered trademarks of Schrödinger, LLC. MCPRO is a trademark of William L. Jorgensen. Desmond is a trademark of D. E. Shaw Research. Desmond is used with the permission of D. E. Shaw Research. All rights reserved. This publication may contain the trademarks of other companies. Schrödinger software includes software and libraries provided by third parties. For details of the copyrights, and terms and conditions associated with such included third party software, see the Legal Notices, or use your browser to open $SCHRODINGER/docs/html/third_party_legal.html (Linux OS) or %SCHRODINGER%\docs\html\third_party_legal.html (Windows OS). This publication may refer to other third party software not included in or with Schrödinger software ("such other third party software"), and provide links to third party Web sites ("linked sites"). References to such other third party software or linked sites do not constitute an endorsement by Schrödinger, LLC. Use of such other third party software and linked sites may be subject to third party license agreements and fees. Schrödinger, LLC and its affiliates have no responsibility or liability, directly or indirectly, for such other third party software and linked sites, or for damage resulting from the use thereof. -
Introduction to Shell Programming Using Bash Part I
Introduction to shell programming using bash Part I Deniz Savas and Michael Griffiths 2005-2011 Corporate Information and Computing Services The University of Sheffield Email [email protected] [email protected] Presentation Outline • Introduction • Why use shell programs • Basics of shell programming • Using variables and parameters • User Input during shell script execution • Arithmetical operations on shell variables • Aliases • Debugging shell scripts • References Introduction • What is ‘shell’ ? • Why write shell programs? • Types of shell What is ‘shell’ ? • Provides an Interface to the UNIX Operating System • It is a command interpreter – Built on top of the kernel – Enables users to run services provided by the UNIX OS • In its simplest form a series of commands in a file is a shell program that saves having to retype commands to perform common tasks. • Shell provides a secure interface between the user and the ‘kernel’ of the operating system. Why write shell programs? • Run tasks customised for different systems. Variety of environment variables such as the operating system version and type can be detected within a script and necessary action taken to enable correct operation of a program. • Create the primary user interface for a variety of programming tasks. For example- to start up a package with a selection of options. • Write programs for controlling the routinely performed jobs run on a system. For example- to take backups when the system is idle. • Write job scripts for submission to a job-scheduler such as the sun- grid-engine. For example- to run your own programs in batch mode. Types of Unix shells • sh Bourne Shell (Original Shell) (Steven Bourne of AT&T) • csh C-Shell (C-like Syntax)(Bill Joy of Univ. -
MEDIPS: Genome-Wide Differential Coverage Analysis of Sequencing Data Derived from DNA Enrichment Experiments
MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments. 1 Lukas Chavez [email protected] May 19, 2021 Contents 1 Introduction ..............................2 2 Installation ...............................2 3 Preparations..............................3 4 Case study: Genome wide methylation and differential cover- age between two conditions .....................5 4.1 Data Import and Preprocessing ..................5 4.2 Coverage, methylation profiles and differential coverage ......6 4.3 Differential coverage: selecting significant windows ........8 4.4 Merging neighboring significant windows .............9 4.5 Extracting data at regions of interest ...............9 5 Quality controls ............................ 10 5.1 Saturation analysis ........................ 10 5.2 Correlation between samples ................... 12 5.3 Sequence Pattern Coverage ................... 13 5.4 CpG Enrichment ......................... 14 6 Miscellaneous............................. 14 6.1 Processing regions of interest ................... 15 6.2 Export Wiggle Files ........................ 15 6.3 Merging MEDIPS SETs ...................... 15 6.4 Annotation of significant windows ................. 16 6.5 addCNV ............................. 16 6.6 Calibration Plot .......................... 16 6.7 Comments on the experimental design and Input data ....... 17 MEDIPS 1 Introduction MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipita- tion (MeDIP) experiments [1] followed by -
CA Workload Automation Ixp Admin Guide
CA Workload Automation iXp Administration Guide Release 7.1 SP1 This Documentation, which includes embedded help systems and electronically distributed materials, (hereinafter referred to as the “Documentation”) is for your informational purposes only and is subject to change or withdrawal by CA at any time. This Documentation may not be copied, transferred, reproduced, disclosed, modified or duplicated, in whole or in part, without the prior written consent of CA. This Documentation is confidential and proprietary information of CA and may not be disclosed by you or used for any purpose other than as may be permitted in (i) a separate agreement between you and CA governing your use of the CA software to which the Documentation relates; or (ii) a separate confidentiality agreement between you and CA. Notwithstanding the foregoing, if you are a licensed user of the software product(s) addressed in the Documentation, you may print or otherwise make available a reasonable number of copies of the Documentation for internal use by you and your employees in connection with that software, provided that all CA copyright notices and legends are affixed to each reproduced copy. The right to print or otherwise make available copies of the Documentation is limited to the period during which the applicable license for such software remains in full force and effect. Should the license terminate for any reason, it is your responsibility to certify in writing to CA that all copies and partial copies of the Documentation have been returned to CA or destroyed. TO THE EXTENT PERMITTED BY APPLICABLE LAW, CA PROVIDES THIS DOCUMENTATION “AS IS” WITHOUT WARRANTY OF ANY KIND, INCLUDING WITHOUT LIMITATION, ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR NONINFRINGEMENT. -
MEDIPS: DNA IP-Seq Data Analysis
Package ‘MEDIPS’ September 30, 2021 Type Package Title DNA IP-seq data analysis Version 1.44.0 Date 2020-02-15 Author Lukas Chavez, Matthias Lienhard, Joern Dietrich, Isaac Lopez Moyado Maintainer Lukas Chavez <[email protected]> Description MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP- seq). However, MEDIPS provides functionalities for the analysis of any kind of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples and saturation and correlation analysis. License GPL (>=2) LazyLoad yes Depends R (>= 3.0), BSgenome, Rsamtools Imports GenomicRanges, Biostrings, graphics, gtools, IRanges, methods, stats, utils, edgeR, DNAcopy, biomaRt, rtracklayer, preprocessCore Suggests BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, BiocStyle biocViews DNAMethylation, CpGIsland, DifferentialExpression, Sequencing, ChIPSeq, Preprocessing, QualityControl, Visualization, Microarray, Genetics, Coverage, GenomeAnnotation, CopyNumberVariation, SequenceMatching NeedsCompilation no git_url https://git.bioconductor.org/packages/MEDIPS git_branch RELEASE_3_13 git_last_commit 4df0aee git_last_commit_date 2021-05-19 Date/Publication 2021-09-30 1 2 MEDIPS-package R topics documented: MEDIPS-package . .2 COUPLINGset-class . .3 MEDIPS.addCNV . .4 MEDIPS.correlation . .5 MEDIPS.couplingVector . .6 MEDIPS.CpGenrich . .7 MEDIPS.createROIset . .8 MEDIPS.createSet . 10 MEDIPS.exportWIG . 11 -
Gnu Coreutils Core GNU Utilities for Version 5.93, 2 November 2005
gnu Coreutils Core GNU utilities for version 5.93, 2 November 2005 David MacKenzie et al. This manual documents version 5.93 of the gnu core utilities, including the standard pro- grams for text and file manipulation. Copyright c 1994, 1995, 1996, 2000, 2001, 2002, 2003, 2004, 2005 Free Software Foundation, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation; with no Invariant Sections, with no Front-Cover Texts, and with no Back-Cover Texts. A copy of the license is included in the section entitled “GNU Free Documentation License”. Chapter 1: Introduction 1 1 Introduction This manual is a work in progress: many sections make no attempt to explain basic concepts in a way suitable for novices. Thus, if you are interested, please get involved in improving this manual. The entire gnu community will benefit. The gnu utilities documented here are mostly compatible with the POSIX standard. Please report bugs to [email protected]. Remember to include the version number, machine architecture, input files, and any other information needed to reproduce the bug: your input, what you expected, what you got, and why it is wrong. Diffs are welcome, but please include a description of the problem as well, since this is sometimes difficult to infer. See section “Bugs” in Using and Porting GNU CC. This manual was originally derived from the Unix man pages in the distributions, which were written by David MacKenzie and updated by Jim Meyering. -
Processes in Linux/Unix
Processes in Linux/Unix A program/command when executed, a special instance is provided by the system to the process. This instance consists of all the services/resources that may be utilized by the process under execution. • Whenever a command is issued in unix/linux, it creates/starts a new process. For example, pwd when issued which is used to list the current directory location the user is in, a process starts. • Through a 5 digit ID number unix/linux keeps account of the processes, this number is call process id or pid. Each process in the system has a unique pid. • Used up pid’s can be used in again for a newer process since all the possible combinations are used. • At any point of time, no two processes with the same pid exist in the system because it is the pid that Unix uses to track each process. Initializing a process A process can be run in two ways: 1. Foreground Process : Every process when started runs in foreground by default, receives input from the keyboard and sends output to the screen. When issuing pwd command $ ls pwd Output: $ /home/geeksforgeeks/root When a command/process is running in the foreground and is taking a lot of time, no other processes can be run or started because the prompt would not be available until the program finishes processing and comes out. 2. Backround Process : It runs in the background without keyboard input and waits till keyboard input is required. Thus, other processes can be done in parallel with the process running in background since they do not have to wait for the previous process to be completed.