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Types and Programming Languages by Benjamin C
< Free Open Study > . .Types and Programming Languages by Benjamin C. Pierce ISBN:0262162091 The MIT Press © 2002 (623 pages) This thorough type-systems reference examines theory, pragmatics, implementation, and more Table of Contents Types and Programming Languages Preface Chapter 1 - Introduction Chapter 2 - Mathematical Preliminaries Part I - Untyped Systems Chapter 3 - Untyped Arithmetic Expressions Chapter 4 - An ML Implementation of Arithmetic Expressions Chapter 5 - The Untyped Lambda-Calculus Chapter 6 - Nameless Representation of Terms Chapter 7 - An ML Implementation of the Lambda-Calculus Part II - Simple Types Chapter 8 - Typed Arithmetic Expressions Chapter 9 - Simply Typed Lambda-Calculus Chapter 10 - An ML Implementation of Simple Types Chapter 11 - Simple Extensions Chapter 12 - Normalization Chapter 13 - References Chapter 14 - Exceptions Part III - Subtyping Chapter 15 - Subtyping Chapter 16 - Metatheory of Subtyping Chapter 17 - An ML Implementation of Subtyping Chapter 18 - Case Study: Imperative Objects Chapter 19 - Case Study: Featherweight Java Part IV - Recursive Types Chapter 20 - Recursive Types Chapter 21 - Metatheory of Recursive Types Part V - Polymorphism Chapter 22 - Type Reconstruction Chapter 23 - Universal Types Chapter 24 - Existential Types Chapter 25 - An ML Implementation of System F Chapter 26 - Bounded Quantification Chapter 27 - Case Study: Imperative Objects, Redux Chapter 28 - Metatheory of Bounded Quantification Part VI - Higher-Order Systems Chapter 29 - Type Operators and Kinding Chapter 30 - Higher-Order Polymorphism Chapter 31 - Higher-Order Subtyping Chapter 32 - Case Study: Purely Functional Objects Part VII - Appendices Appendix A - Solutions to Selected Exercises Appendix B - Notational Conventions References Index List of Figures < Free Open Study > < Free Open Study > Back Cover A type system is a syntactic method for automatically checking the absence of certain erroneous behaviors by classifying program phrases according to the kinds of values they compute. -
The Gnu Binary Utilities
The gnu Binary Utilities Version cygnus-2.7.1-96q4 May 1993 Roland H. Pesch Jeffrey M. Osier Cygnus Support Cygnus Support TEXinfo 2.122 (Cygnus+WRS) Copyright c 1991, 92, 93, 94, 95, 1996 Free Software Foundation, Inc. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modi®ed versions of this manual under the conditions for verbatim copying, provided also that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modi®ed versions. The GNU Binary Utilities Introduction ..................................... 467 1ar.............................................. 469 1.1 Controlling ar on the command line ................... 470 1.2 Controlling ar with a script ............................ 472 2ld.............................................. 477 3nm............................................ 479 4 objcopy ....................................... 483 5 objdump ...................................... 489 6 ranlib ......................................... 493 7 size............................................ 495 8 strings ........................................ 497 9 strip........................................... 499 Utilities 10 c++®lt ........................................ 501 11 nlmconv .................................... -
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 -
Gene Discovery and Annotation Using LCM-454 Transcriptome Sequencing Scott J
Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press Methods Gene discovery and annotation using LCM-454 transcriptome sequencing Scott J. Emrich,1,2,6 W. Brad Barbazuk,3,6 Li Li,4 and Patrick S. Schnable1,4,5,7 1Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, Iowa 50010, USA; 2Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50010, USA; 3Donald Danforth Plant Science Center, St. Louis, Missouri 63132, USA; 4Interdepartmental Plant Physiology Graduate Major and Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, Iowa 50010, USA; 5Department of Agronomy and Center for Plant Genomics, Iowa State University, Ames, Iowa 50010, USA 454 DNA sequencing technology achieves significant throughput relative to traditional approaches. More than 261,000 ESTs were generated by 454 Life Sciences from cDNA isolated using laser capture microdissection (LCM) from the developmentally important shoot apical meristem (SAM) of maize (Zea mays L.). This single sequencing run annotated >25,000 maize genomic sequences and also captured ∼400 expressed transcripts for which homologous sequences have not yet been identified in other species. Approximately 70% of the ESTs generated in this study had not been captured during a previous EST project conducted using a cDNA library constructed from hand-dissected apex tissue that is highly enriched for SAMs. In addition, at least 30% of the 454-ESTs do not align to any of the ∼648,000 extant maize ESTs using conservative alignment criteria. These results indicate that the combination of LCM and the deep sequencing possible with 454 technology enriches for SAM transcripts not present in current EST collections. -
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. -
Computer Matching Agreement
COMPUTER MATCHING AGREEMENT BETWEEN NEW tvIEXICO HUMAN SERVICES DEPARTMENT AND UNIVERSAL SERVICE ADMINISTRATIVE COMPANY AND THE FEDERAL COMMUNICATIONS COMMISSION INTRODUCTION This document constitutes an agreement between the Universal Service Administrative Company (USAC), the Federal Communications Commission (FCC or Commission), and the New Mexico Human Services Department (Department) (collectively, Parties). The purpose of this Agreement is to improve the efficiency and effectiveness of the Universal Service Fund (U SF’) Lifeline program, which provides support for discounted broadband and voice services to low-income consumers. Because the Parties intend to share information about individuals who are applying for or currently receive Lifeline benefits in order to verify the individuals’ eligibility for the program, the Parties have determined that they must execute a written agreement that complies with the Computer Matching and Privacy Protection Act of 1988 (CMPPA), Public Law 100-503, 102 Stat. 2507 (1988), which was enacted as an amendment to the Privacy Act of 1974 (Privacy Act), 5 U.S.C. § 552a. By agreeing to the terms and conditions of this Agreement, the Parties intend to comply with the requirements of the CMPPA that are codified in subsection (o) of the Privacy Act. 5 U.S.C. § 552a(o). As discussed in section 11.B. below, U$AC has been designated by the FCC as the permanent federal Administrator of the Universal Service funds programs, including the Lifeline program that is the subject of this Agreement. A. Title of Matching Program The title of this matching program as it will be reported by the FCC and the Office of Management and Budget (0MB) is as follows: “Computer Matching Agreement with the New Mexico Human Services Department.” B. -
Project1: Build a Small Scanner/Parser
Project1: Build A Small Scanner/Parser Introducing Lex, Yacc, and POET cs5363 1 Project1: Building A Scanner/Parser Parse a subset of the C language Support two types of atomic values: int float Support one type of compound values: arrays Support a basic set of language concepts Variable declarations (int, float, and array variables) Expressions (arithmetic and boolean operations) Statements (assignments, conditionals, and loops) You can choose a different but equivalent language Need to make your own test cases Options of implementation (links available at class web site) Manual in C/C++/Java (or whatever other lang.) Lex and Yacc (together with C/C++) POET: a scripting compiler writing language Or any other approach you choose --- must document how to download/use any tools involved cs5363 2 This is just starting… There will be two other sub-projects Type checking Check the types of expressions in the input program Optimization/analysis/translation Do something with the input code, output the result The starting project is important because it determines which language you can use for the other projects Lex+Yacc ===> can work only with C/C++ POET ==> work with POET Manual ==> stick to whatever language you pick This class: introduce Lex/Yacc/POET to you cs5363 3 Using Lex to build scanners lex.yy.c MyLex.l lex/flex lex.yy.c a.out gcc/cc Input stream a.out tokens Write a lex specification Save it in a file (MyLex.l) Compile the lex specification file by invoking lex/flex lex MyLex.l A lex.yy.c file is generated -
EMBL-EBI Powerpoint Presentation
Processing data from high-throughput sequencing experiments Simon Anders Use-cases for HTS · de-novo sequencing and assembly of small genomes · transcriptome analysis (RNA-Seq, sRNA-Seq, ...) • identifying transcripted regions • expression profiling · Resequencing to find genetic polymorphisms: • SNPs, micro-indels • CNVs · ChIP-Seq, nucleosome positions, etc. · DNA methylation studies (after bisulfite treatment) · environmental sampling (metagenomics) · reading bar codes Use cases for HTS: Bioinformatics challenges Established procedures may not be suitable. New algorithms are required for · assembly · alignment · statistical tests (counting statistics) · visualization · segmentation · ... Where does Bioconductor come in? Several steps: · Processing of the images and determining of the read sequencest • typically done by core facility with software from the manufacturer of the sequencing machine · Aligning the reads to a reference genome (or assembling the reads into a new genome) • Done with community-developed stand-alone tools. · Downstream statistical analyis. • Write your own scripts with the help of Bioconductor infrastructure. Solexa standard workflow SolexaPipeline · "Firecrest": Identifying clusters ⇨ typically 15..20 mio good clusters per lane · "Bustard": Base calling ⇨ sequence for each cluster, with Phred-like scores · "Eland": Aligning to reference Firecrest output Large tab-separated text files with one row per identified cluster, specifying · lane index and tile index · x and y coordinates of cluster on tile · for each -
An Open-Sourced Bioinformatic Pipeline for the Processing of Next-Generation Sequencing Derived Nucleotide Reads
bioRxiv preprint doi: https://doi.org/10.1101/2020.04.20.050369; this version posted May 28, 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. An open-sourced bioinformatic pipeline for the processing of Next-Generation Sequencing derived nucleotide reads: Identification and authentication of ancient metagenomic DNA Thomas C. Collin1, *, Konstantina Drosou2, 3, Jeremiah Daniel O’Riordan4, Tengiz Meshveliani5, Ron Pinhasi6, and Robin N. M. Feeney1 1School of Medicine, University College Dublin, Ireland 2Division of Cell Matrix Biology Regenerative Medicine, University of Manchester, United Kingdom 3Manchester Institute of Biotechnology, School of Earth and Environmental Sciences, University of Manchester, United Kingdom [email protected] 5Institute of Paleobiology and Paleoanthropology, National Museum of Georgia, Tbilisi, Georgia 6Department of Evolutionary Anthropology, University of Vienna, Austria *Corresponding Author Abstract The emerging field of ancient metagenomics adds to these Bioinformatic pipelines optimised for the processing and as- processing complexities with the need for additional steps sessment of metagenomic ancient DNA (aDNA) are needed in the separation and authentication of ancient sequences from modern sequences. Currently, there are few pipelines for studies that do not make use of high yielding DNA cap- available for the analysis of ancient metagenomic DNA ture techniques. These bioinformatic pipelines are tradition- 1 4 ally optimised for broad aDNA purposes, are contingent on (aDNA) ≠ The limited number of bioinformatic pipelines selection biases and are associated with high costs. -
Sequence Alignment/Map Format Specification
Sequence Alignment/Map Format Specification The SAM/BAM Format Specification Working Group 3 Jun 2021 The master version of this document can be found at https://github.com/samtools/hts-specs. This printing is version 53752fa from that repository, last modified on the date shown above. 1 The SAM Format Specification SAM stands for Sequence Alignment/Map format. It is a TAB-delimited text format consisting of a header section, which is optional, and an alignment section. If present, the header must be prior to the alignments. Header lines start with `@', while alignment lines do not. Each alignment line has 11 mandatory fields for essential alignment information such as mapping position, and variable number of optional fields for flexible or aligner specific information. This specification is for version 1.6 of the SAM and BAM formats. Each SAM and BAMfilemay optionally specify the version being used via the @HD VN tag. For full version history see Appendix B. Unless explicitly specified elsewhere, all fields are encoded using 7-bit US-ASCII 1 in using the POSIX / C locale. Regular expressions listed use the POSIX / IEEE Std 1003.1 extended syntax. 1.1 An example Suppose we have the following alignment with bases in lowercase clipped from the alignment. Read r001/1 and r001/2 constitute a read pair; r003 is a chimeric read; r004 represents a split alignment. Coor 12345678901234 5678901234567890123456789012345 ref AGCATGTTAGATAA**GATAGCTGTGCTAGTAGGCAGTCAGCGCCAT +r001/1 TTAGATAAAGGATA*CTG +r002 aaaAGATAA*GGATA +r003 gcctaAGCTAA +r004 ATAGCT..............TCAGC -r003 ttagctTAGGC -r001/2 CAGCGGCAT The corresponding SAM format is:2 1Charset ANSI X3.4-1968 as defined in RFC1345. -
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.