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Bibliografická citace

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BK
Příručka
First edition
Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo : O’Reilly, 2015
xxv, 507 stran : ilustrace ; 24 cm

objednat
ISBN 978-1-449-36737-4 (brožováno)
Terminologický slovník
Obálkový podnázev: reproducible and robust research with open source tools
Obsahuje bibliografii na stranách 479-482 a rejstřík
001451742
Table of Contents // Preface... xiii // Part I. Ideology: Data Skills for Robust and Reproducible Bioinformatics // 1. How to Learn Bioinformatics... 1 // Why Bioinformatics? Biology’s Growing Data 1 // Learning Data Skills to Learn Bioinformatics 4 // New Challenges for Reproducible and Robust Research 5 // Reproducible Research 6 // Robust Research and the Golden Rule of Bioinformatics 8 // Adopting Robust and Reproducible Practices Will Make Your Life Easier, Too 9 Recommendations for Robust Research 10 // Pay Attention to Experimental Design 10 // Write Code for Humans, Write Data for Computers 11 // Let Your Computer Do the Work For You 12 // Make Assertions and Be Loud, in Code and in Your Methods 12 // Test Code, or Better Yet, Let Code Test Code 13 // Use Existing Libraries Whenever Possible 14 // Treat Data as Read-Only 14 // Spend Time Developing Frequently Used Scripts into Tools 15 // Let Data Prove That Its High Quality 15 // Recommendations for Reproducible Research 16 // Release Your Code and Data 16 // Document Everything 16 // Make Figures and Statistics the Results of Scripts 17 // Use Code as Documentation 17 // Continually Improving Your Bioinformatics Data Skills 17 // Part II. Prerequisites: Essential Skills for Getting Started with a Bioinformatics Project // 2. Setting Up and Managing a Bioinformatics Project... 21 // Project Directories and Directory Structures 21 // Project Documentation 24 // Use Directories to Divide Up Your Project into Subprojects 26
// Organizing Data to Automate File Processing Tasks 26 // Markdown for Project Notebooks 31 // Markdown Formatting Basics 31 // Using Pandoc to Render Markdown to HTML 35 // 3. Remedial Unix Shell... 37 // Why Do We Use Unix in Bioinformatics? Modularity and the Unix Philosophy 37 // Working with Streams and Redirection 41 // Redirecting Standard Out to a File 41 // Redirecting Standard Error 43 // Using Standard Input Redirection 45 // The Almighty Unix Pipe: Speed and Beauty in One 45 // Pipes in Action: Creating Simple Programs with Grep and Pipes 47 // Combining Pipes and Redirection 48 // Even More Redirection: A tee in Your Pipe 49 // Managing and Interacting with Processes 50 // Background Processes 50 // Killing Processes 51 // Exit Status: How to Programmatically Tell Whether Your Command Worked 52 // Command Substitution 54 // 4. Working with Remote Machines... 57 // Connecting to Remote Machines with SSH 57 // Quick Authentication with SSH Keys 59 // Maintaining Long-Running Jobs with nohup and tmux 61 // nohup 61 // Working with Remote Machines Through Tmux 61 // Installing and Configuring Tmux 62 // Creating, Detaching, and Attaching Tmux Sessions 62 // Working with Tmux Windows 64 // vi I Table of Contents // 5. Git for Scientists...67 // Why Git Is Necessary in Bioinformatics Projects 68 // Git Allows You to Keep Snapshots of Your Project 68 // Git Helps You Keep Track of Important Changes to Code 69 // Git Helps Keep Software Organized and Available After People
Leave 69 // Installing Git 70 // Basic Git: Creating Repositories, Tracking Files, and Staging and Committing Changes 70 // Git Setup: Telling Git Who You Are 70 // git init and git clone: Creating Repositories 70 // Tracking Files in Git: git add and git status Part I 72 // Staging Files in Git: git add and git status Part II 73 // git commit: Taking a Snapshot of Your Project 76 // Seeing File Differences: git diff 77 // Seeing Your Commit History: git log 79 // Moving and Removing Files: git mv and git rm 80 // Telling Git What to Ignore: .gitignore 81 // Undoing a Stage: git reset 83 // Collaborating with Git: Git Remotes, git push, and git pull 83 // Creating a Shared Central Repository with GitHub 86 // Authenticating with Git Remotes 87 // Connecting with Git Remotes: git remote 87 // Pushing Commits to a Remote Repository with git push 88 // Pulling Commits from a Remote Repository with git pull 89 // Working with Your Collaborators: Pushing and Pulling 90 // Merge Conflicts 92 // More GitHub Workflows: Forking and Pull Requests 97 // Using Git to Make Life Easier: Working with Past Commits 97 // Getting Files from the Past: git checkout 97 // Stashing Your Changes: git stash 99 // More git diff: Comparing Commits and Files 100 // Undoing and Editing Commits: git commit —amend 102 // Working with Branches 102 // Creating and Working with Branches: git branch and git checkout 103 // Merging Branches: git merge 105 // Branches and Remotes 106 // Continuing Your Git Education 108
// 6. Bioinformatics Data... 109 // Retrieving Bioinformatics Data 110 // Downloading Data with wget and curl 110 // Rsync and Secure Copy (scp) 113 // Table of Contents // vii // Data Integrity 114 // SHA and MD5 Checksums 115 // Looking at Differences Between Data 116 // Compressing Data and Working with Compressed Data 118 // gzip 119 // Working with Gzipped Compressed Files 120 // Case Study: Reproducibly Downloading Data 120 // Part III. Practice: Bioinformatics Data Skills // 7. Unix Data Tools... 125 // Unix Data Tools and the Unix One-Liner Approach: Lessons from Programming Pearls 125 // When to Use the Unix Pipeline Approach and How to Use It Safely 127 // Inspecting and Manipulating Text Data with Unix Tools 128 // Inspecting Data with Head and Tail 129 // less 131 // Plain-Text Data Summary Information with wc, Is, and awk 134 // Working with Column Data with cut and Columns 138 // Formatting Tabular Data with column 139 // The All-Powerful Grep 140 // Decoding Plain-Text Data: hexdump 145 // Sorting Plain-Text Data with Sort 147 // Finding Unique Values in Uniq 152 // Join 155 // Text Processing with Awk 157 // Bioawk: An Awk for Biological Formats 163 // Stream Editing with Sed 165 // Advanced Shell Tricks 169 // Subshells 169 // Named Pipes and Process Substitution 171 // The Unix Philosophy Revisited 173 // 8. A Rapid Introduction to the R Language... 175 // Getting Started with R and RStudio 176 // R Language Basics 178 // Simple Calculations in R, Calling Functions,
and Getting Help in R 178 // Variables and Assignment 182 // Vectors, Vectorization, and Indexing 183 // Working with and Visualizing Data in R 193 // Loading Data into R 194 // viii I Table of Contents // Exploring and Transforming Dataframes 199 // Exploring Data Through Slicing and Dicing: Subsetting Dataframes 203 // Exploring Data Visually with ggplot2 I: Scatterplots and Densities 207 // Exploring Data Visually with ggplot2 II: Smoothing 213 // Binning Data with cut() and Bar Plots with ggplot2 215 // Merging and Combining Data: Matching Vectors and Merging Dataframes 219 Using ggplot2 Facets 224 // More R Data Structures: Lists 228 // Writing and Applying Functions to Lists with lapplyQ and sapplyQ 231 // Working with the Split-Apply-Combine Pattern 239 // Exploring Dataframes with dplyr 243 // Working with Strings 248 // Developing Workflows with R Scripts 253 // Control Flow: if, for, and while 253 // Working with R Scripts 254 // Workflows for Loading and Combining Multiple Files 257 // Exporting Data 260 // Further R Directions and Resources 261 // 9. Working with Range Data... 263 // A Crash Course in Genomic Ranges and Coordinate Systems 264 // An Interactive Introduction to Range Data with GenomicRanges 269 // Installing and Working with Bioconductor Packages 269 // Storing Generic Ranges with IRanges 270 // Basic Range Operations: Arithmetic, Transformations, and Set Operations 275 Finding Overlapping Ranges 281 // Finding Nearest Ranges and Calculating Distance 290
// Run Length Encoding and Views 292 // Storing Genomic Ranges with GenomicRanges 299 // Grouping Data with GRangesList 303 // Working with Annotation Data: GenomicFeatures and rtracklayer 308 // Retrieving Promoter Regions: Flank and Promoters 314 // Retrieving Promoter Sequence: Connection GenomicRanges with Sequence Data 316 // Getting Intergenic and Intronic Regions: Gaps, Reduce, and Setdiffs in Practice 319 // Finding and Working with Overlapping Ranges 324 // Calculating Coverage of GRanges Objects 328 // Working with Ranges Data on the Command Line with BEDTools 329 // Computing Overlaps with BEDTools Intersect 330 // BEDTools Slop and Flank 333 // Coverage with BEDTools 335 // Table of Contents | ix // Other BEDTools Subcommands and pybedtools // 336 // 10. Working with Sequence Data... 339 // The PASTA Format 339 // The FASTQ Format 341 // Nucleotide Codes 343 // Base Qualities 344 // Example: Inspecting and Trimming Low-Quality Bases 346 // A FASTA/FASTQ Parsing Example: Counting Nucleotides 349 // Indexed FASTA Files 352 // 11. Working with Alignment Data...355 // Getting to Know Alignment Formats: SAM and BAM 356 // The SAM Header 356 // The SAM Alignment Section 359 // Bitwise Flags 360 // CIGAR Strings 363 // Mapping Qualities 365 // Command-Line Tools for Working with Alignments in the SAM Format 365 // Using samtools view to Convert between SAM and BAM 365 // Samtools Sort and Index 367 // Extracting and Filtering Alignments with samtools view 368 // Visualizing
Alignments with samtools tview and the Integrated Genomics Viewer 372 // Pileups with samtools pileup, Variant Calling, and Base Alignment Quality 378 Creating Your Own SAM/BAM Processing Tools with Pysam 384 // Opening BAM Files, Fetching Alignments from a Region, and Iterating Across Reads 384 // Extracting SAM/BAM Header Information from an AlignmentFile Object 387 Working with AlignedSegment Objects 388 // Writing a Program to Record Alignment Statistics 391 // Additional Pysam Features and Other SAM/BAM APIs 394 // 12. Bioinformatics Shell Scripting, Writing Pipelines, and Parallelizing Tasks. 395 // Basic Bash Scripting 396 // Writing and Running Robust Bash Scripts 396 // Variables and Command Arguments 398 // Conditionals in a Bash Script: if Statements 401 // Processing Files with Bash Using for Loops and Globbing 405 // Automating File-Processing with find and xargs 411 // Using find and xargs 411 // Finding Files with find 412 // x I Table of Contents // find’s Expressions 413 // find’s -exec: Running Commands on find’s Results 415 // xargs: A Unix Powertool 416 // Using xargs with Replacement Strings to Apply Commands to Files 418 // xargs and Parallelization 419 // Make and Makefiles: Another Option for Pipelines 421 // 13. Out-of-Memory Approaches: Tabix and SQLite... 425 // Fast Access to Indexed Tab-Delimited Files with BGZF and Tabix 425 // Compressing Files for Tabix with Bgzip 426 // Indexing Files with Tabix 427 // Using Tabix 427 // Introducing Relational
Databases Through SQLite 428 // When to Use Relational Databases in Bioinformatics 429 // Installing SQLite 431 // Exploring SQLite Databases with the Command-Line Interface 431 // Querying Out Data: The Almighty SELECT Command 434 // SQLite Functions 441 // SQLite Aggregate Functions 442 // Subqueries 447 // Organizing Relational Databases and Joins 448 // Writing to Databases 455 // Dropping Tables and Deleting Databases 458 // Interacting with SQLite from Python 459 // Dumping Databases 465 // 14. Conclusion... 467 // Where to Go From Here? 468 // Glossary...471 // Bibliography...479 // Index... 483 // Table of Contents // xi

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