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Book Reviews – Bioinformatics Data Skills

Book Reviews - Bioinformatics Data SkillsThis practical book teaches the skills that scientists need for turning large sequencing datasets into reproducible and robust biological findings. Many biologists begin their bioinformatics training by learning scripting languages like Python and R alongside the Unix command line. But there’s a huge gap between knowing a few programming languages and being prepared to analyze large amounts of biological data.
Rather than teach bioinformatics as a set of workflows that are likely to change with this rapidly evolving field, this book demsonstrates the practice of bioinformatics through data skills. Rigorous assessment of data quality and of the effectiveness of tools is the foundation of reproducible and robust bioinformatics analysis. Through open source and freely available tools, you’ll learn not only how to do bioinformatics, but how to approach problems as a bioinformatician.
Go from handling small problems with messy scripts to tackling large problems with clever methods and tools
Focus on high-throughput (or “next generation”) sequencing data
Learn data analysis with modern methods, versus covering older theoretical concepts
Understand how to choose and implement the best tool for the job
Delve into methods that lead to easier, more reproducible, and robust bioinformatics analysis

Part I. Ideology: Data Skills for Robust and Reproducible Bioinformatics
1. How to Learn Bioinformatics
Why Bioinformatics? Biology’s Growing Data
Learning Data Skills to Learn Bioinformatics
New Challenges for Reproducible and Robust Research
Reproducible Research
Robust Research and the Golden Rule of Bioinformatics
Adopting Robust and Reproducible Practices Will Make Your Life Easier, Too
Recommendations for Robust Research
Pay Attention to Experimental Design
Write Code for Humans, Write Data for Computers
Let Your Computer Do the Work For You
Make Assertions and Be Loud, in Code and in Your Methods
Test Code, or Better Yet, Let Code Test Code
Use Existing Libraries Whenever Possible
Treat Data as Read-Only
Spend Time Developing Frequently Used Scripts into Tools
Let Data Prove That It’s High Quality
Recommendations for Reproducible Research
Release Your Code and Data
Document Everything
Make Figures and Statistics the Results of Scripts
Use Code as Documentation
Continually Improving Your Bioinformatics Data Skills
Part II. Prerequisites: Essential Skills for Getting Started with a Bioinformatics Project
2. Setting Up and Managing a Bioinformatics Project
Project Directories and Directory Structures
Project Documentation
Use Directories to Divide Up Your Project into Subprojects
Organizing Data to Automate File Processing Tasks
Markdown for Project Notebooks
Markdown Formatting Basics
Using Pandoc to Render Markdown to HTML
3. Remedial Unix Shell
Why Do We Use Unix in Bioinformatics? Modularity and the Unix Philosophy
Working with Streams and Redirection
Redirecting Standard Out to a File
Redirecting Standard Error
Using Standard Input Redirection
The Almighty Unix Pipe: Speed and Beauty in One
Pipes in Action: Creating Simple Programs with Grep and Pipes
Combining Pipes and Redirection
Even More Redirection: A tee in Your Pipe
Managing and Interacting with Processes
Background Processes
Killing Processes
Exit Status: How to Programmatically Tell Whether Your Command Worked
Command Substitution
4. Working with Remote Machines
Connecting to Remote Machines with SSH
Quick Authentication with SSH Keys
Maintaining Long-Running Jobs with nohup and tmux
Working with Remote Machines Through Tmux
Installing and Configuring Tmux
Creating, Detaching, and Attaching Tmux Sessions
Working with Tmux Windows
5. Git for Scientists
Why Git Is Necessary in Bioinformatics Projects
Git Allows You to Keep Snapshots of Your Project
Git Helps You Keep Track of Important Changes to Code
Git Helps Keep Software Organized and Available After People Leave
Installing Git
Basic Git: Creating Repositories, Tracking Files, and Staging and Committing Changes
Git Setup: Telling Git Who You Are
git init and git clone: Creating Repositories
Tracking Files in Git: git add and git status Part I
Staging Files in Git: git add and git status Part II
git commit: Taking a Snapshot of Your Project
Seeing File Differences: git diff
Seeing Your Commit History: git log
Moving and Removing Files: git mv and git rm
Telling Git What to Ignore: .gitignore
Undoing a Stage: git reset
Collaborating with Git: Git Remotes, git push, and git pull
Creating a Shared Central Repository with GitHub
Authenticating with Git Remotes
Connecting with Git Remotes: git remote
Pushing Commits to a Remote Repository with git push
Pulling Commits from a Remote Repository with git pull
Working with Your Collaborators: Pushing and Pulling
Merge Conflicts
More GitHub Workflows: Forking and Pull Requests
Using Git to Make Life Easier: Working with Past Commits
Getting Files from the Past: git checkout
Stashing Your Changes: git stash
More git diff: Comparing Commits and Files
Undoing and Editing Commits: git commit –amend
Working with Branches
Creating and Working with Branches: git branch and git checkout
Merging Branches: git merge
Branches and Remotes
Continuing Your Git Education
6. Bioinformatics Data
Retrieving Bioinformatics Data
Downloading Data with wget and curl
Rsync and Secure Copy (scp)
Data Integrity
SHA and MD5 Checksums
Looking at Differences Between Data
Compressing Data and Working with Compressed Data
Working with Gzipped Compressed Files
Case Study: Reproducibly Downloading Data
Part III. Practice: Bioinformatics Data Skills
7. Unix Data Tools
Unix Data Tools and the Unix One-Liner Approach: Lessons from Programming Pearls
When to Use the Unix Pipeline Approach and How to Use It Safely
Inspecting and Manipulating Text Data with Unix Tools
Inspecting Data with Head and Tail
Plain-Text Data Summary Information with wc, ls, and awk
Working with Column Data with cut and Columns
Formatting Tabular Data with column
The All-Powerful Grep
Decoding Plain-Text Data: hexdump
Sorting Plain-Text Data with Sort
Finding Unique Values in Uniq
Text Processing with Awk
Bioawk: An Awk for Biological Formats
Stream Editing with Sed
Advanced Shell Tricks
Named Pipes and Process Substitution
The Unix Philosophy Revisited
8. A Rapid Introduction to the R Language
Getting Started with R and RStudio
R Language Basics
Simple Calculations in R, Calling Functions, and Getting Help in R
Variables and Assignment
Vectors, Vectorization, and Indexing
Working with and Visualizing Data in R
Loading Data into R
Exploring and Transforming Dataframes
Exploring Data Through Slicing and Dicing: Subsetting Dataframes
Exploring Data Visually with ggplot2 I: Scatterplots and Densities
Exploring Data Visually with ggplot2 II: Smoothing
Binning Data with cut() and Bar Plots with ggplot2
Merging and Combining Data: Matching Vectors and Merging Dataframes
Using ggplot2 Facets
More R Data Structures: Lists
Writing and Applying Functions to Lists with lapply() and sapply()
Working with the Split-Apply-Combine Pattern
Exploring Dataframes with dplyr
Working with Strings
Developing Workflows with R Scripts
Control Flow: if, for, and while
Working with R Scripts
Workflows for Loading and Combining Multiple Files
Exporting Data
Further R Directions and Resources
9. Working with Range Data
A Crash Course in Genomic Ranges and Coordinate Systems
An Interactive Introduction to Range Data with GenomicRanges
Installing and Working with Bioconductor Packages
Storing Generic Ranges with IRanges
Basic Range Operations: Arithmetic, Transformations, and Set Operations
Finding Overlapping Ranges
Finding Nearest Ranges and Calculating Distance
Run Length Encoding and Views
Storing Genomic Ranges with GenomicRanges
Grouping Data with GRangesList
Working with Annotation Data: GenomicFeatures and rtracklayer
Retrieving Promoter Regions: Flank and Promoters
Retrieving Promoter Sequence: Connection GenomicRanges with Sequence Data
Getting Intergenic and Intronic Regions: Gaps, Reduce, and Setdiffs in Practice
Finding and Working with Overlapping Ranges
Calculating Coverage of GRanges Objects
Working with Ranges Data on the Command Line with BEDTools
Computing Overlaps with BEDTools Intersect
BEDTools Slop and Flank
Coverage with BEDTools
Other BEDTools Subcommands and pybedtools
10. Working with Sequence Data
The FASTA Format
The FASTQ Format
Nucleotide Codes
Base Qualities
Example: Inspecting and Trimming Low-Quality Bases
A FASTA/FASTQ Parsing Example: Counting Nucleotides
Indexed FASTA Files
11. Working with Alignment Data
Getting to Know Alignment Formats: SAM and BAM
The SAM Header
The SAM Alignment Section
Bitwise Flags
CIGAR Strings
Mapping Qualities
Command-Line Tools for Working with Alignments in the SAM Format
Using samtools view to Convert between SAM and BAM
Samtools Sort and Index
Extracting and Filtering Alignments with samtools view
Visualizing Alignments with samtools tview and the Integrated Genomics Viewer
Pileups with samtools pileup, Variant Calling, and Base Alignment Quality
Creating Your Own SAM/BAM Processing Tools with Pysam
Opening BAM Files, Fetching Alignments from a Region, and Iterating Across Reads
Extracting SAM/BAM Header Information from an AlignmentFile Object
Working with AlignedSegment Objects
Writing a Program to Record Alignment Statistics
Additional Pysam Features and Other SAM/BAM APIs
12. Bioinformatics Shell Scripting, Writing Pipelines, and Parallelizing Tasks
Basic Bash Scripting
Writing and Running Robust Bash Scripts
Variables and Command Arguments
Conditionals in a Bash Script: if Statements
Processing Files with Bash Using for Loops and Globbing
Automating File-Processing with find and xargs
Using find and xargs
Finding Files with find
find’s Expressions
find’s -exec: Running Commands on find’s Results
xargs: A Unix Powertool
Using xargs with Replacement Strings to Apply Commands to Files
xargs and Parallelization
Make and Makefiles: Another Option for Pipelines
13. Out-of-Memory Approaches: Tabix and SQLite
Fast Access to Indexed Tab-Delimited Files with BGZF and Tabix
Compressing Files for Tabix with Bgzip
Indexing Files with Tabix
Using Tabix
Introducing Relational Databases Through SQLite
When to Use Relational Databases in Bioinformatics
Installing SQLite
Exploring SQLite Databases with the Command-Line Interface
Querying Out Data: The Almighty SELECT Command
SQLite Functions
SQLite Aggregate Functions
Organizing Relational Databases and Joins
Writing to Databases
Dropping Tables and Deleting Databases
Interacting with SQLite from Python
Dumping Databases
14. Conclusion
Where to Go From Here?

Book Reviews – Bioinformatics Data Skills

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