Mastering Python for Bioinformatics

Name: Mastering Python for Bioinformatics | How to Write Flexible, Documented, Tested Python Code for Research Computing
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How to Write Flexible, Documented, Tested Python Code for Research Computing

Ken Youens-Clark(Author)

O'Reilly (Publisher)

Published on 5. May 2021

456 pages

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978-1-0981-0083-4 (ISBN)

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Content

Cover
Copyright
Table of Contents
Preface
Who Should Read This?
Programming Style: Why I Avoid OOP and Exceptions
Structure
Test-Driven Development
Using the Command Line and Installing Python
Getting the Code and Tests
Installing Modules
Installing the new.py Program
Why Did I Write This Book?
Conventions Used in This Book
Using Code Examples
O'Reilly Online Learning
How to Contact Us
Acknowledgments
Part I. The Rosalind.info Challenges
Chapter 1. Tetranucleotide Frequency: Counting Things
Getting Started
Creating the Program Using new.py
Using argparse
Tools for Finding Errors in the Code
Introducing Named Tuples
Adding Types to Named Tuples
Representing the Arguments with a NamedTuple
Reading Input from the Command Line or a File
Testing Your Program
Running the Program to Test the Output
Solution 1: Iterating and Counting the Characters in a String
Counting the Nucleotides
Writing and Verifying a Solution
Additional Solutions
Solution 2: Creating a count() Function and Adding a Unit Test
Solution 3: Using str.count()
Solution 4: Using a Dictionary to Count All the Characters
Solution 5: Counting Only the Desired Bases
Solution 6: Using collections.defaultdict()
Solution 7: Using collections.Counter()
Going Further
Review
Chapter 2. Transcribing DNA into mRNA: Mutating Strings, Reading and Writing Files
Getting Started
Defining the Program's Parameters
Defining an Optional Parameter
Defining One or More Required Positional Parameters
Using nargs to Define the Number of Arguments
Using argparse.FileType() to Validate File Arguments
Defining the Args Class
Outlining the Program Using Pseudocode
Iterating the Input Files
Creating the Output Filenames
Opening the Output Files
Writing the Output Sequences
Printing the Status Report
Using the Test Suite
Solutions
Solution 1: Using str.replace()
Solution 2: Using re.sub()
Benchmarking
Going Further
Review
Chapter 3. Reverse Complement of DNA: String Manipulation
Getting Started
Iterating Over a Reversed String
Creating a Decision Tree
Refactoring
Solutions
Solution 1: Using a for Loop and Decision Tree
Solution 2: Using a Dictionary Lookup
Solution 3: Using a List Comprehension
Solution 4: Using str.translate()
Solution 5: Using Bio.Seq
Review
Chapter 4. Creating the Fibonacci Sequence: Writing, Testing, and Benchmarking Algorithms
Getting Started
An Imperative Approach
Solutions
Solution 1: An Imperative Solution Using a List as a Stack
Solution 2: Creating a Generator Function
Solution 3: Using Recursion and Memoization
Benchmarking the Solutions
Testing the Good, the Bad, and the Ugly
Running the Test Suite on All the Solutions
Going Further
Review
Chapter 5. Computing GC Content: Parsing FASTA and Analyzing Sequences
Getting Started
Parsing FASTA Using Biopython
Iterating the Sequences Using a for Loop
Solutions
Solution 1: Using a List
Solution 2: Type Annotations and Unit Tests
Solution 3: Keeping a Running Max Variable
Solution 4: Using a List Comprehension with a Guard
Solution 5: Using the filter() Function
Solution 6: Using the map() Function and Summing Booleans
Solution 7: Using Regular Expressions to Find Patterns
Solution 8: A More Complex find_gc() Function
Benchmarking
Going Further
Review
Chapter 6. Finding the Hamming Distance: Counting Point Mutations
Getting Started
Iterating the Characters of Two Strings
Solutions
Solution 1: Iterating and Counting
Solution 2: Creating a Unit Test
Solution 3: Using the zip() Function
Solution 4: Using the zip_longest() Function
Solution 5: Using a List Comprehension
Solution 6: Using the filter() Function
Solution 7: Using the map() Function with zip_longest()
Solution 8: Using the starmap() and operator.ne() Functions
Going Further
Review
Chapter 7. Translating mRNA into Protein: More Functional Programming
Getting Started
K-mers and Codons
Translating Codons
Solutions
Solution 1: Using a for Loop
Solution 2: Adding Unit Tests
Solution 3: Another Function and a List Comprehension
Solution 4: Functional Programming with the map(), partial(), and takewhile() Functions
Solution 5: Using Bio.Seq.translate()
Benchmarking
Going Further
Review
Chapter 8. Find a Motif in DNA: Exploring Sequence Similarity
Getting Started
Finding Subsequences
Solutions
Solution 1: Using the str.find() Method
Solution 2: Using the str.index() Method
Solution 3: A Purely Functional Approach
Solution 4: Using K-mers
Solution 5: Finding Overlapping Patterns Using Regular Expressions
Benchmarking
Going Further
Review
Chapter 9. Overlap Graphs: Sequence Assembly Using Shared K-mers
Getting Started
Managing Runtime Messages with STDOUT, STDERR, and Logging
Finding Overlaps
Grouping Sequences by the Overlap
Solutions
Solution 1: Using Set Intersections to Find Overlaps
Solution 2: Using a Graph to Find All Paths
Going Further
Review
Chapter 10. Finding the Longest Shared Subsequence: Finding K-mers, Writing Functions, and Using Binary Search
Getting Started
Finding the Shortest Sequence in a FASTA File
Extracting K-mers from a Sequence
Solutions
Solution 1: Counting Frequencies of K-mers
Solution 2: Speeding Things Up with a Binary Search
Going Further
Review
Chapter 11. Finding a Protein Motif: Fetching Data and Using Regular Expressions
Getting Started
Downloading Sequences Files on the Command Line
Downloading Sequences Files with Python
Writing a Regular Expression to Find the Motif
Solutions
Solution 1: Using a Regular Expression
Solution 2: Writing a Manual Solution
Going Further
Review
Chapter 12. Inferring mRNA from Protein: Products and Reductions of Lists
Getting Started
Creating the Product of Lists
Avoiding Overflow with Modular Multiplication
Solutions
Solution 1: Using a Dictionary for the RNA Codon Table
Solution 2: Turn the Beat Around
Solution 3: Encoding the Minimal Information
Going Further
Review
Chapter 13. Location Restriction Sites: Using, Testing, and Sharing Code
Getting Started
Finding All Subsequences Using K-mers
Finding All Reverse Complements
Putting It All Together
Solutions
Solution 1: Using the zip() and enumerate() Functions
Solution 2: Using the operator.eq() Function
Solution 3: Writing a revp() Function
Testing the Program
Going Further
Review
Chapter 14. Finding Open Reading Frames
Getting Started
Translating Proteins Inside Each Frame
Finding the ORFs in a Protein Sequence
Solutions
Solution 1: Using the str.index() Function
Solution 2: Using the str.partition() Function
Solution 3: Using a Regular Expression
Going Further
Review
Part II. Other Programs
Chapter 15. Seqmagique: Creating and Formatting Reports
Using Seqmagick to Analyze Sequence Files
Checking Files Using MD5 Hashes
Getting Started
Formatting Text Tables Using tabulate()
Solutions
Solution 1: Formatting with tabulate()
Solution 2: Formatting with rich
Going Further
Review
Chapter 16. FASTX grep: Creating a Utility Program to Select Sequences
Finding Lines in a File Using grep
The Structure of a FASTQ Record
Getting Started
Guessing the File Format
Solution
Guessing the File Format from the File Extension
I Love It When a Plan Comes Together
Combining Regular Expression Search Flags
Reducing Boolean Values
Going Further
Review
Chapter 17. DNA Synthesizer: Creating Synthetic Data with Markov Chains
Understanding Markov Chains
Getting Started
Understanding Random Seeds
Reading the Training Files
Generating the Sequences
Structuring the Program
Solution
Going Further
Review
Chapter 18. FASTX Sampler: Randomly Subsampling Sequence Files
Getting Started
Reviewing the Program Parameters
Defining the Parameters
Nondeterministic Sampling
Structuring the Program
Solutions
Solution 1: Reading Regular Files
Solution 2: Reading a Large Number of Compressed Files
Going Further
Review
Chapter 19. Blastomatic: Parsing Delimited Text Files
Introduction to BLAST
Using csvkit and csvchk
Getting Started
Defining the Arguments
Parsing Delimited Text Files Using the csv Module
Parsing Delimited Text Files Using the pandas Module
Solutions
Solution 1: Manually Joining the Tables Using Dictionaries
Solution 2: Writing the Output File with csv.DictWriter()
Solution 3: Reading and Writing Files Using pandas
Solution 4: Joining Files Using pandas
Going Further
Review
Appendix A. Documenting Commands and Creating Workflows with make
Makefiles Are Recipes
Running a Specific Target
Running with No Target
Makefiles Create DAGs
Using make to Compile a C Program
Using make for a Shortcut
Defining Variables
Writing a Workflow
Other Workflow Managers
Further Reading
Appendix B. Understanding $PATH and Installing Command-Line Programs
Epilogue
Index
About the Author
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Mastering Python for Bioinformatics

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