Mastering Python for Bioinformatics (ebook) Chorzów

Life scientists today urgently need training in bioinformatics skills. Too many bioinformatics programs are poorly written and barely maintained--usually by students and researchers who've never learned basic programming skills. This practical guide shows postdoc bioinformatics professionals and students how to exploit the best parts of Python to solve problems in biology while creating documented, tested, reproducible software.Ken Youens-Clark, author of Tiny Python Projects (Manning), demonstrates not only how to write effective Python code but also how to use tests to write and refactor scientific programs. You'll learn the latest Python features and toolsâ??including linters, formatters, type checkers, and testsâ??to create documented and tested programs. You'll also tackle 14 challenges in Rosalind, a problem-solving platform for learning bioinformatics and programming.Create command-line Python programs to document and validate parametersWrite tests to verify refactor programs and confirm they're correctAddress bioinformatics ideas using Python data structures and modules such as BiopythonCreate reproducible shortcuts and workflows using makefilesParse essential bioinformatics file formats such as FASTA and FASTQFind patterns of text using regular expressionsUse higher-order functions in Python like filter(), map(), and reduce() Spis treści: 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 OReilly Online Learning How to Contact Us Acknowledgments I. The Rosalind.info Challenges 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 2. Transcribing DNA into mRNA: Mutating Strings, Reading and Writing Files Getting Started Defining the Programs 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 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 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 5. Computing GC Content: Parsing FASTA and Analyzing Sequences Getting Started Get 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 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 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 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 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 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 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 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 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 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 II. Other Programs 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 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 Going Further Review 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 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 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 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 B. Understanding $PATH and Installing Command-Line Programs Epilogue Index