Reproducible Bioinformatics with Python: How to Write Flexible, Documented, Tested Python Code for Research Computing By: Ken Youens-Clark

Author: Ken Youens-Clark

Life scientists today urgently need training in bioinformatics skills. Too many bioinformatics programs are poorly written and barely maintained–usually by students and postdoc researchers who’ve never learned basic programming skills. This practical guide shows how to exploit the best parts of Python for solving problems in biology while also creating documented, tested, reproducible software.

Ken Youens-Clark, author of Tiny Python Projects (Manning), demonstrates how to write effective Python code and how to use tests to write and refactor scientific programs. You’ll learn the latest Python features and tools–such as linters, formatters, type checkers, and tests–to write documented and tested programs.

Create command-line Python programs that document and validate parameters
Write tests to verify refactor programs and confirm they’re correct
Address bioinformatics ideas using Python data structures (strings, lists, and sets) and modules such as Biopython
Create reproducible shortcuts and workflows using makefiles
Parse essential bioinformatics file formats such as FASTA, FASTQ, and SwissProt
Find patterns of text using regular expressions
Use higher-order functions in Python like filter() and map()

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