tiny_python_projects VS biofx_python

My book, Mastering Python for Bioinformatics (O'Reilly, 2021), uses many biofx challenges from the Rosalind.info site, but it's not necessarily a beginner book. The most important thing I teach is the use of tests to verify that a program/function is correct (or at least behaves predictably). You can see https://github.com/kyclark/biofx_python for all the code/tests. To learn more about Python and testing, I would recommend you start with other books such at my Tiny Python Projects (Manning, 2020). Code and tests are at https://github.com/kyclark/tiny_python_projects. I recorded videos showing how to write and test all those programs at tinypythonprojects.com. Best of luck!

From Clark's Tiny Python Projects (the corresponding code shared on GitHub) I learned the concept of test driven development (specific to Python, the book elected pytest for quality control) which equally can be applied for other programming languages. For me, continuous integration tests (some projects on GitHub use), or unit tests tap into this field.

What the 101 beginner courses sometimes/often skip (because there isn't enough time, attendees become tired, etc) is the next level, automated testing. As an example, pytest for Python allows you to set up "a test bank" to monitor if the output of your program's result are reasonable. This then is test driven development (e.g., Clark's Tiny Python Projects).

Only as recommendation: If the lines of the source code (here: you C code you aim to document) are kept short, in manageable bytes (similar to entries parser.add_argument in Clark's "Tiny Python Projects", example seldomly pass beyond the frequently recommended threshold of 80 characters/line), reporting with listings becomes easier (equally, the reading of the difference logs/views by git and vimdiff), than with lines of say 120 characters per line. Though we no longer are constrained to 80 characters per line by terminals/screens and punch cards (when Fortran still was FORTRAN), this is a reason e.g., yapf for Python allows you to choose between 4 spaces/indentation (PEP8 style), or 2 spaces/indentation (Google style).

Rosalind.info is, indeed, a great resource for bioinformatics challenges. If you'd like some structured education on how to go about writing *and testing* Python code to solve these, I humbly recommend my book, Mastering Python for Bioinformatics (O'Reilly, 2021). You can look at the code/inputs/tests at https://github.com/kyclark/biofx_python to see if you'd like to learn how to write Python that follows industry best practices like using types, linters, formatters, tests, etc.

My book, Mastering Python for Bioinformatics (O'Reilly, 2021), uses many biofx challenges from the Rosalind.info site, but it's not necessarily a beginner book. The most important thing I teach is the use of tests to verify that a program/function is correct (or at least behaves predictably). You can see https://github.com/kyclark/biofx_python for all the code/tests. To learn more about Python and testing, I would recommend you start with other books such at my Tiny Python Projects (Manning, 2020). Code and tests are at https://github.com/kyclark/tiny_python_projects. I recorded videos showing how to write and test all those programs at tinypythonprojects.com. Best of luck!

I would encourage you to learn some best practices around coding such as how to format, lint, and test code. I show how to do this in my book, Mastering Python for Bioinformatics (O'Reilly, 2021), but the principles apply to any language you use. The first 14 chapters use challenges from Rosalind.info, which is a great resource for learning basic bioinformatics algorithms. You can view all the code, inputs, and tests at https://github.com/kyclark/biofx_python. Best of luck!

I used this problem in Mastering Python for Bioinformatics (O'Reilly, 2021). I have several solutions at https://github.com/kyclark/biofx_python/tree/main/04_fib you might find useful.

If you are interested in improving your Python, I would humbly recommend a book I wrote to explain how to organize, document, and test bioinformatics programs. You can find the title in my bio and I'm happy to share a link. All the code is in https://github.com/kyclark/biofx_python

It depends on exactly how much direction you're looking for. This sounds a bit similar to the Rosalind.info http://rosalind.info/problems/gc/ problem where you are given several sequences and you look for the one with the highest GC content. If you are looking for a general structure for writing a program in Python to solve this, I could offer https://github.com/kyclark/biofx_python/tree/main/05_gc as a template. I don't know what your input files are. If DNA, do you need to translate to proteins, etc.? There are other programs in that repo you might find useful.

I'm the author of a new book from O'Reilly called Mastering Python for Bioinformatics. You can download a PDF of the preface and first chapter. The first 14 chapters tackle basic bioinformatics problems from Rosalind.info, and the final 5 chapters work through examples of more complicated programs I've written. The goal is for you to write your own versions of the programs using test-driven development with the tests I provide. This will to teach you how to write a well-structured, documented, tested Python program that leverages type hints and the use of tools like pylint, flake8, pytest, and mypy. All the code, data, and tests are freely available in my GitHub repo.

Take a look at the code in https://github.com/kyclark/biofx_python/tree/main/19_blastomatic to see if that can help you parse this. I'll DM more info.

I'm publishing a new Python/biofx book with O'Reilly that would get you up to speed on the language, testing, algorithms, the command line, Makefiles, etc. I'd be happy to share the first five chapters with you. All the code and tests are at https://github.com/kyclark/biofx_python. DM for a link to the PDF.