Bioinformatics in Python

Dive into the world of bioinformatics with this comprehensive 4.5-hour tutorial series focused on Python programming. Learn to develop a DNA toolkit, starting with nucleotide validation and counting, and progressing through transcription, reverse complement, GC content calculation, translation, codon usage, and protein search. Tackle Rosalind problems, explore the FASTA file format, and set up essential development tools. Master concepts like open reading frames, Fibonacci sequences in rabbit populations, and searching for real proteins in the NCBI database. Refactor code into a bio_seq class, work with RNA, and implement helper functions. Gain practical skills in data processing and learn to calculate Hamming distance, all while solving real-world bioinformatics challenges using Python.

Bioinformatics in Python: Intro.
Bioinformatics in Python: DNA Toolkit. Part 1: Validating and counting nucleotides..
Rosalind Problems: Counting DNA Nucleotides.
Rosalind Problems: Python Village.
Bioinformatics in Python: DNA Toolkit. Part 2: Transcription, Reverse Complement.
Rosalind Problems: Transcription and Reverse Complement.
Bioinformatics in Python: DNA Toolkit. Part 3: GC Content Calculation.
Rosalind Problems: GC Content, FASTA File Format, Data Processing.
Bioinformatics in Python: DNA Toolkit. Part 4: Translation, Codon Usage.
Bioinformatics Tips & Tricks: Development Tools Setup.
Bioinformatics in Python: DNA Toolkit. Part 5: Open Reading Frames.
Rosalind Problems: Fibonacci, Rabbits and Recurrence Relations.
Bioinformatics in Python: DNA Toolkit. Part 6: Protein search in a reading frame.
Bioinformatics in Python: DNA Toolkit. Part 7: A search for a real protein from NCBI database.
Bioinformatics in Python: DNA Toolkit. Part 8.1: Code refactoring into a bio_seq class.
Bioinformatics in Python: DNA Toolkit. Part 8.2: Code refactoring into a bio_seq class.
Bioinformatics in Python: DNA Toolkit. Part 9: RNA, Helper functions.
Bioinformatics Tips & Tricks: Hamming Distance.