Python for Bioinformatics for learning Python
summary
Argues Python is both concise and practical for bioinformatics by showing actual solutions: uses Rosalind problems (about 300 exercises, nearly 200 solved), implements GC content with str.count, reverse complement with dicts and join, Hamming distance via zip, k-mer counting with collections.Counter, a generator then numpy.cumsum for skew minima, and computes the 150th Fibonacci exactly with big integers. Demonstrates code size, runtime choices and how many problems move from hour to multi‑day to unsolved.
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chapters (14)
- 0:00Intro
- 0:45About me
- 2:02What is Bioinformatics
- 3:19GC Content of DNA
- 4:16Count
- 5:27Reverse complement
- 7:20Hamming distance
- 9:54Finding motifs
- 13:05Fibonacci
- 14:46Skew
- 19:22Hidden Markov Models
- 20:46Transitions
- 22:23Conclusion
- 24:03Questions
