Chapter 5: Computing GC Content
Computing GC Content: Parsing FASTA and Analyzing Sequences
In Chapter 1, you counted all the bases in a string of DNA. In this exercise, you need to count the Gs and Cs in a sequence and divide by the length of the sequence to determine the GC content as described on the Rosalind GC page. GC content is informative in several ways. A higher GC content level indicates a relatively higher melting temperature in molecular biology, and DNA sequences that encode proteins tend to be found in GC-rich regions. There are many ways to solve this problem, and
they all start with using Biopython to parse a FASTA file, a key file format in bioinformatics. I’ll show you how to use the Bio.SeqIO module to iterate over the sequences in the file to identify the sequence with the highest GC content.
You will learn:
• How to parse FASTA format using Bio.SeqIO
• How to read STDIN (pronounced standard in)
• Several ways to express the notion of a for loop using list comprehensions,
filter(), and map()
• How to address runtime challenges such as memory allocation when parsing
large files
• More about the sorted() function
• How to include formatting instructions in format strings
• How to use the sum() function to add a list of numbers
• How to use regular expressions to count the occurrences of a pattern in a string
Code/tests: https://github.com/kyclark/biofx_pyth...
Segments:
00:00 Intro
03:23 Getting Started
13:41 Parsing FASTA Using Biopython
21:53 Iterating the Sequences Using a for Loop
27:11 Solution 1: Using a List
41:35 Solution 2: Type Annotations and Unit Tests
53:10 Solution 3: Keeping a Running Max Variable
1:09:07 Solution 4: Using a List Comprehension with a Guard
1:13:13 Solution 5: Using the filter() Function
1:15:55 Solution 6: Using the map() Function and Summing Booleans
1:20:30 Solution 7: Using Regular Expressions to Find Patterns
1:23:55 Solution 8: A More Complex find_gc() Function
1:30:55 Benchmarking
1:32:42 Going Further/Review
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