Read alignment: approximate matching

Often, reads do not exactly match the reference genome, owing to natural genomic variations or errors introduced during sequencing. In such instances, approximate matching algorithms facilitate identifying similarities between the query and reference sequences, enabling the alignment of reads. This approach is especially useful when working with organisms that are closely related, as their genomes may have evolved somewhat differently over time.

In this section you will learn how approximate matching algorithms work.

Approximate matching, Hamming and edit distance

Pigeonhole principle

Practical: Implementing the pigeonhole principle

Solving the edit distance problem

Using dynamic programming for edit distance

Practical: Implementing dynamic programming for edit distance

Edit distance for approximate matching

Problems to solve

Try to solve these problems after completing the section.

1. Counting Point Mutations

2. Finding a Shared Motif

3. Enumerating Gene Orders

4. Enumerating k-mers Lexicographically

If these were too easy for you, try unlocking the following set of advanced problems

1. Longest Increasing Subsequence

2. k-Mer Composition

3. Finding a Shared Spliced Motif

4. Edit Distance

5. Edit Distance Alignment

6. Counting Optimal Alignments

Congratulations!

If you made it here, then congratulations! You have successfully completed this section. Move to the next portion of the guide with the arrow buttons below.