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# String Processing and Pattern Matching Algorithms

## Overview

The world and internet are full of textual information. We search for information using textual queries and read websites, books and e-mails.

These are all strings from a computer science point of view. To make sense of all this information and make search efficient, search engines use many string algorithms. Moreover, the emerging field of personalized medicine uses many search algorithms to find disease-causing mutations in the human genome.

In this course, part of the Algorithms and Data Structures MicroMasters program, you will learn about:

• suffix trees;
• suffix arrays;
• how other brilliant algorithmic ideas help doctors to find differences between genomes;
• power lightning-fast Internet searches.

## Syllabus

Weeks 1 and 2: Suffix Trees
How would you search for a longest repeat in a string in LINEAR time? In 1973, Peter Weiner came up with a surprising solution that was based on suffix trees, the key data structure in pattern matching. Computer scientists were so impressed with his algorithm that they called it the Algorithm of the Year. In this lesson, we will explore some key ideas for pattern matching that will - through a series of trials and errors - bring us to suffix trees.

Week 3 and 4: Burrows-Wheeler Transform and Suffix Arrays
Although EXACT pattern matching with suffix trees is fast, it is not clear how to use suffix trees for APPROXIMATE pattern matching. In 1994, Michael Burrows and David Wheeler invented an ingenious algorithm for text compression that is now known as Burrows-Wheeler Transform. They knew nothing about genomics, and they could not have imagined that 15 years later their algorithm will become the workhorse of biologists searching for genomic mutations. But what text compression has to do with pattern matching??? In this lesson you will learn that the fate of an algorithm is often hard to predict – its applications may appear in a field that has nothing to do with the original plan of its inventors.

### Taught by

Pavel Pevzner and Michael Levin

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