We will learn computational methods -- algorithms and data structures -- for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used. We will use Python to implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets.
Algorithms for DNA Sequencing
Algorithms for DNA Sequencing
This course is part of Genomic Data Science Specialization
Instructors: Ben Langmead, PhD
Sponsored by Johns Hopkins University
43,208 already enrolled
Gain insight into a topic and learn the fundamentals.
4.7
(897 reviews)
12 hours to complete
3 weeks at 4 hours a week
Flexible schedule
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Assessments
8 assignments
Taught in English
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This course is part of the Genomic Data Science Specialization
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There are 4 modules in this course
This module we begin our exploration of algorithms for analyzing DNA sequencing data. We'll discuss DNA sequencing technology, its past and present, and how it works.
What's included
19 videos7 readings2 assignments
In this module, we learn useful and flexible new algorithms for solving the exact and approximate matching problems. We'll start by learning Boyer-Moore, a fast and very widely used algorithm for exact matching
What's included
15 videos1 reading2 assignments
This week we finish our discussion of read alignment by learning about algorithms that solve both the edit distance problem and related biosequence analysis problems, like global and local alignment.
What's included
13 videos1 reading2 assignments
In the last module we began our discussion of the assembly problem and we saw a couple basic principles behind it. In this module, we'll learn a few ways to solve the alignment problem.
What's included
13 videos1 reading2 assignments
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4.8 (116 ratings)Offered by
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