Algorithms for DNA Sequencing

About this course: 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.

Created by:  Johns Hopkins University

• Taught by:  Ben Langmead, PhD, Assistant Professor

  Computer Science

• Taught by:  Jacob Pritt

  Department of Computer Science

Basic Info Course 4 of 8 in the Genomic Data Science Specialization Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.8 stars Average User Rating 4.8See what learners said Coursework

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Syllabus


WEEK 1


DNA sequencing, strings and matching
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.


19 videos, 7 readings expand

1. Reading: Welcome to Algorithms for DNA Sequencing
2. Reading: Pre Course Survey
3. Reading: Syllabus
4. Reading: Setting up Python (and Jupyter)
5. Reading: Getting slides and notebooks
6. Reading: Using data files with Python programs
7. Video: Module 1 Introduction
8. Video: Lecture: Why study this?
9. Video: Lecture: DNA sequencing past and present
10. Video: Lecture: Genomes as strings, reads as substrings
11. Video: Lecture: String definitions and Python examples
12. Video: Practical: String basics
13. Video: Practical: Manipulating DNA strings
14. Video: Practical: Downloading and parsing a genome
15. Video: Lecture: How DNA gets copied
16. Video: Optional lecture: How second-generation sequencers work
17. Video: Optional lecture: Sequencing errors and base qualities
18. Video: Lecture: Sequencing reads in FASTQ format
19. Video: Practical: Working with sequencing reads
20. Video: Practical: Analyzing reads by position
21. Video: Lecture: Sequencers give pieces to genomic puzzles
22. Video: Lecture: Read alignment and why it's hard
23. Video: Lecture: Naive exact matching
24. Video: Practical: Matching artificial reads
25. Video: Practical: Matching real reads
26. Reading: Programming Homework 1 Instructions (Read First)

Graded: Module 1
Graded: Programming Homework 1

WEEK 2


Preprocessing, indexing and approximate matching
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


15 videos, 1 reading expand

1. Video: Week 2 Introduction
2. Video: Lecture: Boyer-Moore basics
3. Video: Lecture: Boyer-Moore: putting it all together
4. Video: Lecture: Diversion: Repetitive elements
5. Video: Practical: Implementing Boyer-Moore
6. Video: Lecture: Preprocessing
7. Video: Lecture: Indexing and the k-mer index
8. Video: Lecture: Ordered structures for indexing
9. Video: Lecture: Hash tables for indexing
10. Video: Practical: Implementing a k-mer index
11. Video: Lecture: Variations on k-mer indexes
12. Video: Lecture: Genome indexes used in research
13. Video: Lecture: Approximate matching, Hamming and edit distance
14. Video: Lecture: Pigeonhole principle
15. Video: Practical: Implementing the pigeonhole principle
16. Reading: Programming Homework 2 Instructions (Read First)

Graded: Module 2
Graded: Programming Homework 2

WEEK 3


Edit distance, assembly, overlaps
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.


13 videos, 1 reading expand

1. Video: Module 3 Introduction
2. Video: Lecture: Solving the edit distance problem
3. Video: Lecture: Using dynamic programming for edit distance
4. Video: Practical: Implementing dynamic programming for edit distance
5. Video: Lecture: A new solution to approximate matching
6. Video: Lecture: Meet the family: global and local alignment
7. Video: Practical: Implementing global alignment
8. Video: Lecture: Read alignment in the field
9. Video: Lecture: Assembly: working from scratch
10. Video: Lecture: First and second laws of assembly
11. Video: Lecture: Overlap graphs
12. Video: Practical: Overlaps between pairs of reads
13. Video: Practical: Finding and representing all overlaps
14. Reading: Programming Homework 3 Instructions (Read First)

Graded: Module 3
Graded: Programming Homework 3

WEEK 4


Algorithms for assembly
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.


13 videos, 1 reading expand

1. Video: Module 4 introduction
2. Video: Lecture: The shortest common superstring problem
3. Video: Practical: Implementing shortest common superstring
4. Video: Lecture: Greedy shortest common superstring
5. Video: Practical: Implementing greedy shortest common superstring
6. Video: Lecture: Third law of assembly: repeats are bad
7. Video: Lecture: De Bruijn graphs and Eulerian walks
8. Video: Practical: Building a De Bruijn graph
9. Video: Lecture: When Eulerian walks go wrong
10. Video: Lecture: Assemblers in practice
11. Video: Lecture: The future is long?
12. Video: Lecture: Computer science and life science
13. Video: Lecture: Thank yous
14. Reading: Post Course Survey

Graded: Programming Homework 4
Graded: Module 4