Algorithms for DNA Sequencing
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Algorithms for DNA Sequencing
This course is part of Genomic Data Science Specialization
Instructors: Ben Langmead, PhD
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There are 4 modules in 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.
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
19 videos•Total 112 minutes
- Module 1 Introduction•2 minutes
- Lecture: Why study this?•4 minutes
- Lecture: DNA sequencing past and present•3 minutes
- Lecture: Genomes as strings, reads as substrings•5 minutes
- Lecture: String definitions and Python examples•4 minutes
- Practical: String basics •7 minutes
- Practical: Manipulating DNA strings •7 minutes
- Practical: Downloading and parsing a genome •6 minutes
- Lecture: How DNA gets copied•4 minutes
- Optional lecture: How second-generation sequencers work •7 minutes
- Optional lecture: Sequencing errors and base qualities •7 minutes
- Lecture: Sequencing reads in FASTQ format•5 minutes
- Practical: Working with sequencing reads •11 minutes
- Practical: Analyzing reads by position •7 minutes
- Lecture: Sequencers give pieces to genomic puzzles•6 minutes
- Lecture: Read alignment and why it's hard•3 minutes
- Lecture: Naive exact matching•10 minutes
- Practical: Matching artificial reads •7 minutes
- Practical: Matching real reads •7 minutes
7 readings•Total 70 minutes
- Welcome to Algorithms for DNA Sequencing•10 minutes
- Pre Course Survey•10 minutes
- Syllabus•10 minutes
- Setting up Python (and Jupyter)•10 minutes
- Getting slides and notebooks•10 minutes
- Using data files with Python programs•10 minutes
- Programming Homework 1 Instructions (Read First)•10 minutes
2 assignments•Total 60 minutes
- Module 1•30 minutes
- Programming Homework 1•30 minutes
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
15 videos•Total 114 minutes
- Week 2 Introduction •2 minutes
- Lecture: Boyer-Moore basics•9 minutes
- Lecture: Boyer-Moore: putting it all together•6 minutes
- Lecture: Diversion: Repetitive elements•5 minutes
- Practical: Implementing Boyer-Moore •10 minutes
- Lecture: Preprocessing•7 minutes
- Lecture: Indexing and the k-mer index•11 minutes
- Lecture: Ordered structures for indexing•8 minutes
- Lecture: Hash tables for indexing•7 minutes
- Practical: Implementing a k-mer index •7 minutes
- Lecture: Variations on k-mer indexes•9 minutes
- Lecture: Genome indexes used in research•10 minutes
- Lecture: Approximate matching, Hamming and edit distance•7 minutes
- Lecture: Pigeonhole principle•7 minutes
- Practical: Implementing the pigeonhole principle •10 minutes
1 reading•Total 10 minutes
- Programming Homework 2 Instructions (Read First)•10 minutes
2 assignments•Total 60 minutes
- Module 2•30 minutes
- Programming Homework 2•30 minutes
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
13 videos•Total 92 minutes
- Module 3 Introduction •1 minute
- Lecture: Solving the edit distance problem•13 minutes
- Lecture: Using dynamic programming for edit distance•12 minutes
- Practical: Implementing dynamic programming for edit distance •6 minutes
- Lecture: A new solution to approximate matching•9 minutes
- Lecture: Meet the family: global and local alignment•11 minutes
- Practical: Implementing global alignment •8 minutes
- Lecture: Read alignment in the field•4 minutes
- Lecture: Assembly: working from scratch•3 minutes
- Lecture: First and second laws of assembly•8 minutes
- Lecture: Overlap graphs•8 minutes
- Practical: Overlaps between pairs of reads •4 minutes
- Practical: Finding and representing all overlaps •4 minutes
1 reading•Total 10 minutes
- Programming Homework 3 Instructions (Read First)•10 minutes
2 assignments•Total 60 minutes
- Module 3•30 minutes
- Programming Homework 3•30 minutes
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
13 videos•Total 83 minutes
- Module 4 introduction •1 minute
- Lecture: The shortest common superstring problem•8 minutes
- Practical: Implementing shortest common superstring •5 minutes
- Lecture: Greedy shortest common superstring•8 minutes
- Practical: Implementing greedy shortest common superstring •7 minutes
- Lecture: Third law of assembly: repeats are bad•6 minutes
- Lecture: De Bruijn graphs and Eulerian walks•9 minutes
- Practical: Building a De Bruijn graph •5 minutes
- Lecture: When Eulerian walks go wrong•10 minutes
- Lecture: Assemblers in practice•8 minutes
- Lecture: The future is long?•10 minutes
- Lecture: Computer science and life science•5 minutes
- Lecture: Thank yous •1 minute
1 reading•Total 10 minutes
- Post Course Survey•10 minutes
2 assignments•Total 60 minutes
- Programming Homework 4•30 minutes
- Module 4•30 minutes
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Reviewed on Sep 2, 2020
Very well explained, a lot of the gaps of the previous courses got cleared up. This course should be an example on how to teach a subject. Thanks!!!
Reviewed on Jul 3, 2018
Very well prepared, from basics up to all commonly used techniques in bioinformatics. Prerequisites in Python is a plus, but not even necessary.
Reviewed on Oct 18, 2018
I loved this course a lot. It's well organized. The lectures are clear. And the practicals are highly useful. Also, the assignments are helpful.
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