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 |
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
- Reading: Welcome to Algorithms for DNA Sequencing
- Reading: Pre Course Survey
- Reading: Syllabus
- Reading: Setting up Python (and Jupyter)
- Reading: Getting slides and notebooks
- Reading: Using data files with Python programs
- Video: Module 1 Introduction
- Video: Lecture: Why study this?
- Video: Lecture: DNA sequencing past and present
- Video: Lecture: Genomes as strings, reads as substrings
- Video: Lecture: String definitions and Python examples
- Video: Practical: String basics
- Video: Practical: Manipulating DNA strings
- Video: Practical: Downloading and parsing a genome
- Video: Lecture: How DNA gets copied
- Video: Optional lecture: How second-generation sequencers work
- Video: Optional lecture: Sequencing errors and base qualities
- Video: Lecture: Sequencing reads in FASTQ format
- Video: Practical: Working with sequencing reads
- Video: Practical: Analyzing reads by position
- Video: Lecture: Sequencers give pieces to genomic puzzles
- Video: Lecture: Read alignment and why it's hard
- Video: Lecture: Naive exact matching
- Video: Practical: Matching artificial reads
- Video: Practical: Matching real reads
- 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
- Video: Week 2 Introduction
- Video: Lecture: Boyer-Moore basics
- Video: Lecture: Boyer-Moore: putting it all together
- Video: Lecture: Diversion: Repetitive elements
- Video: Practical: Implementing Boyer-Moore
- Video: Lecture: Preprocessing
- Video: Lecture: Indexing and the k-mer index
- Video: Lecture: Ordered structures for indexing
- Video: Lecture: Hash tables for indexing
- Video: Practical: Implementing a k-mer index
- Video: Lecture: Variations on k-mer indexes
- Video: Lecture: Genome indexes used in research
- Video: Lecture: Approximate matching, Hamming and edit distance
- Video: Lecture: Pigeonhole principle
- Video: Practical: Implementing the pigeonhole principle
- 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
- Video: Module 3 Introduction
- Video: Lecture: Solving the edit distance problem
- Video: Lecture: Using dynamic programming for edit distance
- Video: Practical: Implementing dynamic programming for edit distance
- Video: Lecture: A new solution to approximate matching
- Video: Lecture: Meet the family: global and local alignment
- Video: Practical: Implementing global alignment
- Video: Lecture: Read alignment in the field
- Video: Lecture: Assembly: working from scratch
- Video: Lecture: First and second laws of assembly
- Video: Lecture: Overlap graphs
- Video: Practical: Overlaps between pairs of reads
- Video: Practical: Finding and representing all overlaps
- 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
- Video: Module 4 introduction
- Video: Lecture: The shortest common superstring problem
- Video: Practical: Implementing shortest common superstring
- Video: Lecture: Greedy shortest common superstring
- Video: Practical: Implementing greedy shortest common superstring
- Video: Lecture: Third law of assembly: repeats are bad
- Video: Lecture: De Bruijn graphs and Eulerian walks
- Video: Practical: Building a De Bruijn graph
- Video: Lecture: When Eulerian walks go wrong
- Video: Lecture: Assemblers in practice
- Video: Lecture: The future is long?
- Video: Lecture: Computer science and life science
- Video: Lecture: Thank yous
- Reading: Post Course Survey
Graded: Programming Homework 4
Graded: Module 4
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|---|---|---|
| Access to course materials | Available | Available |
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Ratings and Reviews
This course provided me a very quick overview of all the core concepts pertaining to DNA sequencing. It is very well organized, crystal clear demonstration of concepts and I really enjoyed the course.
the course was clear and instructive. i've enjoyed every moment of it, thx to instructors.
Great course! The materials explain everything from scratch. The Practical lessons are very helpful! Thank you for this course.
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Very good course! You end up with a repertoire of nice algorithms that might be useful in many kinds of genomic analysis. While these are far from the optimal algorithms used by the actual genomic programs, they provide a nice introduction to the subject. It would be very good to have another course for "Advanced algorithms for DNA sequencing" where strategies for dealing with the several complicators of actual genomic data are added on to the knowledge obtained in this course.