Algorithms for DNA Sequencing

Algorithms for DNA Sequencing

This course is part of Genomic Data Science Specialization

Instructors: Ben Langmead, PhD

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47,956 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

932 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

96%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

932 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

96%

Most learners liked this course

Skills you'll gain

Tools you'll learn

Python Programming

Details to know

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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

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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Instructors

Instructor ratings

(125 ratings)

Ben Langmead, PhD

Johns Hopkins University

1 Course 47,956 learners

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Johns Hopkins University

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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 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 Mar 11, 2016

Excellent intro to the computational challenges in analyzing genomic sequences.Lectures and programming exercises explain algorithms very clearly even for beginners.

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