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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

48,625 already enrolled

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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

Shareable certificate

Add to your LinkedIn profile

Assessments

8 assignments

Taught in English

91% of learners achieved a positive career outcome

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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.

Module details

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 videosTotal 112 minutes

Module 1 Introduction2 minutes
Lecture: Why study this?4 minutes
Lecture: DNA sequencing past and present3 minutes
Lecture: Genomes as strings, reads as substrings5 minutes
Lecture: String definitions and Python examples4 minutes
Practical: String basics 7 minutes
Practical: Manipulating DNA strings 7 minutes
Practical: Downloading and parsing a genome 6 minutes
Lecture: How DNA gets copied4 minutes
Optional lecture: How second-generation sequencers work 7 minutes
Optional lecture: Sequencing errors and base qualities 7 minutes
Lecture: Sequencing reads in FASTQ format5 minutes
Practical: Working with sequencing reads 11 minutes
Practical: Analyzing reads by position 7 minutes
Lecture: Sequencers give pieces to genomic puzzles6 minutes
Lecture: Read alignment and why it's hard3 minutes
Lecture: Naive exact matching10 minutes
Practical: Matching artificial reads 7 minutes
Practical: Matching real reads 7 minutes

7 readingsTotal 70 minutes

Welcome to Algorithms for DNA Sequencing10 minutes
Pre Course Survey10 minutes
Syllabus10 minutes
Setting up Python (and Jupyter)10 minutes
Getting slides and notebooks10 minutes
Using data files with Python programs10 minutes
Programming Homework 1 Instructions (Read First)10 minutes

2 assignmentsTotal 60 minutes

Module 130 minutes
Programming Homework 130 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 videosTotal 114 minutes

Week 2 Introduction 2 minutes
Lecture: Boyer-Moore basics9 minutes
Lecture: Boyer-Moore: putting it all together6 minutes
Lecture: Diversion: Repetitive elements5 minutes
Practical: Implementing Boyer-Moore 10 minutes
Lecture: Preprocessing7 minutes
Lecture: Indexing and the k-mer index11 minutes
Lecture: Ordered structures for indexing8 minutes
Lecture: Hash tables for indexing7 minutes
Practical: Implementing a k-mer index 7 minutes
Lecture: Variations on k-mer indexes9 minutes
Lecture: Genome indexes used in research10 minutes
Lecture: Approximate matching, Hamming and edit distance7 minutes
Lecture: Pigeonhole principle7 minutes
Practical: Implementing the pigeonhole principle 10 minutes

1 readingTotal 10 minutes

Programming Homework 2 Instructions (Read First)10 minutes

2 assignmentsTotal 60 minutes

Module 230 minutes
Programming Homework 230 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 videosTotal 92 minutes

Module 3 Introduction 1 minute
Lecture: Solving the edit distance problem13 minutes
Lecture: Using dynamic programming for edit distance12 minutes
Practical: Implementing dynamic programming for edit distance 6 minutes
Lecture: A new solution to approximate matching9 minutes
Lecture: Meet the family: global and local alignment11 minutes
Practical: Implementing global alignment 8 minutes
Lecture: Read alignment in the field4 minutes
Lecture: Assembly: working from scratch3 minutes
Lecture: First and second laws of assembly8 minutes
Lecture: Overlap graphs8 minutes
Practical: Overlaps between pairs of reads 4 minutes
Practical: Finding and representing all overlaps 4 minutes

1 readingTotal 10 minutes

Programming Homework 3 Instructions (Read First)10 minutes

2 assignmentsTotal 60 minutes

Module 330 minutes
Programming Homework 330 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.