4.7
stars
796 ratings

Ben Langmead, PhD +1 more instructor

34,390 already enrolled

Offered By

Johns Hopkins University

About this Course

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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.

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Approx. 12 hours to complete

English

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Skills you will gain

Bioinformatics Algorithms
Algorithms
Python Programming
Algorithms On Strings

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Approx. 12 hours to complete

English

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Try Coursera for Business

Instructors

Instructor rating

4.79/5 (95 Ratings)

Ben Langmead, PhD

Jacob Pritt

Department of Computer Science

34,390 Learners

1 Course

Offered by

Johns Hopkins University

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Syllabus - What you will learn from this course

Content Rating96%(3,947 ratings)

Week

Week 1

4 hours to complete

DNA sequencing, strings and matching

4 hours to complete

19 videos (Total 112 min), 7 readings, 2 quizzes

19 videos

Module 1 Introduction1m

Lecture: Why study this?4m

Lecture: DNA sequencing past and present3m

Lecture: Genomes as strings, reads as substrings5m

Lecture: String definitions and Python examples3m

Practical: String basics 7m

Practical: Manipulating DNA strings 7m

Practical: Downloading and parsing a genome 6m

Lecture: How DNA gets copied3m

Optional lecture: How second-generation sequencers work 7m

Optional lecture: Sequencing errors and base qualities 6m

Lecture: Sequencing reads in FASTQ format4m

Practical: Working with sequencing reads 11m

Practical: Analyzing reads by position 6m

Lecture: Sequencers give pieces to genomic puzzles5m

Lecture: Read alignment and why it's hard3m

Lecture: Naive exact matching10m

Practical: Matching artificial reads 6m

Practical: Matching real reads 7m

7 readings

Welcome to Algorithms for DNA Sequencing10m

Pre Course Survey10m

Syllabus10m

Setting up Python (and Jupyter)10m

Getting slides and notebooks10m

Using data files with Python programs10m

Programming Homework 1 Instructions (Read First)10m

2 practice exercises

Module 130m

Programming Homework 130m

Week

Week 2

3 hours to complete

Preprocessing, indexing and approximate matching

3 hours to complete

15 videos (Total 114 min), 1 reading, 2 quizzes

15 videos

Week 2 Introduction 1m

Lecture: Boyer-Moore basics8m

Lecture: Boyer-Moore: putting it all together6m

Lecture: Diversion: Repetitive elements5m

Practical: Implementing Boyer-Moore 10m

Lecture: Preprocessing7m

Lecture: Indexing and the k-mer index10m

Lecture: Ordered structures for indexing8m

Lecture: Hash tables for indexing7m

Practical: Implementing a k-mer index 7m

Lecture: Variations on k-mer indexes9m

Lecture: Genome indexes used in research9m

Lecture: Approximate matching, Hamming and edit distance6m

Lecture: Pigeonhole principle6m

Practical: Implementing the pigeonhole principle 9m

1 reading

Programming Homework 2 Instructions (Read First)10m

2 practice exercises

Module 230m

Programming Homework 230m

Week

Week 3

3 hours to complete

Edit distance, assembly, overlaps

3 hours to complete

13 videos (Total 92 min), 1 reading, 2 quizzes

13 videos

Module 3 Introduction 1m

Lecture: Solving the edit distance problem12m

Lecture: Using dynamic programming for edit distance12m

Practical: Implementing dynamic programming for edit distance 6m

Lecture: A new solution to approximate matching9m

Lecture: Meet the family: global and local alignment10m

Practical: Implementing global alignment 8m

Lecture: Read alignment in the field4m

Lecture: Assembly: working from scratch2m

Lecture: First and second laws of assembly8m

Lecture: Overlap graphs8m

Practical: Overlaps between pairs of reads 4m

Practical: Finding and representing all overlaps 3m

1 reading

Programming Homework 3 Instructions (Read First)10m

2 practice exercises

Module 330m

Programming Homework 330m

Week

Week 4

3 hours to complete

Algorithms for assembly

3 hours to complete

13 videos (Total 83 min), 1 reading, 2 quizzes

13 videos

Module 4 introduction 1m

Lecture: The shortest common superstring problem8m

Practical: Implementing shortest common superstring 4m

Lecture: Greedy shortest common superstring7m

Practical: Implementing greedy shortest common superstring 7m

Lecture: Third law of assembly: repeats are bad5m

Lecture: De Bruijn graphs and Eulerian walks8m

Practical: Building a De Bruijn graph 4m

Lecture: When Eulerian walks go wrong9m

Lecture: Assemblers in practice8m

Lecture: The future is long?9m

Lecture: Computer science and life science5m

Lecture: Thank yous 43s

1 reading

Post Course Survey10m

2 practice exercises

Programming Homework 430m

Module 430m

Reviews

4.7

191 reviews

5 stars
80.81%
4 stars
14.85%
3 stars
2.97%
2 stars
0.49%
1 star
0.86%

TOP REVIEWS FROM ALGORITHMS FOR DNA SEQUENCING

by KSJul 20, 2021

An excellent course designed to prepare students for real challenges. Thank you Dr. Langmead, JHU and Coursera for designing such a beautiful course!

by SHAug 27, 2022

John Hopkin University provides an excellent course. It is effective for postdoctoral fellows and early career researchers, primarily those in the STEM fields !

by NFMar 9, 2021

very engaging and well-presented course material.

intermediate difficulty while conveying the basics of how even recent real-world genome alignment and assembly tools work. great course design!

by MDNov 9, 2016

This was really fun. Really enjoyed the a-ha of the algorithms and the fun of solving the alignment and assembly problems. Feel mildly powerful after assembling a virus genome.

View all reviews

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Algorithms for DNA Sequencing

About this Course

Skills you will gain