4.7
stars
827 ratings

Ben Langmead, PhD +1 more instructor

35,859 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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Instructors

Instructor rating

4.8/5 (98 Ratings)

Ben Langmead, PhD

Jacob Pritt

Department of Computer Science

35,859 Learners

1 Course

Offered by

Johns Hopkins University

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Syllabus - What you will learn from this course

Content Rating96%(4,046 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

198 reviews

5 stars
80.55%
4 stars
14.85%
3 stars
3.14%
2 stars
0.48%
1 star
0.96%

TOP REVIEWS FROM ALGORITHMS FOR DNA SEQUENCING

by LJMar 14, 2016

I had a lot of fun doing the little programming assignments in this course. I did not know I like algorithms this much. The pace is good and it is well taught.

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.

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!

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Algorithms for DNA Sequencing

About this Course

Skills you will gain