4.7
stars
837 ratings

Ben Langmead, PhD +1 more instructor

36,580 already enrolled

Offered By

Johns Hopkins University

About this Course

34,571 recent views

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 (101 Ratings)

Ben Langmead, PhD

Jacob Pritt

Department of Computer Science

36,580 Learners

1 Course

Offered by

Johns Hopkins University

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Syllabus - What you will learn from this course

Content Rating96%(4,072 ratings)

Week1

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 Introduction1mLecture: Why study this?4mLecture: DNA sequencing past and present3mLecture: Genomes as strings, reads as substrings5mLecture: String definitions and Python examples3mPractical: String basics 7mPractical: Manipulating DNA strings 7mPractical: Downloading and parsing a genome 6mLecture: How DNA gets copied3mOptional lecture: How second-generation sequencers work 7mOptional lecture: Sequencing errors and base qualities 6mLecture: Sequencing reads in FASTQ format4mPractical: Working with sequencing reads 11mPractical: Analyzing reads by position 6mLecture: Sequencers give pieces to genomic puzzles5mLecture: Read alignment and why it's hard3mLecture: Naive exact matching10mPractical: Matching artificial reads 6mPractical: Matching real reads 7m

7 readings

Welcome to Algorithms for DNA Sequencing10mPre Course Survey10mSyllabus10mSetting up Python (and Jupyter)10mGetting slides and notebooks10mUsing data files with Python programs10mProgramming Homework 1 Instructions (Read First)10m

2 practice exercises

Module 130mProgramming Homework 130m

Week2

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 1mLecture: Boyer-Moore basics8mLecture: Boyer-Moore: putting it all together6mLecture: Diversion: Repetitive elements5mPractical: Implementing Boyer-Moore 10mLecture: Preprocessing7mLecture: Indexing and the k-mer index10mLecture: Ordered structures for indexing8mLecture: Hash tables for indexing7mPractical: Implementing a k-mer index 7mLecture: Variations on k-mer indexes9mLecture: Genome indexes used in research9mLecture: Approximate matching, Hamming and edit distance6mLecture: Pigeonhole principle6mPractical: Implementing the pigeonhole principle 9m

1 reading

Programming Homework 2 Instructions (Read First)10m

2 practice exercises

Module 230mProgramming Homework 230m

Week3

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 1mLecture: Solving the edit distance problem12mLecture: Using dynamic programming for edit distance12mPractical: Implementing dynamic programming for edit distance 6mLecture: A new solution to approximate matching9mLecture: Meet the family: global and local alignment10mPractical: Implementing global alignment 8mLecture: Read alignment in the field4mLecture: Assembly: working from scratch2mLecture: First and second laws of assembly8mLecture: Overlap graphs8mPractical: Overlaps between pairs of reads 4mPractical: Finding and representing all overlaps 3m

1 reading

Programming Homework 3 Instructions (Read First)10m

2 practice exercises

Module 330mProgramming Homework 330m

Week4

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 1mLecture: The shortest common superstring problem8mPractical: Implementing shortest common superstring 4mLecture: Greedy shortest common superstring7mPractical: Implementing greedy shortest common superstring 7mLecture: Third law of assembly: repeats are bad5mLecture: De Bruijn graphs and Eulerian walks8mPractical: Building a De Bruijn graph 4mLecture: When Eulerian walks go wrong9mLecture: Assemblers in practice8mLecture: The future is long?9mLecture: Computer science and life science5mLecture: Thank yous 43s

1 reading

Post Course Survey10m

2 practice exercises

Programming Homework 430mModule 430m

Reviews

4.7

202 reviews

5 stars
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4 stars
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3 stars
3.22%
2 stars
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1 star
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TOP REVIEWS FROM ALGORITHMS FOR DNA SEQUENCING

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 KPMay 18, 2020

Very good course this is. Just a little advice, please make the quiz questions more clear and more specific because one shouldn't waste time to understand a question which is very easy to implement.

by MPJan 23, 2023

Challenging course and took me longer than four weeks to finish but they explained the concepts well and the discussion forum posts from other students were very helpful.

by SSJul 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.

View all reviews

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Algorithms for DNA Sequencing

About this Course

Skills you will gain