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Johns Hopkins University

Algorithms for DNA Sequencing

Johns Hopkins University via Coursera


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.


  • DNA sequencing, strings and matching
    • 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.
  • Preprocessing, indexing and approximate matching
    • 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
  • Edit distance, assembly, overlaps
    • 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.
  • Algorithms for assembly
    • 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.

Taught by

Ben Langmead and Jacob Pritt


4.5 rating, based on 17 Class Central reviews

4.7 rating at Coursera based on 882 ratings

Start your review of Algorithms for DNA Sequencing

  • Profile image for Mark Wang
    Mark Wang
    Hello! I am a developer who's considering moving into bioinformatics. I took the course to get an understanding of the type of problems that computer scientists face in bio. Wanted to give you some feedback. Pros 1:) Good introduction to the proble…
  • Daria
    This course deals with the algorithms employed by mapping and genome assembly programs commonly used. It was only after completing the course that I realised that the course instructor, Ben Langmead, is actually the first author of the bowtie paper…
  • I feel that I have been finally introduced to the real world problems, their potential solutions and the paths for the improvement of such solutions. I feel that I've learned a lot about implementing the knowledge I've acquired so far and not only t…
  • Leif Ulstrup
    I have taken six other courses in this JHU Genomics series on Coursera and many others in Data Science @ JHU, Cybersecurity @ the U of Maryland etc on Coursera. I think this module is one of the very best I have taken. The video lectures with theory…
  • Tyler Devlin
    A solid course from a great instructor. Unlike some of the other courses in the Genomic Data Science Specialization, which have been shallow or poorly taught, this course is challenging (but not undoable) and the lectures are very well organized.

    The course is short, so you should not expect to get a full introduction to the field. But for its length, the course delivers a lot.
  • Profile image for Chrys
    Really good course. Compared to the other courses in the specialisation this one is really awesome and helpful. The other python course does not prepare you for the level of this course because it gets quite tricky , quite fast but stick with it. The instructors are amazing and really cool.
  • Anonymous
    Good course for all levels. Not much previous experience needed and a good learning curve. Recommended for people of all backgrounds who want to learn how sequencing works.
  • Allison Cooper
    This was a very nice course. The instructor explained things really well and the problem sets were fun, interesting, challenging enough to be motivating but doable. I had taken the San Diego bioinformatics courses, and this course helped me solidify some of the material that flew by in that other course. Highly recommended!
  • Anonymous
    This course was organized excellently. For a course with a duration of just 4 weeks it covers an amazing amount of required DNA sequencing background material and algorithms. Lectures were great and practicals were amazing since they covered coding of algorithms. Course offers plenty of scope for future learning.
  • Anonymous
    I found the lectures and the assignments to be very helpful and interesting. The course also provided an excuse to learn some of the Python programming language, which I encounter from time to time in my current career.
  • Great course.
    Thanks to wonderful instructors:
    Ben Langmead, PhD and Jacob Pritt.
    Highly recommended to all interested in algorithms design in general, applied computer science and modern bioinformatics.
  • This was the high point of an otherwise dreadful specialization. It was well-taught. And both the exercises & tutorial format were well-suited to the course.
  • Profile image for Radu Dragusin
    Radu Dragusin
  • Anonymous
    This course has a serious problem for anyone who has only taken the previous Introduction to Python course. This course fails to cover how to install and use the Python IDE (e.g. Anaconda, Jupyter, Notebook). The instructors just go ahead and presen…
  • Anonymous
    As any course in both "specializations" from Johns Hopkins, this is a just a random collection of things with no structure. Don't waste your time, you will not learn much.
  • Anonymous

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