Algorithms for DNA Sequencing

With genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome, and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome.This Specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, along with a variety of software implementation tools like Python, R, Biocon

Created by: Ben Langmead

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Overall Score : 96 / 100

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Course Description

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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Instructor Details

Ben Langmead

Ben Langmead is an Assistant Professor in the Department of Computer Science at Johns Hopkins University. He earned his Ph.D. in Computer Science from the University of Maryland in 2012. His group seeks to make high-throughput biological datasets easy for biomedical researchers to use. The group has released several high-impact software tools for genomics. He is the recipient of a Sloan Research Fellowship (2014) and a National Science Foundation CAREER award (2014).

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Reviews

4.8

94 total reviews

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By Genyu Z on 12-Nov-18

The course is very helpful to me,especially the code that the professor assistants wrote in the class. There are some algorithms have mentioned and completed ,but I think if the class talked about the software like BWA, BFAST and other DNA sequence or De novo assembly software, it will be more perfect and helpful. Finally ,thank you for your work

By Walt M on 6-Jan-19

This course was a really awesome course for anyone that has a light background in Python and wants to look more into bioinformatics. The instructors were very passionate and clear, and there was a good balance between learning about biology and the programming aspect. However, for somebody that only has the programming knowledge from Course 3 of the Genomic Data Science Specialization (Python for Genomic Data Science), that course, I believe, is too light for the new Python concepts taught in this course. I believe that before taking this course, you may need more background knowledge for Python. However, this is still a stellar course that I greatly enjoyed, with the homework and assignments being appropriately challenging.

By Rohan V on 11-Jan-19

This is one of the best courses I've seen so far, that explains and showcases main principles, fundamentals of genomics, algorithms. Lectures are very informative and use metaphors for person to understand complex tasks in simple manner. I loved practicals as you could go along with the lecture in parallel, seeing how such algorithms were made and what steps to take while doing so. 5/5

By Evan S on 22-Mar-19

Great material, nicely presented.

By Le N P on 13-Aug-18

This is more of my area... just starting the first videos and I'm already excited.

By Sai K on 24-Aug-18

this is just so good. i did take a lot of courses online and in my university on bioinformatics and this is the best course design i saw so far. i had to take pauses while watching the lectures to appreciate how much effort the creators of the course put to make it this connected and comprehensive. thank you!

By Arun K P on 19-Oct-18

I loved this course a lot. It's well organized. The lectures are clear. And the practicals are highly useful. Also, the assignments are helpful.

By XIAO N on 24-Sep-18

This is a super nice course.

By Bhisham J M on 31-Aug-18

This is an excellent course. Lectures are very well prepared, practicals provide step-by-step explanations of the scripts (which is especially useful for people with little coding experience) and homeworks are well thought through, so that they force students to use the knowledge gained in the module. Some of the homeworks are challenging, but all the information needed to do the exercises is provided in lectures and practicals. All the notebooks containing scripts are provided, which makes it easy to take notes and better understand the scripts by running some examples. The way the concepts are explained in the lectures (the computational problem is described in details and then the ways of dealing with it are carefully explained in order of increasing complexity) provides insight into not only how these algorithms work but also why (what is the purpose/cause/reason behind these solutions). I can imagine how much work and thought went into preparation of these lectures and I honestly admire the teachers for their efforts. Taking this course was a great experience: I learned a lot and enjoyed it a lot. A big thank you! Please, keep up the good work.

By Nagendra K M R on 11-Mar-16

Awesome, you will learn a lot about how DNA assemblers work, but very challenging and time demand in, especially if your background is in life science and not computer science.

By Magdi M on 10-Nov-16

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 Rahul K on 3-May-17

Great but too short. I realize that it is hard to know where to start the course from but it is true that the Introductory Course for the Genomics Specialization has a lot of over lapping material. I suggest getting rid of your intro material and go just a little farther in depth.Thanks, MC