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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Genomic Data Science SpecializationJohns Hopkins UniversityAbout this Course
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Learner Career Outcomes
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started a new career after completing these courses
33%
got a tangible career benefit from this course
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Approx. 12 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish
Skills you will gain
Bioinformatics AlgorithmsAlgorithmsPython ProgrammingAlgorithms On Strings
Learner Career Outcomes
43%
started a new career after completing these courses
33%
got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 8 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 12 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish
Offered by
Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Syllabus - What you will learn from this course
4 hours to complete
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.
4 hours to complete
19 videos (Total 112 min), 7 readings, 2 quizzes
19 videos
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
3 hours to complete
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
3 hours to complete
15 videos (Total 114 min), 1 reading, 2 quizzes
15 videos
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
3 hours to complete
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.
3 hours to complete
13 videos (Total 92 min), 1 reading, 2 quizzes
13 videos
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
3 hours to complete
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.
3 hours to complete
13 videos (Total 83 min), 1 reading, 2 quizzes
13 videos
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
TOP REVIEWS FROM ALGORITHMS FOR DNA SEQUENCING
by VKAug 7, 2017
This course provided me a very quick overview of all the core concepts pertaining to DNA sequencing. It is very well organized, crystal clear demonstration of concepts and I really enjoyed the course.
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 MYOct 9, 2020
One of the most useful courses I have joined. Many thanks to the instructors for these quality lectures and for the time spent to deliver the information as easily as possible!
by JDDec 30, 2020
Highly eloquent instructors that make intricate concepts accessible and fun to learn. Would definitely help if you have some programming or bioinformatics background beforehand.
About the Genomic Data Science Specialization
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.
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