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Algorithms for DNA Sequencing
This course is part of Genomic Data Science.
Course Cost
Free course
Intermediate
Skill Level
11 Hours
Self-paced Video lessons
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Genomic Data Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.7
42,785 Enrolled
English
What you'll learn
Implement DNA sequence analysis algorithms in Python
Master string matching and pattern recognition techniques
Develop skills in preprocessing and indexing genomic data
Understand and apply DNA assembly algorithms
Gain practical experience with real sequencing datasets
Skills you'll gain
This course includes:
6.6 Hours PreRecorded video
8 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
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Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
CREATED BY
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There are 4 modules in this course
This course teaches computational methods and algorithms for analyzing DNA sequencing data. Students learn about DNA sequencing technology and implement key algorithms in Python. The curriculum covers string matching, preprocessing, indexing, approximate matching, and assembly algorithms. Through practical programming assignments, students gain hands-on experience working with real genomic data and DNA sequencing datasets. The course combines theoretical understanding with practical implementation skills for bioinformatics applications.
DNA sequencing, strings and matching
Module 1 · 4 Hours to complete
Preprocessing, indexing and approximate matching
Module 2 · 3 Hours to complete
Edit distance, assembly, overlaps
Module 3 · 2 Hours to complete
Algorithms for assembly
Module 4 · 2 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Genomic Data Science
Reviews
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
4.8
117
Reviews
43,547
Students
1
Course
Dr. Ben Langmead serves as an Assistant Professor in the Department of Computer Science at Johns Hopkins University, having earned his Ph.D. from the University of Maryland in 2012. His research focuses on making high-throughput biological datasets more accessible to biomedical researchers, leading to the development of several influential genomics software tools. His exceptional contributions to the field have been recognized with prestigious awards, including a Sloan Research Fellowship and a National Science Foundation CAREER award in 2014.
4.8
117
Reviews
43,547
Students
1
Course
Jacob Pritt combines academic excellence and practical expertise in computational biology, holding a PhD in Computer Science from Johns Hopkins University and a BA from Harvard University. As a Graduate Research Assistant at Johns Hopkins, he specialized in developing tools for analyzing large datasets and improving system scalability. His notable contributions include co-creating the "Algorithms for DNA Sequencing" course with Professor Ben Langmead, which is part of the Genomic Data Science Specialization. His research work has garnered 153 citations8, and he currently serves as Lead Computational Scientist at Mimetics1, where he applies his expertise in computational methods and DNA sequencing algorithms.
Frequently asked Questions
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