Algorithms for DNA Sequencing
Learn computational methods and algorithms for analyzing DNA sequence data. Master Python implementation for genomic analysis.
Learn computational methods and algorithms for analyzing DNA sequence data. Master Python implementation for genomic analysis.
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.
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English
پښتو, বাংলা, اردو, 3 more
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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
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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
Instructors
Genomics Software Pioneer and Computer Science Innovator at Johns Hopkins
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.
Computational Science Expert and DNA Sequencing Algorithm Developer
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.
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4.7 course rating
889 ratings
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