This course is part of Bioinformatics Specialization.
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 Bioinformatics 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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Instructors:
English
پښتو, বাংলা, اردو, 3 more
What you'll learn
Implement efficient DNA read mapping algorithms
Master the Burrows-Wheeler transform for pattern matching
Develop solutions for inexact pattern matching
Apply hidden Markov models to sequence analysis
Analyze highly mutated protein sequences
Use profile HMMs for protein classification
Skills you'll gain
This course includes:
2.5 Hours PreRecorded video
3 assignments, 1 peer review
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This advanced bioinformatics course focuses on computational methods for finding mutations in DNA and proteins. The first half covers efficient algorithms for mapping DNA fragments to reference genomes using combinatorial pattern matching and the Burrows-Wheeler transform. The second half explores hidden Markov models for identifying protein functions, particularly in rapidly mutating sequences like HIV. Students learn both theoretical foundations and practical applications of these advanced bioinformatics tools.
Introduction to Read Mapping
Module 1 · 4 Hours to complete
The Burrows-Wheeler Transform
Module 2 · 4 Hours to complete
Speeding Up Burrows-Wheeler Read Mapping
Module 3 · 3 Hours to complete
Introduction to Hidden Markov Models
Module 4 · 3 Hours to complete
Profile HMMs for Sequence Alignment
Module 5 · 4 Hours to complete
Bioinformatics Application Challenge
Module 6 · 3 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: Bioinformatics Specialization
Instructors
Pioneering Bioinformatics Scholar and Computational Biology Innovator
Pavel Arkadevich Pevzner serves as the Ronald R. Taylor Professor of Computer Science at the University of California, San Diego, and director of the NIH Center for Computational Mass Spectrometry, where he has revolutionized the field of computational biology since 2000. After receiving his Ph.D. in mathematics and physics from the Moscow Institute of Physics and Technology, he completed postdoctoral work with Michael Waterman at USC, before establishing himself through positions at Penn State and USC. His groundbreaking research spans bioinformatics algorithms, genome rearrangements, DNA sequencing, and computational proteomics, leading to significant advances in genome assembly and antibiotics discovery. His academic excellence has been recognized through numerous prestigious honors, including the Howard Hughes Medical Institute Professorship (2006), ACM Fellowship (2010), ISCB Fellowship (2012), and the ACM Paris Kanellakis Theory and Practice Award (2018). As an educator, he has transformed bioinformatics education through innovative approaches, including the development of massive open online courses that have reached over half a million students, and authored influential textbooks including "Computational Molecular Biology: An Algorithmic Approach" and "Bioinformatics Algorithms: An Active Learning Approach"
Computational Biology Educator and Educational Innovation Pioneer
Phillip Compeau is an Assistant Teaching Professor in the Carnegie Mellon University Computational Biology Department, where he serves as Assistant Director of the Master's in Computational Biology program. He holds a Ph.D. in mathematics from UC San Diego and completed his Master's degree at Cambridge University. Phillip co-founded Rosalind, an online platform for learning bioinformatics. A retired tennis player, he dreams of one day going pro in golf.
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