Explore advanced algorithms and data analysis techniques to uncover meaningful genomic patterns and extract valuable biological understanding
Explore advanced algorithms and data analysis techniques to uncover meaningful genomic patterns and extract valuable biological understanding
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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English
پښتو, বাংলা, اردو, 3 more
What you'll learn
Master algorithms for finding DNA replication origins
Learn to identify molecular clock patterns in DNA
Apply randomized algorithms to biological problems
Analyze recurring biological motifs in genes
Develop practical bioinformatics software skills
Understand computational DNA pattern analysis
Skills you'll gain
This course includes:
1.35 Hours PreRecorded video
4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This course explores computational methods for finding hidden patterns in DNA sequences. The curriculum covers two main areas: identifying DNA replication origins in bacterial genomes and discovering molecular clock patterns in DNA. Students learn to apply algorithmic approaches, including randomized algorithms, to solve real biological problems. The course combines theoretical concepts with practical applications, culminating in a hands-on bioinformatics challenge analyzing Mycobacterium tuberculosis genes.
Welcome
Module 1 · 3 Hours to complete
Finding Replication Origins
Module 2 · 2 Hours to complete
Hunting for Regulatory Motifs
Module 3 · 3 Hours to complete
How Rolling Dice Helps Us Find Regulatory Motifs
Module 4 · 2 Hours to complete
Bioinformatics Application Challenge
Module 5 · 3 Hours to complete
Fee Structure
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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