Master graph algorithms for genome assembly and evolutionary tree construction in this 3-week bioinformatics course from UC San Diego.
Master graph algorithms for genome assembly and evolutionary tree construction in this 3-week bioinformatics course from UC San Diego.
This specialized course, part of the Algorithms and Data Structures MicroMasters program, explores graph algorithms' applications in modern biology. Students learn how to use graph theory for assembling genome sequences from DNA fragments and constructing evolutionary trees. The course covers genome sequencing fundamentals, Eulerian path approaches for DNA assembly, and practical implementations for real-world genomic analysis.
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Instructors:
English
English
What you'll learn
Apply graph algorithms to assemble genome sequences
Implement Eulerian path approaches for DNA fragment assembly
Construct evolutionary trees from genomic data
Understand practical considerations in genome sequencing
Master fundamental concepts in computational genomics
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 2 modules in this course
The course focuses on applying graph algorithms to solve fundamental problems in modern biology. Students learn techniques for genome assembly using graph theory to piece together DNA fragments into complete sequences. The curriculum covers both theoretical foundations and practical implementations, including Eulerian path approaches and their adaptation to real-world genomic data. Students also explore methods for constructing evolutionary trees from genome data.
Introduction to Genome Sequencing and Graphs
Module 1
Assembling Genomes from Tiny Fragments
Module 2
Fee Structure
Fee Breakdown:
Base Course Fee (Audit): ₹0 (Free)
Certificate Fee: ₹12,375
Additional Costs: None
Total Cost:
Audit (No Certificate): ₹0 (Free)
With Certificate: ₹12,375
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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