Genome Sequencing (Bioinformatics II)
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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English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Master genome assembly using graph theory algorithms
Implement DNA sequencing techniques with computational methods
Analyze antibiotic sequences using brute force algorithms
Develop skills in Python programming for bioinformatics
Apply practical genome assembly tools to bacterial sequences
Skills you'll gain
This course includes:
1.8 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course explores genome sequencing through computational methods, focusing on graph theory and brute force algorithms. Students learn to assemble genomes from DNA fragments using graph-based approaches and study antibiotic sequencing through algorithmic analysis. The course combines theoretical foundations with practical applications in bioinformatics, covering both genome assembly techniques and antibiotic peptide sequencing methods.
Introduction to Genome Sequencing
Module 1 · 4 Hours to complete
Applying Euler's Theorem to Assemble Genomes
Module 2 · 3 Hours to complete
Sequencing Antibiotics
Module 3 · 3 Hours to complete
From Ideal to Real Spectra for Antibiotics Sequencing
Module 4 · 3 Hours to complete
Bioinformatics Application Challenge
Module 5 · 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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