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Finding Hidden Messages in DNA (Bioinformatics I)

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.

4.3

(1,013 ratings)

1,10,761 already enrolled

English

پښتو, বাংলা, اردو, 3 more

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Finding Hidden Messages in DNA (Bioinformatics I)

This course includes

15 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

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

Bioinformatics
Algorithmic Analysis
DNA Pattern Recognition
Python Programming
Computational Biology
Molecular Biology
Genomics
Data Analysis
Motif Finding
Sequence Analysis

This course includes:

1.35 Hours PreRecorded video

4 assignments

Access on Mobile, Tablet, Desktop

FullTime access

Shareable certificate

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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

Pavel Pevzner
Pavel Pevzner

8,33,777 Students

16 Courses

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"

Phillip Compeau
Phillip Compeau

4.1 rating

282 Reviews

2,88,261 Students

8 Courses

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.

Finding Hidden Messages in DNA (Bioinformatics I)

This course includes

15 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

Testimonials

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Frequently asked questions

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