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Bioinformatics Capstone: Big Data in Biology

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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Bioinformatics Capstone: Big Data in Biology

This course includes

12 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

What you'll learn

  • Master genome assembly and annotation techniques

  • Analyze SARS-CoV-2 variants and classification

  • Explore protein structure analysis methods

  • Use cloud-based bioinformatics platforms

  • Apply RNA-Sequencing analysis to real data

  • Compare whole genome and exome sequencing approaches

Skills you'll gain

Bioinformatics
BaseSpace Platform
Genome Assembly
SARS-CoV-2 Analysis
RNA Sequencing
Protein Structure
Variant Detection
Cloud Computing
Data Analysis

This course includes:

0.9 Hours PreRecorded video

3 peer reviews

Access on Mobile, Tablet, Desktop

FullTime access

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There are 3 modules in this course

This capstone course focuses on practical applications of bioinformatics using Illumina's BaseSpace cloud platform. Students tackle real-world challenges including genome assembly for tracking food poisoning outbreaks, RNA-Sequencing analysis for tissue-level gene expression, and comparing whole genome vs. exome sequencing approaches. The course emphasizes hands-on experience with SARS-CoV-2 genome analysis, including assembly, annotation, variant detection, and protein structure analysis.

Assembly and Annotation of the SARS-CoV-2 Genome

Module 1 · 4 Hours to complete

Variant Detection and Classification of SARS-CoV-2

Module 2 · 4 Hours to complete

SARS-CoV-2 Spike Protein Structure Analysis

Module 3 · 4 Hours to complete

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.

Bioinformatics Capstone: Big Data in Biology

This course includes

12 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

Testimonials

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