This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed.
Introduction to Genomic Technologies
Introduction to Genomic Technologies
This course is part of Genomic Data Science Specialization
Instructors: Steven Salzberg, PhD
Sponsored by Johns Hopkins University
108,002 already enrolled
Gain insight into a topic and learn the fundamentals.
4.6
(4,633 reviews)
6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
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Assessments
5 assignments
Taught in English
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This course is part of the Genomic Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
In this Module, you can expect to study topics of "Just enough molecular biology", "The genome", "Writing a DNA sequence", "Central dogma", "Transcription", "Translation", and "DNA structure and modifications".
What's included
8 videos2 readings1 assignment
In this module, you'll learn about polymerase chain reaction, next generation sequencing, and applications of sequencing.
What's included
3 videos1 assignment
The lectures for this module cover a few basic topics in computing technology. We'll go over the foundations of computer science, algorithms, memory and data structures, efficiency, software engineering, and computational biology software.
What's included
6 videos1 assignment
In this module on Data Science Technology, we'll be covering quite a lot of information about how to handle the data produced during the sequencing process. We'll cover reproducibility, analysis, statistics, question types, the central dogma of inference, analysis code, testing, prediction, variation, experimental design, confounding, power, sample size, correlation, causation, and degrees of freedom.
What's included
10 videos2 readings2 assignments
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4.7 (781 ratings)Offered by
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