Introduction to Genomic Technologies
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Introduction to Genomic Technologies
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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan .
- Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
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About this course: 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. This is the first course in the Genomic Data Science Specialization.
Created by: Johns Hopkins University-
Taught by: Steven Salzberg, PhD, Professor
Biomedical Engineering, Computer Science…
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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan .
- Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
- Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.
About this course: 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. This is the first course in the Genomic Data Science Specialization.
Created by: Johns Hopkins University-
Taught by: Steven Salzberg, PhD, Professor
Biomedical Engineering, Computer Science, and Biostatistics -
Taught by: Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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Johns Hopkins University The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.Syllabus
WEEK 1
Overview
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".
8 videos, 2 readings expand
- Reading: Syllabus
- Reading: Pre-Course Survey
- Video: Why Genomics?
- Video: What Is Genomics?
- Video: What Is Genomic Data Science?
- Video: Just Enough Cell Biology
- Video: Important Molecules in Molecular Biology
- Video: The Human Genome Project
- Video: Molecular Biology Structures
- Video: From Genes to Phenotypes
Graded: Quiz 1: Overview and Molecular Biology
WEEK 2
Measurement Technology
In this module, you'll learn about polymerase chain reaction, next generation sequencing, and applications of sequencing.
3 videos expand
- Video: Polymerase Chain Reaction
- Video: Next Generation Sequencing
- Video: Applications of Sequencing
Graded: Quiz 2: Measurement Technology
WEEK 3
Computing Technology
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.
6 videos expand
- Video: What Is Computer Science?
- Video: Algorithms
- Video: Memory and Data Structures
- Video: Efficiency
- Video: Software Engineering
- Video: What is Computational Biology Software
Graded: Quiz 3: Computing Technology
WEEK 4
Data Science Technology
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.
10 videos, 2 readings expand
- Video: Why Care About Statistics?
- Video: What Went Wrong?
- Video: The Central Dogma of Statistics
- Video: Data Sharing Plans
- Video: Getting Help with Statistics
- Video: Plotting Your Data
- Video: Sample Size and Variability
- Video: Statistical Significance
- Video: Multiple Testing
- Video: Study Design, Batch Effects, and Confounding
- Reading: Course Project Instructions and Reading
- Reading: Post-Course Survey
Graded: Quiz 4: Data Science Technology
Graded: Course Project
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