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
| Basic Info | Course 1 of 8 in the Genomic Data Science Specialization |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
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
- 阅读: Syllabus
- 阅读: Pre-Course Survey
- 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
- 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
- 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
- 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
- 阅读: Course Project Instructions and Reading
- 阅读: Post-Course Survey
Graded: Quiz 4: Data Science Technology
Graded: Course Project
FAQs
How It Works
课程作业
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
来自同学的帮助
与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。
证书
获得正式认证的作业，并与朋友、同事和雇主分享您的成功。
Creators
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.
Pricing
| Purchase Course | |
|---|---|
| Access to course materials | Available |
| Access to graded materials | Available |
| Receive a final grade | Available |
| Earn a shareable Course Certificate | Available |
Ratings and Reviews
good
A nice overview of all the subfields that make up bioinformatics.
it was interesting course but the part of statistics was severely complicated and should be taught in simple language.
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this was good course. Its really appreciating that you are teaching students sitting at home and learning online. this course was also good but i feel difficulty in learning 4th week course. Especially calculating p value and plotting data . I think that should be more precisely and should be describe with more examples. Thank you so much for this course and for increasing my knowledge.