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.
提供方
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 2 小時
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 個視頻 （總計 81 分鐘）, 2 個閱讀材料, 1 個測驗
8 個視頻
Why Genomics?12分鐘
What Is Genomics?7分鐘
What Is Genomic Data Science?8分鐘
Just Enough Cell Biology8分鐘
Important Molecules in Molecular Biology7分鐘
The Human Genome Project17分鐘
Molecular Biology Structures9分鐘
From Genes to Phenotypes9分鐘
2 個閱讀材料
Syllabus10分鐘
Pre-Course Survey10分鐘
1 個練習
Quiz 1: Overview and Molecular Biology20分鐘
週
2完成時間為 1 小時
Measurement Technology
In this module, you'll learn about polymerase chain reaction, next generation sequencing, and applications of sequencing.
3 個視頻 （總計 25 分鐘）, 1 個測驗
1 個練習
Quiz 2: Measurement Technology20分鐘
週
3完成時間為 1 小時
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 個視頻 （總計 39 分鐘）, 1 個測驗
6 個視頻
Algorithms4分鐘
Memory and Data Structures6分鐘
Efficiency3分鐘
Software Engineering8分鐘
What is Computational Biology Software11分鐘
1 個練習
Quiz 3: Computing Technology20分鐘
週
4完成時間為 2 小時
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 個視頻 （總計 55 分鐘）, 2 個閱讀材料, 2 個測驗
10 個視頻
What Went Wrong?4分鐘
The Central Dogma of Statistics3分鐘
Data Sharing Plans4分鐘
Getting Help with Statistics2分鐘
Plotting Your Data5分鐘
Sample Size and Variability7分鐘
Statistical Significance6分鐘
Multiple Testing7分鐘
Study Design, Batch Effects, and Confounding7分鐘
2 個閱讀材料
Course Project Instructions and Reading10分鐘
Post-Course Survey10分鐘
2 個練習
Quiz 4: Data Science Technology20分鐘
Course Project20分鐘
4.5
256 個審閱35%
完成這些課程後已開始新的職業生涯
29%
通過此課程獲得實實在在的工作福利
來自基因组技术基础的熱門評論
Relatively nice introduction course, contents are maybe rather limited, yet as an instructive course, it does provide a clear overview which correlates well with the required answers for the quizzes!
Very good introductory course. It's a lot of material to cover for a newbie, but if you have some background, you will find you progress pretty quickly. Enjoyable, engaging, fun and useful.
講師
Steven Salzberg, PhD
ProfessorBiomedical Engineering, Computer Science, and Biostatistics
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
關於 约翰霍普金斯大学
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.
關於 基因组数据科学 專項課程
This specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, Python, R, Bioconductor, and Galaxy. The sequence is a stand alone introduction to genomic data science or a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics.
To audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit.
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