Introduction to Genomic Technologies

Start Date: 07/05/2020

Course Type: Common Course

Course Link:

About 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.

Course Syllabus

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.

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Course Introduction

Introduction to Genomic Technologies This course introduces the fundamental technologies and principles underlying genome-wide data analysis and annotation, including data quality assurance and annotation. The course emphasizes the diverse data sources used by genome-wide data analysts and the importance of maintaining knowledge of the different data sources. The course also describes the tools used to explore and visualize genomic data. The course emphasizes the diverse data sources used by genome-wide data analysts and the importance of maintaining knowledge of the different data sources. The course also describes the tools used to explore and visualize genomic data. Introduction to Genetics for Genomic Data Analysis Annotation and Data Validation Tools for Exploring Genomic Data Tools for Validation in Genomic Data Introduction to Graph Theory This course is an introduction to graph theory as applied to data science. Graphs are the building blocks of data science. We will introduce the fundamental ideas of graph theory, including the basic elements of data science, and explore the building blocks of graph theory: trees, rocks, and galaxies. We will then apply some of the most important ideas in graph theory to solve problems in data science, while also developing an introductory knowledge of the different areas of graph theory and their unique characteristics. We’ll also look at the building blocks of graph theory: edges, edges of streams, edges of trees, and bridges. We’ll also look at some of the most important problems in graph

Course Tag

Bioinformatics Statistics Data Science Computational Biology

Related Wiki Topic

Article Example
Compression of Genomic Re-Sequencing Data High-throughput sequencing technologies have led to a dramatic decline of genome sequencing costs and to an astonishingly rapid accumulation of genomic data. These technologies are enabling ambitious genome sequencing endeavours, such as the 1000 Genomes Project and 1001 ("Arabidopsis thaliana") Genomes Project. The storage and transfer of the tremendous amount of genomic data have become a mainstream problem, motivating the development of high-performance compression tools designed specifically for genomic data. A recent surge of interest in the development of novel algorithms and tools for storing and managing genomic re-sequencing data emphasizes the growing demand for efficient methods for genomic data compression.
Genomic imprinting In placental species, parent-offspring conflict can result in the evolution of strategies, such as genomic imprinting, for embryos to subvert maternal nutrient provisioning. Despite several attempts to find it, genomic imprinting has not been found in the platypus, reptiles, birds or fish. The absence of genomic imprinting in a placental reptile, the southern grass skink, is interesting as genomic imprinting was thought to be associated with the evolution of viviparity and placental nutrient transport.
Genomic counseling Genomic counseling is the process by which a person gets informed about his or her genome. In contrast to genetic counseling, which focuses on Mendelian diseases and typically involves person-to-person communication with a medical genetics expert, genomic counseling is not limited to currently clinically relevant information and includes other genomic information that is of interest for the informed person, such as increased risk for complex disease (for example diabetes or obesity), genetically determined non-disease related traits (for example baldness), or genetic genealogy data. Given the less sensitive nature of this information, genomic advice can be given impersonally, for example over the internet (virtual genomic counseling).
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