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Genome Assembly Programming Challenge
Coursera
Course
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Genome Assembly Programming Challenge

University of California San Diego

Learn to use bioinformatics programming to assemble and analyze the genome of a pathogenic bacterium during an outbreak in Germany, exploring its evolutionary origins.

Unknown3 weeksEnglish47,019 enrolled

About this Course

In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome

What You'll Learn

  • Understand the unknown pathogen’s role in the outbreak
  • Apply bioinformatics programming to assemble genomes
  • Analyze pathogen characteristics through genomic data

Prerequisites

  • Prior hands-on experience with core concepts
  • Ability to apply main tools or methods independently

Instructors

N

Neil Rhodes

Adjunct Faculty

M

Michael Levin

Visiting Scholar

M

Michael Levin

Lecturer

P

Pavel Pevzner

Professor

Topics

Algorithms
Computer Science
Health Informatics
Health
Molecular Biology
Program Development
Software Development
Bioinformatics
Graph Theory
Infectious Diseases

Course Info

PlatformCoursera
LevelUnknown
PacingUnknown
PriceFree

Skills

الخوارزميات
علوم الحاسوب
معلوماتية الصحة
الصحة
علم الأحياء الجزيئي
تطوير البرمجيات
البرمجة
المعلوماتية الحيوية
Graph Theory
Infectious Diseases

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