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.
Este curso forma parte de Programa Especializado - Genomic Data Science
Introduction to Genomic Technologies
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Acerca de este Curso
37,206 vistas recientes
Habilidades que obtendrás
BioinformaticsStatisticsData ScienceComputational Biology
Programa - Qué aprenderás en este curso
Semana
12 horas para completar
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 (Total 81 minutos), 2 lecturas, 1 cuestionario
8 videos
What Is Genomics?7m
What Is Genomic Data Science?8m
Just Enough Cell Biology8m
Important Molecules in Molecular Biology7m
The Human Genome Project17m
Molecular Biology Structures9m
From Genes to Phenotypes9m
2 lecturas
Syllabus10m
Pre-Course Survey10m
1 ejercicio de práctica
Quiz 1: Overview and Molecular Biology20m
Semana
21 hora para completar
Measurement Technology
In this module, you'll learn about polymerase chain reaction, next generation sequencing, and applications of sequencing.
3 videos (Total 25 minutos), 1 cuestionario
1 ejercicio de práctica
Quiz 2: Measurement Technology20m
Semana
31 hora para completar
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 (Total 39 minutos), 1 cuestionario
6 videos
Algorithms4m
Memory and Data Structures6m
Efficiency3m
Software Engineering8m
What is Computational Biology Software11m
1 ejercicio de práctica
Quiz 3: Computing Technology20m
Semana
42 horas para completar
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 (Total 55 minutos), 2 lecturas, 2 cuestionarios
10 videos
What Went Wrong?4m
The Central Dogma of Statistics3m
Data Sharing Plans4m
Getting Help with Statistics2m
Plotting Your Data5m
Sample Size and Variability7m
Statistical Significance6m
Multiple Testing7m
Study Design, Batch Effects, and Confounding7m
2 lecturas
Course Project Instructions and Reading10m
Post-Course Survey10m
2 ejercicios de práctica
Quiz 4: Data Science Technology20m
Course Project20m
35%
comenzó una nueva carrera después de completar estos cursos
28%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Principales revisiones sobre Introduction to Genomic Technologies
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!
This course provides a gentle introduction to the concepts and terminology associated with genomics. As someone who never took a biology course in high school or university, this course is perfect.
Instructores
Steven Salzberg, PhD
ProfessorBiomedical Engineering, Computer Science, and Biostatistics
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Acerca de Universidad Johns Hopkins
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.
Acerca de Programa especializado Genomic Data Science
With genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome, and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome.
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, along with a variety of software implementation tools like Python, R, Bioconductor, and Galaxy.
This Specialization is designed to serve as both a standalone introduction to genomic data science or as a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics, for scientists in these fields seeking to gain familiarity in data science and statistical tools to better interact with the data in their everyday work.
To audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit. Please note that you will not receive a Certificate of Completion if you choose to Audit.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
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