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ChIP-seq with Bioconductor in R

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Course Report - ChIP-seq with Bioconductor in R

Course Report

Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.

Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Bioinformatics has become a crucial branch. It allows us to see the inner workings of our cells. All of them share the same genome. This is true regardless of whether they're brain cells that help you read this webpage or immune cells that check your body for microorganisms. The active genes are what distinguish them. Complex proteins activate and deactivate various genes to determine these genes. If this regulatory machinery becomes too complex, it can lead to cancer or other serious diseases. The function and causes behind disease can be analyzed using ChIP-seq. It can provide insights into how we can intervene to prevent cells from spinning out of control. This course will show you how to analyze ChIP–seq data with R.

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Introduction to Bioconductor in R

Intermediate R

What You Will Learn

In this course, you will explore a real dataset while learning how to process and analyze ChIP-seq data in R

Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset

Course Instructors

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Peter Humburg

Statistician

Peter Humburg has extensive experience in the analysis of genomic data as a Bioinformatician. His doctoral work focused on the development of statistical methods for the analysis of ChIP-seq data. Pe...
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