Learn ChIP-Seq & Single Cell ATAC-Seq Data Analysis Using Linux and R

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Learn ChIP-Seq & Single Cell ATAC-Seq Data Analysis Using Linux and R

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About Course

Are you ready to dive into the world of gene regulation and chromatin accessibility using cutting-edge bioinformatics tools? This course, “Master ChIP-Seq and ATAC-Seq Analysis Using Linux and R,” is your gateway to understanding and performing real-world epigenomics data analysis from scratch.

Designed for students, researchers, and professionals in bioinformatics, biotechnology, and genomics, this hands-on course will take you on a comprehensive journey through ChIP-Seq (Chromatin Immunoprecipitation Sequencing) and ATAC-Seq (Assay for Transposase-Accessible Chromatin using sequencing) data analysis using powerful open-source tools.

You’ll start by understanding the theoretical concepts behind ChIP-Seq and ATAC-Seq technologies, including how they’re used to investigate protein-DNA interactions and chromatin accessibility in cells. These insights are essential in fields like cancer research, developmental biology, immunology, and beyond.

What You’ll Learn

  • The complete bioinformatics pipeline for ChIP-Seq and ATAC-Seq

  • Setting up a Linux environment (even on Windows) for analysis

  • Running quality checks and trimming raw sequencing reads

  • Aligning reads to a reference genome and handling alignment files

  • Performing peak calling with MACS2

  • Annotating peaks and discovering DNA motifs using HOMER

  • Understanding how single-cell ATAC-Seq works

  • Analyzing scATAC-Seq datasets in R using the Signac and Seurat packages

  • Visualizing chromatin accessibility and comparing multiple samples

Hands-On Linux and R Training

No prior experience with Linux or R? No problem. This course includes a full section on Linux for bioinformatics, where you’ll learn to navigate the file system, run basic commands, and handle biological data on the command line.

When working with single-cell ATAC-Seq data, we’ll shift to R—a powerful language for statistical analysis and visualization. You’ll get comfortable using RStudio, installing relevant packages, and working with real datasets from publicly available repositories like 10X Genomics and ENCODE.

Real Data. Real Tools. Real Skills.

Throughout this course, you’ll work with real ChIP-Seq and ATAC-Seq data to apply what you learn immediately. By the end of the course, you’ll be able to build your own analysis pipelines, interpret results, and even contribute to publications or research projects.

We cover essential tools used by professionals worldwide, including:

  • FastQC for quality control

  • BWA or Bowtie2 for alignment

  • MACS2 for peak calling

  • HOMER for peak annotation and motif discovery

  • Signac and Seurat for scATAC-Seq analysis in R

Who This Course Is For

  • Students in bioinformatics, computational biology, or life sciences

  • Researchers analyzing epigenetic or chromatin data

  • Professionals looking to upskill in genome data analysis

  • Anyone curious about sequencing data and open-source tools

No Prior Experience Required

Whether you’re a beginner or someone with basic knowledge of sequencing technologies, this course is structured to build your skills progressively from theory to practice.

Join now and gain the confidence to analyze and interpret ChIP-Seq and ATAC-Seq data like a pro. Let’s uncover the regulatory code of the genome one peak at a time.

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What Will You Learn?

  • Understand the principles and applications of ChIP-Seq and ATAC-Seq in studying protein-DNA interactions and chromatin accessibility.
  • Set up and navigate a Linux environment for bioinformatics, including using WSL (Windows Subsystem for Linux) and installing necessary tools.
  • Download, compress, and perform quality control of raw ChIP-Seq and ATAC-Seq data using command-line bioinformatics tools.
  • Index reference genomes and align sequencing reads using BWA or Bowtie2 and manage alignment files (SAM/BAM).
  • Perform peak calling with MACS2 and interpret the significance of enriched regions in ChIP-Seq data.
  • Annotate peaks and identify motifs using HOMER to gain functional insights into regulatory elements.
  • Learn the theory and workflow of single-cell ATAC-Seq, including its experimental setup and data structure.
  • Analyze scATAC-Seq data using R, Signac, and Seurat, including filtering cells, dimensionality reduction, and clustering.
  • Compare chromatin accessibility between different samples or conditions and visualize the results using R plots.
  • Build complete ChIP-Seq and scATAC-Seq analysis pipelines that can be adapted to various research and publication needs.

Course Content

Course Introduction

  • What You Will Learn !!!
    05:46

Introduction to Chip-Seq Data And Pipeline

Linux for Bioinformatics

ChIP-Seq Data Analysis Using Linux

Project 1: Chip-Seq Data analysis

Introduction to Single-Cell ATAC-Seq

Getting Started with R

Single-Cell ATAC-Seq Data Analysis Using R

Project 2: ScATAC Data Analysis

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Your Instructor

Abdul Rehman Ikram

Bioinformatician | Data Analyst | Computational Biologist

Abdul is a distinguished bioinformatician, data analyst, and computational biologist known for his exceptional contributions to the field of biomedical research. With a passion for integrating technology and biology, Abdul has carved a niche for himself, leveraging cutting-edge computational techniques to unravel complex biological data.

Driven by a curiosity to decode the complexities of life, Abdul believes in the power of interdisciplinary approaches. He is committed to mentoring the next generation of scientists, fostering a culture of innovation and continuous learning.

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