# Intro-to-ChIPseq

**Repository Path**: mayu95/Intro-to-ChIPseq

## Basic Information

- **Project Name**: Intro-to-ChIPseq
- **Description**: Intro to ChIPseq using HPC
- **Primary Language**: CSS
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-06-29
- **Last Updated**: 2024-12-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## Introduction to ChIP-seq using high performance computing


| Audience | Computational Skills | Prerequisites | Duration |
:----------|:----------|:----------|:----------|
| Biologists | Beginner/Intermediate | None | 3-day workshop (~19.5 hours of trainer-led time)|

### Description

This repository has teaching materials for a 3-day Introduction to ChIP-sequencing data analysis workshop. This workshop focuses on teaching basic computational skills to enable the effective use of an high-performance computing environment to implement a ChIP-seq data analysis workflow. It includes an introduction to shell (bash) and shell scripting. In addition to running the ChIP-seq workflow from FASTQ files to peak calls and nearest gene annotations, the workshop covers best practice guidlelines for ChIP-seq experimental design and data organization/management and quality control.

> These materials were developed for a trainer-led workshop, but are also amenable to self-guided learning.

### Learning Objectives

1.	Understand the necessity for, and use of, the command line interface (bash) and HPC for analyzing high-throughput sequencing data.
2.	Understand best practices for designing a ChIP-seq experiment and analysis the resulting data.

### Lessons
**[Click here](https://hbctraining.github.io/Intro-to-ChIPseq/schedule/) for links to lessons and the suggested schedule**

### Dataset
[Introduction to Shell: Dataset](https://www.dropbox.com/s/3lua2h1oo18gbug/unix_lesson.tar.gz?dl=1)

***
*These materials have been developed by members of the teaching team at the [Harvard Chan Bioinformatics Core (HBC)](http://bioinformatics.sph.harvard.edu/). These are open access materials distributed under the terms of the [Creative Commons Attribution license](https://creativecommons.org/licenses/by/4.0/) (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.*

* *Some materials used in these lessons were derived from work that is Copyright © Data Carpentry (http://datacarpentry.org/). 
All Data Carpentry instructional material is made available under the [Creative Commons Attribution license](https://creativecommons.org/licenses/by/4.0/) (CC BY 4.0).*