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RNAseq Data analysis using Shell scripting and R
Rating: 3.9 out of 5(17 ratings)
107 students

RNAseq Data analysis using Shell scripting and R

Become a master in performing RNAseq analysis on linux command-line and use R to perform DE analysis and clustering
Created bySumukh Deshpande
Last updated 5/2023
English

What you'll learn

  • Basics of NGS data analysis and how to perform Differential gene expression analysis for RNAseq dataset
  • Generating Quality Control metrics and statistics
  • Mapping Reads to the genome
  • Differential gene expression
  • Using Conda for installation of bioinformatics tools
  • Processing RNA sequencing data
  • UNIX command-line tools for processing the data
  • Transcript quantification
  • Performing Principal Component Analysis (PCA)
  • Performing Clustering analysis using gene expression data

Course content

10 sections • 20 lectures • 5h 15m total length

  • Introduction5:51
  • Installing Conda4:01
  • Installing Bioinformatics tools using Conda13:04

    Learn how to install Bioinformatics tools using Conda environment on Unix.

Requirements

  • Background knowledge of biology
  • Interest in working with UNIX command-line tools
  • Interest in bioinformatics tools installation
  • Interest in genomics as well as applying computational methods for processing transcriptomics datasets
  • No programming experience needed. You will learn everything you need to know
  • All downloadable resources provided

Description

In this course, you will learn how to perform RNAseq data analysis via linux command line. This course provides a comprehensive introduction to RNAseq data analysis, covering the key concepts and tools needed to perform differential expression analysis and functional annotation of RNAseq data. Students will learn how to preprocess raw sequencing data, perform quality control, and align reads to a reference genome or transcriptome. The course will also cover differential expression analysis using statistical methods and visualisation of results using popular tools such as R. You will learn how to do end-to-end RNAseq data analysis which includes pre-processing of RNAseq data, Quality Control analysis, Differential Gene Expression analysis, Clustering and Principal Component Analysis of the gene expression data. You will also learn how to download data, install the bioinformatics/IT softwares using Conda/Anaconda on Mac, Windows or Linux platforms. I will guide you through performing differential expression analysis on RStudio (graphical user interface for R language).

Throughout the course, students will work with real-world datasets and gain hands-on experience with popular bioinformatics tools and software packages. By the end of the course, students will have a thorough understanding of RNAseq data analysis and will be able to perform their own analyses of gene expression data. This course is ideal for researchers, scientists, and students who are interested in understanding the molecular basis of gene expression and exploring the potential applications of RNAseq technology. No prior bioinformatics or programming experience is required, but a basic knowledge of molecular biology and genetics is recommended.

Who this course is for:

  • People interested in learning Next Generation Sequencing data analysis methods
  • Beginner Bioinformatician looking to understand end-to-end pipeline for transcriptomics data analysis
  • People looking to understand differential gene expression analysis
  • People interested to carry out bioinformatics analysis with command-line tools

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