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. The course will also cover differential expression analysis using statistical methods and visualisation of results using popular tools such as R. Students will learn how to preprocess raw sequencing data, perform quality control, and align reads to a reference genome or transcriptome. 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.

In this course, you will learn how to perform RNAseq data analysis via linux command line. You will learn everything you need to know Interest in genomics as well as applying computational methods for processing transcriptomics datasets Interest in bioinformatics tools installation Interest in working with UNIX command-line tools Performing Clustering analysis using gene expression data Performing Principal Component Analysis (PCA) UNIX command-line tools for processing the data Using Conda for installation of bioinformatics tools Generating Quality Control metrics and statistics Language: English | Size: 2.71 GB | Duration: 5h 16mīecome a master in performing RNAseq analysis on linux command-line and use R to perform DE analysis and clusteringīasics of NGS data analysis and how to perform Differential gene expression analysis for RNAseq dataset Rnaseq Data Analysis Using Shell Scripting And R

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