Genomic Data Analysis NGS data processing on the CLI and GUI

Hands on tutorial for Transcriptomics/NGS data analysis using GUI and Command line Interfaces
What you'll learn
The basics of Next Generation Sequencing and how it can be used for Differential gene expression analysis via RNA sequencing.
Quality Control of NGS data
Trimming the Reads of NGS Data
Different tools for aligning reads to genome
Differential Expression.
Ultimately understand how technologies like RNA sequencing could be used to identify specific genes that can cause certain conditions.
Heatmap Generation of Results
Interpret the results of DEG's
Understanding Bioinformatics Pipeline concept
Use of Galaxy for NGS data processing
Requirements
Background knowledge of Biology and genetics
Knowledge of the composition and function of DNA & RNA.
Understanding gene expression in different conditions.
Description
This course will take you through an example genomic analysis of high-throughput (NGS) sequencing data and will help you build the skills for reproducible research.
Phase 1:
GUI Phase Will introduce you to the galaxy which is already done
Phase 2:
This will introduce the Linux and WSL environment
Phase 3:
Genomic Data Analysis: NGS data processing on the command line
Introduction phase:
As we saw in the bash introductory lesson, the Linux shell is a powerful system for interacting with genomic data. Because NGS data files are so large and are often processed end-to-end, the Unix tool/pipe metaphor works particularly well for high-throughput sequencing experiments.
What does the command line approach offer over GUIs?
Repetition
You will often need to repeat the same tasks with multiple input files. As the number of input files grows, the advantages of a command-line interface over a graphical user interface (GUI) also grow.
Reproducibility
Command-line tools provide greater reproducibility than GUIs: if you need to change the parameters, you can do so and regenerate all of the downstream results.
Project organization
As you work with more data, you will generate more results. It’s not uncommon for a single project to generate hundreds of data files. If you are working at a command line interface, you can think about how you want to organize those files and do so in a consistent way.
Scaling from desktops to servers
GUIs are great for interactive use on an individual computer, but if you need the power of a server there is likely no GUI application to meet your needs. Command line interfaces scale well to server-based computing.

Who this course is for:
People generally interested in new research methdologies and would like to try them themselves!
Beginner Bioinformaticians looking to understand the process of RNA sequencing
People interested in researching the effects of different pathologies on gene expression or even how gene expression changes over the course of a cell's growth curve.
People looking to carry out differential gene expression and gene ontology analysis.
People who want to carry out bioinformatic analysis without the need for complex code.

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