Genomic Data Analysis NGS data processing on the CLI and GUI

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Abdul Rehman Ikram

4:00:46

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1. Introduction

1. Introduction of Course.mp4

04:55

2. Bioinformatics Pipelines.mp4

07:55

2. NGS On Graphical User Interface Using Galaxy

1. What is NGS and why we are using the NGS for data analysis.mp4

02:56

2. Next-generation sequencing.mp4

03:24

3. Generations of Sequencing.mp4

06:12

4. NGS Workflow.mp4

04:07

5. SRA Database introduction.mp4

03:22

6. SRA File.mp4

02:54

7. Galaxy Server Intro to Goals.mp4

07:04

8. Galaxy Server And Objects.mp4

04:48

9. Getting Onto Galaxy.mp4

02:14

10. Tools For NGS Data Analysis.mp4

03:35

11. Getting SRA Runs from Databases And platform.mp4

03:25

12. Ncbi Genome to Galaxy.mp4

03:32

13. Getting Sra Runs To Galaxy.mp4

05:12

14. Fastqc Tool To Dataset Generated Dataset.mp4

05:22

15. Trimmomatic Tool On Dataset.mp4

06:09

16. Alignmentgenome Mapping.mp4

04:57

17. Abundance Estimation Tool On Dataset.mp4

05:30

18. From Values To Visuals (Heatmap).mp4

07:41

3. CLI in Bioinformatics

1. Introduction and Why CLI in Bioinformatics.mp4

06:02

2. CLI and GUI Explanation.mp4

07:07

3. if we already have Graphical user interface system why we should use CLI.mp4

03:24

4. Short Practical with Programming Language.mp4

03:15

5. Why Would You Use CLI over GUI.mp4

03:44

6. Foundation behind CLI Shell explanation.mp4

07:40

7. Drawbacks of CLI and GUI.mp4

02:53

8. Linux Introduction and Usage Over years.mp4

05:29

9. Linux Distros.mp4

05:03

10. Why Ubuntu Operating System.mp4

03:20

11. WSL Explanation.mp4

02:05

12. Linux Vs Unix.mp4

04:21

13. (Practical) Making A Subsystem For Linux In Windows OS.mp4

05:06

14. Linux File Handling Commands.mp4

11:05

15. Accessing And Creating Files In Windows Os.mp4

03:28

16. Basic Process Management Commands for Linux OS.mp4

07:07

17. E-utilities on the Linux Command Line.mp4

03:44

18. Installing NCBI through CLi.mp4

01:19

19. Entrez Direct Functions.mp4

02:17

20. Mrna And Protein Seq Retrieval.mp4

03:08

21. Batch Retrieval of Protein Using Taxon Id.mp4

02:09

22. Retrieving CDS From Reference Genome.mp4

01:16

23. Explaining Different Commands.mp4

01:13

4. NGS data Analysis on Command Line

1. Getting the SRA Reads.mp4

08:18

2. Checking the Quality of Data.mp4

07:39

3. Quality Trimming of data.mp4

04:34

4. Aligners and Aligning Reads to genome.mp4

12:48

5. SAM and Bam File Indexing and Sorting.mp4

07:15

6. Feature Extraction.mp4

08:43

7. Pipeline Code.html

Description

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

Who is this 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.

More details

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. Itâ€™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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My teaching skills might not be as good as experienced and professional instructors on Udemy who made handsome profits from their courses, but I am sure that I have a sincere passion to teach, to broaden my professional networks, and teach that I love to do in my bioinformatics work.My goal is to help you to understand highly complex biological data and improve your computational skills in the analysis of Genomics, Proteomics, and Transcriptomics DATA using various Bioinformatics Approaches.if you have any queries about any of my work please let me know in my Udemy inbox section I'll be happy to answer all of your questions.That was All Folks!!

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