Bioinformatics Data Analysis: Master Python, R and Linux

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Abdul Rehman Ikram

12:07:49

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1. Introduction

1. Introduction to bioinformatics scripting.mp4

08:48

2. Introduction to bash for bioinformatics

1. Introduction to linux (bash for bioinformatics).mp4

18:40

2. Bash Basic Commands.mp4

15:47

3. Ncbi E-utilities on bash (Sequence Analysis).mp4

16:43

4. Famous Bioinformatics Tools (Installation and Introduction).mp4

09:09

5. Blast for Linux (Sequences Homology).mp4

12:16

6. Sequence Alignment Analysis.mp4

05:59

7. Phylogenetic Analysis (Tree Construction).mp4

14:24

8. GitHub Repository.html

3. GitHub for Bioinformatics (Not Compulsory)

1. Introducing GitHub.mp4

11:04

2. Profile and first Repository Setup.mp4

12:18

3. Bioinformatics Projects Hunting.mp4

08:13

4. Cloning and Forking Repositories.mp4

15:21

5. Collaborating on GitHub.mp4

06:34

6. GitHub for Project Mangement.mp4

07:21

4. CLI in Bioinformatics (Not Compulsory)

1. Introduction and Why CLI in Bioinformatics.mp4

06:02

2. CLI and GUI Explanation.mp4

03:24

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

07:07

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

5. WSL as Linux Alternative on Windows

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

05:06

2. Linux File Handling Commands.mp4

11:05

3. Accessing And Creating Files In Windows Os.mp4

03:28

4. Basic Process Management Commands for Linux OS.mp4

07:07

5. E-Direct Introduction.mp4

03:44

6. Installing NCBI through CLi.mp4

01:19

7. Code Used in Lectures.html

8. Entrez Direct Functions.mp4

02:17

9. Mrna And Protein Seq Retrieval.mp4

03:08

10. Batch Retrieval of Protein Using Taxon Id.mp4

02:09

11. Retrieving CDS From Reference Genome.mp4

01:16

12. Explaining Different Commands.mp4

01:13

6. Bioinformatics Pipeline

1. Pipeline Explanation.mp4

07:55

7. NGS data Analysis on Bash

1. Introduction.mp4

04:55

2. Getting the SRA Reads.mp4

08:18

3. Checking the Quality of Data.mp4

07:39

4. Quality Trimming of data.mp4

04:34

5. Aligners and Aligning Reads to genome.mp4

12:48

6. SAM and Bam File Indexing and Sorting.mp4

07:15

7. Feature Extraction.mp4

08:43

8. Pipeline Code.html

8. Variant Calling analysis on Bash

1. Introduction.mp4

03:51

2. Variants and Types.mp4

07:20

3. Understanding the Metadata and Softwares.mp4

05:22

4. Getting Data From SRA Using SRA Toolkit.mp4

07:18

5. Quality Control and Trimming.mp4

09:46

6. Sam and Bcf Tools and Fixing NS and Calling Variants.mp4

07:49

7. Alignment to Reference Genome.mp4

10:15

8. Separation of SNPs and Indels Variants.mp4

05:17

9. Visualizing Variants Using IGV and UCSC Browser.mp4

03:37

10. Pipeline Code.html

9. Python Section

1. Introduction to Bioinformatics and Why Python.mp4

09:17

2. BioPython Introduction.mp4

09:58

3. GitHub Repository for Python.html

4. Setting up Coding Environment.mp4

14:02

5. Explaining the libraries for the course.mp4

15:25

6. Advance File Formats of Bioinformatics with BioPython.mp4

19:23

7. Sequence Analysis Using Biopython.mp4

16:17

8. Database RetrievalAccessing Using Biopython.mp4

11:33

9. Working With Genomes Using Biopython.mp4

19:08

10. Phylogenetic Tree Construction using Biopython.mp4

14:09

11. Proteomics Analysis Using Biopython.mp4

19:56

12. Machine Learning in Bioinformatics.mp4

11:45

10. R Section

1. Introduction to Bioinformatics and R Exploring the Intersection of Biology.mp4

08:02

2. Getting Started with R Installation and Variables Understanding.mp4

10:15

3. Working with R Packages Installing, Loading, and Exploring Bioinformatics.mp4

09:34

4. Differential Gene Expression Analysis with Deseq2 Preparing Data.mp4

05:27

5. Deseq2 Code Understanding.mp4

17:43

6. Converting Ensembl Gene IDs to Gene Symbols Using R Techniques and Packages.mp4

11:30

7. Visualizing Gene Expression Data Creating Stunning Plots with ggplot2.mp4

10:14

8. Introduction to Single-Cell RNA Sequencing (scRNA-seq) Data Analysis.mp4

07:15

9. Exploring scRNA-seq Code Cell Trajectories and Gene Expression Dynamics.mp4

21:55

10. GitHub Source Code for R.html

11. Microarray Data Analysis Using R

1. Introduction of Microarray.mp4

18:49

2. Microarray Databases.mp4

12:20

3. Microarray Analysis Using GEO2R.mp4

18:33

4. Microarray Analysis on R.mp4

33:45

5. Source Code for Microarray Section.html

12. Thank You for taking the Course

1. Thankyou Note.html

Description

Mastering Python, R, and Bash for Efficient Biological Data Processing and Analysis

What You'll Learn?

Fundamentals of Python, R, and Bash scripting: Learn the basics of these scripting languages, including syntax, data types, variables, and control structures.
Data parsing and manipulation: Understand how to parse and manipulate various biological data types, such as DNA sequences, protein structures, and gene express
Data visualization: Learn how to visualize biological data using Python and R, including creating plots, charts, and graphs to gain insights from the data.
Statistical analysis: Explore statistical analysis techniques in R for analyzing biological datasets, including hypothesis testing, regression analysis, and clu
Automation and pipeline development: Learn how to automate repetitive tasks and build efficient data processing pipelines using Bash scripting.
Real-life data analysis projects: Apply the skills learned throughout the course to real-life biological datasets, gaining hands-on experience in bioinformatics
Best practices in bioinformatics scripting: Learn best practices for writing clean, efficient, and maintainable scripts for bioinformatics analysis.

Who is this for?

Biologists: Biologists who want to enhance their computational skills and learn how to analyze biological data using scripting languages.

Programmers: Programmers interested in applying their skills to biological research and learning about the unique challenges of bioinformatics data analysis.

Students: Students studying bioinformatics, biology, computer science, or related fields who want to gain practical skills in bioinformatics scripting.

Professionals: Professionals working in the field of bioinformatics who want to update their skills and learn new techniques for data analysis.

Anyone interested in bioinformatics: Individuals with a general interest in bioinformatics and a desire to learn more about how biological data is analyzed using scripting languages.

What You Need to Know?

Basic biology knowledge: Understanding of fundamental biological concepts such as DNA, genes, proteins, and biological processes.

Computer skills: Comfortable using a computer and basic software applications. No prior programming experience is required.

Command line familiarity: Basic familiarity with the command line interface (e.g., navigating directories, executing commands) will be helpful but is not mandatory.

Hardware and software requirements: Access to a computer with internet connectivity and the ability to install software (Python, R, and Bash) as needed for the course.

More details

Description
Welcome to "Bioinformatics Scripting: From Data Parsing to Analysis," a comprehensive course designed to equip you with the essential skills in Python, R, and Bash scripting for effective biological data processing and analysis.
In the rapidly evolving field of bioinformatics, the ability to efficiently analyze and interpret biological data is crucial. This course is designed to help you master the scripting languages commonly used in bioinformaticsâ€”Python, R, and Bashâ€”and apply them to real-life biological datasets.
The course begins with an introduction to the fundamentals of Python, R, and Bash scripting, including basic syntax, data structures, and control flow. You will then learn how to parse different types of biological data, such as DNA sequences, protein structures, and gene expression profiles, using these scripting languages.
As you progress through the course, you will explore advanced topics such as data visualization, statistical analysis, and machine learning in Python and R. You will also learn how to automate repetitive tasks and build efficient data processing pipelines using Bash scripting.
By the end of the course, you will have the skills and confidence to tackle complex bioinformatics problems and conduct meaningful analyses of biological data. Whether you are a biologist looking to enhance your computational skills or a programmer interested in applying your skills to biological research, this course will provide you with the knowledge and tools you need to succeed in the field of bioinformatics.
Join us on this exciting journey and take your bioinformatics skills to the next level!
Who this course is for:
Biologists: Biologists who want to enhance their computational skills and learn how to analyze biological data using scripting languages.
Programmers: Programmers interested in applying their skills to biological research and learning about the unique challenges of bioinformatics data analysis.
Students: Students studying bioinformatics, biology, computer science, or related fields who want to gain practical skills in bioinformatics scripting.
Professionals: Professionals working in the field of bioinformatics who want to update their skills and learn new techniques for data analysis.
Anyone interested in bioinformatics: Individuals with a general interest in bioinformatics and a desire to learn more about how biological data is analyzed using scripting languages.

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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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