Bioinformatics Scripting: Python, R, Linux For Data Analysis

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.

Requirements

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.

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 bioinformatics—Python, R, and Bash—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!

Overview

Section 1: Introduction

Lecture 1 Introduction to bioinformatics scripting

Section 2: Introduction to bash for bioinformatics

Lecture 2 Introduction to linux (bash for bioinformatics)

Lecture 3 Bash Basic Commands

Lecture 4 Ncbi E-utilities on bash (Sequence Analysis)

Lecture 5 Famous Bioinformatics Tools (Installation and Introduction)

Lecture 6 Blast for Linux (Sequences Homology)

Lecture 7 Sequence Alignment Analysis

Lecture 8 Phylogenetic Analysis (Tree Construction)

Lecture 9 GitHub Repository

Section 3: GitHub for Bioinformatics (Not Compulsory)

Lecture 10 Introducing GitHub

Lecture 11 Profile and first Repository Setup

Lecture 12 Bioinformatics Projects Hunting

Lecture 13 Cloning and Forking Repositories

Lecture 14 Collaborating on GitHub

Lecture 15 GitHub for Project Mangement

Section 4: CLI in Bioinformatics (Not Compulsory)

Lecture 16 Introduction and Why CLI in Bioinformatics

Lecture 17 CLI and GUI Explanation

Lecture 18 if we already have Graphical user interface system why we should use CLI?

Lecture 19 Short Practical with Programming Language

Lecture 20 Why Would You Use CLI over GUI?

Lecture 21 Foundation behind CLI Shell explanation

Lecture 22 Drawbacks of CLI and GUI

Lecture 23 Linux Introduction and Usage Over years

Lecture 24 Linux Distros

Lecture 25 Why Ubuntu Operating System

Lecture 26 WSL Explanation

Lecture 27 Linux Vs Unix

Section 5: WSL as Linux Alternative on Windows

Lecture 28 (Practical) Making A Subsystem For Linux In Windows OS

Lecture 29 Linux File Handling Commands

Lecture 30 Accessing And Creating Files In Windows Os

Lecture 31 Basic Process Management Commands for Linux OS

Lecture 32 E-Direct Introduction

Lecture 33 Installing NCBI through CLi

Lecture 34 Code Used in Lectures

Lecture 35 Entrez Direct Functions

Lecture 36 Mrna And Protein Seq Retrieval

Lecture 37 Batch Retrieval of Protein Using Taxon Id

Lecture 38 Retrieving CDS From Reference Genome

Lecture 39 Explaining Different Commands

Section 6: Bioinformatics Pipeline

Lecture 40 Pipeline Explanation

Section 7: NGS data Analysis on Bash

Lecture 41 Introduction

Lecture 42 Getting the SRA Reads

Lecture 43 Checking the Quality of Data

Lecture 44 Quality Trimming of data

Lecture 45 Aligners and Aligning Reads to genome

Lecture 46 SAM and Bam File Indexing and Sorting

Lecture 47 Feature Extraction

Lecture 48 Pipeline Code

Section 8: Variant Calling analysis on Bash

Lecture 49 Introduction

Lecture 50 Variants and Types

Lecture 51 Understanding the Metadata and Softwares

Lecture 52 Getting Data From SRA Using SRA Toolkit

Lecture 53 Quality Control and Trimming

Lecture 54 Sam and Bcf Tools and Fixing NS and Calling Variants

Lecture 55 Alignment to Reference Genome

Lecture 56 Separation of SNP's and Indels Variants

Lecture 57 Visualizing Variants Using IGV and UCSC Browser

Lecture 58 Pipeline Code

Section 9: Python Section

Lecture 59 Introduction to Bioinformatics and Why Python

Lecture 60 BioPython Introduction

Lecture 61 GitHub Repository for Python

Lecture 62 Setting up Coding Environment

Lecture 63 Explaining the libraries for the course

Lecture 64 Advance File Formats of Bioinformatics with BioPython

Lecture 65 Sequence Analysis Using Biopython

Lecture 66 Database Retrieval/Accessing Using Biopython

Lecture 67 Working With Genomes Using Biopython

Lecture 68 Phylogenetic Tree Construction using Biopython

Lecture 69 Proteomics Analysis Using Biopython

Lecture 70 Machine Learning in Bioinformatics

Section 10: R Section

Lecture 71 Introduction to Bioinformatics and R: Exploring the Intersection of Biology

Lecture 72 Getting Started with R: Installation and Variables Understanding

Lecture 73 Working with R Packages: Installing, Loading, and Exploring Bioinformatics

Lecture 74 Differential Gene Expression Analysis with Deseq2: Preparing Data

Lecture 75 Deseq2 Code Understanding

Lecture 76 Converting Ensembl Gene IDs to Gene Symbols: Using R Techniques and Packages

Lecture 77 Visualizing Gene Expression Data: Creating Stunning Plots with ggplot2

Lecture 78 Introduction to Single-Cell RNA Sequencing (scRNA-seq) Data Analysis

Lecture 79 Exploring scRNA-seq Code: Cell Trajectories and Gene Expression Dynamics

Lecture 80 GitHub Source Code for R

Section 11: Microarray Data Analysis Using R

Lecture 81 Introduction of Microarray

Lecture 82 Microarray Databases

Lecture 83 Microarray Analysis Using GEO2R

Lecture 84 Microarray Analysis on R

Lecture 85 Source Code for Microarray Section

Section 12: Thank You for taking the Course

Lecture 86 Thankyou Note

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.