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دسته بندی دوره ها

Bioinformatics Data Analysis: Master Python, R and Linux

سرفصل های دوره

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


1. Introduction
  • 1. Introduction to bioinformatics scripting

  • 2. Introduction to bash for bioinformatics
  • 1. Introduction to linux (bash for bioinformatics)
  • 2. Bash Basic Commands
  • 3. Ncbi E-utilities on bash (Sequence Analysis)
  • 4. Famous Bioinformatics Tools (Installation and Introduction)
  • 5. Blast for Linux (Sequences Homology)
  • 6. Sequence Alignment Analysis
  • 7. Phylogenetic Analysis (Tree Construction)
  • 8. GitHub Repository.html

  • 3. GitHub for Bioinformatics (Not Compulsory)
  • 1. Introducing GitHub
  • 2. Profile and first Repository Setup
  • 3. Bioinformatics Projects Hunting
  • 4. Cloning and Forking Repositories
  • 5. Collaborating on GitHub
  • 6. GitHub for Project Mangement

  • 4. CLI in Bioinformatics (Not Compulsory)
  • 1. Introduction and Why CLI in Bioinformatics
  • 2. CLI and GUI Explanation
  • 3. if we already have Graphical user interface system why we should use CLI
  • 4. Short Practical with Programming Language
  • 5. Why Would You Use CLI over GUI
  • 6. Foundation behind CLI Shell explanation
  • 7. Drawbacks of CLI and GUI
  • 8. Linux Introduction and Usage Over years
  • 9. Linux Distros
  • 10. Why Ubuntu Operating System
  • 11. WSL Explanation
  • 12. Linux Vs Unix

  • 5. WSL as Linux Alternative on Windows
  • 1. (Practical) Making A Subsystem For Linux In Windows OS
  • 2. Linux File Handling Commands
  • 3. Accessing And Creating Files In Windows Os
  • 4. Basic Process Management Commands for Linux OS
  • 5. E-Direct Introduction
  • 6. Installing NCBI through CLi
  • 7. Code Used in Lectures.html
  • 8. Entrez Direct Functions
  • 9. Mrna And Protein Seq Retrieval
  • 10. Batch Retrieval of Protein Using Taxon Id
  • 11. Retrieving CDS From Reference Genome
  • 12. Explaining Different Commands

  • 6. Bioinformatics Pipeline
  • 1. Pipeline Explanation

  • 7. NGS data Analysis on Bash
  • 1. Introduction
  • 2. Getting the SRA Reads
  • 3. Checking the Quality of Data
  • 4. Quality Trimming of data
  • 5. Aligners and Aligning Reads to genome
  • 6. SAM and Bam File Indexing and Sorting
  • 7. Feature Extraction
  • 8. Pipeline Code.html

  • 8. Variant Calling analysis on Bash
  • 1. Introduction
  • 2. Variants and Types
  • 3. Understanding the Metadata and Softwares
  • 4. Getting Data From SRA Using SRA Toolkit
  • 5. Quality Control and Trimming
  • 6. Sam and Bcf Tools and Fixing NS and Calling Variants
  • 7. Alignment to Reference Genome
  • 8. Separation of SNPs and Indels Variants
  • 9. Visualizing Variants Using IGV and UCSC Browser
  • 10. Pipeline Code.html

  • 9. Python Section
  • 1. Introduction to Bioinformatics and Why Python
  • 2. BioPython Introduction
  • 3. GitHub Repository for Python.html
  • 4. Setting up Coding Environment
  • 5. Explaining the libraries for the course
  • 6. Advance File Formats of Bioinformatics with BioPython
  • 7. Sequence Analysis Using Biopython
  • 8. Database RetrievalAccessing Using Biopython
  • 9. Working With Genomes Using Biopython
  • 10. Phylogenetic Tree Construction using Biopython
  • 11. Proteomics Analysis Using Biopython
  • 12. Machine Learning in Bioinformatics

  • 10. R Section
  • 1. Introduction to Bioinformatics and R Exploring the Intersection of Biology
  • 2. Getting Started with R Installation and Variables Understanding
  • 3. Working with R Packages Installing, Loading, and Exploring Bioinformatics
  • 4. Differential Gene Expression Analysis with Deseq2 Preparing Data
  • 5. Deseq2 Code Understanding
  • 6. Converting Ensembl Gene IDs to Gene Symbols Using R Techniques and Packages
  • 7. Visualizing Gene Expression Data Creating Stunning Plots with ggplot2
  • 8. Introduction to Single-Cell RNA Sequencing (scRNA-seq) Data Analysis
  • 9. Exploring scRNA-seq Code Cell Trajectories and Gene Expression Dynamics
  • 10. GitHub Source Code for R.html

  • 11. Microarray Data Analysis Using R
  • 1. Introduction of Microarray
  • 2. Microarray Databases
  • 3. Microarray Analysis Using GEO2R
  • 4. Microarray Analysis on R
  • 5. Source Code for Microarray Section.html

  • 12. Thank You for taking the Course
  • 1. Thankyou Note.html
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