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What you'll learn
Learn Basic Theory & Practical demonstration of Bioinformatics techniques on both command line and graphical user interface
Learn how to explore biological databases and retrieve data using databases and their tools
Learn how to align biological sequences using pairwise and multiple alignment method on both graphical user interface and command line interface
Learn how to perform genomics analysis like prediction of genes, genome annotation, Intron and exon analysis, gene location and gene enrichment using bioinforma
Learn how to perform evolutionary bioinformatics analysis also called phylogenetic analysis on both command line and graphical user interface
Learn to perform proteomics bioinformatics analysis like motifs and domain finding, protein physical parameters, protein-protein interaction, protein enrichment
Learn about structural bioinformatics where you will learn homology modeling on GUI and CLI platforms also you will be able to predict 3D structure of proteins
Learn how to use command line in bioinformatics how to linux subsystem in windows learn some basic use of bash, perform database searching analysis using NCBI E
Learn how to use transcriptomics data and for NGS and variant calling analysis
Learn how to Use R for bioinformatics install packages make some interesting plots with ggplot2 get familiar with Single Cell RNA and perform Gene expression da
Learn how to Use python for bioinformatics use biopython library for sequence analysis, working with genomes, phylogenetics, proteomics and machine learning
Build your very own pipeline for RNA-Seq and variant calling analysis by using Linux
Build Codes for R and Python to perform Automation in bioinformatics analysis
All the codes and pipelines used in the course will be available to all the students
Requirements
Basic knowledge of biology: Understanding of biological concepts such as genes, proteins, and genomes.
Basic computer skills: Ability to navigate and use a computer, as well as familiarity with software installation.
Basic understanding of statistics: Knowledge of basic statistical concepts used in bioinformatics analysis.
Familiarity with programming concepts: Basic understanding of programming concepts such as variables, loops, and functions.
Familiarity with the command line interface (CLI): Basic knowledge of using the command line for tasks such as file navigation and manipulation.
Description
Embark on a journey into the world of bioinformatics with our Complete Bioinformatics Practical Bootcamp from Zero to Hero! This comprehensive training bootcamp is designed for students, academia, and industry professionals looking to extend their understanding of biological data and excel in the field of bioinformatics.Explore the bioinformatics world through hands-on, practical demonstrations that bridge theory with real-world applications. Learn to navigate biological databases, align sequences, and analyze genomics data using cutting-edge tools and techniques. Dive into proteomics, structural bioinformatics, and evolutionary analysis to uncover the hidden secrets of life's building blocks.Our course is designed to equip you with the fundamental skills needed for becoming a excellent computational data analyst. You'll delve into ten key modules:Biological DatabasesSequence AlignmentGenomic BioinformaticsProteomic BioinformaticsEvolutionary BioinformaticsStructural BioinformaticsCommand Line Bioinformatics (Linux for bioinformatics)Transcriptomics BioinformaticsR language for BioinformaticsPython language for BioinformaticsThis unique blend of theory and practical application will empower you to handle biological data with confidence. With 103 lectures, including practical tutorials, 10 assignments, and 11 quizzes and a final project for making your own bioinformatics pipeline for RNA-Seq data analysis on bash, you'll gain a comprehensive understanding of bioinformatics tools and techniques.From biological databases to building your own pipeline for data analysis, you'll explore a variety of tools that are freely available and closely related to the course material. Some tools may require sign-up, but most will be accessible without any registration. All the codes used will be available to GitHub.By the end of this course, you'll see biological data in a whole new light. We believe this investment in your education will be both valuable and rewarding. Don't miss out on this opportunity to transform your bioinformatics skills.Sign up today and take the first step towards mastering bioinformatics!
Overview
Section 1: Module 1: Introduction to Bioinformatics (Theory)
Lecture 1 Introduction to Bioinformatics
Lecture 2 Goals, Scope and Applications of Bioinformatics
Section 2: Module 2: Biological Databases (Theory and Practical)
Lecture 3 Introduction to Databases and Bioinformatics Databases
Lecture 4 Types of Databases
Lecture 5 Practical: Primary Databases & NCBI Database
Lecture 6 Practical: Embl Database
Lecture 7 Practical: Ddbj Database
Lecture 8 Practical: Secondary Databases and PDB Database
Lecture 9 Practical: Uniprot Database
Lecture 10 Practical: GEO Database
Lecture 11 Derived Databases Introduction
Lecture 12 Practical: Derived Databases
Lecture 13 Practical: Literature Specialized Databases
Lecture 14 GitHub Repository
Section 3: Module 3: Biological Sequence Alignment (Theory and Practical)
Lecture 15 Introduction to Sequence Alignment and Pairwise Alignment
Lecture 16 Practical: Pairwise Sequence Alignment
Lecture 17 Introduction to Homology Searching and Blast
Lecture 18 Practical: Homology Searching and Blast
Lecture 19 Introduction to Multiple Sequence Alignment
Lecture 20 Practical: Multiple Sequence Alignment
Lecture 21 GitHub Repository
Section 4: Module 4: Genomics Bioinformatics (Theory and Practical)
Lecture 22 Introduction to Genomics for Bioinformatics
Lecture 23 Introduction to Gene Prediction
Lecture 24 Practical: Prediction of Prokaryotic and Eukaryotic Genes
Lecture 25 Practical: Genome Annotation and visualization
Lecture 26 Practical: Intron and Exon Analysis
Lecture 27 Practical: Gene location on Chromosome Analysis
Lecture 28 Practical: Gene Enrichment Analysis
Lecture 29 GitHub Repository
Section 5: Module 5: Evolutionary Bioinformatics (Theory and Practical)
Lecture 30 Introduction to Phylogenetics
Lecture 31 Procedure and Tools for Constructing Phylogenetic Trees
Lecture 32 Practical: Phylogenetic Trees Construction
Lecture 33 Practical: Annotating Phylogenetic Trees for Publications and Research
Lecture 34 GitHub Repository
Section 6: Module 6: Proteomics Bioinformatics (Theory and Practical)
Lecture 35 Course Introduction and Sequences Analysis
Lecture 36 Practical: Phylogenetic Analysis
Lecture 37 Practical: Motifs and Domains analysis
Lecture 38 Practical: Protein Physical Parameters and Location Analysis
Lecture 39 Practical: Protein-Protein Interaction and Enrichment Analysis
Lecture 40 Practical: Proteins Pathway Analysis
Lecture 41 Practical: Protein Structure Prediction
Lecture 42 Practical: Proteins Visualization Analysis
Section 7: Module 7: Structural Bioinformatics (Theory and Practical)
Lecture 43 Introduction to Structural Bioinformatics
Lecture 44 Explaining Homology Modeling
Lecture 45 Practical: GUI based Modeling of Proteins
Lecture 46 Practical: Command Line based Protein Modeling
Lecture 47 Practical: Performing Protein Prediction through ML and De novo Methods
Lecture 48 GitHub Repository
Section 8: M8: Command Line Bioinformatics (Linux for Bioinformatics) Theory and Practical
Lecture 49 Introduction to Linux and Command line in bioinformatics
Lecture 50 Practical: Making Subsystem for Linux in Windows OS
Lecture 51 Practical: Bash Basic Commands
Lecture 52 Practical: Ncbi E-utilities on bash (Sequence Analysis)
Lecture 53 Practical: Famous Bioinformatics Tools (Installation and Introduction)
Lecture 54 Practical: Blast for Linux (Sequences Homology)
Lecture 55 Practical: Sequence Alignment Analysis
Lecture 56 Practical: Phylogenetic Analysis (Tree Construction)
Lecture 57 GitHub Repository
Section 9: Module 9: Transcriptomics Bioinformatics (Theory and Practical)
Lecture 58 Introduction to Transcriptomics and RNA-Seq
Lecture 59 Understanding Bioinformatics Pipelines
Lecture 60 What is SRA Database and SRA-toolkit
Lecture 61 Practical: Getting the SRA Reads for RNA-Seq Data
Lecture 62 Practical: Checking the Quality of Data
Lecture 63 Practical: Quality Trimming of data
Lecture 64 Practical: Aligners and Aligning Reads to genome
Lecture 65 Practical: SAM and Bam File Indexing and Sorting
Lecture 66 Practical: Feature Extraction
Lecture 67 Introduction to Variant Calling for Bioinformatics
Lecture 68 Variants and Types
Lecture 69 Understanding the Metadata and Software's
Lecture 70 Practical: Getting Data From SRA Using SRA Toolkit
Lecture 71 Practical: Quality Control and Trimming
Lecture 72 Practical: Alignment to Reference Genome
Lecture 73 Practical: Sam and Bcf Tools and Fixing NS and Calling Variants
Lecture 74 Practical: Separation of SNP's and Indels Variants
Lecture 75 Practical: Visualizing Variants Using IGV and UCSC Browser
Lecture 76 GitHub Repository
Section 10: Module 10: The Final Project
Lecture 77 Final Project Instructions
Section 11: Module 11: R for Bioinformatics (Theory and Practical)
Lecture 78 Introduction to Bioinformatics and R
Lecture 79 Practical: Getting Started with R: Installation
Lecture 80 Practical: Working with R Packages: Installing and Loading Packages
Lecture 81 Differential Gene Expression Analysis with Deseq2: Preparing Data
Lecture 82 Practical: Deseq2 Code Understanding
Lecture 83 Practical: Converting Ensembl Gene IDs to Gene Symbols
Lecture 84 Practical: Visualizing Gene Expression dаta: Creating Stunning Plots
Lecture 85 Introduction to Single-Cell RNA Sequencing (scRNA-seq) Data Analysis
Lecture 86 Practical: Exploring scRNA-seq Code: Cell Trajectories and Gene Expression
Lecture 87 GitHub Repository
Section 12: Module 11: Microarray Data Analysis (Theory and Practical)
Lecture 88 Introduction of Microarray
Lecture 89 Microarray Databases
Lecture 90 Practical: Microarray Analysis Using GEO2R
Lecture 91 Practical: Microarray Analysis on R
Lecture 92 GitHub Repository
Section 13: Module 12: Python Section (Theory and Practical)
Lecture 93 BioPython Introduction
Lecture 94 Practical: Setting up Coding Environment
Lecture 95 Explaining the libraries for the course
Lecture 96 Practical: Advance File Formats of Bioinformatics with BioPython
Lecture 97 Practical: Sequence Analysis Using Biopython
Lecture 98 Practical: Database Retrieval/Accessing Using Biopython
Lecture 99 Practical: Working With Genomes Using Biopython
Lecture 100 Practical: Phylogenetic Tree Construction using Biopython
Lecture 101 Practical: Proteomics Analysis Using Biopython
Lecture 102 Practical: Machine Learning in Bioinformatics
Lecture 103 GitHub Repository
Students: Undergraduate or graduate students studying biology, bioinformatics, or related fields who want to enhance their bioinformatics skills.,Professionals: Working professionals in the fields of biology, genetics, or biotechnology who want to learn bioinformatics for their research or career advancement.,Researchers: Scientists and researchers who want to incorporate bioinformatics into their research projects.,Data Analysts: Professionals working with biological data who want to learn bioinformatics tools and techniques for data analysis.,Enthusiasts: Individuals with a keen interest in biology and bioinformatics who want to explore the field and learn new skills.

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