Bioinformatics Guide To Research Projects And Publications

Bioinformatics course for Academia, Students and Research Professionals to learn Research and Publish your first paper

What you'll learn

Biomarker Fundamentals: Understand the fundamental concepts of biomarkers, including their types, significance in health research, and applications

Data Handling: Learn how to access, manage, and preprocess large-scale biological data, including genomics, transcriptomics, and proteomics data.

Bioinformatics Tools: Gain proficiency in using bioinformatics tools and software for data analysis, including R, Python, and specialized biomarker

Statistical Analysis: Master statistical techniques and methodologies for identifying differentially expressed genes or proteins that serve

Data Visualization: Learn to create informative data visualizations to represent findings and insights, aiding in the interpretation of results.

Biomarker Discovery: Explore various methods for identifying and validating biomarkers, with a focus on their role in health research.

Integration of Omics Data: Understand how to integrate data from different omics levels (genomics, transcriptomics, proteomics) to discover multi-dimensional

Ethical Considerations: Explore the ethical and regulatory aspects of biomarker research, including privacy, consent, and data sharing.

Case Studies: Analyze real-world case studies and research papers to see how biomarkers are identified and applied in health research.

Hands-on Experience: Gain practical experience by working with real data, performing analyses, and presenting research findings.

Interdisciplinary Collaboration: Learn the importance of collaboration between bioinformaticians, biologists, clinicians, and other experts in health research.

Emerging Trends: Stay updated with the latest trends and advancements in biomarker discovery and health bioinformatics.

Upon completing this course, students will be well-equipped to conduct biomarker research, interpret data, and contribute to the field of health bioinformatics

Choosing a gene family

Choosing a plant for gene family research

Searching for previously published paper

selecting the office to work with on different platforms

Selecting the references software

How to use Mendeley - Reference Management Software

Sequence Retrieval Analysis

Homology Searching

Sequence Alignment

Sequence identity and Similarity analysis

What is phylogenetics and Tree construction

Gene Structures introns and exons map

Motifs identification Analysis

Domains Visualization Analysis

Chromosomal Mapping (Distribution of Genes on chromosome)

physiochemical characteristics analysis

Cis-acting regulatory elements prediction analysis

Protein Interaction network analysis

Gene Enrichment Analysis

Gene expression Profiles analysis

Sra Database Introduction

NGS sequence Analysis

Use of trimmomatic tool

Quality Control of sample

Genome/Alignment Mapping

Abundance Estimation Tool on Dataset

Heatmap Generation of values

Requirements

No prior knowledge of software's is required as in this course no coding has been used

Description

Embark on a Journey of Bioinformatics Research Projects: Plant Genomics and Cancer BiomarkersAre you ready to unlock the secrets of bioinformatics research and make a real impact in plant biology and health? Join us in this comprehensive course where you will delve into two exciting research projects that will lead to published research papers.In the plant bioinformatics research project, you will conduct a genome-wide study of gene families in plants. Explore the roles of these gene families in various biological processes, gaining valuable insights into plant genomics and bioinformatics analysis.In the health bioinformatics research project, you will discover biomarkers in cancer, using advanced bioinformatics tools and techniques. This project-based approach will sharpen your skills in health informatics and data analysis, equipping you to contribute to cancer research and diagnosis.Throughout the course, you will work with both graphical user interfaces (GUI) and command-line interfaces (CLI), giving you a comprehensive understanding of bioinformatics software and tools. You will receive guidance and mentorship from experienced bioinformatics researchers, ensuring that you are well-equipped to tackle the challenges of bioinformatics research.Why Choose This Course?The course opens doors to an exciting world where data meets biology, enabling you to contribute to groundbreaking health research. Whether you're a biologist, clinician, healthcare practitioner, or aspiring researcher, this course empowers you with the skills to make a real impact. Here's what you can expect:Comprehensive Biomarker Knowledge: You'll dive deep into the significance of biomarkers in diagnostics, prognosis, and treatment. Gain a profound understanding of how biomarkers are transforming the healthcare landscape.Genomic Data Analysis: Harness the power of genomics as you learn to navigate and analyze vast datasets. Master the art of data preprocessing, differential gene expression analysis, and quality control.Hands-On Bioinformatics: We'll take you through the practical side of bioinformatics, using R, Python, and specialized software. From data preprocessing to identifying differentially expressed genes, you'll gain real skills.Discovering Biomarkers: Explore techniques for identifying potential biomarkers from diverse omics data. Uncover the gems that hold the answers to critical health questions.Ethical and Responsible Research: Understand the ethical and regulatory considerations in biomarker research. Our course guides you to conduct research that is not only groundbreaking but also ethical and compliant.Real-World Applications: Walk in the footsteps of researchers. Analyze real datasets and published research papers to understand how biomarkers come to life in actual health research scenarios.Interdisciplinary Collaboration: Collaboration is key. Learn how biologists, clinicians, and data analysts work together to drive discoveries. Interdisciplinary collaboration is a cornerstone of health research.Emerging Trends: The world of biomarker research evolves rapidly. We'll keep you updated with the latest trends and emerging technologies, ensuring you're at the forefront of the field.Make a Difference: Ultimately, this course is your opportunity to play a pivotal role in improving healthcare, disease prevention, and treatment. Be part of a future where health research knows no bounds.Flexible Learning: We understand that your journey is unique. This course offers flexibility, allowing you to learn at your own pace and from anywhere. Whether you're a working professional or a student, you can access the knowledge you need to excel in the field.Certification: Upon successfully completing this course, you'll receive a certification that validates your expertise in health bioinformatics and biomarker discovery, making you stand out in the job market.By the end of the course, you will not only have valuable research experience but also two published research papers to showcase your work. Whether you are a student, researcher, or professional in the field of bioinformatics, this course will provide you with the knowledge and skills needed to excel in bioinformatics research.Enroll now and start your journey to becoming a bioinformatics research expert!

Overview

Section 1: Introduction to Bioinformatic Research

Lecture 1 Introduction and Course Guide

Lecture 2 Understanding bioinformatics research

Section 2: Omics based study of plant gene family

Lecture 3 Bioinformatics Research Guide Introduction

Lecture 4 Choosing A Gene Family

Lecture 5 Choosing a Plant for research

Section 3: Paper Pattern and Discussion

Lecture 6 Searching for previously published paper

Lecture 7 Selecting the references software's-How to use Mendeley

Section 4: Gene Identification and homology Searching Section

Lecture 8 Sequence Retrieval

Lecture 9 Homology Searching

Lecture 10 Renaming the Retrieved Sequences

Lecture 11 Sequence Analysis for duplication and Alignment

Lecture 12 Sequence identity and Similarity analysis

Section 5: Phylogenetic Analysis and Gene Structure

Lecture 13 Phylogenetics and Tree Construction

Lecture 14 Introns and exons and visualization

Lecture 15 Motifs and their visualization

Section 6: Protein Domain Analysis

Lecture 16 Domain Searching and Visualization

Section 7: physicochemical properties of proteins

Lecture 17 physicochemical properties of proteins

Section 8: Gene location on chromosome Analysis

Lecture 18 Gene location on chromosome Analysis

Section 9: Gene Enrichment Analysis

Lecture 19 Gene Enrichment Analysis

Section 10: identification of cis acting regulatory elements

Lecture 20 identification of cis acting regulatory elements

Section 11: Protein-Protein interaction Network Analysis

Lecture 21 Protein-Protein interaction Network Analysis

Section 12: Gene Expression Analysis of NGS Data

Lecture 22 What is NGS and why we are using the NGS for data analysis?

Lecture 23 Next-generation sequencing

Lecture 24 Generations of Sequencing

Lecture 25 NGS Workflow

Lecture 26 SRA Database introduction

Lecture 27 SRA File

Lecture 28 Galaxy Server Intro to Goals

Lecture 29 Galaxy Server And Objects

Lecture 30 Getting Onto Galaxy

Lecture 31 Tools For NGS Data Analysis

Lecture 32 Getting SRA Runs From Databases And platform

Lecture 33 Ncbi Genome To Galaxy

Lecture 34 Getting Sra Runs To Galaxy

Lecture 35 Fastqc Tool To Dataset Generated Dataset

Lecture 36 Trimmomatic Tool On Dataset

Lecture 37 Alignment/genome Mapping

Lecture 38 Abundance Estimation Tool On Dataset

Lecture 39 From Values To Visuals (Heatmap)

Section 13: How to Write Up the Analysis to Research Format!

Lecture 40 How to Write Up the Analysis to Research Format!

Section 14: Bash for Beginners (Not compulsory)

Lecture 41 Introduction to linux (bash for bioinformatics)

Lecture 42 Bash Basic Commands

Lecture 43 Ncbi E-utilities on bash (Sequence Analysis)

Lecture 44 Famous Bioinformatics Tools (Installation and Introduction)

Lecture 45 Blast for Linux (Sequences Homology)

Lecture 46 Sequence Alignment Analysis

Lecture 47 Phylogenetic Analysis (Tree Construction)

Lecture 48 Github Repo

Section 15: NGS data analysis on Command Line

Lecture 49 Understanding bioinformatics pipeline

Lecture 50 Introduction

Lecture 51 Getting the SRA Reads

Lecture 52 Checking the Quality of Data

Lecture 53 Quality Trimming of data

Lecture 54 Aligners and Aligning Reads to genome

Lecture 55 SAM and Bam File Indexing and Sorting

Lecture 56 Feature Extraction

Lecture 57 Pipeline Code

Section 16: Health Bioinformatics research Discover biomarkers using datasets

Lecture 58 Introduction of Course

Lecture 59 Bioinformatics Resources Used for Course

Lecture 60 Microarray Data Source of Lung Cancer

Lecture 61 DEG's Identification Analysis

Section 17: Microarray Dataset Analysis on R and R Studio (Not compulsory)

Lecture 62 Introduction to R

Lecture 63 Installing R and R Studio

Lecture 64 Working with R Packages

Lecture 65 Microarray Analysis on R

Lecture 66 Protein Sequences to Analysis

Lecture 67 Gene ontology Enrichment Analysis on R

Lecture 68 Github Repo

Section 18: Protein Structure Prediction and Docking Analysis

Lecture 69 Protein Structure Modeling Analysis

Lecture 70 Biomarker Docking Analysis with ligand

Lecture 71 Compiling the Research Paper

Section 19: Where to Publish

Lecture 72 Where to Publish your paper

This course is meant for all the those who have love for life sciences and want to start their research career,This course is meant for all those who are interested in learning Bioinformatics research,This course is meant for those who are interested in bioinformatics,This course is meant for all those who are interested in learning bioinformatics (project based approach)