Gene Expression Analysis using R Programming

Gene Expression Analysis, R Programming, Bioinformatics, Differential Expression, Data Preprocessing, Data Visualization, Statistical Analysis, Pathway Analysis, Biomarker Discovery, Molecular Biology.

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MODE
Virtual (Google Meet)

TYPE
Self Paced

LEVEL
Moderate

DURATION
1 Month

VIDEO LENGTH
1

About

Program on Gene Expression Analysis using R Programming is a comprehensive and practical program designed to equip participants with the essential skills and knowledge to conduct sophisticated gene expression analyses. Gene expression analysis plays a pivotal role in understanding the molecular basis of biological processes and diseases, and R programming offers a powerful and flexible platform for performing these analyses.

Aim

The aim of Gene Expression Analysis using R Programming is to provide participants with the necessary knowledge and skills to effectively explore, analyze, and interpret gene expression data using the R programming language. This program seeks to empower researchers, biologists, bioinformaticians, and students with a powerful computational toolset to gain valuable insights into the molecular basis of biological processes and diseases.

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Courses Objectives

Introduce participants to the fundamentals of gene expression analysis.
Familiarize attendees with R programming for bioinformatics.
Teach data preprocessing and quality control techniques for gene expression data.
Enable participants to perform differential expression analysis using R.
Train participants in creating effective visualizations of gene expression data.
Provide an understanding of statistical interpretation and significance in gene expression analysis.
Introduce pathway analysis methods to interpret gene expression results in a biological context.
Offer hands-on experience through practical exercises and real-world datasets.
Promote best practices to ensure accurate and reliable gene expression analysis.
Facilitate collaborative learning and knowledge-sharing among participants.

Courses Structure

Day 1:

Introduction to RNA seq and R
Data types structures in R
Installing packages in R

Day 2:

Quality control and preprocessing
Read Mapping using HISAT2
Quantification of reads using FeatureCount

Day 3:

Differential Expression for RNA-seq using DESeq2
Visualization DEG and generation of heatmap
Perform functional analysis on gene lists with R-based tools
Pathway analysis

Installation Requirements

Download the most recent versions of R and RStudio for your laptop:

R
RStudio

Packages to be installed: FASTQC, Trimmomatic, HTSeq2, FeatureCount

Participant’s Eligibility

Graduates, Post Graduates, Research Scholars, Academicians, Industry Professionals Bioinformatics, Computational Biology, Biology and Life Sciences,Biotechnology and Pharmaceutical Sciences, Medical and Clinical Research, Genomics and Genetics,

Important Dates

Registration Ends

2023-09-01
Indian Standard Timing 03:00 PM

Courses Dates

2023-09-01 to 2023-09-03
Indian Standard Timing 04:00 PM

Courses Outcomes

Proficiency in R programming for gene expression analysis.
Hands-on data analysis skills with real-world gene expression datasets.
Interpretation of gene expression patterns to identify differentially expressed genes.
Data visualization and effective presentation of gene expression analysis results.
Understanding of data quality control and normalization for reliable analysis.
Interpretation of gene expression data in the context of biological pathways and networks.
Application of statistical methods for gene expression analysis and hypothesis testing.
Knowledge of best practices and reproducibility in gene expression analysis.
Collaborative learning and networking with peers and instructors.
Confidence in conducting independent gene expression analysis using R programming.

Fee Structure

Student

INR. 999
USD. 40

Ph.D. Scholar / Researcher

INR. 1199
USD. 45

Academician / Faculty

INR. 1499
USD. 50

Industry Professional

INR. 1999
USD. 75

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