Transcriptomics: RNA to Single Cell Applications

Decoding the Language of Cells: From RNA to Revolutionary Insights

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MODE
Online/ e-LMS

TYPE
Self Paced

LEVEL
Moderate

DURATION
1 Month

About

This one-month intensive program delves into the intricate world of transcriptomics, emphasizing the transformative role of RNA analysis in understanding cellular functions and disease mechanisms. Through a combination of lectures, and case studies, participants will explore the latest technologies and methodologies in RNA sequencing, data analysis, and their applications in single cell studies. The program is designed to bridge the gap between traditional molecular biology techniques and modern computational approaches, offering insights into the genomic structure and functional dynamics of cells in various biological contexts.

Aim

This program aims to equip participants with the foundational and advanced knowledge of transcriptomics, from RNA sequencing to single cell analysis techniques. We focus on integrating theoretical concepts with practical applications to prepare learners for the evolving landscape of biological research and biotechnology.

Program Objectives

Understand the fundamental principles of RNA biology and transcriptome analysis.
Master the techniques for extracting, sequencing, and analyzing RNA from single cells.
Apply computational tools and software in the analysis of transcriptomic data.
Interpret and correlate transcriptomic data with biological functions and disease states.
Develop skills necessary for conducting independent research in transcriptomics.

Program Structure

Week 1: Introduction to Transcriptomics

The Basics of Transcriptomics: Understanding the transcriptome and its importance in genomics.
Key Techniques in Transcriptomic Analysis: Overview of RNA extraction, sequencing technologies, and RNA-seq.
Data Quality and Preparation: Pre-processing steps for RNA-seq data, including quality control.
Introduction to Computational Tools: Basic tools and software for transcriptomic data analysis.

Week 2: Deep Dive into RNA-seq Analysis

Mapping Reads and Annotation: Aligning sequences to reference genomes and understanding annotations.
Quantifying Gene Expression: Methods for measuring expression levels in RNA-seq data.
Normalization Techniques: Approaches to correct data for various biases in transcriptome data.
Differential Expression Analysis: Identifying significant changes in expression between conditions.

Week 3: Single Cell Transcriptomics

Introduction to Single Cell RNA-seq (scRNA-seq): Understanding the technology and its transformative impact.
Preparing Single Cell Libraries: Techniques for single cell isolation and library preparation.
Analyzing Single Cell Data: Exploring data analysis pipelines specific to scRNA-seq.
Visualizing Single Cell Data: Tools and methods for data visualization in single cell studies.

Week 4: Advanced Topics and Applications

Integrative Analysis Techniques: Combining transcriptomic data with other data types (e.g., proteomics).
Spatial Transcriptomics: An overview of techniques to map gene expression in tissue sections.
Transcriptomics in Disease Research: Case studies on how transcriptomics is used to understand diseases.
Future Directions in Transcriptomics: Emerging trends and technologies in transcriptomic research.

Participant’s Eligibility

Undergraduate or postgraduate degree in Biology, Bioinformatics, Genetics, or related fields.
Laboratory technicians and research associates looking to upgrade their skills in genomic analysis.
Biotechnologists and medical professionals interested in the applications of transcriptomics in diagnostics and therapeutic developments.

Program Outcomes

Proficient in RNA sequencing technologies and their applications.
Able to perform and analyze experiments related to transcriptomics.
Capable of integrating transcriptomic data for biological insight.
Skilled in using software tools for data analysis in transcriptomics.
Prepared for roles in research, development, and clinical applications.

Reviewers

Mentor, Reviewer

Dr. Neeta Bhagat

Senior Assistant Professor

Professional Biotechnology Freelancer
Biography

Fee Structure

Standard Fee: INR ~~4,998~~ USD ~~110~~

Discounted Fee: INR 2499 USD 55

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!

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