Self Paced

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, hands-on labs, 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.


Fee Structure

Standard Fee:           INR 11,998           USD 240

Discounted Fee:       INR 5999             USD 120

Batches

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Certificate

Program Assessment

Certification to this program will be based on the evaluation of following assignment (s)/ examinations:

Exam Weightage
Mid Term Assignments 20 %
Final Online Exam 30 %
Project Report Submission (Includes Mandatory Paper Publication) 50 %

To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.

Program Deliverables

  • Access to e-LMS
  • Real Time Project for Dissertation
  • Project Guidance
  • Paper Publication Opportunity
  • Self Assessment
  • Final Examination
  • e-Certification
  • e-Marksheet

Future Career Prospects

  • Genomic Data Analyst
  • Clinical Research Coordinator
  • Biotechnology Research Scientist
  • Pharmaceutical Development Specialist
  • Academic Researcher in Molecular Biology
  • Bioinformatics Consultant

Enter the Hall of Fame!

Take your research to the next level!

Publication Opportunity
Potentially earn a place in our coveted Hall of Fame.

Centre of Excellence
Join the esteemed Centre of Excellence.

Networking and Learning
Network with industry leaders, access ongoing learning opportunities.

Hall of Fame
Get your groundbreaking work considered for publication in a prestigious Open Access Journal (worth ₹20,000/USD 1,000).

Achieve excellence and solidify your reputation among the elite!


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