DALL·E 2024 08 08 15.03.34 An illustration depicting transcriptomics including elements like RNA sequences transcription processes and data analysis charts. It should be brig scaled

Transcriptomics: RNA to Single Cell Applications

Decoding the Language of Cells: From RNA to Revolutionary Insights

MODE
Mode(Online) TYPE
Self Paced LEVEL
Moderate

Skills you will gain:

This DeepScience-powered, self-paced certificate course delivers a structured journey through the fast-evolving domain of transcriptomics—from RNA sequencing (RNA-seq) fundamentals to cutting-edge single-cell transcriptomics and spatial gene expression technologies. Participants will explore how transcriptomic data unlocks insights into gene regulation, disease biomarkers, and precision medicine.

Designed with real-world application in mind, this course bridges molecular biology with computational genomics. Through expert-led lectures, case studies, and tool-based walkthroughs, you’ll gain actionable skills in biological data science and modern bioinformatics workflows.

Aim:

To build interdisciplinary expertise in transcriptomic sciences, enabling participants to:

  • Decode gene expression dynamics.
  • Apply AI-ready bioinformatics pipelines.
  • Transition into cutting-edge biotech and research roles.

Program Objectives:

  • Grasp core principles of RNA biology and transcriptome profiling.

  • Master RNA extraction, sequencing workflows, and data preprocessing.

  • Utilize industry-standard tools for single-cell data analysis.

  • Interpret transcriptomic outputs in disease modeling and therapeutic targeting.

  • Develop a research-ready mindset and pipeline in DeepTech biology.

What you will learn?

Week 1: Transcriptomics in System Biology

  • RNA Extraction, QC & Sequencing Technologies

  • RNA-seq Data Processing & Quality Control

  • Transcriptomics Software: FASTQC, STAR, Kallisto

Week 2: RNA-seq Data Analysis

  • Read Mapping & Genome Annotation (using HISAT2, Ensembl)

  • Quantification of Gene Expression (TPM, FPKM, raw counts)

  • Data Normalization Techniques for Transcriptomic Integrity

  • Statistical & Machine Learning Approaches to Differential Expression Analysis

Week 3: Single Cell Transcriptomics

  • Introduction to Single Cell RNA-seq (10x Genomics, Smart-seq2)

  • Protocols for Single Cell Library Prep & Barcoding

  • Processing Pipelines: Cell Ranger, Seurat, Scanpy

  • Visualizing Cell Clusters & Gene Markers (UMAP, t-SNE)

Week 4: Advanced Transcriptomic Applications

  • Spatial Transcriptomics & Tissue Mapping Technologies

  • Multi-Omics Integration: Proteomics, Metabolomics & RNA-seq

  • Translational Case Studies in Cancer, Neuroscience, and Immunology

  • DeepTech Trends: AI in Transcriptomics, Synthetic Biology Intersections

Intended For :

  • Life science graduates, postgraduates, and PhD students in biotech, genetics, or bioinformatics

  • Researchers, lab professionals, and R&D executives in genomics & molecular diagnostics

  • Medical professionals exploring precision medicine, genomic data interpretation, and translational bioinformatics

Career Supporting Skills

Sequencing Bioinformatics Data Analysis Computational Modeling Clinical Application

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