OmicsLogic Transcriptomics Data Analysis for Biomedical Data is an online mentor-guided program on next-generation sequencing (NGS) data analysis in application to gene expression data with project examples from infectious diseases, cancer & neuroscience. This program will introduce students to real-world applications of RNA-seq Data Analysis in biomedical research and provide hands-on skills and logical background to extract insights from gene expression data. 

First, we will review methods and the history of quantitative and qualitative analysis of mRNA expression. Practical sessions will guide participants to use the methods we reviewed on several project datasets to practice generating a table of expression from raw fastq files. This is followed by downstream analysis using simple statistical comparison (t-test), complex statistical comparison (DESEQ2), and advanced statistical methods (unsupervised & supervised machine learning) to derive and interpret important biological insights.

The mentor-guided training & research program will be conducted through Online coursework, video tutorials, and session recordings on OmicsLogic Learn Portal and scheduled live sessions via Zoom. For the analysis of transcriptomics data, students will also have access to the T-BioInfo Platform which combines statistical analysis modules into bioinformatics pipelines. To practice R and Python for data analysis, students will receive access to OmicsLogic Code Playground. At the end of the program completion, students have the opportunity to continue their learning journey in transcriptomics through advanced coursework, example projects, and even an opportunity to carry out an independent bioinformatics research project with the possibility to publish.

To learn more, we welcome you to watch the videos below by our expert mentors and register for a free orientation session:

Transcriptomics for Drug Discovery Webinar

Analysis of Bulk and Single Cell Transcriptomics Data on the T-BioInfo Platform: By Julia Panov, TBRC

Transcriptomic Data Analysis in R

Method of the year by Nature: Spatially Resolved Transcriptomics - By Lisa, TBRC

Transcriptomics Project Examples:

 Quantitative and qualitative analysis of RNA
Data preparation using Next Generation Sequencing and preparation of a table of expression from raw FASTq files. Visualization of high-dimensional data using Principal Component Analysis (PCA).

○ Mapping raw reads to reference genome and transcriptome
○ Detecting junctions and assembly of isoforms
○ Quantification of mRNA: Gene, isoform and exon expression table
Advanced analytical approaches
We will look at t-test, then use DESeq2 to run a differential gene expression pipeline and then use Factor Regression Analysis. As a result, you will learn to detect obvious differences between pre-set groups as well as expand that idea to more subtle differences represented by factors that might interact with each other.

○ DIfferential gene and isoform expression
○ Hypothesis testing, p-values, and normalization
○ Multivariate analysis using regressio
Data Exploration and Classification
Supervising and Unsupervised analysis using an example from precision medicine, the methods will be demonstrated to work together to understand cancer subtypes and the use of this information to determine how a new sample can be classified.

○ Data exploration using dimensionality reduction and clustering
○ Classification and discriminant analysis for labeled datasets
○ Cancer subtypes based on gene expression (breast cancer classification)
  Single Cell Transcriptomics   
 scRNA-seq has been demonstrated as a powerful technique for classification of tissue-specific cells and the study of time-course data for thousands of single-cell samples. Data preparation techniques and sparse properties of such data require a new set of methods for its analysis

○ Techniques and protocols for scRNA-seq data preparation
○ Major analytical steps for processing raw single cell sequencing data
○ Analytical techniques to visualize and cluster scRNA-seq data

What's Included?

Choose a program membership type that supports your needs and join a community of our program experts!

45 Days

Online Tutorials
Project Examples
Coding Sessions
Session Recordings
Omicslogic Program Access
Example Demo Cloud Pipelines
✅Online Mentor Support


⭐️ Certificate of Completion

⭐️ Case Studies

60 Days

Asynchronous Courses
Project Examples
Group Sessions
One-on-One Mentor Sessions
Omicslogic Program Access
Education Cloud Pipelines


⭐️ Literature Review

⭐️ Certificate of Completion

⭐️ Research Poster

90 Days

Asynchronous Courses
Project Examples
Group Sessions
One-on-One Mentor Session
Omicslogic Program Access
Education Cloud Pipelines


⭐️ Certificate of Excellence

⭐️ Project Report

⭐️ Research Paper Publication

Below are the payment checkout links for the tenures mentioned above:

Note: For Low-Income Countries scholarship opportunities are available, please reach out to us and our team at: and we will find the best possible way for you to participate and get the best outcomes from the training program.

We also have the option for financial assistance with weekly/monthly installment options.


Program Syllabus : Transcriptomics



Introduction to NGS Transcriptomics
  • mRNA Biology
  • Methods of RNA quantification 
  • mRNA library preparation
  • Next-generation Sequencing
  • Illumina, Microarray, Oxford Nanopore, PacBio


Processing Transcriptomics Data
  • Read quality: FastQC
  • Pre-processing: Trimmomatic and PCR Clean
  • Strategies for mapping
  • Exon and Junction detection
  • Quantification strategie


Precision Medicine Projects
  • Projects: Literature Review and Publication 
  • Modeling Precision Medicine in Breast cancer 
  • Cell Lines and High Throughput Drug Testing
  • Cancer Classification
  • Targeted Treatment


Loading RNA-Seq data in R and Python
  • Loading CSV, Excel or TXT files.
  • Determine numeric and non-numeric data types.
  • Preparing a matrix and a data frame.
  • Creating variables and functions


Normalization and Preparation for Analysis
  • Statistical Properties of Data
  • Quantification strategies
  • Removing NA and Zero values
  • Log scale transformation


Data Visualization in R and Python
  • Computation of summary statistics
  • Boxplots, Scatterplots, and Bar Plots
  • Density Plots, Histograms, and Heatmaps
  • Data transformation and Scaling




Exploratory Analysis
  • Descriptive statistics
  • Finding outliers
  • Variance and Interpretation
  • Adjusting scaling and normalization
  • Removing low value data
  • Addressing common issues


Differential Gene Expression
  • T-test and statistical considerations
  • p-value and Fold change
  • FDR and Benjamini Adjustment
  • DESEQ2 and EDGER
  • Volcano and MA Plots
  • Heat map for selected genes


Regression Analysis
  • Variance and factors, ANOVA
  • Factor levels and preparation for analysis
  • Regression and Factor Regression Analysis
  • Interpretation of results


Biological Interpretation

  • Gene Set Enrichment Analysis (GSEA)
  • KEGG pathways annotation
  • Gene Ontology (GO) Biological Processes
  • Molecular function and Cellular Components




Single Cell RNA-Seq methods and analysis approaches for cell type decomposition

  • Single-cell transcriptomics (SCT) Introduction to SCT, History of SCT,
  • NGS Techniques, Capture techniques, Quantification, scRNA-seq data preparation & Counts
  • ScRNA data analysis, Publication & projects: Drop-seq data from Ye et al., 2017, Results & Interpretation.


Case Studies
  • Examples from oncology, neuroscience science and infectious diseases
  • Components of bioinformatics analysis
  • Literature review, compiling a primary dataset, developing an analysis plan, performing exploratory analysis, data processing and preparation, statistical analysis, biological interpretation and validation


Example Projects & Research Projects by OmicsLogic Research Fellows

Insilico analysis of Affected Biological Pathways in MS: By Rutvi Vaja, Biomedical sciences student at Narvachana University, India

Investigating Transcriptomic Landscape of Patients to Track Tuberculosis Disease Progression: By Aman Kaur, Master's Bioinformatics Student at Amity University, Noida, India

Bioinformatics approach to analyze variants in Retinitis Pigmentosa: By Jeevanjot Kaur, BTech Biotechnology Student at Amity University, Noida, India

Register for the Upcoming Webinar Session:

OmicsLogic Transcriptomics Webinar
Transcriptomics - 3 (1)
OmicsLogic Transcriptomics- T-BioInfo Server

Below are the payment checkout links for the tenures mentioned above:

Note: For Low-Income Countries scholarship opportunities are available, please reach out to us and our team at: and we will find the best possible way for you to participate and get the best outcomes from the training program.

We also have the option for financial assistance with weekly/monthly installment options.


Resources and Training Session Examples

Omics Logic T-BioInfo User-friendly-1 OL Transcriptomics Module OL BioInfo in R OL BioInfo in Python Omics Logic Project Case Studies
Learn how to analyze RNA-Seq data on the T-BioInfo platform Understand methods and approaches for RNA-seq  Start running R code for visualization and analysis Learn Python code for visualization and analysis Explore Project Case Studies in various topics
Natalie Chang
"This lesson provided really helpful explanations of why data should be normalized during preprocessing, as well as examples of methods of normalization (log and quantile)".
- Natalie Chang, HighSchool Student
Poonam Sen
"The course outlined the basics of molecular biology that is needed for understanding the sequencing results. Complex concepts are explained in simple words".
- Poonam Sen, PhD Fellow
Emeka Patrick
"It has been a long journey for me. I will always find myself coming here to gain the much needed insight, knowledge and skill of bioinformatics, programming and research".
- Emeka Patrick, Hematologist/ Physician-scientist