transccriptomics program

An online program on next generation sequencing (NGS) Data Analysis in application to gene expression data with project examples from infectious diseases, cancer & neuroscience hosted by Pine Biotech

This online program will introduce real-world applications of RNA-seq data analysis in biomedical research and provide participants with hands-on skills and logical background to extract insights from gene expression data. We will review methods and history of quantitative and qualitative analysis of mRNA expression. Practical sessions will guide participants to use the methods we review on several project datasets to practice generating a table of expression from raw FASTq files and perform subsequent analysis of this table of gene and isoform expression.

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

Program Syllabus : Transcriptomics

Introduction to the NGS Transcriptomics Introduction to NGS Transcriptomics
  • mRNA Biology
  • Methods of RNA quantification 
  • mRNA library preparation
  • Next generation Sequencing
  • Illumina, Microarray, Oxford Nanopore, PacBio
Associated Online Resources: 
Processing Transcriptomic Data Processing Transcriptomics Data
  • Read quality: FastQC
  • Pre-processing: Trimmomatic and PCR Clean
  • Strategies for mapping
  • Exon and Junction detection
  • Quantification strategies
Associated Online Resources:
Precision Medicine Project 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

Associated Online Resources:

Loading RNA-Seq dataset 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

Associated Online Resources:

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

Associated Online Resources:

  • Practical: Normalization & PCA of Gene Expression Data
  • RNA Quantification
  • Statistical Tests
Data visualization in R and Python Data Visualization in R and Python
  • Computation of summary statistics
  • Boxplots, Scatterplots, and Bar Plots
  • Density Plots, Histograms, and Heatmaps
  • Data transformation and Scaling

Associated Online Resources:

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

Associated Online Resources:

Differential Gene Expression 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

Associated Online Resources:

Regression Analysis Regression Analysis
  • Variance and factors, ANOVA
  • Factor levels and preparation for analysis
  • Regression and Factor Regression Analysis
  • Interpretation of results
Associated Online Resources:
Biological Interpretation

Biological Interpretation

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

Associated Online Resources:

Single Cell RNA transcriptomics

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.

Associated Online Resources:


Case studies on transcriptomics 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
Associated Online Resources:


Register for the Upcoming Webinar Session:

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