Transcriptomics for Chronic Diseases

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

To learn more, we welcome you to explore the topics on this page and join the orientation session on

June-August 2022

Register Today For The Program :

NAIPI - Click Here

LSU| LBRN - Click Here

Key Topics Covered:

 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 regression
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 : BioMMed Transcriptomics

Session Title Description
Slide9 Introduction to NGS Transcriptomics
    • mRNA Biology
    • Methods of RNA quantification 
    • mRNA library preparation
    • Next generation Sequencing
    • Illumina, Microarray, Oxford Nanopore, PacBio
Associated Online Resources

Biology of transcription

RNA Quantification

RNA-seq and NGS sequencing

Analysis of Raw RNA-seq Data: Logical Steps

Practical: RNA-Seq

Slide10 Processing Transcriptomics Data
    • Read quality: FastQC
    • Pre-processing: Trimmomatic and PCR Clean
    • Strategies for mapping
    • Exon and Junction detection
    • Quantification strategies
Associated Online Resources-

Practical: RNA-Seq

Data Preparation for Downstream Analysis

RNA Quantification

Slide11 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 -

Designing a Bioinformatics Research Project

Modeling Cancer Precision Medicine

Cell line Data and Preparation

Machine Learning: Classification

Slide12 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 -

How to load data and check for variable data

Loading Data

Data Preparation for Downstream Analysis

Slide13 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

Slide14 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 -

Data Processing & visualization

Data visualization

Data Manipulation, visualization & Analysis

Slide15 Exploratory Analysis
  • Descriptive statistics
  • Finding outliers
  • Variance and Interpretation
  • Adjusting scaling and normalization
  • Removing low value data
  • Addressing common issues

Associated Online Resources -

Principal component Analysis (PCA ):Tutorial

Dimensionality reduction: PCA, t-SNE & visualization

Practical: Normalization & PCA of Gene Expression Data

Slide16 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 -

Statistical Tests
Differential Gene Expression & Gene Enrichment Analysis
Differential Gene Expression Analysis & Gene set enrichment Analysis (R Code)

Slide17 Regression Analysis
    • Variance and factors, ANOVA
    • Factor levels and preparation for analysis
    • Regression and Factor Regression Analysis
    • Interpretation of results

Associated Online Resources -
Regression & Factor Regression Analysis
Statistical Analysis Tests
Mutability Analysis & Interpretation
Slide18 Biological Interpretation
    • DAVID and ENRICHr
    • Gene Set Enrichment Analysis (GSEA)
    • KEGG pathways annotation
    • Gene Ontology (GO) Biological Processes
    • Molecular function and Cellular Components

Associated Online Resources -

Principal component Analysis (PCA ):Tutorial
Cell line Data and Preparation
Unsupervised Machine Learning (Clustering)
Supervised Machine Learning
Supervised Machine Learning: Feature Selection
Slide19 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 -
Analysis of Raw RNA-seq Data: Logical Steps
TCGA Liver Cancer Project Precision Oncology
Patient Derived Xenograft Models

 

If you need help finalizing registration, contact Farhana Musarrat, Ph.D., Post-Doctoral Researcher, Kousoulas Lab (fmusar1@lsu.edu | office 225-578-9084 | mobile 504-265-6777)or join the orientation session for this program to learn how to do that. You have to register for the orientation using the form below. For LSU or LBRN members, you can complete your registration via the BIOMMED iLab link below - 

Register for the Upcoming Webinar Session:

Certificate

Training Certificate from the Louisiana Biomedical Research Network or LSU BioMMED:

  • Certification of Training Requirement Completion
  • Recognition within the Network and Other IDeA States
  • Advancement for Research, Faculty and Student participants within the LBRN Network
  • Training certification for LSU through the Center for Biotechnology and Biomolecular Medicine at the Louisiana State University
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

 

Resources and Training Session Examples:

081419_paulrider-4
"This is the most brilliant piece of work that I have seen (ever) to address data in the biological sciences and genetics. My mind was blown today. Quite enlightening to follow such a course. There is hardly any exposure to all this in regular curricula nor much talk in wider media. Happy that I got exposed to the world of NGS".
- Dr. Paul Rider, PhD
YU_Xiuping_200x300-1
"I already have experience with RNAseq data, and was interested to hear another perspective on data analysis. I ran over the lectures so I might not be a reliable resource for feedback but overall good introduction on the topic of transcriptomics. Would recommend it for anyone wanting to get to know this intricate field of analysis".
- Dr. Xiuping Yu, Ph.D Fellow
Thao Vo-3
"I have increased my knowledge substantially throughout this course. The software has created a great opportunity for me to practice what I've learned and could be very useful for my career development. "I liked this course and will surely recommend it to my friends. Overall, it was an extremely useful class. Thank you for the wonderful instruction!"
- Thao Vo, Ph.D. Fellow