
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 :
Key Topics Covered:
○ Mapping raw reads to reference genome and transcriptome
○ Detecting junctions and assembly of isoforms
○ Quantification of mRNA: Gene, isoform and exon expression table
○ DIfferential gene and isoform expression
○ Hypothesis testing, p-values, and normalization
○ Multivariate analysis using regression
○ Data exploration using dimensionality reduction and clustering
○ Classification and discriminant analysis for labeled datasets
○ Cancer subtypes based on gene expression (breast cancer classification)
○ 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 |
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Introduction to NGS Transcriptomics
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Processing Transcriptomics Data
Data Preparation for Downstream Analysis |
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Precision Medicine Projects
Associated Online Resources - Designing a Bioinformatics Research Project Modeling Cancer Precision Medicine |
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Loading RNA-Seq data in R and Python
Associated Online Resources - How to load data and check for variable dataLoading Data |
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Normalization and Preparation for Analysis
Associated Online Resources - Practical: Normalization & PCA of Gene Expression Data Statistical Tests |
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Data Visualization in R and Python
Associated Online Resources - |
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Exploratory Analysis
Associated Online Resources - Principal component Analysis (PCA ):TutorialDimensionality reduction: PCA, t-SNE & visualization |
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Differential Gene Expression
Associated Online Resources - Statistical Tests |
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Regression Analysis
Associated Online Resources - Regression & Factor Regression Analysis Statistical Analysis Tests Mutability Analysis & Interpretation |
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Biological Interpretation
Associated Online Resources - Principal component Analysis (PCA ):TutorialCell line Data and Preparation Unsupervised Machine Learning (Clustering) Supervised Machine Learning Supervised Machine Learning: Feature Selection |
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Case Studies
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:

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
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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:


