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

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
	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*
	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
	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
	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
	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 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*
	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
	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)
	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
	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*
	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


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

Key Topics Covered:

Program Syllabus : BioMMed Transcriptomics

Data Preparation for Downstream Analysis

How to load data and check for variable data

Loading Data

Statistical Tests

Principal component Analysis (PCA ):Tutorial

Dimensionality reduction: PCA, t-SNE & visualization

Register for the Upcoming Webinar Session:

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

Resources and Training Session Examples:

LINKS

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