US Summer programs for NAIPI LBRN.005

Omics Logic Transcriptomics for Chronic Diseases is an online training program that focuses on the practical skills and applications of RNA-seq data analysis in biomedical research. The program will provide participants with hands-on training and the theoretical background needed to effectively apply big data processing and analytical methods to extract biological insights from gene expression data.

Transcriptomics for Chronic Diseases program sessions, we review modern methods of quantitative and qualitative analysis of mRNA abundance with a major focus on bulk and single-cell Next Generation Sequencing (RNA-Seq). Practical sessions will use a case study approach using curated project datasets to learn about generating a table of expression from raw FASTq files and perform subsequent analysis of this table of gene and isoform expression.

We will cover standard statistical methods of analysis like differential gene expression as well as advanced data mining and machine learning for large-scale datasets. In conclusion, you will also be able to plan how to take what you have learned and apply it to an independent research project using the tools and methods covered in the program.

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

Learn more and access: Transcriptomics for Chronic Diseases

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