OmicsLogic: Transcriptomics

An Online Program on Next Generation Sequencing Data Analysis
January 2020
ONLINE

   About the Program:

OmicsLogic-Transcriptomics1

The OmicsLogic Transcriptomics program will introduce real-world applications of RNA-seq and provide participants with hands-on skills and a logical background to the full RNA-seq analysis approach. We will review methods of quantitative and qualitative analysis of mRNA expression in a sample. Other sessions will focus on how data is generated using Next Generation Sequencing. 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 to perform subsequent analysis of this table of gene and isoform expression.

PROGRAM TOPICS

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Gene and Isoform Expression
Microbial communities affecting human life and health
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Next Generation Sequencing
Learn to process and analyze NGS High-throughput sequencing
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Working with Gene Expression Data
Learn to analyze sequence data using visualization and machine learning
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Practical Project Datasets
Apply analysis to projects in oncology, biotech and agriculture

OmicsLogic: Transcriptomics Program

You can learn more about the topics we will cover in this program by watching the video below:

 

Jan
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14
OmicsLogic Transcriptomics Program overview
Overview of the entire course, Overview of the tools and resources for this program (edu.t-bio.info courses, projects and datasets, and the T-BioInfo Analytics Platform), Expectations and schedule review for the training program, Important deadlines
Jan
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17
Processing of Gene expression NGS data
Role of pre-processing in standard RNA-seq pipelines (Trimmomatic and PCR-clean), Mapping techniques: mapping on the transcriptome, Mapping on the genome and combined strategies (Bowtie, BWA, and TopHat/HiSat), Quantification and Generating a table of expression: RSEM
Jan
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21
Differential Gene Expression
Quantification and Generating a table of expression: RSEM, HTSeq, and Sailfish, Case study & Hands-on using Cell line project, Case study & Hands-on using Angelman Project
Jan
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24
Exploratory Data Analysis
Filtering, Removing noise and Normalization Techniques, Correlation – detecting correlation of features and factors, Regression, factors, and features – Factor Regression Analysis overview
Jan
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28
Analysis challenges - Analysis of Gene Expression
Unsupervised machine learning (PCA, H-Clust, K-means), Clustering of samples using gene expression profiles, Clustering of genes by expression profiles across samples
Jan
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31
Data Mining using the Multidimensional Expression Data
Supervised Machine learning techniques, Factor Regression Analysis, Decision Trees, Random Forest, Challenges associated with different kinds of machine learning
Feb
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04
Using Machine Learning for Expression Data
Discriminant Analysis and Support Vector Machines, Feature Selection and expanding the list of features, and Data Visualization
Feb
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07
Interpretation
Annotation using Gene Ontology, Human GAGE: Gene Set Enrichment Analysis, Statistical Significance and Reproducibility
Feb
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11
Single-cell Transcriptomics
Single-cell transcriptomics (SCT) Introduction to SCT, History of SCT, NGS Techniques, Capture techniques, Quantification, ScRNA-seq data preparation & Counts
Feb
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13
Single-cell Transcriptomics Data Analysis
ScRNA data analysis, Publication & projects: Drop-seq data from Ye et al., 2017, Results & Interpretation
Mar
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14
Projects
Planning your project, 2 Q&A sessions, 1 Presentation, Case Studies & Publications, Datasets

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