OmicsLogic Transcriptomics Training Program

The OmicsLogic Transcriptomics program will introduce real-world applications of RNA-seq and provide participants with hands-on skills and 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 session 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 perform subsequent analysis of this table of gene and isoform expression.

Upcoming Events

  1. 14
    January
    Session 1 – Introduction
    09:30 - 10:30
    Online

    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),...

  2. 17
    January
    Processing of Gene expression NGS data
    09:30 - 10:30
    Online

    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,...

  3. 21
    January
    Differential Gene Expression
    09:30 - 10:30
    Online

    Quantification and Generating a table of expression: RSEM, HTSeq, and Sailfish Case study & Hands-on using Cell line project  Case study & Hands-on using...

  4. 24
    January
    Exploratory Data Analysis
    09:30 - 10:30
    Online

    Filtering, removing noise and Normalization Techniques Correlation – detecting correlation of features and factors Regression, factors, and features – Factor Regression Analysis overview

  5. 28
    January
    Analysis of Gene Expression
    09:30 - 10:30
    Online

    Unsupervised machine learning (PCA, H-Clust, K-means) Clustering of samples using gene expression profiles Clustering of genes by expression profiles across samples

  6. 31
    January
    Data Mining using the Multi-Dimensional Expression Data
    09:30 - 10:30
    Online

    Supervised Machine learning techniques  Factor Regression Analysis, Decision Trees, Random Forest  Challenges associated with different kinds of machine learning

  7. 04
    February
    Machine Learning for Expression Data
    09:30 - 10:30
    Online

    Discriminant Analysis and Support Vector Machines Feature Selection and expanding the list of features Data Visualization

  8. 07
    February
    Interpretation
    09:30 - 10:30
    Online

    Annotation using Gene Ontology Human GAGE: Gene Set Enrichment Analysis Statistical Significance and Reproducibility

  9. 11
    February
    Single-Cell Transcriptomics
    09:30 - 10:30
    Online

    Single-cell transcriptomics (SCT) Introduction to SCT, History of SCT, NGS Techniques, Capture techniques, Quantification, scRNA-seq data preparation & Counts

  10. 13
    February
    ScRNA Transcriptomics Data Analysis
    09:30 - 10:30
    Online

    ScRNA data analysis, Publication & projects: Drop-seq data from Ye et al., 2017, Results & Interpretation.

  11. 14
    February
    Independent project work
    09:30 - 10:30
    Online

    Planning your project, 2 Q&A sessions, 1 Presentation Case Studies & Publications Datasets

  12. 14
    March

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