Bioinformatics for Next Generation Sequencing

The rapid growth of high-throughput data, including -omics technologies, gave rise to data-driven discovery in life sciences. As a result, there is a significant demand for data science skills and experience with bioinformatics methods of analysis. Once a peripheral discipline you could easily outsource, bioinformatics is quickly becoming mainstream in life science research.  Many high impact journals will only publish if a rigorous bioinformatics analysis is included in the results and methods portions of the publication. In this course, we will explain the logic of bioinformatics analysis and allow for user-friendly applications of bio-statistical and machine learning techniques to a variety of biomedical challenges.

Upcoming Events

  1. 13
    January
    Program Overview
    09:00 - 10:00
    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. 16
    January
    Next-Generation Sequencing and Omics technologies
    09:00 - 10:00
    Online

    Overview of Processing NGS Data (from raw data to structured data),  Standard tools and concepts for genomics, transcriptomics, epigenomics, metagenomics. What kinds of challenges...

  3. 20
    January
    High throughput: Omics Data Processing
    09:00 - 10:00
    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,...

  4. 23
    January
    Processing of NGS data and differential gene expression
    09:00 - 10:00
    Online

    Filtering, removing noise and Normalization Techniques Least error Predictive model, Fitting a regression line Regression, factors, and features – Factor Regression Analysis

  5. 30
    January
    Transcriptomics Data Analysis: Onsite Workshop
    09:00 - 10:00
    Online

    Review of the first Five-Session, Hands-on training, and support, One-on-one and group meetings & project presentation by Students.          

  6. 03
    February
    Exploratory Data Analysis: PCA, K-Means and H-clustering
    09:00 - 10:00
    Online

    Review of Factor Regression Analysis  Unsupervised machine learning (PCA, H-Clust, K-means,) Case study & Hands-on using Cell line project

  7. 06
    February
    Data Mining using the Random forests
    09:00 - 10:00
    Online

    Review of unsupervised machine learning methods  Decision Trees, Random Forest  Challenges associated with different kinds of machine learning

  8. 10
    February
    Using Machine Learning for Expression Data
    09:00 - 10:00
    Online

    Review Supervised ML Discriminant Analysis (LDA, SwLDA, QDA) and Support Vector Machines Data Visualization and classification

  9. 13
    February
    Biological Interpretation
    09:00 - 10:00
    Online

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

  10. 20
    February
    Machine Learning to Transcriptomic Data to Critical Genes: Onsite Workshop
    -
    Onsite

    Review of the last Five-Session, Hands-on training, and support, One-on-one and group meetings & Project Presentation by Students.  

  11. 27
    February
    Planning your Project: Educational Project
    09:00 - 10:00
    Online

    Planning your project, Projects on the Educational Platform. One-on-One interactions: Data access and project abstract Case Studies & Publications Datasets Independent Projects* (Requires Research...

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Program License

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Bioinformatics for Next Generation Sequencing
$60.00
70 Days

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The rapid growth of high-throughput data, including -omics technologies, gave rise to data-driven discovery in life sciences. As a result, there is a significant demand for data science skills and experience with bioinformatics methods of analysis. Once a peripheral discipline you could easily outsource, bioinformatics is quickly becoming mainstream in life science research.  Many high impact journals will only publish if a rigorous bioinformatics analysis is included in the results and methods portions of the publication. In this course, we will explain the logic of bioinformatics analysis and allow for user-friendly applications of bio-statistical and machine learning techniques to a variety of biomedical challenges.
PCA: biomedical data visualization in R
Machine Learning for Biomedical Data
Transcriptomics 4
Transcriptomics 3
Transcriptomics 2
Transcriptomics 1

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