Louisiana Tech University LBRN Bioinformatics Training Program

Bioinformatics is becoming a priority for researchers and biology students, however resources easily accessible to students are limited and not well developed for both research and training. Louisiana Tech University recognizes the need for quality bioinformatics training and has partnered with the Louisiana Biomedical Research Network and with Pine Biotech to offer a modern project-based curriculum for qualified students.

Upcoming Events

  1. 14
    Introductory meeting

    Event Details: 1. Goal of the program and introduction 2. Overview of the tools and resources for this program ( courses, projects and datasets and the...

  2. 21
    Introduction to Bioinformatics

    Event Details: 1. Why big data in biology (“small data” vs “big data” - high throughput experiments) 2. Data driven vs. hypothesis driven (how can data...

  3. 28
    Processing of NGS data

    Event Details: (Pre-processing, Alignment and Quantification - RNA-seq example) 1. Role of quality control and re-processing in standard RNA-seq pipelines (Trimmomatic/PCR-clean) 2. Mapping techniques: mapping on transcriptome,...

  4. 04
    Exploratory Data Analysis

    Event Details: 1. Filtering, removing noise and Normalization Techniques 2. Exploring multi-dimensional data using PCA visualization 3. Principal Components and variance – outliers, filtering, normalization

  5. 11
    Analysis of Gene Expression – hypothesis testing and biostatistics

    Event Details: 1. Correlation – detecting correlation of features and factors 2. Clustering of samples using gene expression profiles 3. Clustering of genes by expression profiles across...

  6. 18
    DNA Variation, Gene and Isoform expression, DNA Methylation and Histone Modification

    Event Details: 1. Detecting SNVs and measuring frequency of mutations (example - Ebola) 2. Somatic Mutations in Cancer 3. Epigenetic regulation in neurodegenerative diseases 4. Multi Omics integration...

  7. 25
    Introduction to Data Mining

    Event Details: 1. What is machine learning, categories of methods 2. Regression, factors and features – Factor Regression Analysis 3. Challenges associated with different kinds of machine...

  8. 02
    Using Machine Learning for Expression Data

    Event Details: 1. Decision Trees, Discriminant Analysis and Support Vector Machines 2. Feature Selection and expanding the list of features 3. Data Visualization

  9. 09

    Event Details: 1. Annotation using Gene Ontology 2. Human GAGE: Gene Set Enrichment Analysis 3. Statistical Significance and Reproducibility

  10. 16
    Independent project work

    Event Details: Participants will be asked to work on the independent project which they can choose from the list of the projects or can work...

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