Genomic Data Analysis is a 3-month program that will provide an in-depth overview of standard tools and data types used to study genomics datasets. Each week, participants will meet online to discuss various aspects of genomics analysis and interpretation and work on independent projects throughout this course. Read more about the program and important dates: http://bit.ly/31WX0tj
Program Overview: tools and resources for successful learning Expectations, schedule review, and important deadlines Introduction to materials, speakers, and presenters
What is DNA, Variation, and Significance Next Generation Sequencing (WES, WGS, Targeted panels) Read length/quality, Mapping NGS data to a reference Genome
Germline variation Somatic mutations – reference and “normal” sample Non-point variations (CNV, chromosomal instability, rearrangements)
Accurate detection – the significance of coverage distribution Filtering – limiting the number of potentially significant variants Statistical methods for significance, stratification
Types of variants missense, synonymous, nonsense Prediction of variant effects on protein structure/function, P-P interaction, abnormal splicing Using reference databases Population genetics – phylogeny,...
Regression, factors, and features – Factor Regression Analysis Supervised Machine Learning (Classification, Discriminant Analysis) Mutational Profiling, Signatures, and biomarkers
Cancer studies (several examples) Development of treatment resistance (Tuberculosis) Case-control studies, GWAS (Breast Cancer Risk)