JMP® for Genomic Data Analysis
Whether you're working in agriculture, pharmacogenomics, biotechnology, or other areas of genomic research, JMP Genomics provides tools to analyze rare and common variants, detect differential expression patterns, find signals in next-generation sequencing data, discover reliable biomarker profiles, and visualize patterns through integrated genomics data analysis workflows.
- Plant breeders and crop bioscientists
- Biomarker scientists
- Statistical geneticists
JMP Genomics: Overview and Q&A
In our webinar, Dr. Ruth Hummel will cover three common “-omics” analyses using JMP Genomics as we explore a small portion of the breadth and depth in JMP Genomics software. We will also provide opportunities for Q&A and point out other features of interest within the software. Please choose the session at your most convenient start time below.
Differential Expression Analysis – Expression analysis can relate to microarray data, Nextgen RNAseq, proteomics, metabolomics, or any scenario where you collect data on the amount of expression for various genes or exons or markers. We will look at an example of what JMP Genomics can do for expression data, including importing tools, cleaning and filtering missing data or poor-quality reads, adjusting for multiple comparisons, visualizing and interacting with results in volcano plots and Manhattan plots, using Venn diagrams to find significant markers for combinations of comparisons, and creating input data sets for pathway analysis. (10 minutes)
Q&A on expression tools (5 minutes)
Basic Genetic Analysis (GWAS and similar) – Genetic association testing often involves case-control comparisons, or multi-comparisons of more groups, typically called GWAS – Genome-Wide Association Study. We will cover case-control and SNP-Trait Association Testing (types of GWAS), and we will advance the genetic analysis to estimate and control for familial similarities (kinship) and population similarities, as well as allowing random effects into a Q-K Mixed Model framework. We will explore data importing and recoding, cleaning and filtering, and these increasing complex modeling options, including adjusting for multiple comparisons, visualizing and interacting with results in volcano plots and Manhattan plots, and using Venn diagrams to find significant markers for combinations of comparisons. (10 minutes)
Q&A on genetic tools (5 minutes)
Advancing Plant Breeding with Predictive Modeling and ProgenySimulation – Our final example will show the power of using predictive modeling to predict phenotypic behavior for specific genotypic markers. We will build and compare predictive models using genetic data, and then we will use the best model to predict traits as we explore the best progeny. We will filter to find the best progeny to cross, cross them in-silico, predict the traits of the offspring, and repeat this for several generations. We will use caterpillar plots to visualize the improvement of various traits over generations of crosses. (10 minutes)
Q&A on predictive modeling and progeny simulation (5 minutes)
Check out how companies and universities are using JMP for genomic analysis: