Advanced genomic data analysis software that helps you visualize your data and discover more
JMP Genomics provides a unique integrated analysis solution for biological experiments. Whether you need to detect differential expression patterns, find signals in next-generation sequencing data, discover reliable biomarker profiles, perform single-cell sequencing analysis or increase genetic diversity for plant traits in silico, JMP Genomics takes you from data to understanding faster.
Using cutting-edge algorithms based on the lastest agronomic research, high-paced breeding cycles executed in JMP Genomics allow plant breeders and crop scientists to drive the selection of healthier crops by modeling genetic variability, optimizing breeding choices, simulating multiple-trait breeding programs and balancing trade-offs across key desirable traits.
Cellular composition analysis using bulk genomics data enables translational scientists to visually explore complex and rare cell populations, detect differential gene expression patterns across cell types through clustering analysis, profile transcriptional changes in immune cells to help develop vaccines or treatment, and uncover the evolutionary dynamics of tumor progression, metastasis and treatment resistance.
Biomarker scientists gather more data on drug response and disease biology than ever before. But extracting statistically valid insights from all that data has never been more challenging. JMP Genomics helps you design and analyze complex experiments, apply cutting-edge predictive modeling and machine learning algorithms to large genomic datasets and associate biological signals with clinical outcomes.
With the increasing accessibility of sequenced genomes, quantitative geneticists are now flooded with rich data to investigate the genetic basis of disease. JMP Genomics lets you analyze genome-wide association studies (GWAS), explore genetic variability and structure with advanced statistical association models and build complex genetic risk models to understand the biological underpinnings of disease and health outcomes.