JMP® Genomics

Advanced genomic data analysis software that helps you visualize your data and discover more

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.

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Plant breeders and crop bioscientists

The need for innovative, sustainable agricultural practices is one of the world’s most pressing concerns. JMP Genomics allows plant breeders and crop bioscientists 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.

Biomarker scientists

Advances in technology allow biomarker scientists to 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 visually explore the genetic underpinnings of disease and drug response, design and analyze complex experiments, and conduct reproducible research.

Statistical geneticists

With the rise of cost-effective and increasingly rapid genotyping technology, statistical geneticists are now flooded with rich genotype data with which to investigate the genetic basis of disease. JMP Genomics lets you analyze complex genome-wide association studies (GWAS), explore genetic variability and structure with advanced statistical association models, and identify and understand rare variants.


The Core Capabilities of JMP Genomics®

  • Genomic Selection for Crop Improvement

    Explore genotype-environment interactions, and uncover optimal combinations of markers to produce desirable traits and simulate the progeny of potential crosses.

  • Pharmacogenomics

    Analyze integrated genomic patterns from DNA, RNA, metabolite and protein expression to discover the biological roles in disease and drug response.

  • Expression

    Analyze microarray or RNA-seq studies through point-and-click workflows tailored to quality control, normalization and ANOVA modeling for differential gene and exon expression.

  • Statistical Genetics

    Examine associations with comprehensive GWAS capabilities. Apply techniques from simple case-control association to complex mixed models, and easily control for population structure and cryptic relatedness.

  • Next-Gen Sequencing

    Streamline the statistical analysis of next-gen data through a variety of tailored modeling methods and workflows.

  • Linkage Mapping

    Create optimal linkage maps for experimental populations, visualize marker maps and perform extensive QTL analyses.

  • Predictive Modeling

    Identify biomarkers from high-dimensional data sets. Build and compare multiple models for multiple traits through cross-validated predictive modeling reviews with extensive predictor screening capabilities.

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