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JMP Genomics

JMP Genomics analyzes and visualizes genomics data, combining the power of JMP with SAS Analytics and customized applications tailored for vast genomic data sets


A unique pedigree: visual discovery and analytics


JMP Genomics 6.0 offers enhanced features for agronomic applications and more. Download the product brief and learn more about the visual paradigm that helps even novices quickly begin to make discoveries in genomics data.

Key Features in JMP® Genomics 6.0


ID shared and unique variations:-


New segmentation summary plots can be filtered interactively to identify shared regions of copy number loss or gain.


Analytics for many platforms

Overlay continuous variables such as p-values, intensities, counts or fold changes on simple and complex genomes to identify interesting regions, then drill down to view detailed statistical results and tracks.


Comprehensive genetics toolkit

Identify genomic regions that contain marker genotypes shared identical by state between related or unrelated individuals.


Linkage Mapping :-

Improve crops with analytics

Visualize linkage maps created in JMP Genomics or imported from other software


Analyze counts and variants

Scale count data across samples using TMM normalization, compare TMM factors between samples, and view kernel density plots of normalized data.

Scale count data across samples using TMM normalization, compare TMM factors between samples, and view kernel density plots of normalized data.


Predictive Modeling :-

Find reliable biomarkers

View ROC curves and assess your predictive models using a variety of ROC statistics.


Zoom out to pathway level

Examine a summary volcano plot to identify pathways that are over – or under– represented in your significant gene list, using a variety of enrichment tests


JMP Genomics imports data from a variety of formats, including:

Read counts, intensities or genotypes in single or multiple text files.

Aligned reads in SAM or BAM format and variants in VCF files.

Complete Genomics pipeline and testvariant output files.

CLC Bio SNP and indel summary files.

Illumina BeadStudio or GenomeStudio expression, SNP, copy number and other data types.

A variety of Affymetrix CEL and CHP files, as well as BAR, LOHCHP, CNCHP, CNAT, and Cytogenetics CEL and CHP files.

GenePix, QuantArray, one-color and two-color Agilent files.

Excel and comma-separated files, including data formats from multiple NimbleGen platforms.


Flexible workflows for new and experienced users:

JMP Genomics Wizard guides import of new data sets.

Basic workflows for expression, exon, RNA-seq, copy number and linkage map construction.

Intermediate options for expression QC and analysis.

Q-K and rare variant association workflows.

Workflow Builder for creation of custom workflows.


Integrate statistics into next-gen sequencing workflows to:

Normalize and analyze sequence counts generated at the exon, transcript, or gene level by various pipelines.


Assess RNA-seq data using point-and-click workflows.


Test for association between traits and rare or common variants.


Perform cross-correlation analysis to relate sequence counts to other numeric genomic measures.


Assess genome-wide variant data sets to:

Examine individual and marker missing data patterns.


Summarize marker properties including allele and genotype frequencies, HWE, heterozygosity and diversity.


Filter data sets by marker or sample attributes.


Explore associations with one or more binary or quantitative traits.


Calculate interactions and adjust for covariates.


Test for associations using imputed SNP data.


Calculate and visualize linkage disequilibrium and LD blocks.


Visualize and adjust for population structure using PCA or MDS.


Perform GWAS meta-analysis using p-values or effects.


Expand analysis options for marker data to incorporate:

Testing SNP variants grouped within a locus or pathway.


Rare variant association tests with permutation options.


Genetic distance matrices for individuals or populations.


Calculation of IBD, IBS and allele-sharing relationship matrices.


Compression of K matrices to save computational time.


Association test corrections for relatedness and structure.


Identification of genomic regions shared identical by state.


Estimation of haplotypes and haplotype-trait association.


Selection of haplotype tagSNPs or LD tagSNPs.


Reconciliation of strand differences between studies.


Improve crop and livestock breeding strategies by:

Visualizing categorical and continuous phenotype distributions among individuals, genotypes, or lines.


Assessing genotypes in multi-environment trials.


Identifying linkage groups from experimental cross data.


Ordering markers within linkage groups using advanced optimization algorithms.


Visualizing linkage maps created in JMP Genomics and other mapping software packages.


Performing single-marker, interval and composite-interval QTL mapping with permutation option.

Assess the quality of large expression data sets to:

Identify data quality issues and remove outliers.


Pinpoint factors that explain variance in your data.


Visualize distributions, PCA plots, and sample clusters.


Normalize across samples.


Remove batch effects due to technical variables.


Adjust count distributions with TMM and KDMM.


Perform loess, quantile, RMA, GCRMA, and ANOVA normalization or standardize to a variety of statistics.


Apply trusted statistical modeling methods to:

Discover significant differences using ANOVA and generalized linear models.


Apply a variety of multiple test adjustments.


Adjust for covariates and random effects.


Screen for allele-specific expression.


Analyze censored survival data.


Plot expression profiles by sample or group with dynamic selection and filtering.


Perform hierarchical and K-means clustering.


Use advanced predictive modeling analysis tools to allow:

Identification of biomarkers from wide data sets.


Selection of predictors from multiple data types.


Customized predictor filtering during model construction.


Lock in of key class or continuous predictors.


Performance comparison across eight different methods.


Cross-validation with many hold-out and iteration options.


Learning Curve analysis to assess sample size impact.


Assess copy number data sets to:

Examine data quality with PCA and distribution analysis.


Adjust copy number measures using paired or grouped controls.


View segments detected by circular binary segmentation (CBS).


Visualize shared patterns of copy number loss or gain.


Find genomic areas that display statistically significant differences between groups, or individuals and a control group.


Use JMP Genomics annotation tools to:

Merge functional information with statistical results.


Upload results to Ingenuity Pathways Analysis to seek points of interaction between SNP, gene and protein lists and color pathways.


Perform enrichment analysis using functional information from Ingenuity Pathways Analysis.


Merge pathway information from mSigDB, KEGG or other sources to perform enrichment analysis or gene set scoring.


Visualize sets of co-regulated genes in KEGG pathways.


Download annotation and library files from Affymetrix NetAffx.


Create Venn diagrams to assess overlap of significant results.


Create genome-level views that allow you to:

Visualize chromosomes with customizable color themes.


Compare multiple experiments to find regions of shared significance.


Overlay gene, SNP, bar chart, and color map tracks on results.


The JMP software platform provides:

Graph Builder for visual exploration of data patterns.


Point-and-click creation of a variety of custom graphics.


Easy copy-and-paste into Word and PowerPoint.


Built-in scripts for capturing and sharing findings.

Add-in capabilities for external analytics (e.g., R, SAS).


Interactive graphics generated automatically during analysis:

Are organized into dynamic reports linked to underlying data.


Offer point-and-click selection and easy subset creation.


Can be queried dynamically using the JMP Data Filter.


Can be converted to static reports.


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