JMP® Genomics

A unique pedigree: visual discovery and analytics

Whether you’re working in agriculture, pharmacogenomics, biotechnology, or other areas of genomic research, JMP Genomics provides comprehensive tools to analyze rare and common variants, detect differential expression patterns, understand NGS data, discover reliable biomarker profiles, and incorporate pathway information into your analysis workflows.

Linkage mapping, QTL analysis, and genomic selection functionalities guide crop and livestock breeding strategies, uncovering markers for increased yield or resistance to disease, pests and extreme weather conditions.

Extensive capabilities for expression analysis and genetic association studies simplify molecular marker discovery for disease prognosis or treatment response, identifying sources of patient variability and providing clinically relevant results in pharmacogenomics applications.

The unique pedigree of JMP Genomics combines interactive JMP graphics and robust SAS® Analytics, allowing researchers to see and explore large genomic data from every angle, understand it and share analysis results with colleagues.

Explore the core capabilities of JMP Genomics.

JMP Genomics Manhattan Plot

Explore the Core Capabilities of JMP® Genomics

Genomic Selection for Crop Improvement

JMP Genomics serves as your experimental design and analysis platform for crop-breeding programs. Biologists and breeders can employ linkage mapping and QTL analysis for experimental populations, and complex association methods for diversity populations to identify markers for pest and pathogen resistance.

Comprehensive predictive modeling reviews perform genomic selection with multiple traits of interest to improve breeding selection and accuracy, allowing insights that aren’t possible with classical phenotypic selection. These genomic selection tools uncover optimal combinations of markers to produce desirable traits, which in turn can be used to design and evaluate future crosses to produce healthier foods.

  • Perform molecular marker-assisted selection to improve crop viability and performance.
  • Utilize multi-trait predictive modeling reviews to optimize breeding programs.
  • Evaluate potential crosses and simulate their progeny based on genomic selection model scoring to determine optimal breeding strategies for future performance.
Identify ideal sequence candidates for breeding with the Pareto Frontier tool

Identify ideal sequence candidates for breeding with the Pareto Frontier tool.

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Pharmacogenomics

Use Learning Curves to identify optimal predictive models in your clinical genomics data

Use Learning Curves to identify optimal predictive models in your clinical genomics data.

JMP Genomics allows you to import variant data sets from VCF files, CLCbio SNP and indel reports, summary files from Complete Genomics, Plink text and binary files, and common output formats from SNP arrays. Once imported, choose from extensive association analysis options from simple case-control association to complex linear models supporting covariates, interactions and random effects.

Select from an extensive set of rare variant association methods to group rare SNP variants within genes, pathways or positional groups, or identify genomic regions shared identical by state (IBS) between related or unrelated individuals. You can also analyze patterns of linkage disequilibrium, correct for population structure and discover SNP-SNP interactions. Create, compress and easily integrate relationship matrices into association tests to simultaneously correct for population structure and relatedness with Q-K mixed model analysis.

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Expression

With support for common intensity, aligned read, and count data formats, JMP Genomics lets you normalise and analyse both array data and summaries from next-gen studies. Point-and-click workflows simplify gene and exon expression and RNA-seq analysis for new users.

JMP Genomics 6 offers several new scaling methods tailored for count data sets, and updates standard methods like quantile and loess normalisation for use with count data. You can also screen for allele-specific expression, filter intensities or counts, perform batch normalisation, and easily apply sample and gene filters to reanalyse subsets of your data quickly.

Overlay Continuous Variables

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.

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Genetics

JMP Genomics allows you to import variant data sets from VCF files, CLCbio SNP and indel reports, summary files from Complete Genomics, Plink text and binary files, and common output formats from SNP arrays. Once imported, choose from extensive association analysis options from simple case-control association to complex linear models supporting covariates, interactions and random effects.

Select from an extensive set of rare variant association methods to group rare SNP variants within genes, pathways or positional groups, or identify genomic regions shared identical by state (IBS) between related or unrelated individuals. You can also analyse patterns of linkage disequilibrium, correct for population structure and discover SNP-SNP interactions. Create, compress and easily integrate relationship matrices into association tests to simultaneously correct for population structure and relatedness with Q-K mixed model analysis.

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Next-Gen Sequencing

JMP Genomics provides downstream statistical analysis capabilities for aligned reads from state-of-the-art sequence analysis pipelines. Import counts from text formats, or summarize counts from SAM and BAM files to take advantage of normalisation and generalized linear modeling methods tailored for count data. Basic workflows for RNA-seq and miRNA-seq streamline steps in statistical analysis.

You can import genotypes directly from a variety of text formats or VCF files, or call variants from a set of BAM files using a reference genome. JMP Genomics supports a wide range of methods for association analysis of rare and common SNP variants and can identify regions identical by state (IBS) between related or unrelated individuals. You can screen for significant correlations between different data types and also view results in the JMP Genomics Browser to place your statistical results in genomic context.

Scale Count Data Across Samples

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

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Linkage Mapping

JMP Genomics provides a suite of interactive processes for working with genetic marker linkage maps used in efforts to improve various agronomic crops. You can create new linkage groups using genotypes from experimental inbred crosses, order markers within linkage groups using advanced optimisation algorithms, and visualise newly created or imported marker maps. JMP Genomics 6.1 adds support for framework markers, interactive marker filtering and reversal of marker order within linkage groups, and automated identification of linkage groups based on genetic distance or recombination fraction.

In addition, you may explore genotype-environment interactions in multienvironment trials, summarize phenotype information with interactive graphics, and perform QTL analysis using newly constructed marker maps. New Haley-Knott regression and permutation options expand capabilities for interval and composite interval mapping of QTLs.

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Predictive Modeling

JMP Genomics excels at predictive modeling, offering a broad and robust array of methods, as well as options for predictor filtering, predictor lock-in, and cross-validation. The software guides you through comprehensive exploratory analyses of separate and paired data types and permits you to combine multiple predictor types to build, test and cross-validate biomarker signatures with a choice of hold-out methods.

Extensive participation of JMP developers in the MicroArray Quality Control consortium has influenced the development of predictive modeling functions in JMP Genomics. Replication and iteration strategies implemented in the software seek to reduce bias, with honest cross-validation approaches that can accurately assess the relative performance of hundreds of different models at a time.

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Pathways

Volcano Plot to Identify Pathways

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 helps link pathway information to analysis results. Click to upload gene lists to partner tool Ingenuity Pathways Analysis to view and color pathways, and add IPA information to analysis data sets to perform gene set enrichment tests. JMP Genomics offers gene set scoring, which summarizes individual measurements of gene expression at the pathway level to detect related but heterogeneous patterns of differential expression.

You can also incorporate gene set and pathway information from MSigDB into analysis data sets, group genes by cytoband, or create custom annotation groups using positional information.

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Copy Number

Explore copy number and loss of heterozygosity (LOH) data for groups or individuals with JMP Genomics. You can assess data quality to identify outlier samples and data points, and adjust copy number or LOH data sets using paired or grouped reference samples. Perform partition analysis with fast circular binary segmentation (CBS) to visualise shared patterns across samples.

ANOVA-based approaches are also available to find statistically significant differences between experimental groups, or to compare individual samples to a reference group. Interactive graphical displays and the JMP Genomics Browser make it simple to identify shared regions of interest as well as unique variations in copy number.

Segmentation Summary Plots

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

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Key Features of JMP® Genomics

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:

Normalise and analyse 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 visualise linkage disequilibrium and LD blocks.
Visualise 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:

Visualising 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 optimisation algorithms.
Visualising 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.
Visualise distributions, PCA plots, and sample clusters.
Normalise across samples.
Remove batch effects due to technical variables.
Adjust count distributions with TMM and KDMM.
Perform loess, quantile, RMA, GCRMA, and ANOVA normalisation 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.
Analyse 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 high-dimensional data sets.
Selection of predictors from multiple data types.
Customised 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).
Visualise 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 or other sources to perform enrichment analysis or gene set scoring.
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:

Visualise chromosomes with customisable 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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