JMP Genomics is an Affymetrix® GeneChip-compatible™ Software Solution.
JMP® Genomics Features
See and explore your genomics data from every angle. Features in JMP Genomics include:
Customized SAS Analytics running behind a JMP user interface:
Support 32- and 64-bit Professional, Business and Enterprise editions of Windows XP, Vista and Windows 7 for desktops and servers.
Offer point-and-click menus and options so users can get started quickly.
Drive powerful and robust data import, quality control, analyses, annotation and pattern discovery features using well-documented and innovative methods.
Requires no previous SAS programming experience.
The JMP software platform provides:
Dynamic, drag-and-drop interface for visual exploration of data patterns with Graph Builder.
Point-and-click creation of custom graphics: 2-D and 3-D scatterplots, box, parallel, overlay, contour, trellis, and bubble plots.
An add-in infrastructure that simplifies the integration of external analytics into JMP.
R integration capabilities to let R users leverage JMP’s interactive graphics to display analytic results.
Tools for R programmers to build and package user interfaces that let them share customized R analytics with a broader audience.
Easy copy-and-paste into Word and PowerPoint.
Built-in JMP Scripting Language (JSL) and auto-generated graphics scripts that make it easy to capture and share important findings.
Options for creating tailored dialogs for custom analysis processes.
Interactive graphics generated automatically during analysis:
Produce easy-to-understand summaries of large data sets with extensive drill-down capabilities.
Are organized into tabbed reports and linked to underlying data tables.
Offer point-and-click selection and easy subset creation.
Can be queried dynamically to create tailored views of your data, using the JMP Data Filter or a variety of other selection tools.
Can be converted to static RTF or PDF reports with new Create Report buttons on tabbed reports.
Flexible workflows offer options for numerous scenarios:
JMP Genomics Wizard guides the import of new data sets.
Basic Workflows for expression, exon, genetics, copy number, tiling and miRNA.
- New Basic RNA-seq Workflow offers normalization and modeling options for count and continuous data.
- Updated Basic miRNA expression/miRNA-seq Workflow offers greater flexibility for miRNA analysis.
Intermediate Workflows for expression quality control and analysis.
Q-K Analysis and Genetics Rare Variants workflows.
Expression and copy number workflows include variance components analysis to help identify important factors to include in statistical models.
Workflow Builder offers complete control for expert users who wish to create their own custom workflows.
Journal Builder creates a journal that links results from analyses not originally run in a workflow.
JMP Genomics imports data from a variety of formats, including:
Summarized read count, RPM, RPKM, or genotype data in text format.
Raw read counts for direct analysis or summarization using gene model information in UCSC format or BED files.
Aligned sequence reads in SAM, BAM, and Eland file formats.
- Generate counts, RPM, and RPKM values at the exon or gene level using gene models in BED, UCSC or text format or for fixed positional bins of user-specified size.
- Call variants in a set of BAM files using samtools.
Complete Genomics variant, dbSNP, and gene variant summary files.
CLC Bio SNP and indel summary files.
VCF v4.0 files, a standard format of the 1000 Genomes Project.
Illumina BeadStudio or GenomeStudio output files for expression, SNP, genetic marker, copy number and other data types.
- Multiple expression and miRNA Final Report files and their associated sample files may now be imported and combined.
- Multiple SNP Final Report or Full Data tables may now be imported simultaneously using the same map file.
Exon, whole transcript, miRNA and 3’ expression CEL and CHP files from GCOS and Affymetrix Command and Expression Console.
Tiling CEL files and BAR files from Affymetrix Tiling Array Software.
CEL, CHP, LOHCHP and CNCHP files from Affymetrix Genotyping Console, and CNAT files.
Cytogenetics CEL and CHP files.
GenePix, QuantArray, one-color and two-color Agilent files.
Genomics data contained within single text files or multiple text files.
Excel and comma-separated files, including data formats from multiple NimbleGen platforms.
Integrate statistics into next-gen sequencing workflows to:
Analyze sequence counts at the SNP, exon or transcript level generated by pipelines from Illumina, the National Center for Genome Resources, GenoLogics, CLC Bio, or summarized by other software.
Normalize and analyze RNA-seq data using point and click workflows.
Test for association between rare and common variant alleles and traits using a variety of different methods.
Perform cross-correlation analysis to relate sequence counts to other numeric genomic measures.
- Now incorporating multiple testing adjustment.
Assess genome-wide data sets to:
Examine missing data patterns for individuals and genetic markers.
Summarize allele and genotype frequencies, HWE, number of missing values, heterozygosity and diversity.
Filter data sets by marker properties prior to statistical analysis, including filtering by HWE values for a subgroup (e.g., controls only).
Calculate and visualize linkage disequilibrium measures, then zoom into interesting regions with interactive triangular plots.
Identify and visualize linkage disequilibrium blocks.
Generate distributions of categorical and continuous phenotypes.
Perform whole-genome and candidate-gene SNP analysis to:
Analyze GWAS data sets as large as 1.5 million SNPs for 15,000 samples on a 32-bit desktop work station.
Tackle even larger data sets on a 64-bit desktop or server.
Explore associations between genetic markers and binary or quantitative traits while adjusting for covariates, with experimental permutation options.
- Association trend tests now include volcano plots.
Test for association between SNPs and multiple traits, either separately or jointly, while adjusting for covariates.
Test for associations using imputed SNP data.
Perform meta-analysis to combine results for the same SNP across multiple GWAS studies using p-values or effects as input.
- Reconcile strand differences by comparing study annotations and flipping strands of major and minor alleles as needed.
Visualize and correct for population structure prior to association tests with Principal Components Analysis (PCA) or Multidimensional Scaling (MDS).
Expand analysis options for marker data to incorporate:
Statistical testing on common or rare SNP variants collapsed within a locus or pathway in Genetics Rare Variants Workflow.
Five new methods and a general framework for rare variants association that allows modifications or combinations of methods, using permutation to assess significance.
- The Rare Variants Tutorial explains approaches implemented in Genetics Rare Variants Workflow and Rare Variants Association.
Computation and clustering of genetic distance matrices for individuals or populations.
- Calculate Fst as a measure of distance between populations.
Calculation of IBD, IBS and allele-sharing individual relationship matrices.
- Output pairs exceeding a given IBS or IBD threshold.
Identification of genomic regions that contain marker genotypes shared identical by state between related or unrelated individuals.
Compression of K matrices to save computational time during Q-K analysis.
Correction of association tests for relatedness and population structure simultaneously with Q-K Mixed Model analysis.
Haplotype estimation and discovery of haplotype-trait associations.
- New option to output only the most probable haplotype pair.
Selection of tagSNPs for haplotypes or linkage disequilibrium blocks.
Improve crop and livestock breeding strategies by:
Examining distributions of categorical and continuous phenotypes among different individuals, genotypes, or lines.
Analyzing genotype performance in a multi-environment trial, displaying stability measures, genotype and GxE least square means from linear-bilinear models, PCA biplots, and heritabilities.
Identifying linkage groups using experimental cross design information and clustering of pairwise recombination rates.
Generating marker order solutions within linkage groups using multidimensional scaling (MDS) or optimization methods in PROC OPTMODEL from SAS/OR®.+
- Drill down to visualize pairwise recombination rates for adjacent markers with interactive triangle plots.
Visualizing linkage maps created in JMP Genomics or other mapping software packages.
Performing single-marker, interval and composite-interval QTL mapping.
Assess large expression data sets with confidence to:
Identify data quality issues and remove outliers prior to statistical analysis.
Visualize intensity distributions, 2-D and 3-D PCA plots, and sample clustering patterns to explore the impact of experimental and technical effects.
Pinpoint experimental and technical factors that contribute to the variance explained by each principal component.
Normalize within and across samples to remove confounding sources of variation to:
Perform batch normalization and scoring, or utilize PLS normalization to remove known technical effects.
Adjust for sample-to-sample variability in count data using TMM and KDMM normalization.
Use loess (within or between arrays), quantile, RMA, GCRMA, factor analysis, and ANOVA normalizations as well as standardization to a variety of statistics (e.g., mean, median, IQR).
Standardize using a shifting factor and perform log2 transformation after standardization.
Specify a baseline data set to apply reference information to a new data set during between-array loess or quantile normalization.
Use kernel density information in loess and quantile normalization.
Perform MAT and quantile normalization for Affymetrix tiling arrays.
Apply trusted statistical methods with flexible options to:
Perform gene-by-gene modeling to discover statistically significant differences at the probe, exon, or gene level while correcting for multiple tests and adjusting for covariates and random effects.
- Analyze count data using general linear models supported by SAS PROC GLIMMIX with automatic use of a multiplicative over-dispersion parameter.
Screen paired DNA and RNA intensities for allele-specific expression.
Perform row-by-row analysis of censored survival data.
- Specify least squares means effects, custom differences and estimates, and class and continuous covariates.
Use sample characteristics to easily specify subsets for analysis.
Output adjusted p-values and t-statistics for statistical tests of differential expression.
Select sets of comparisons for inclusion in output and reverse the order of differences.
Plot and color profiles of raw or normalized intensities by sample or by group with dynamic data filtering to pinpoint key patterns.
Cluster samples or genes with hierarchical and K-means analyses.
Apply advanced predictive modeling analysis tools that allow:
Identification of reliable biomarkers from large, wide data sets.
Assessment of multiple data types from different experiments.
Customized predictor filtering during model construction.
- Now lock in key class or continuous predictors.
Comparison of relative performance across eight different predictive modeling methods using cross-validation with adjustable hold-out and iteration options.
- New option for specifying different costs for classification with binary or nominal outcomes.
Predictive modeling for survival analysis with Harrell’s C statistic and integration with Cross-Validation Model Comparison.
Computation of survival residuals for further analysis with other predictive modeling methods.
Calculation of principal components on a primary data set and scoring of components in a secondary data set.
Depiction of partition tree information graphically for standard models.
Learning Curve analysis assessing the impact of sample size.
Assess copy number data sets to:
Examine data quality with PCA and distribution analysis.
Analyze intensities or counts directly or import copy number values generated by a variety of algorithms.
Adjust intensities or counts for experimental samples using paired or grouped control samples.
Compare breakpoints within and between samples identified by circular binary segmentation.
- Visualize shared patterns of copy number loss or gain in new interactive summary graphics.
- Explore segment means across samples in new output data sets using dynamic filtering tools.
Filter or shade segmentation results by mean intensity, with optional segment mean intensity lines.
Find genomic areas that display statistically significant differences between groups (or individuals and a control group) using ANOVA.
Use JMP Genomics annotation tools to:
Merge functional information with statistical results.
Download annotation and library files from Affymetrix NetAffx.
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.
- New option to specify multiple annotation categories.
Visualize sets of co-regulated genes in KEGG pathways.
Create Venn diagrams to assess overlap of up to five categories of significant results simultaneously, with proportional area option for one-, two- and three-way diagrams.
Compare list membership for up to five groups and display overlaps with Venn diagrams using common gene identifiers.
Create genome-level views to:
Color chromosomes using custom themes based on annotation information or summarized statistical results.
Use a variety of continuous measures for summarization.
Overlay information from multiple comparisons or experiments to find regions of shared significance.
Drill down on interesting regions to plot p-values and view gene, SNP, bar chart, and color map tracks.
* Green text denotes features introduced in JMP Genomics 5.1
+ SAS/OR must be licensed separately. Please contact for more information.
Find out more
JMP Genomics Product Brief
(PDF File 1.6 MB)
Next Steps
Request Information or Schedule a Demonstration
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877.594.6567 (US)
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