See and explore your genomics data from every angle. Features in JMPGenomics 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 desktop and server operating systems.

Offer point-and-click menus and options so users can get started quickly.

Power robust data import, quality control, analyses, annotation and pattern discovery features using well-documented methods.

Require no previous SAS programming experience.

The JMP software platform provides:

New integration capabilities 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.

A new add-in infrastructure that simplifies the integration of external analytics into JMP.

Dynamic, drag-and-drop interface for visual exploration of data patterns with Graph Builder.

Unparalleled flexibility for point-and-click creation of custom graphics: 2-D and 3-D scatterplots, parallel, overlay, contour and bubble plots.

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.

Are organized into tabbed reports and linked to underlying data tables.

Offer point-and-click selection and easy creation of subset data tables.

Can be queried dynamically to create tailored views of your data, using the JMP Data Filter or a variety of other selection tools.

Flexible workflows offer options for all users:

JMP Genomics Wizard guides the import of new data sets.

Basic Workflows for expression, exon, genetics, copy number, tiling andmiRNA.

Intermediate Workflows for expression quality control and analysis.

New Q-K Analysis and Rare Variants workflows.

Expression and copy number workflows incorporate variance components analysis to guide statistical model selection.

Workflow Builder, which offers complete control for expert users who wish to create their own custom workflows.

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

Aligned sequence reads stored in SAM files.

Illumina BeadStudio or GenomeStudio output files for expression, SNP, genetic marker, copy number and other data types.

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.

Assess genome-wide data sets to:

Examine missing data patterns for individuals and genetic markers.

Summarize characteristics of genetic marker data sets: 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 to zoom into interesting regions with interactive triangular plots.

• Now identify and visualize linkage disequilibrium blocks.

Generate distributions of categorical and continuous phenotypes.

Perform candidate-gene or whole-genome SNP analysis to:

Analyze 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.

Summarize across common or rare variants to perform statistical testing and test grouped SNPs within a locus or pathway.

Explore associations between genetic markers and binary or quantitative traits while adjusting for covariates.

• New options to output residuals and R-square for each SNP.
• Compute LS Means and differences when performing genotype tests with continuous traits or random effects.
• Experimental permutation options now available.

Test for association between SNPs and multiple traits, either separately or jointly, while adjusting for covariates.

Correct association tests for relatedness and population structure simultaneously.

• New workflow streamlines the steps in performing Q-K Mixed Model analysis.
• Save computational time by creating a compressed K matrix and using it as input to Q-K analysis.

Test for associations using imputed SNP data.

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:

Haplotype estimation and discovery of haplotype-trait associations.

Selection of tagSNPs for haplotypes or linkage disequilibrium blocks.

Computation and clustering of genetic distance matrices for individuals or populations.

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

New option to output pairs of individuals exceeding a user-specified IBS threshold.

Single-marker, interval and composite-interval QTL mapping.

Assess large expression data sets with confidence to:

Identify data quality issues and remove outlier arrays 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 arrays to remove confounding sources of variation to:

Perform batch normalization and scoring, or utilize PLS normalization to remove known technical effects.

Use loess (within or between arrays), quantile, 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.

Apply RMA and GCRMA for Affymetrix 3’ expression arrays.

Use RMA, mean and median standardization and summary for Affymetrix exon and miRNA arrays.

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, transcript or exon level while correcting for multiple tests and adjusting for covariates and random effects.

Use sample characteristics to easily specify subsets for analysis.

Output adjusted p-values and t-statistics for statistical tests of differential expression.

Screen paired DNA and RNA intensities for allele-specific expression.

Select sets of comparisons for inclusion in output and reverse the order of differences with new Difference Chooser.

Reveal biological insights with pattern discovery tools.

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 to allow:

Identification of reliable biomarkers from large, wide data sets.

Assessment of multiple data types from different experiments.

Predictive modeling for survival analysis with Harrell’s assessment method and integration with Cross-Validation Model Comparison.

Calculation of principal components on a primary data set and scoring of components in a secondary data set.

Comparison of results across eight different predictive modeling methods.

• Depict partition tree information graphically for standard models with new Tree Viewer.

Customized predictor filtering during model construction.

Cross-validation with adjustable hold-out and iteration options to enable comparison of relative performance across multiple 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 SNP intensities 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.

Look for shared genomic areas that display statistically significant differences using ANOVA.

Compare breakpoints within and between samples identified by circular binary segmentation.

• Filter or shade segments by mean intensity, with an option to display segment mean intensity and set a reference value for shading.

Integrate statistical analysis into next-gen sequencing workflows to:

Import sequence counts at the SNP, exon or transcript level generated by partner software from Illumina, the National Center for Genome Resources or GenoLogics, or summarized by other software.

Summarize counts using gene model information downloaded in UCSC format.

Examine mRNA-seq data using trusted methods implemented in existing exon and expression workflows.

Test for association between variant alleles and traits.

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

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.

Perform enrichment analysis using functional information from Ingenuity Pathways Analysis.+

Retrieve KEGG IDs and pathways to visualize sets of co-regulated genes and perform enrichment analysis.

Using significance indicators, create Venn diagrams to assess overlap of up to five categories simultaneously, with proportional area option for one-, two- and three-way diagrams.

• Using a common identifier, compare list membership for up to five groups and display overlaps with Venn diagrams.

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 or SNP tracks.

• New bar chart track allows summarization of reads or intensities.
• New color map track displays heat plots of information for individual subjects.

*Green text denotes feature introduced in JMP Genomics 5.

+ This feature is available to licensed users of both JMP Genomics and IPA. Existing customers can contact support@ingenuity.com. To obtain a trial license, go to www. ingenuity.com.

Find out more

JMP® Genomics Home

JMP Genomics Product Brief
(PDF File 486KB)

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Call JMP Genomics Sales
877.594.6567 (US)

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Contact JMP Genomics Sales

Request Information

877.594.6567 (US)

International Sales via Worldwide SAS Offices