JMP® Genomics

Explore copy number and loss of heterozygosity (LOH) data for groups or individuals with JMP Genomics.  You can assess data quality with distribution and principal components analysis, 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) and customize your view of results to display segment means, shade segments relative to a reference value, and display only segments meeting your cutoff.  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 genome browser make it simple to identify genomic regions of interest.

Easy-to-use workflows in JMP Genomics simplify analysis of gene and exon expression and mRNA-seq data sets for new users. Simply point and click to select quality control, normalization, analysis and pattern discovery methods, and explore analysis results displayed in interactive tabbed reports.

Sophisticated analysts will find even greater flexibility in JMP Genomics 5, with new options to screen for allele-specific expression, then drill down on interesting candidate genes for detailed analysis. Quantile and loess normalization now use kernel density information to improve model fit.  Sample and gene filters let you re–analyze subsets of your data quickly.

Read about expression analysis features in JMP Genomics.

JMP Genomics provides case-control analysis options for data sets as large as 1.5 million SNPs for 15,000 individuals on a 32-bit PC workstation. A 64-bit workstation or server permits analysis of even larger data sets.With JMP Genomics 5, you can:

• Test SNP-trait associations for various traits types, with experimental permutation options. 
• Examine SNPs and multiple continuous traits with MANOVA, or overlay single-trait test results with Venn diagrams.
• Discover and test SNP-SNP interactions and group rare SNP variants within genes, pathways or positional groups.
• Create and compress relationship matrices for integration in association tests that correct for population structure and relatedness.

New workflows for Q-K and rare variant analysis streamline these processes.

JMP Genomics provides sophisticated downstream statistical analysis capabilities to users of state-of-the-art sequence analysis pipelines. Import genotypes or sequence counts from your experiments directly from text formats.  Counts can be summarized by positional bins or using existing gene models.  Support for SAM files simplifies the creation of analysis-ready data sets.

New tools allow optional recoding or group-level analysis of rare and common SNP variants. Perform flexible screens for correlations between paired data types. Counts or statistical analysis results may be viewed in the JMP Genomics Browser, with histogram and heat plot tracks with individual- or group-level summaries overlaid to complement SNP and gene tracks.

JMP Genomics excels at predictive modeling, offering a broad and robust array of methods, as well as options for predictor filtering and cross-validation. JMP Genomics 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.

JMP Genomics 5 helps link pathway information to analysis results. Click to upload gene lists to partner tool Ingenuity Pathways Analysis to view pathway information, and add IPA pathway information to analysis data sets to perform gene set enrichment tests.

You can also incorporate gene set and pathway information from MsigDB or KEGG into analysis data sets. Use existing cytoband groupings for genes, or create custom annotation groups using positional information. Gene Set Scoring, new in JMP Genomics 5, summarizes individual measurements of gene expession at the pathway level to detect related but heterogeneous patterns of differential expression.

Read more about tools for pathway analysis.