JMP® Genomics

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 visualize 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 genomic regions of interest.

With support for common intensity, aligned read, and count data formats, JMP Genomics makes it simple to perform analysis of array data and summaries from next-gen studies. Easy-to-use workflows simplify analysis of gene and exon expression and RNA-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. You can also screen for allele-specific expression, filter intensities or counts, perform batch normalization, and easily apply sample and gene filters to reanalyze subsets of your data quickly.

Sophisticated analysts will find even greater flexibility in JMP Genomics 5.1, with tailored normalization and modeling options for count data that take advantage of SAS Analytics to handle these large data sets. Row-by-row analysis of survival data now includes options for specifying least squares means effects with custom differences and estimates as well as class and continuous covariates.

Read about expression analysis features in JMP Genomics.

JMP Genomics provides association analysis options ranging from simple case-control association to complex linear models supporting covariates, interactions and random effects. You can 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. With licensing options for JMP Genomics 5.1 on a 64-bit workstation or server, you can tackle larger data sets than ever before.

With JMP Genomics 5.1, you can now:

• Import variant information from VCF files, CLCbio SNP and indel reports, Complete Genomics summary files, and a variety of other text-based formats.
• Select from an extensive set of rare variant association methods to group rare SNP variants within genes, pathways or positional groups.
• Resolve strand differences between studies and perform meta-analysis on GWAS data.
• Identify genomic regions shared identical by state (IBS) between related or unrelated individuals.

JMP Genomics 5.1 features a new suite of interactive processes for the construction, optimization and visualization of marker linkage maps used to efforts to improve various agronomic crops. With these new processes, you can:

• Estimate new linkage groups using genotype data from experimental crosses.
• Order markers within linkage groups or marker groups from a predefined consensus map with multidimensional scaling (MDS) or advanced marker order optimization methods.*
• Visualize newly created or imported marker maps using simple interactive graphics or high quality multi-chromosome views.
• Compare marker groupings and orders between an existing map and a new map using interactive tools.

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.

*SAS/OR® must be licensed separately. Please contact for more information

JMP Genomics provides sophisticated downstream statistical analysis capabilities for analysis of aligned reads from state-of-the-art sequence analysis pipelines. Import counts from text formats or summarize counts from SAM, BAM, and Eland input files to take advantage of new normalization and general linear modeling methods tailored for count data. New 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 elect to call variants from a set of BAM files using a reference genome. JMP Genomics 5.1 supports an expanded list of methods for association analysis of rare and common SNP variants and a new process for identifying regions identical by state (IBS) between related or unrelated individuals. Finally, screen for significant correlations between different data types and view results in the JMP Genomics Browser with tracks overlaid to put your statistical results in genomic context.

JMP Genomics 5.1 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.

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 or KEGG into analysis data sets, group genes by cytoband, or create custom annotation groups using positional information. Easily retrieve and color KEGG pathways to highlight differentially expressed genes.

Read more about tools for pathway analysis.