- Copy Number
- Linkage Mapping
- Predictive Modeling
A unique pedigree: visual discovery and analytics
Whether you’re working with large data sets from next-gen sequencing studies or microarrays, JMP Genomics provides the tools you need to analyze rare and common variants, detect differential expression patterns, discover reliable biomarker profiles, and incorporate pathway information into your analysis workflows. In addition, our latest release extends the software’s capabilities for creating and manipulating genetic linkage maps, and then utilizing these maps in downstream QTL mapping for important agronomic crops.
The unique pedigree of JMP Genomics combines dynamically interactive JMP graphics and robust SAS® Analytics, so you can see and explore your data from every angle, understand it and share analysis results with colleagues.
JMP Genomics automatically organizes results into tabbed reports and lets you customize your view of analysis options. With capabilities for integration with R, Excel and other tools, JMP Genomics becomes your analytic hub.
ID shared and unique variations
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 shared regions of interest as well as unique variations in copy number.
Analytics for many platforms
With support for common intensity, aligned read, and count data formats, JMP Genomics lets you normalize and analyze 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 normalization for use with count data. 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.
Comprehensive genetics toolkit
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.
Improve crops with analytics
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 optimization algorithms, and visualize newly created or imported marker maps. JMP Genomics 6 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.
Analyze counts and variants
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 normalization 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.
Find reliable biomarkers
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.
Zoom out to pathway level
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.