JMP Genomics 3.1

Category Genomics>Gene Expression Analysis/Profiling/Tools

Abstract JMP Genomics is a statistical discovery software solution. It can be used to uncover meaningful patterns in high-throughput genetics, gene expression microarray and proteomics data. Interactive graphics and analysis dialog boxes are provided to help you explore data relationships using a comprehensive set of traditional and advanced statistical algorithms. JMP Genomics is designed for life science students, biologists, chemists and biostatisticians engaged in analyzing data. Point-and-click menus let you select from a variety of prebuilt SAS analytic processes and choose customized options appropriate for your genomic data sets.

JMP Genomics features include:

Customized SAS analytics running behind a JMP user interface which -

1) Requires No previous SAS programming knowledge;

2) Offers 100 analytic processes (APs) for data import, quality control, preprocessing, analysis, annotation and pattern discovery, plus all standard features of the JMP software platform.

Interactive graphics and summary tables that are generated automatically during analysis to -

1) Link points in a graph to lines in a corresponding table for easy viewing;

2) Allow selection of sets of interesting points to subset to new tables;

3) Produce easy-to-understand summaries of large data sets.

JMP Genomics imports a variety of data formats, including -

1) Affymetrix and Illumina SNP and standard genotype data;

2) Affymetrix, Agilent and Illumina gene expression data;

3) GenePix, QuantArray and several other popular image processors;

3) ABI Analyst, single- and multiple-text files;

4) Text, Excel and comma-separated formats.

Mine genetic marker data from families and unrelated individuals to -

1) Perform whole-genome SNP analysis;

2) Summarize information about phenotypes and genetic markers;

3) Explore associations between genetic markers and binary or quantitative traits;

4) Select tagSNPs for haplotypes or areas of high linkage disequilibrium

5) Reconstruct haplotypes and discover haplotype-trait associations.

Identify key genes from large microarray data sets to -

1) Assess quality control metrics to identify and remove outlier arrays;

2) Normalize within and across arrays to remove effects of experimental biases;

3) Perform gene-by-gene modeling to discover statistically significant differences;

4) Reveal biological insight with pattern discovery and predictive modeling tools.

Find protein biomarkers, using spectral data to -

1) Bin, detrend and find peaks in 2-D spectra;

2) Plot 2-D and 3-D spectra for graphical browsing;

3) Align 3-D spectra across multiple data sets

4) Analyze preprocessed proteomics data with ANOVA and other methods.

Annotate results using public or proprietary sources of information to -

1) Merge in annotation files during analysis;

2) Search public databases to construct tables of annotation hyperlinks;

3) Upload results to Ingenuity Pathways Analysis (see G6G Abstract Number 20017U) to seek points of interaction between significantly changing gene and protein lists;

4) Color KEGG pathways to identify co-regulated genes and more.

Note: For additional info and features for JMP Genomics 3.1 please click here.

System Requirements


Manufacturer Web Site SAS Worldwide Headquarters

Price Contact manufacturer.

G6G Abstract Number 20007

G6G Manufacturer Number 102325