JMP Genomics Version 10 released

For scientists in agriculture, pharmacogenomics, biotechnology, or other areas of genomic research, JMP Genomics is the go-to-tool to analyze rare and common variants, detect differential expression patterns, find signals in next-generation sequencing data, discover reliable biomarker profiles, and visualize patterns through integrated genomics data analysis workflows.

What`s new in JMP Genomics Version 10

In Version 10 a lot of new features, including releasing with JMP 15.1 with PRO capabilities, add value to users in academia and government, pharmaceutical organizations and agronomic companies.

Here are some new Features in Version 10 I like to highlight:

Translational scientists will get a lot of value out of the new Basic single cell RNA-Seq workflow which enables you to perform standard exploration on a Single-Cell RNA-Seq data set to analyze gene expression patterns at the cellular level and facilitates identification of cell type clusters showing differential expression. This new technology is especially useful in immunology and oncology studies. Additionally, a Feature-Barcode Matrices importer, Variable Gene Selection and dimension reduction embedding methods (t-SNE and UMAP) are new features to aid in single cell RNA-Seq analysis.  For more details please see:  https://www.jmp.com/en_us/events/ondemand/mastering-jmp/single-cell-rna-sequencing.html

Population Admixture improves the set of tools in JMP Genomics for statistical geneticists to estimate ancestral origins to account for genetic diversity in genome-wide association studies (GWAS).  For agronomic research, genomic selection model updates enhance cross evaluation and progeny simulations to perform high paced breeding cycles driving the selection of healthier crops by modeling genetic variability.  Please take a look at this describing these new features:  https://www.jmp.com/en_us/events/ondemand/mastering-jmp/analyzing-genetic-diversity-plant-breeding.html

Predictive Modeling enhancements include a new Model Summary and Ensemble utility to improve machine learning applications for biomarker discovery. New Add-in routines also enable Genomic Bayesian and XGBoost models via popular R and Python packages.  

A more seamless integration with SAS, R and Python allows our customers to use their implementation of choice for new algorithms.  This can help with reproduction of publication data, improve the variety of analyses and expand modeling choices within JMP Genomics. 

How to implement JMP Genomics

Together with our Life Science Team I`d appreciate to explore possible use cases for JMP Genomics within your organization together with you. Please reach out to me via DM or fill out this form and we`ll get in contact with you.