JMP Genomics 10: New features and tools for translational and breeding scientists
May 8, 2020 7:50 AM
| Last Modified: May 14, 2020 8:31 AM
JMP Genomics 10, the newest software release in the JMP family of products, is here, and there’s a lot to be excited about. If you are a plant breeder or crop scientist interested in accelerating breeding cycles or a translational scientist interested in analyzing Single-Cell RNA-Seq experiments, there are new analyses available for you, so buckle up.
In JMP Genomics 10, the most sophisticated set of tools are ready for you to test drive to learn how they can help you tackle your scientific problems.
Let’s first take a look at some of the new features.
Visualize the relationships of genes using constellation plots.
Translational scientists will get a lot of value out of the new Basic Single-Cell RNA-Seq workflow. With it, you can perform standard exploration on a Single-Cell RNA-Seq data set to analyze gene expression patterns at the cellular level, facilitating identification of cell type clusters that show 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. If you are interested in a demo, make sure to check out Meijian Guan’s presentation, Analyzing Single-Cell RNA-Sequencing Data Using JMP Genomics.
But don’t just take Meijian’s word for how interesting JMP Genomics makes his scientific research. Take a look at some recent publications to get a more complete understanding of what JMP Genomics provides. For translational scientists who are evaluating the efficacy of vaccines, predicting blood based biomarkers (and a subsequent assay) for breast cancer identification, or applying for patents to diagnose cutaneous melanoma, JMP Genomics adds a new Basic Single-Cell RNA-Seq workflow to users’ arsenal of data science tools.
The addition of Population Admixture to JMP Genomics gives statistical geneticists another arrow in their quiver for estimating 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, supporting the selection of healthier crops by modeling genetic variability. The latest video from Luciano da Costa e Silva describes these new features. For a more recent high-level discussion of our software, check out Dr. Kelci Miclaus’s JMP On Air segment: Plant Breeding with JMP Genomics.
This release of JMP Genomics is the latest step toward our promise of providing more convenient access to SAS, R and Python. With JMP Pro 15.1 and SAS 9.4M6, the release has predictive modeling enhancements including 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.
All of these advancements are made possible by a team of top-notch scientists, led by Russ Wolfinger, JMP Life Sciences Director of R&D. His election to the American Association for the Advancement of Science in 2012 backs up his accomplishments with the application of mixed models to clinical trials and genomics analysis, as well as research in many other areas of statistical analysis. His most recent accolades come from Kaggle competitions, most notably just a few weeks ago when he placed first on the private leaderboard for global forecasting of COVID-19. His JMP On Air interview, Lessons from a Kaggle Winner, gives more details on his experiences with these competitions.