In a recent article, we shared that JMP Pro 17 has expanded capabilities to analyze the types of data commonly used for genomics. We have created a comprehensive resource, Genomic Analysis with JMP Pro, to help you learn how to apply JMP Pro for this type of analysis. @TomPedersen, Principal Technical Writer in the JMP Life Sciences R&D Team, is the primary author and shares his experience developing this new book.
What was the motivation for writing this new book?
One of the great features of our previous JMP Genomics software was the availability of various import engines along with a series of dedicated analysis workflows. These features enabled users to import their data from many proprietary-formatted files and then analyze it. We want to give users the knowledge they need to do similar analyses in JMP Pro. Here, we see that JMP Pro can open a series matrix file, parse its organization, and prepare it for importing.
What did you discover about JMP Pro while working on the book?
That JMP Pro is FAST. Analyses that once took hours in JMP Genomics now take only minutes in JMP Pro. JMP Genomics relied on SAS software to do its "heavy lifting." During lengthy analyses, SAS would not "remember" data; instead, it would read it from the disk and then write it repeatedly. Your analysis depended on how quickly your computer could read and write the data. JMP Pro, on the other hand, keeps everything in memory, so the analysis is much faster. In addition, JMP Pro is POWERFUL. With version 17, JMP Pro has improved significantly to work efficiently and effectively with the very large and wide datasets typically found in Genomics studies. Here are two examples of JMP Pro revealing important clues in your data. The volcano plots enable the identification of genetic variants that influence specific phenotypes. Plant breeders working on crop improvement are using rice plants crossed in silico to predict the best crosses to make in the field.
How do you think this book will help our customers?
The Genomic Analysis with JMP Pro book is organized to show the user how to apply typical genomic data analysis flows (see image below). It begins with showing users how to import their data and then proceeds through data transformation, quality control, and normalization. It then moves on to genetic marker analysis, exploring patterns and structure in the data, and finally to statistical and predictive modeling. This approach enables users to uncover the information hidden in their data and to make important scientific discoveries. We also include a set of real example studies and provide users with a step-by-step guide on analyzing the data.
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