Goal: Determine if IGB can run faster and better in JDK21 versus JDK8?

Ideas for stress-testing IGB:

• Users have noticed that when several genome graphs are loaded across an entire chromosome, scrolling and zooming gets a bit laggy. This is due to the large number of computations that have to happen to display a large amount of data during navigation. This could be a way to investigate improvements in speed.
• Another way to answer this question would be to investigate how memory management may have improved - or not - with the release of JDK21. When a user loads data into IGB, IGB displays in the bottom right corner information about how much memory IGB is using. It's possible that we are now able to load more data into IGB thanks to improvements in Java. For example, you could load the same data into IGB 10 and IGB 9 and observe whether the number reporting memory usage is different, or not.

Other thoughts on this:

• Possibility to write a testing script and time IGB
• NF: comments that we need this to be repeatable.
• AL: to start, create a narrative describing what you would do to investigate. Fore example, you could use the RNA-Seq coverage graphs from human genome, using the MEOX1 scenario as starting point. See: https://www.slideshare.net/AnnLoraine/use-integrated-genome-browser-to-explore-analyze-and-publish-genomic-data. Example: Load a scaled coverage graph for each data set for chromosome 1 of the human genome and then observe if IGB responsiveness changes as you pan and zoom through the data. An example task that a user might actually want to do with data like this would be to search visually for differentially expressed genes across the chromosome (or genome). Once all the genome graphs (coverage graphs) are scaled the same, places where peaks are noticeably different in height represent differentially expressed genes. Note that the dogma about differential expression analysis is that you need statistical inference methods to detect DE genes. But probably this is not entirely true for genes that are VERY differentially expressed (e.g., > 2x) in part because genes with BIG differences in expression are probably the most important biologically.
• Question: Should we investigate this on every platform? AL's opinion: check to see if there are BIG differences between the platforms.