AgQHub, a JMP Add-in for Breeding Pipeline Data Analysis and Reporting (2021-US-EPO-930)
Thomas Walk, Large Plant Breeding Pipeline DB Manger, North Dakota State University
Ana Heilman Morales, Large Plant Breeding Pipeline DB Manger, North Dakota State University
Didier Murillo, Data Analyst, North Dakota State University
Richard Horsley, Head of the Department of Plant Sciences, North Dakota State University
Crop breeders, often managing numerous experiments involving thousands of experimental breeding lines grown at multiple locations over many years, have developed valuable data management and analysis tools. Here, we report on more efficient crop evaluation with a suite of tools integrated into the JMP add-in dubbed AgQHub. This add-in provides an interface for users to first query MS SQL Server databases, and then calculate best linear unbiased predictors (BLUPs) of crop performance through the mixed model features of JMP. Then, to further assist in selection processes, users can sort and filter data within the add-in, with filtered data available for building reports in an interactive dashboard. Within the dashboard, users segregate selected crop genotypes into test and check categories. Separate variety release tables are automatically generated for each test line in head-to-head comparisons with selected check varieties. The dashboard also provides users the option to produce figures for quickly comparing results across tested lines and multiple traits. The tables and figures produced in the dashboard can be output to files that users can readily incorporate into variety release documentation. In short, AgQHub is a one-stop add-in that crop breeders can use to query databases, calculate BLUPs, and generate report tables and figures.
Auto-generated transcript...
Speaker | Transcript |
| Curt Hinrichs | Alright, Tom Walk, with Anna and Didier |
| with their poster on AG.Q.Hub. Tom, take it away. | |
| Tom Walk | Thank you so much, Curt, and thank you to the JMP community for inviting us to this presentation. We're so glad to be here to show you our work. |
| Today we're going to talk about a tool we're building at North Dakota State University in the Department of Plant Sciences called AG.Q.Hub. Its primarily the work of our team, the plant breeding database management team, of myself and | |
| Anna and Didier | |
| and Rich Horsley who's the department chair of plant sciences. | |
| And what we've done is we're trying to help the plant breeders who've had this long established cycle. You can imagine that if you want to | |
| improve crops, it's going to take a long process. If you want to do it right and consistently, you have to have set up a lot of experiments. You're not just going to | |
| get lucky very often and choose the best crop. So what you have to do is, you have to set up a lot of crosses | |
| and a lot of trials with thousands of lines initially, and from those, you have to go through this decade-long cycle or more, and | |
| choose...make choices every year about which were the best lines to advance. and this is...all would change with environmental conditions, so we have to get that right combination | |
| of genes with the environment. And you have to have the right analyses and experimental designs to do that, so this gets very complicated for a | |
| plant breeding team. And it's a long process to make any variety selections. And what we want to do is to make the selection processes easier every year. It'd be nice to shorten this whole process, but our more immediate goal is to | |
| to make the process more efficient, the selections more efficient at each stage. | |
| And what we've done for that, is we're developing this tool AG.Q.Hub, and we were using that with our breeding programs. We have | |
| 10 breeding programs within plant sciences at NDSU and over 60 users. We've incorporated also two research extension centers with more variety trials and field sites. And this is...this list is growing. | |
| We're trying to add more users and will probably add in more research extension centers. | |
| So what what's nice about AG.Q.Hub and the reasons why we have these users is because we have the functionality at AG.Q.Hub and it allows you to connect to the database directly and you can see data from decades worth of experiments. And once you have that you can do this analysis. | |
| You can look at experimental designs, you can view the histories of your varieties, you could look at distributions of data for individual experiments, you can calculate blups | |
| and make those predicted values. And once you have those predicted values, you can get those head-to-head | |
| comparisons, and | |
| if you have those compare...once you have those head-to-heads, then we can start building reports. | |
| And that's where we're going to get into is, we want to be able to make reports with subsetting data and build tables | |
| and the visualizations that make it a lot easier for our users. And all this is going to be done within seconds, with a few clicks in AG.Q.Hub and it's saving what's up to weeks of time in the past where users using spreadsheets and workbooks. | |
| So just to give you an idea of a workflow in AG.Q.Hub, here's one cycle of generating data for reports. | |
| And so what users will go into AG.Q.Hub, they'll select a database they want to use, and what type of analysis or query they want to have, and the output they want to have. | |
| And once they...then they'll click start and then after that, another window will pop open that will prompt them for the parameters for the queries, such as the experiments they want to query, the years, the | |
| traits or treatments that they want to look at. And after they select their parameters and click OK, then the data will pop up in these data tables and the data tables are in... | |
| within the AG.Q.Hub add-in, so all the data tables are compiled nicely within these tabs in here in AG.Q.Hub. And then here are some of the newest features we have | |
| is that users can fill...they can select the varieties they're interested in, and they can sort, and they could select, and then they could do some filtering, and then they can make these filtered variety tables. | |
| And with with these filtered variety tables, they can export those into their reports into Excel or other documents. | |
| And once you're done with one, you can start over and move on to another set of experiments. And click cancel after you do as many analyses as you wish to. | |
| We're still working on this, it's a work in progress and we get a lot of great ideas from our users. What we want to do... | |
| we're always...we're always expanding this to more users and research extension centers. That's been helpful for us to build this up. As we do that, we're looking to compile templates | |
| and release...of release tables used by the programs. | |
| With that we can build up some sort of output tables that make it easier for the users to produce head-to-head tables and variety release tables. | |
| And then we'd also like to make it easier by adding visualizations for making quicker variety comparisons. And Anna has some great ideas with that, with her experience as a plant breeder. | |
| Finally, we there's always...we're always looking to make the interface more dynamic with maybe changing options as users click things. And with that, | |
| that's my talk, but I would like to start this video, this short video to give you an idea...give you a better idea of what AG.Q.Hub does. | |
| And Curt, | |
| thank you for this opportunity again. | |
| With that, I do have one more thing I'm excited to show you and I'm going to request the share screen. I want to show our users one more thing, and this is the newest things with AG.Q.Hub. | |
| This is what we're excited...this is the direction we're going. What we have...what we have here is once the users make their selections and filtering and what they want to make tables.... | |
| the varieties they want to make tables with, we're making dashboards that open up and they can select among their filtered varieties, for which ones they want to be check lines. | |
| Like, for example, we want to be this historical varieties to be our check lines, and we want to compare our new test lines against those check lines. | |
| And we're going to select the different types of traits we want to look at, the traits we've seen in the field versus traits we measure in a laboratory or traits | |
| such as disease traits. And then we click make tables and it'll output these tables. I'm not going to do that right now to save you a little time. | |
| But what it's going to do is output tables for each of these traits, | |
| for each of these varieties, and you can see it's still needs some work. I need to put the names of the varieties in, but it's still...we're working on this, but I'm very excited and I wanted to show you this before I end. | |
| And that's the direction we're going and we're going to build on this dashboard to keep making these tables and make outputs for our users so they can put these the better formats for Excel. | |
| They can format on further outputs to Excel and Word or whatnot, and build visualizations where we can do head-to-head comparisons by comparing how these...this variety does against this variety. So we're building up on these dashboards. We are very excited about this and | |
| I'm so happy to show this and share this with the JMP community. And before I go, I want to thank all the North Dakota State University | |
| Anna, Didier, Rich and myself, and all our other users. | |
| Thank you so much. |
