I decided to simplify things by going back to Google Drive. Using OneDrive or Sharepoint or whatever it was turned out to be one tool too many to keep track of. I had made the move from Google Drive to OneDrive a couple years ago when I realized that Google Drive was stamping our time-lapse image sets with the date and time of upload, getting in the way of one of the ways I like to confirm the frequency of image capture for an experiment. It was still possible to get the actual image capture date from the EXIF info, but you couldn’t just sort by that within Google Drive. OneDrive didn’t do that, and we were experimenting with OneNote for lab notes, so it seemed like a good move. Now we’re no longer using OneNote because notebooks were not preserved when students graduated! Fortunately I had backups of notebooks, but this made it untenable.
I feel like using the Google shared drive is better supported by our IT support, and that’s worth something alone. I think the file date problem can be circumvented by just checking the image EXIF info if I need to double-check the interval between images, so even though it isn’t as easy as looking at the file date, at least it’s not lost permanently. Plus students are already using the shared drive to keep their notes and other info, so it’s easier for them to just use the same for data.
The students just log into their student Google account and can access the shared lab drive from there. When I moved things back, I used Transmit, the FTP client from Panic, to log into Google Drive and move large sets of files and folders easily. I think it might be a little more fault-tolerant than the web uploading/downloading interface.
There’s a brief story about the lab in yesterday’s edition of the local paper. It highlights a recent paper I was a co-author on, focused on the development of a pipeline for standardizing analysis of RNA-seq data from spaceflight experiments. Pleased to see some positive local press coverage!
I’m dabbling a little with PlantCV, an open-source image analysis package with lots of pre-built features for doing plant image analysis. It can be installed using the Conda package manager system, which means I won’t have to mess around with all the dependencies. It can also be installed on a RasPi, which is also very interesting. It isn’t clear to me how well it works on roots, as most of the examples I recall seeing seem to focus on leaves and stems. I can’t think of why this could be a problem, but I’d like to poke around a little bit with some example root files once I capture them using the RasPi hq-cam next week.
I dabbled a little today with the RPi high quality camera using the same lens I use for the other image capture systems (a 75 mm fixed C-mount lens) with a 70 mm extension tube. I took a photo of a ruler and measured the distance of 3 mm in pixels, which I found to be 2144 pixels. The vertical distance in the frame was around 4 mm, which seems to be in the neighborhood of what I want for an Arabidopsis root experiment. This may mean I don’t really need a panel of multiple LEDs to achieve even background illumination, at least when using this lens and tube setup for a single root tracking experiment. OG ROTATO scaling is 664 µm per 100 pixels, or 6.64 µm/px. The RPi HQ camera has a resolution of 3000 µm in 2144 pixels, or 1.4 µm/px. This is 4.75x that of the old system. If I’m going to use it for regular root experiments, I might remove the IR filter that’s glued to the sensor.
I’ve started to dip my toe in the pool of bioinformatics methods, using our recent data sets as an incentive to learn what I’m doing. In reading more about using Salmon, it seems I should build a “decoy-aware” index. So the commands below source accomplish that.
This pegs the CPU at 100% but does not fill the 8 GB of RAM on this machine. This took about 7 min on this machine.
After this I will do another trial run with a few reads and compare the mapping rate with this decoy-aware index compared to the raw transcriptome I used yesterday (I renamed the output from yesterday quants_1).
I’m looking forward to the day that my seeds are transported to the ISS on one of these babies! There’s still a long way to go before my project is even ready to apply for a flight position, but I’ve started working with support scientists to schedule all the tests that need to be done. It’s going to be a very busy summer around my lab!
Whenever I teach on seeds, either in my non-majors Food class or my Plant Physiology class for majors, I can’t help describing them as the children of the mother plant. I know, not exactly creative, but it helps to paint a picture of the roles of the parent plant and the seed. I like to talk about how the endosperm or other food reserve is like a packed lunch, put there by the caring mother to feed the baby plant as it germinates and becomes able to feed itself. And what kind of parent sends its babies out without a coat? It usually gets a few chuckles, at least, to put this all in human terms.
That coat on the seed? Sometimes it’s a jacket, and other times it’s more like a down coat, and the mother plant chooses based on the temperature. I’m not making this up. In a study published this week, plant scientists link the toughness/thickness of the seed coat to the temperature endured by the mother plant. If the mother experienced warmer temperatures, it will make more of a protein that limits the production of tannins in the fruit. Less tannin makes for a thinner seed coat and faster germination. On the other hand lower temperatures cause the mother plant to make more tannins, leading to a thicker coat. Simple, yet remarkable.