Top Tweets for #cryolo
Forward-looking preview by @WUBin10069342 to our latest paper @Structure_CP by @AndrewNChang highlighting the power of #AI in #cryoEM of patient-derived samples:
https://t.co/dYEB4JCSpB
Paper:
https://t.co/PQi3OAwaiM
Thanks to @pixelbaumarkt @Intein for #crYOLO, & @cryosparc
The @MarlovitsLab and collaborators at @helmholtz_image/@desy developed PickYOLO, an ultrafast particle finder for cryo electron tomograms. @MileticSean @Rorydhj #cryoEM
Find out more: https://t.co/NE5xHRL7Nh
Really grateful for #crYOLO from @pixelbaumarkt which saves our hands from RSI! Filament picking works a treat. Also #ISOLDE from @CrollTristan made modelling a breeze.
@IsraelF96135088 @GuiomarPedro @GenZadChemE Both #cryolo and #topaz have been extremely helpful for our projects too!
Especially for enriching rare particles orientations.
If anyone can get the structure to better than 2.5 Γ
, please take a crack at it. I realize now that including ptcl coords with raw movies is kind of pointless, since re-doing motion correction may make coords off. But programs like #cryolo should make fast work of these fibrils.
New entry EMPIR-10494
"The Ξ±-synuclein hereditary mutation E46K unlocks a more stable, pathogenic fibril structure"
https://t.co/jgxQb7uuCw
4919 super-resolution K2 movies in TIFF (897.9 GB)
Successfully got #cryolo to pick some pretty challenging amyloid fibril cryo-EM images about as good or better than a human! Should save weeks of time manually picking fibrils.
At last, here's @intein's lab take on the structure of Lifeact bound to actin. Kudos to twitterless Alex Belyy and @oleg_sitsel (Made with @SPHIRE_Package)
#cytoskeleton #cryoem
https://t.co/zBd9G9vNgC
Want to improve the processing of your #cryoem data? Check thisπ
The evolution of @SPHIRE_Package #crYOLO particle picking and its application in automated #cryoem processing workflows - @pixelbaumarkt & @Intein @maxplanckpress in @CommsBio #DeepLearning
https://t.co/tjQ4dgQWzG
Yes, we routinely use #crYOLO now. It's was very helpful for rapidly picking difficult cryoEM/liposome data. The filament picking mode has also saved me hours of picking! @pixelbaumarkt
The evolution of SPHIRE-crYOLO particle picking and its application in automated cryo-EM processing workflows https://t.co/xn0NW44hQW
Another challenging complex analyzed using #SPHIRE #cryolo (#membrane #nanodisc #VPP #flexibility #SignalSubtraction #ISAC)
Our very first preprint in now online! https://t.co/rsK0H8n0rr
The peroxisomal receptor docking complex, in collaboration with the Warscheid @UniFreiburg and Erdmann @ruhrunibochum labs.
#nanodisc #cryoEM #VPP #nMS #peroxisomes
Just tried #crYOLO with a big iridoviruses. Only trained it with 191 particles on 20 #cryoEM micrographs. Extensive data augmentation made it possible. #SPHIRE_Package
Finally our #crYOLO paper is out for good in @CommsBio .
You can get the paper here https://t.co/82fkKMiD79 and the software from our @SPHIRE_Package page https://t.co/YNe8mTrWdr
Kudos to Thorsten (@pixelbaumarkt)!
Thanks again @pixelbaumarkt for making cryolo available early (+ via biorxiv). #crYOLO has completely redefined how I process my data now. Especially tricky liposome data!
The #crYOLO users out there should check this out. Thorsten checked it for a couple of examples and the program will work fine with sparse picking. Check the wiki link.
#CryoEM #machinelearning
We've just updated our crYOLO preprint! More about the general model (Fig. 9), information regarding SNR dependency (Fig. 6) and an analysis on KLH (Fig. 7). Surprising result for me: We trained crYOLO successfully on two KLH mics with only ~40 particles https://t.co/8Gfyq3V5ET
@CosmicCryoEM Are you interested in posting your built containers in our public Container Library? (https://t.co/QeH3InhvFK)
We have started down the @SingularityApp road since we needed to build #crYOLO for running on @SDSC_UCSD Comet https://t.co/fEL8KEyxtw
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