CZII’s particle picking machine learning challenge is up and running. There are already 1000 signups on Kaggle. Follow this page for an introduction and links to all sorts of material and resources, including two preprints: cryoetdataportal.czscience.c…

Annotating CryoET Volumes: A Machine Learning Challenge 1. This study tackles a major bottleneck in cryo-electron tomography (cryoET)—the challenging and time-consuming process of annotating 3D cellular volumes. To address this, the authors introduced a machine learning challenge to drive innovation in automated particle labeling for cryoET datasets. 2. A key innovation is the creation of a “phantom” sample that mimics the cellular environment, allowing for the generation of high-quality ground truth annotations. This approach provides a diverse dataset that includes various protein complexes, each with distinct shapes and sizes, to train and benchmark machine learning algorithms. 3. The challenge aims to foster collaboration between cryoET and ML experts. By creating a standardized, annotated dataset, the authors hope to push the limits of current particle-picking algorithms, making the annotation of cellular tomograms more efficient and accurate. 4. The dataset, available on the CryoET Data Portal, consists of 492 tomograms featuring six distinct particle types. The dataset serves as a resource for participants to develop ML models capable of recognizing multiple protein classes across various cellular environments. 5. The evaluation metric for the challenge prioritizes models that can accurately label smaller particles, with weighted scoring emphasizing hard-to-detect particles. This is crucial for advancing the accuracy and reliability of cryoET analyses in biological research. 6. The study highlights several newly developed tools, including DenoisET, Copick, and DeepFindET, which enhance cryoET data processing and annotation. These tools were instrumental in curating the phantom dataset and are open to the community for further cryoET advancements. 7. The authors anticipate that this challenge will serve as the foundation for future contests, aiming to solve more complex annotation problems, such as distinguishing particles in crowded cellular environments and labeling membrane-bound proteins. @kisharrington @bcarra2 @DanielSerwas @emontabana @kimanius 📜Paper: biorxiv.org/content/10.1101/… #CryoET #MachineLearning #StructuralBiology #Bioinformatics #MLChallenge #ProteinAnnotation #CellBiology

First short article on the CZII CryoET Data Portal: nature.com/articles/s41592-0… Comments/feedback welcome.

I am at Photonics West till Monday. Please let me know if I should come to your talk or poster, especially if you may be interested in openings at @czbiohub. I just gave an invited talk (spie.org/PW24B/conferencedet…) on our recently preprinted work (x.com/czbiohub/status/173757…).

We are hiring! Check out our open positions! czimaginginstitute.org/caree…

Looking forward to having you as a colleague!

We are still looking! 🔬

Thrilled to announce that I started a new position at the Chan Zuckerberg Imaging Institute (Chan Zuckerberg Biohub Network)! I look forward to the incredible research endeavors ahead.

I love how the @eLife announcement is blowing up my timeline… what is your opinion on this? @AcademicChatter

I am attending my first in-person symposium since Jan 2020: #BAMPS2022 @UCSF. Looking forward to give my presentation on a novel method: multicolor organelle flow #MCOF. Stay tuned!

If you are a Ukrainian citizen and would like to look for a position in the field of proteomics, mass spectrometry or (bio)informatics in Vienna funded by us, you can contact me at any time. I would be happy if this content can be shared.

Congratulations @3rdFloorTom! Very impressive work!

Somehow January 2022 feels like January 2021. Just with a 1-year-old instead of a newborn.

I started this poll after two conversations with different academic parents who told me that kids don’t get sick and there is nothing to worry about. Thank you twitter for showing me that others share my fears. Helped to keep me sane. @Momademia

Great to see @SchurLab and @Avinoam_lab on this list! Congratulations!

Last #cellbio2021 poster discussion session will be today. We combined in situ cryo-electron tomography and mathematical modeling to study actin function in endocytosis. If you want to hear more, come to my poster! @MatthewAkamatsu @DavidGDrubin @RangamaniUCSD.

I'm happy that I was able to contribute something to this story. Based on the yeast extract reconstitution assay, we developed a cryo-correlative light microscopy and electron tomography approach, showing kinetochore complexes binding to the sides and tips of microtubules.

Here is my first last-author manuscript led by Rulan Yi, a graduate from @berkeleyMCB and Emily Chen, a former @thelowell student. We compared 6 fluorescent proteins for their properties as model antigen. Thanks to @immuno_ed and @MaxKrummel for support doi.org/10.1101/2021.10.30.4…

2nd day of the @DGZ_2021 International Meeting. There were many great presentations on day 1. My personal favorite was the talk by Pia Brinkert on WASH-mediated endocytosis. Looking forward to the poster session today.

I'm excited to share our latest collaborative work with @RangamaniUCSD. We obtained new mechanistic insights into actin-based force production during clathrin-mediated endocytosis by integrating quantitative #cryoET results into mathematical modeling. biorxiv.org/content/10.1101/…