New tools added: LocBfactor, LocSpiral, LocOccupancy

We have added new 3D volume enhancement tools to COSMIC²:

  • LocSpiral
  • LocBfactor
  • LocBfactor Estimation
  • LocOccupancy

Preprint:

S. Kaur, J. Gomez-Blanco, A. Khalifa, S. Adinarayanan, R. Sanchez-Garcia, D. Wrapp, J. S. McLellan, K. H. Bui, J. Vargas, Local computational methods to improve the interpretability and analysis of cryo-EM maps. bioRxiv

These tools provide local sharpened, which helps to have a consistent sharpening across a given 3D reconstruction. For example, typically there are flexible regions in a reconstruction that require different filters + B-factor sharpening in order to visualize for model building. These tools allow users to have different sharpening values for different areas of the map.

As an example, here is an example from the print highlighting the improvement of map density for  Spike protein:

Try it out! Please send questions or comments to cosmic2support@umich.edu.

New tool added: cryoDRGN

We are excited to include cryoDRGN into the suite of software available on the COSMIC² science gateway.  cryoDRGN is a deep-learning-based heterogenous reconstruction algorithm that utilizes variational autoencoders to embed single-particle images into a latent space for decoding into conformations and populations.

What does this mean for your single-particle cryoEM project? cryoDRGN allows you to assess underlying heterogeneity after performing a consensus 3D refinement into a single 3D reconstruction. An example of how this can be used was highlighted in the preprint, where the authors separated distinct conformations of assembling ribosomes based on the latent space-embedding:

As explained by the authors on their Github repo page, this is experimental and requires empirically determining the best way for the variational autoencoder to encode and decode the data into latent space. Let us know if it works for you and if you have additional features or options to help you use cryoDRGN.

Links:

To help users navigate outputs from cryoDRGN, be sure to check out our Youtube tutorial on downloading and visualizing the results.

New tool added: DeepEMhancer

We have added a new tool to the platform: DeepEMhancer. DeepEMhancer utilizes a deep-learning-based approach for non-linear processing of 3D reconstructions to produce a sharpening-like effect on the data.

We are seeing great results so far on datasets we’ve analyzed in the Cianfrocco lab as well as that shown by Dr. Oliver Clarke on Twitter:

–> Preprint publication for DeepEMhancer on bioRxiv

–> Read about the tool on COSMIC² here

Step-by-step tutorial added

To help users learn about data formatting, job submission, and job retrieval, we have created a publicly shared Globus endpoint for a small dataset (~1000 particles) from Thermoplasma acidophilum 20S proteasome (T20S) from Campbell et al. 2015, a dataset that is despite into the EMPIAR cryoEM archive as EMPIAR-10025.

After successful completion, users will generate 2D class averages of T20S such these shown here:

–> Link to Tutorial

Video tutorials added

To help engage new users of the gateway we have started a series of video tutorials highlighting how to use the gateway. They are incorporated throughout the site and you can also find them on our Youtube playlist!

Singularity on COSMIC2

We are really excited to start using Singularity containers on COSMIC². For those who don’t know, Singularity (and it’s predecessor, Docker) are methods to ‘containerize’ your software. This means you can design and build a custom operating system with all of the correct software dependencies

We have been wanting to do this for a little while, but were finally forced when we started to incorporate crYOLO into our software platform. In short, since crYOLO runs deep learning software Tensorflow, we needed to be running the latest version of Linux CenOS 7. However, SDSC Comet is still running CentOS 6, which means that we were at an impasse for running this software.

Enter Singularity – these containers allowed us to install Ubuntu and crYOLO into its own ‘image’, which is a standalone environment capable of running crYOLO anywhere. With this new image, all we have to do in order to run crYOLO on any CPU machine is type:

$ singularity exec sdsc-comet-ubuntu-cryolo-cpu.simg cryolo_predict.py -c config.json -w gmodel_phosnet_20181221_loss0037.h5 -i micrographs/ -o micrographs/cryolo -t 0.2

Which is a big step for those who have tried this before!

Since the built images are ~7 GB, we can’t share them directly on Github, so instead we are sharing the definition files. Please take a look and try it out if you are so inclined!

Benchmarking RELION2 GPU-accelerated jobs on Comet-GPU nodes

Below you will find the results of running the standard RELION2 benchmark on a number of different node configurations to optimize speed vs. ‘cost’ (in service units, SUs).

Optimal configuration for COSMIC² users: 8 x K80 GPUs (which is across 2 nodes) is worth it – nearly double the speed, but only a fraction more SUs to pay for it.

Fastest analysis: 12 x P100 GPUs (which made it also the most ‘expensive’)

RELION benchmarking test set (link)

  • Job type: RELION 3D Classification – v2.1.b1; 25 iterations
  • Data info: 105,247 particles; 360 x 360 pixels
  • Elapsed time: 3 hr 14 min
  • Compute type: GPU (4 x P100)
  • SUs: 19.5

 

  • Job type: RELION 3D Classification – v2.1.b1; 25 iterations
  • Data info: 105,247 particles; 360 x 360 pixels
  • Elapsed time: 1 hr 43 min
  • Compute type: GPU (8 x P100)
  • SUs: 21

 

  • Job type: RELION 3D Classification – v2.1.b1; 25 iterations
  • Data info: 105,247 particles; 360 x 360 pixels
  • Elapsed time: 1 hr 25 min
  • Compute type: GPU (12 x P100)
  • SUs: 23

 

  • Job type: RELION 3D Classification – v2.1.b1; 25 iterations
  • Data info: 105,247 particles; 360 x 360 pixels
  • Elapsed time: 3 hr 42 min
  • Compute type: GPU (4 x K80)
  • SUs: 15

 

  • Job type: RELION 3D Classification – v2.1.b1; 25 iterations
  • Data info: 105,247 particles; 360 x 360 pixels
  • Elapsed time: 2 hr 2 min
  • Compute type: GPU (8 x K80)
  • SUs: 16

 

  • Job type: RELION 3D Classification – v2.1.b1; 25 iterations
  • Data info: 105,247 particles; 360 x 360 pixels
  • Elapsed time: 1 hr 42 min
  • Compute type: GPU (12 x K80)
  • SUs: 20.4