Molecular Dynamics

CryoSPARC v3.2 Now Available

March 29, 2021
4 min read
blog-cryosparc-v3.2.jpg

CryoSPARC Overview

CryoSPARC (Cryo-EM Single Particle Ab-initio Reconstruction and Classification) is a software package for processing cryo-electron microscopy (cryo-EM) single particle data, used in research and drug discovery.

As a complete solution for cryo-EM processing, CryoSPARC allows:

    • Ultra-fast end-to-end processing of raw cryo-EM data and reconstruction of electron density maps, ready for ingestion into model building software
    • Optimized algorithms and GPU acceleration at all stages, from pre-processing through particle picking, 2D particle classification, 3D ab-initio structure determination, high resolution refinement, and heterogeneity analysis
    • Specialized and unique tools for therapeutically relevant targets, membrane proteins, continuously flexible structure
    • Interactive, visual and iterative experimentation for even the most complex workflows

Interested in getting faster results?
Learn more about CryoSPARC Optimized GPU Workstations


CryoSPARC v3.2 Release

The newest update is packed with new features, performance optimizations and various stability enhancements.

Download the latest version here.

We highly recommend making a backup of your cryoSPARC database before updating.
Update instructions are available here.

What’s New, Updated & Fixed

  • New - Added option for sorting jobs by date and title. Projects and workspaces can be sorted by title as well
  • Update - Substantially improved performance in iterative particle-processing jobs (2D Classification, Refinement) when particles are stored on some types of filesystems (including FSx for Lustre), resolving cases where some users found that jobs run in v3.1 were slower than in v2.15
  • Update - Deep Particle Picking jobs (Deep Picker Train, Deep Picker Inference) now require CUDA Toolkit 11+
  • Update - The 'Metadata' tab within the job preview dialog is more performant and features expandable sections and color highlighting based on data type
  • Update - Import Particles no longer ignores the rlnImagePixelSize column while trying to import a particle .star file
  • Update - CryoSPARC processes are now less likely to become orphaned due to more robust supervisor management configuration
  • Update - Removed unused cuBLAS dependency that caused multiple GPU contexts on all GPUs in a system to be created even when using only a single GPU
  • Update - GPU status info is now updated when a worker is connected for the first time, or when a worker connection is updated
  • Update - New option to work around bug in CUDA on CentOS 7 that causes cuMemHostAlloc failed errors in multiple job types. To engage this, add export CRYOSPARC_NO_PAGELOCK=true to the cryosparc_worker/config.sh file.
  • Update - Patch CTF Estimation no longer fails when encountering an error on a specific micrograph. Failed micrographs are outputted to a separate group if exceptions are encountered and processing continues.
  • Fixed - Issue in Patch Motion that causes dose weighting to be incorrectly applied in some cases. This was caused by the introduction of variable-dose support in v3.1.
  • Fixed - Patch CTF Estimation fails with IndexError: index 0 is out of bounds for axis 0 with size 0
  • Fixed - Patch CTF Estimation fails with ValueError: Axis limits cannot be NaN or Inf
  • Fixed - Patch CTF Estimation sometimes reports underestimated CTF fit resolution in cryoSPARC Live, in cases where the correlation between the fit and the actual signal is very poor at low resolutions
  • Fixed - Issue that can cause exposure groups set up at import to be lost during processing, specifically through wrapper jobs for GCTF or CTFFIND
  • Fixed - Issue where Deep Picker Train job would fail reporting that there are no training particles on any micrographs
  • Fixed - Topaz wrapper TypeError during negative stain processing: can't multiple sequence by non-int of type numpy.float
  • Fixed - Issue causing IndexError in wrapper for Motioncor2
  • Fixed - Issue in particle simulator job where all particles would be modulated by the same CTF regardless of specified input CTF parameters
  • Fixed - Issue in cryosparcm cli delete_user command that prevented an admin user from being authenticated
  • Fixed - Issue in Extract From Micrographs (CPU) job requiring a GPU dependency, causing it to fail on CPU-only workstations

Learn how to update your CryoSPARC instance here


Have any questions about CryoSPARC or other applications for molecular dynamics?
Contact Exxact Today


Topics

blog-cryosparc-v3.2.jpg
Molecular Dynamics

CryoSPARC v3.2 Now Available

March 29, 20214 min read

CryoSPARC Overview

CryoSPARC (Cryo-EM Single Particle Ab-initio Reconstruction and Classification) is a software package for processing cryo-electron microscopy (cryo-EM) single particle data, used in research and drug discovery.

As a complete solution for cryo-EM processing, CryoSPARC allows:

    • Ultra-fast end-to-end processing of raw cryo-EM data and reconstruction of electron density maps, ready for ingestion into model building software
    • Optimized algorithms and GPU acceleration at all stages, from pre-processing through particle picking, 2D particle classification, 3D ab-initio structure determination, high resolution refinement, and heterogeneity analysis
    • Specialized and unique tools for therapeutically relevant targets, membrane proteins, continuously flexible structure
    • Interactive, visual and iterative experimentation for even the most complex workflows

Interested in getting faster results?
Learn more about CryoSPARC Optimized GPU Workstations


CryoSPARC v3.2 Release

The newest update is packed with new features, performance optimizations and various stability enhancements.

Download the latest version here.

We highly recommend making a backup of your cryoSPARC database before updating.
Update instructions are available here.

What’s New, Updated & Fixed

  • New - Added option for sorting jobs by date and title. Projects and workspaces can be sorted by title as well
  • Update - Substantially improved performance in iterative particle-processing jobs (2D Classification, Refinement) when particles are stored on some types of filesystems (including FSx for Lustre), resolving cases where some users found that jobs run in v3.1 were slower than in v2.15
  • Update - Deep Particle Picking jobs (Deep Picker Train, Deep Picker Inference) now require CUDA Toolkit 11+
  • Update - The 'Metadata' tab within the job preview dialog is more performant and features expandable sections and color highlighting based on data type
  • Update - Import Particles no longer ignores the rlnImagePixelSize column while trying to import a particle .star file
  • Update - CryoSPARC processes are now less likely to become orphaned due to more robust supervisor management configuration
  • Update - Removed unused cuBLAS dependency that caused multiple GPU contexts on all GPUs in a system to be created even when using only a single GPU
  • Update - GPU status info is now updated when a worker is connected for the first time, or when a worker connection is updated
  • Update - New option to work around bug in CUDA on CentOS 7 that causes cuMemHostAlloc failed errors in multiple job types. To engage this, add export CRYOSPARC_NO_PAGELOCK=true to the cryosparc_worker/config.sh file.
  • Update - Patch CTF Estimation no longer fails when encountering an error on a specific micrograph. Failed micrographs are outputted to a separate group if exceptions are encountered and processing continues.
  • Fixed - Issue in Patch Motion that causes dose weighting to be incorrectly applied in some cases. This was caused by the introduction of variable-dose support in v3.1.
  • Fixed - Patch CTF Estimation fails with IndexError: index 0 is out of bounds for axis 0 with size 0
  • Fixed - Patch CTF Estimation fails with ValueError: Axis limits cannot be NaN or Inf
  • Fixed - Patch CTF Estimation sometimes reports underestimated CTF fit resolution in cryoSPARC Live, in cases where the correlation between the fit and the actual signal is very poor at low resolutions
  • Fixed - Issue that can cause exposure groups set up at import to be lost during processing, specifically through wrapper jobs for GCTF or CTFFIND
  • Fixed - Issue where Deep Picker Train job would fail reporting that there are no training particles on any micrographs
  • Fixed - Topaz wrapper TypeError during negative stain processing: can't multiple sequence by non-int of type numpy.float
  • Fixed - Issue causing IndexError in wrapper for Motioncor2
  • Fixed - Issue in particle simulator job where all particles would be modulated by the same CTF regardless of specified input CTF parameters
  • Fixed - Issue in cryosparcm cli delete_user command that prevented an admin user from being authenticated
  • Fixed - Issue in Extract From Micrographs (CPU) job requiring a GPU dependency, causing it to fail on CPU-only workstations

Learn how to update your CryoSPARC instance here


Have any questions about CryoSPARC or other applications for molecular dynamics?
Contact Exxact Today


Topics