relion
Lets your agent validate and run analyses on microscopy images. It checks data quality and helps refine results. It's useful for scientists using this technique to study very small structures.
What it does
This program helps refine and process single-particle cryo-electron microscopy (cryo-EM) data. It checks if the data is correct and runs the analysis steps to obtain a high-resolution 3D image of your sample.
When to use it
- ✓When you have extracted particles and want to refine a 3D structure.
- ✓To check the quality of your data before and after refinement.
- ✓To generate final resolution maps and assess image quality.
Example of what to ask your AI
“Validate my particle data and start the 3D refinement for my protein X sample, using a diameter of 150 Angstroms and C1 symmetry.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The program checks data files (like acquisition and particle data) to ensure they are in the correct format. It then uses this data to run the refinement and post-processing steps, generating high-quality results.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add K-Dense-AI/scientific-agent-skills --skill relionOr download just the file:
curl -L https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/main/skills/relion/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
RELION single-particle refinement
Use for a RELION single-particle project, especially extracted particles → homogeneous selected particle subset → gold-standard refinement → half-map validation and postprocessing. The bundled runner starts from CTF-annotated extracted particles and an initial 3D reference. It does not replace motion correction, picking, 2D/3D selection, or a biological interpretation of map quality. For tomography, helical reconstruction, Blush, or heterogeneous-state modeling, use the appropriate upstream workflow rather than forcing those data into this bounded SPA runner.
Preserve acquisition and coordinate conventions
Read references/acquisition-and-restarts.md when starting from movies or resuming jobs. Confirm pixel size in Å/pixel, voltage in kV, spherical aberration in mm, defocus in Å, amplitude contrast as a fraction, and the symmetry justified by the specimen. Do not “correct” a suspicious value by guessing its units.
data_optics describes acquisition/image groups; data_particles references them through
_rlnOpticsGroup. Particle filenames use one-based index@stack.mrcs; leading zeros such as
00000001@stack.mrcs are valid. Relative paths resolve
from the RELION project directory, not the STAR file's directory. Keep optics groups when merging
or subsetting STAR files. _rlnOriginXAngst/_rlnOriginYAngst are Å translations, not pixels.
Run from this skill directory with paths to the real project:
python scripts/spa_workflow.py validate-star project/particles.star --project project
This opens referenced stacks and checks optics membership, finite acquisition/CTF values, indices,
box sizes, duplicate particle references and existing half-set assignments. Use --metadata-only
only when stacks are genuinely unavailable; the JSON records stack_checks_performed: false.
It does not scan every particle pixel for corruption or establish correct image normalization.
Physical-range warnings are review prompts, not proof that unusual microscope settings are wrong.
Refine a selected particle population
Before running, inspect representative particles and class averages, defocus distributions, CTF fits, particle orientation distribution, and the initial reference. Ensure the map and particle boxes/pixel sizes agree after any downsampling. The runner deliberately supports one effective box/pixel size across optics groups; handle heterogeneous sampling with an explicit upstream resampling workflow. Use conventionally extracted, normalized particles that have not already been phase-flipped or Wiener-filtered; this runner does not configure those special input cases.
python scripts/spa_workflow.py refine \
--star project/particles.star --reference project/initial.mrc \
--project project --diameter 180 --symmetry C1 \
--initial-lowpass 40 --mpi-ranks 3 --threads 2 --output project/RefinePilot
The diameter and low-pass filter above are illustrative Å values. Use specimen-appropriate
values. Refinement executes mpirun -np 3 relion_refine_mpi with --auto_refine,
--split_random_halves, --ctf, and a low-pass starting reference. Gold-standard splitting
requires MPI; the plain sequential relion_refine executable cannot perform this split.
Use odd ranks ≥3 (master plus balanced half-set workers), with a matching MPI installation.
The CPU command is useful for a bounded pilot; choose a documented GPU/MPI launch for full data.
The runner keeps the command, native version and log in a new output directory, records an
explicit random seed (default 1), surfaces runtime warnings, stops on process failure, and
requires converged unfiltered half maps before reporting success. It does not automatically retry
expensive jobs or silently discard failed-job artifacts. Keep _optimiser.star, model/sampling
STAR files, and referenced particle paths for restart. Use the original job's optimiser rather
than starting a new random split from a partially processed table.
Inspect independent half maps
Use the two independently refined unfiltered half maps, never two copies of the combined, sharpened map. Matching headers cannot establish statistical independence; the independent particle assignments and refinement history provide that evidence. Inspect directional anisotropy, preferred orientation and local resolution as well as a global FSC curve.
…
FAQ
What is cryo-EM?
Cryo-EM (Cryo-electron microscopy) is a technique used to determine the three-dimensional structure of biological molecules at high resolution by rapidly freezing them in ice.
What does 'refinement' mean?
Refinement is the process of improving the quality and resolution of an initial 3D model using data from individual particles.
What are 'half maps'?
These are two 3D maps generated independently from different halves of the particle dataset. They are used to verify the reliability of the final result.
Details
Original description by the author
Validates and executes RELION single-particle cryo-EM refinement and half-map postprocessing. Supports STAR optics/acquisition checks, particle-stack consistency, gold-standard half sets, soft-mask validation, diagnostic Fourier shell correlation, and restart guidance.
Popularity
- GitHub stars
- 51k
- Ranking position
- #1962
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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