relion – skill Viết lách & Nội dung cho agent AI

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](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 [email protected]; leading zeros such as

[email protected] 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.

python scripts/spa_workflow.py fsc \
  project/RefinePilot/run_half1_class001_unfil.mrc \
  project/RefinePilot/run_half2_class001_unfil.mrc --output diagnostic-fsc.tsv

This checks map dimensions, finite values, pixel size, origin, axis order and duplicate maps, then

writes an unmasked diagnostic FSC. The reported 0.143 crossing uses linear interpolation;

null means no downward crossing was detected, not infinite resolution. Nyquist resolution is

2 × pixel size. This diagnostic is limited to even cubic maps ≤256³; use RELION's native

relion_image_handler --fsc for larger maps. It does not substitute for mask-corrected FSC.

The helper requires real-space maps with canonical axes, zero MRC start indices and orthogonal

cell angles. Convert other grids explicitly with provenance; merely editing headers can misalign

density. Matching headers and FSC cannot determine absolute handedness.

Postprocess with a soft mask

Construct the solvent mask from an appropriately low-pass-filtered density, with an expanded

boundary and a smooth edge. Inspect all slices; a tight mask can inflate correlation. Avoid a

mask derived from high-frequency noise shared between half maps.

python scripts/spa_workflow.py postprocess \
  --half1 project/RefinePilot/run_half1_class001_unfil.mrc \
  --half2 project/RefinePilot/run_half2_class001_unfil.mrc \
  --mask project/soft_mask.mrc --output project/PostProcessPilot

The helper checks a nonconstant mask in [0,1], soft-edge voxels and matching map grids, then runs

relion_postprocess with explicit half maps, mask and pixel size. RELION performs its own

mask/randomization correction and writes postprocess.star. The bounded command leaves the

B-factor at zero (no automatic B-factor estimation); add automatic/manual sharpening only after choosing a defensible fit

range and inspecting map quality. A valid range and some fractional mask voxels do not prove the

mask is scientifically appropriate. Inspect the phase-randomized masked FSC near the reported

resolution: residual correlation calls for a smoother/wider mask and another postprocessing run.

See [references/runtime-and-validation.md](references/runtime-and-validation.md) for the tested

native utilities and the distinction between pipeline execution and reconstruction validation.

Primary references

• [RELION 5.0 installation](https://relion.readthedocs.io/en/release-5.0/Installation.html).
• [STAR and map conventions](https://relion.readthedocs.io/en/release-5.0/Reference/Conventions.html).
• [Single-particle tutorial](https://relion.readthedocs.io/en/release-5.0/SPA_tutorial/index.html).
• [Gold-standard refinement](https://relion.readthedocs.io/en/release-5.0/SPA_tutorial/Refine3D.html).
• [Mask creation and postprocessing](https://relion.readthedocs.io/en/release-5.0/SPA_tutorial/Mask.html).