Summary#

The postprocess.py tool analyzes the output of match_template.py.

postprocess.py --help

Depending on the downstream use case, different output-format options are available and outlined below.

Create a tab-separated file containing x, y, z translations, euler_x, euler_y and euler_z Euler angles in counter-clockwise zyz format, corresponding score and peak caller details.

postprocess.py \
    --input-file output.pickle \
    --output-format orientations \
    --mask-edges \
    --min-boundary-distance 20 \
    --num-peaks 1000

Create a STAR file compatible with RELION 4 and 5. Relion 4 uses voxel coordinates, Relion 5 centers coordinates and scales them by the voxel size. The version difference is reflected in the header.

postprocess.py \
    --input-file output.pickle \
    --output-format relion4 \
    --mask-edges \
    --min-boundary-distance 20 \
    --angles-clockwise \
    --num-peaks 1000

Note

RELION expects --angles-clockwise, which need to be multiplied with negative one to be compatible with our format again.

Transform template into identified orientations and write the output to disk as {output_prefix}_{index}.{extension}. Index 0 corresponds to the top-scoring peak. This format is useful for assessing the result of fitting templates to maps.

postprocess.py \
    --input-file output.pickle \
    --output-format alignment \
    --mask-edges \
    --min-boundary_distance 20 \
    --num-peaks 10

Extract subvolumes around identified peaks and write the output to disk as {output_prefix}_{index}.{extension}. Index 0 corresponds to the top-scoring peak. This format is useful for assessing the result of particle picking.

postprocess.py \
    --input-file output.pickle \
    --output-format extraction \
    --mask-edges \
    --min-boundary-distance 20 \
    --num-peaks 100

Compute an average of subvolumes based on identified peaks.

postprocess.py \
    --input-file output.pickle \
    --output-format average \
    --mask-edges \
    --min-boundary-distance 20 \
    --num-peaks 100

Create a new pickle file incorporating input files and backgrounds. The output can be used for visual assessment of the normalization procedure and be reused for postprocess.py. The created pickle file can not be used to distinguish between different entities presented by different input files.

postprocess.py \
    --input-file output.pickle \
    --output-prefix output_norm \
    --output-format pickle \
    --background-file background1.pickle background2.pickle

Tip

From version 0.3.0 onwards, postprocessing supports advanced multi-input and background correction. Multiple input files can be specified to distinguish between different macromolecular species, with corresponding class identifiers made available in orientations and RELION output formats. Additionally, multiple background corrections can be applied simultaneously via --background-file, enabling users to account for various noise sources beyond single backgrounds (e.g., from --scramble-phases) and incorporate complex cellular environments such as membrane backgrounds.

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