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![[Figure 1]](ic5122fig1mag.jpg)
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Figure 1
relion_live.py, a web-based dashboard to follow on-the-fly pre-processing. (a) Schematic representation of the proposed workflow for the use of relion_live.py. The dashed boxes indicate the steps enabled by relion_live.py. After starting data collection, the relion_prep Scheme is launched to pre-process (motion correction, CTF estimation, ice thickness estimation) batches of unaligned movies coming from the direct electron detector. Launching the relion_png Scheme starts png_out.py, which collects the averaged micrographs and CTF results to display a PNG preview of both. All results are then aggregated in the relion_live.py dashboard, in which thresholds are manually selected for images to be filtered on the fly. Images that are compatible with all manual thresholds are used as input for the relion_proc Scheme for further processing. (b) Ice scores of all micrographs in a data set containing 35 000 movies. The score correlates directly with the signal intensity in the spatial frequency range 1/4–1/3.6 Å−1 (see the arrows in the rightmost CTF inset). High-scoring micrograph clusters indicate squares with suboptimal ice thickness in the grid. (c) Screenshot of the appearance of the RELION Live Dashboard. The header contains the number of images pending processing in the selected MotionCorr, CTFFIND and ice.py jobs. Below, the key pre-processing results are aggregated in interactive plots. (d) Clicking on a point in any of the plots displays the corresponding micrograph and its estimated CTF. (e) It is possible to manually select thresholds for each individual parameter. In the example depicted, all micrographs with an accumulated motion, as calculated by MotionCorr, of over 50 Å are discarded during Live Filtering. |
![[Figure 1]](ic5122fig1.jpg)
 | STRUCTURAL BIOLOGY |
ISSN: 2059-7983
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