leonarski_fandClaude Opus 5
6b738713e8
--model re-fractionalized the model into the data cell and then left it there. On a non-isomorphous pair that is a placement error, not a cell error: the box is squeezed, the body inside it is not moved. Six parameters now recover it - an angle-axis rotation about the model's centroid and a translation, refined over a 6 / 4.5 / 3.5 A ladder, the scale (k_overall, anisotropic B, k_sol, b_sol) re-fitted at every evaluation so the target measures the placement and not the scale. The refinement sees only the working reflections and the step is committed only if R-free, on the free set it never saw, drops; otherwise the model goes back where it was read. Measured on merged lysozyme data against a deposited lysozyme model whose cell differs by 3.4% in c: R-work 0.559 -> 0.400, R-free 0.591 -> 0.383. Over the same 3.5 A range the external arbiter (REFMAC rigid body through dimple) works in, 0.524 -> 0.330 against REFMAC's 0.522 -> 0.355, and the recovered movement agrees with REFMAC's to 0.25 deg and 0.03 A (3.05 deg / 1.04 A vs 2.76 / 0.98). 2.4 s of added wall clock, 234 structure-factor evaluations. The map coefficients become 2mFo-DFc and mFo-DFc. sigma_A is estimated by maximum likelihood per resolution shell on the free reflections only, with the number of shells taken from the size of the free set so no shell is thin; centric and acentric reflections carry their own likelihoods, and a centric reflection's bias-free coefficient is mFo. Cross-checked against CCP4 SIGMAA on the same reflections: mean FOM 0.404 against its 0.396, with the same per-shell structure. The figure of merit is written to _maps.mtz so the weighting can be undone. The per-shell scaling refusal in fit_model stands - Fobs is never rescaled and the R-factors are untouched - but m and D are per-dataset, so maps from one campaign are no longer scaled identically. That is argued at the code and in docs/CPU_DATA_ANALYSIS_DECISIONS.md 14.4. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EFEJG6WBQv8th4UJFNe53N
21 KiB
Acknowledgements
Citation: F. Leonarski, M. Bruckner, C. Lopez-Cuenca, A. Mozzanica, H.-C. Stadler, Z. Matej, A. Castellane, B. Mesnet, J. Wojdyla, B. Schmitt and M. Wang "Jungfraujoch: hardware-accelerated data-acquisition system for kilohertz pixel-array X-ray detectors" (2023), J. Synchrotron Rad., 30, 227-234 doi:10.1107/S1600577522010268.
The project is supported by :
- Innosuisse via Innovation Project "NextGenDCU high data rate acquisition system for X-ray detectors in structural biology applications" (101.535.1 IP-ENG; Apr 2023 - Sep 2025).
- ETH Domain via Open Research Data Contribute project (Jan - Dec 2023)
- AMD University Program with donation of licenses of Ethernet IP cores and Vivado software
Decoding bitshuffle+LZ4 images on the GPU, rather than decompressing them on the host and uploading the result, follows Jon Wright (ESRF): "Experiences with GPU decompression for bitshuffle + LZ4 data", HDF5 User Group meeting (2021), and bslz4decoders. The CUDA kernels in Jungfraujoch are its own, but the approach is his.
Spot extraction groups strong pixels into spots with the sparse connected-component labelling of the ACTS traccc project: P. Gessinger, H. M. Gray, A. Krasznahorkay, C. Leggett, J. Niermann, A. Salzburger, S. N. Swatman and B. Yeo, "traccc: GPU track reconstruction library for HEP experiments" (2025), arXiv:2505.22822; traccc. The CPU spot extractor adapts its SparseCCL source, and the CUDA spot extractor follows the design of its GPU counterpart - a backward-neighbour graph over a sorted hit list, resolved by a parallel union-find. traccc is MPL-2.0; see THIRD_PARTY_NOTICES.md. The SparseCCL algorithm itself is A. Hennequin, B. Couturier, V. V. Gligorov and L. Lacassagne, "SparseCCL: Connected Components Labeling and Analysis for sparse images" (2019), DASIP 2019, 65-70 doi:10.1109/DASIP48288.2019.9049184.
This software uses Viridis, Magma and Inferno colormaps from Matplotlib under its BSD-compatible license
Public diffraction data used for testing
In addition to in-house datasets collected at SLS 2.0, Jungfraujoch is tested against public diffraction data collected on other people's beamlines, on detectors and in file formats we do not produce ourselves - most of it at other facilities, a few sets at the Swiss Light Source but not by this system. That data was collected and published by other people. Every dataset used, the DOI to cite for it, and the deposition it belongs to are listed in EXTERNAL_TEST_DATA; we thank the depositors, and the repositories that make the data findable and citable.
IRRMC, the Integrated Resource for Reproducibility in Macromolecular Crystallography (Minor lab, University of Virginia), is the source of most of them. IRRMC releases its data under CC0 and asks that the DOI of the dataset be cited; those DOIs are in the table. M. Grabowski, K. M. Langner, M. Cymborowski, P. J. Porebski, P. Sroka, H. Zheng, D. R. Cooper, M. D. Zimmerman, M.-A. Elsliger, S. K. Burley and W. Minor, "A public database of macromolecular diffraction experiments" (2016), Acta Cryst. D72, 1181-1193 doi:10.1107/S2059798316014716; M. Grabowski, M. Cymborowski, P. J. Porebski, T. Osinski, I. G. Shabalin, D. R. Cooper and W. Minor, "The Integrated Resource for Reproducibility in Macromolecular Crystallography: Experiences of the first four years" (2019), Struct. Dyn. 6, 064301 doi:10.1063/1.5128672.
SBGrid Data Bank supplied nine of the datasets. P. A. Meyer, S. Socias, J. Key, E. Ransey, E. C. Tjon, A. Buschiazzo et al., "Data publication with the structural biology data grid supports live analysis" (2016), Nat. Commun. 7, 10882 doi:10.1038/ncomms10882.
Zenodo hosts eleven, deposited there directly by the groups that collected them. European Organization for Nuclear Research and OpenAIRE, "Zenodo" (2013), CERN doi:10.25495/7GXK-RD71. Three of those datasets were published as IUCrData Raw Data Letters; the letters are cited on the EXTERNAL_TEST_DATA page, beside the datasets they describe.
The beamline, resolution, space group and unit cell quoted for each dataset are the values deposited with the corresponding PDB entry, read from the RCSB PDB data API. H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov and P. E. Bourne, "The Protein Data Bank" (2000), Nucleic Acids Res. 28, 235-242 doi:10.1093/nar/28.1.235.
File formats read from a published specification
CBF / imgCIF - the native miniCBF reader implements the x-CBF_BYTE_OFFSET compression scheme
and reads the imgCIF _axis table (the laboratory directions of the image's fast and slow pixel
directions, of the goniometer axes and of a 2theta arm) from the specification alone; no CBFlib or
other CBF code is used, so there is no licence obligation, only this credit.
H. J. Bernstein and A. P. Hammersley, "Specification of the Crystallographic Binary File
(CBF/imgCIF)" (2006), International Tables for Crystallography Vol. G, 37-43
doi:10.1107/97809553602060000729;
A. P. Hammersley, H. J. Bernstein and J. D. Westbrook, "Image dictionary (imgCIF)" (2006),
International Tables for Crystallography Vol. G, 444-458
doi:10.1107/97809553602060000746.
Crystallographic methods adopted from other packages
The analysis pipeline reimplements methods first published, and in most cases first implemented, by other crystallographic software. The code below is Jungfraujoch's own; the methods are theirs, and are acknowledged here. Where a package's source was consulted this is said explicitly. None of these packages is linked or vendored, with the single exception of GEMMI (see THIRD_PARTY_NOTICES.md).
XDS — rotation geometry and notation, the reciprocal Lorentz and
partiality treatment, the maximum-likelihood mosaicity estimate, the MINPK criterion for rejecting
a reflection whose predicted profile is not cleanly its own, the intensity-based test for a
centred lattice, and the scaling correction surfaces indexed by image number and detector region. W. Kabsch, "XDS" (2010), Acta Cryst. D66, 125-132
doi:10.1107/S0907444909047337; W. Kabsch, "Integration,
scaling, space-group assignment and post-refinement" (2010), Acta Cryst. D66, 133-144
doi:10.1107/S0907444909047374.
Profile fitting with reweighted, de-biased variances is the Kabsch/Otwinowski iteration, from the second XDS paper above and from Z. Otwinowski and W. Minor, "Processing of X-ray diffraction data collected in oscillation mode" (1997), Methods Enzymol. 276, 307-326 doi:10.1016/S0076-6879(97)76066-X.
The two-dimensional integration architecture — integrating each image in the detector plane and only afterwards assembling a reflection's partials into a full across images, as against three-dimensional profile fitting through the image stack — is the architecture of DENZO/SCALEPACK and MOSFLM, and it is the one rugnux's rotation pipeline follows (per-image profile-fitted integration, then partials combined into fulls; §9 and §10.6 of the analysis reference). The Otwinowski & Minor citation above and the MOSFLM citations below carry the credit for the paradigm as well as for the specifics taken from each.
DIALS — the resolution cutoff from the CC1/2 fall-off, per-observation outlier rejection at merge, the scaling error model, and the treatment of a reflection whose background is contaminated. Its published behaviour, and in places its source, settled several choices here. G. Winter, D. G. Waterman, J. M. Parkhurst et al., "DIALS: implementation and evaluation of a new integration package" (2018), Acta Cryst. D74, 85-97 doi:10.1107/S2059798317017235; D. G. Waterman, G. Winter, R. J. Gildea et al., "Diffraction-geometry refinement in the DIALS framework" (2016), Acta Cryst. D72, 558-575 doi:10.1107/S2059798316002187; J. Beilsten-Edmands, G. Winter, R. Gildea et al., "Scaling diffraction data in the DIALS software package: algorithms and new approaches for multi-crystal scaling" (2020), Acta Cryst. D76, 385-399 doi:10.1107/S2059798320003198; J. M. Parkhurst, G. Winter, D. G. Waterman et al., "Robust background modelling in DIALS" (2016), J. Appl. Cryst. 49, 1912-1921 doi:10.1107/S1600576716013595.
POINTLESS (CCP4) — the space-group search. Stage A scores each candidate rotation operator by the correlation of I(h) with I(Rh) on resolution-normalised intensities (E²), as POINTLESS does — both arms of a symmetry pair sit at the same |s|, so on raw intensities the resolution fall-off is variance shared between them and lifts a false operator's correlation as much as a true one's; the screw-axis test scores a predicted-absent class against the rest of its own axial row rather than against a global mean or a fixed cut, and lets confidence fall away with the number of axial reflections instead of refusing below a count. P. Evans, "Scaling and assessment of data quality" (2006), Acta Cryst. D62, 72-82 doi:10.1107/S0907444905036693; P. R. Evans, "An introduction to data reduction: space-group determination, scaling and intensity statistics" (2011), Acta Cryst. D67, 282-292 doi:10.1107/S090744491003982X; P. R. Evans and G. N. Murshudov, "How good are my data and what is the resolution?" (2013), Acta Cryst. D69, 1204-1214 doi:10.1107/S0907444913000061; J. Agirre, M. Atanasova, H. Bagdonas et al., "The CCP4 suite: integrative software for macromolecular crystallography" (2023), Acta Cryst. D79, 449-461 doi:10.1107/S2059798323003595.
MOSFLM — the Rossmann FFT autoindexing algorithm and post-refinement practice, including which parameters are safe to refine per image and which must be refined over a wedge. The autoindexing algorithm itself — projecting the reciprocal-space points onto many directions and Fourier-transforming the 1D projection histograms — is I. Steller, R. Bolotovsky and M. G. Rossmann, "An algorithm for automatic indexing of oscillation images using Fourier analysis" (1997), J. Appl. Cryst. 30, 1036-1040 doi:10.1107/S0021889897008777; MOSFLM is the implementation whose practice is followed. A. G. W. Leslie and H. R. Powell, "Processing diffraction data with MOSFLM" (2007), in Evolving Methods for Macromolecular Crystallography, NATO Science Series II, vol. 245, 41-51 doi:10.1007/978-1-4020-6316-9_4; T. G. G. Battye, L. Kontogiannis, O. Johnson, H. R. Powell and A. G. W. Leslie, "iMOSFLM: a new graphical interface for diffraction-image processing with MOSFLM" (2011), Acta Cryst. D67, 271-281 doi:10.1107/S0907444910048675; H. R. Powell, T. G. G. Battye, L. Kontogiannis, O. Johnson and A. G. W. Leslie, "Integrating macromolecular X-ray diffraction data with the graphical user interface iMosflm" (2017), Nat. Protoc. 12, 1310-1325 doi:10.1038/nprot.2017.037.
CrystFEL — spot finding, the three-ring integration
region, the serial/stills processing model, and the per-frame indexing acceptance test
(indexing_peak_check() in peaks.c). T. A. White, R. A. Kirian, A. V. Martin, A. Aquila, K. Nass,
A. Barty and H. N. Chapman, "CrystFEL: a software suite for snapshot serial crystallography" (2012),
J. Appl. Cryst. 45, 335-341 doi:10.1107/S0021889812002312.
The self-calibrating spot finder's per-resolution-ring background statistics, with the Bragg peaks
excluded by iterated clipping, follow Cheetah's peakfinder8: A. Barty, R. A. Kirian,
F. R. N. C. Maia, M. Hantke, C. H. Yoon, T. A. White and H. N. Chapman, "Cheetah: software for
high-throughput reduction and analysis of serial femtosecond X-ray diffraction data" (2014),
J. Appl. Cryst. 47, 1118-1131
doi:10.1107/S1600576714007626.
fast-feedback-indexer — the
known-cell indexer for serial stills (-X ffbidx) is PSI's fast-feedback-indexer library, linked at
build time (BSD-3-Clause; see THIRD_PARTY_NOTICES.md), which implements
the TORO algorithm: P. Gasparotto, L. Barba, H.-C. Stadler, G. Assmann, H. Mendonça, A. W. Ashton,
M. Janousch, F. Leonarski and B. Béjar, "TORO Indexer: a PyTorch-based indexing algorithm for
kilohertz serial crystallography" (2024), J. Appl. Cryst. 57, 931-944
doi:10.1107/S1600576724003182.
GEMMI — symmetry operations, unit-cell and
structure-factor machinery, and MTZ / XDS_ASCII I/O. Vendored in gemmi_gph/, so it also carries a
licence obligation. M. Wojdyr, "GEMMI: A library for structural biology" (2022), J. Open Source
Softw. 7, 4200 doi:10.21105/joss.04200.
Křivý & Gruber's Niggli reduction, and the lattice-character table — the reduction that puts every candidate cell in a comparable form is I. Křivý and B. Gruber, "A unified algorithm for determining the reduced (Niggli) cell" (1976), Acta Cryst. A32, 297-298 doi:10.1107/S0567739476000636, used through GEMMI's implementation; the table of lattice characters that maps a reduced cell to Bravais lattices and centrings follows International Tables for Crystallography Vol. A, Table 9.2.5.1.
Grosse-Kunstleve, Sauter & Adams's numerically stable cell reduction - the magnitude-scaled tolerance that decides the sign of a structurally-zero scalar product, and with it the Niggli type a reduced cell is presented in. R. W. Grosse-Kunstleve, N. K. Sauter and P. D. Adams, "Numerically stable algorithms for the computation of reduced unit cells" (2004), Acta Cryst. A60, 1-6 doi:10.1107/S010876730302186X.
Le Page's metric-symmetry search - the obliquity of each of the 81 candidate two-folds of a reduced cell, which is what tells a run that its lattice metric hosts more rotational symmetry than the group its intensities supported. Used through GEMMI's implementation of it. Y. Le Page, "The derivation of the axes of the conventional unit cell from the dimensions of the Buerger-reduced cell" (1982), J. Appl. Cryst. 15, 255-259 doi:10.1107/S0021889882011959.
Hexagonal-ice ring positions — the eleven ring d spacings from 3.895 to 1.522 Å that the
ice-ring score, the ice-ring flagging and the ice calibrant are all built on are taken from the
measurements of, not enumerated from a cell. D. W. Moreau, H. Atakisi and R. E. Thorne, "Ice in
biomolecular cryocrystallography" (2021), Acta Cryst. D77, 540-554
doi:10.1107/S2059798321001170.
That list ends at 1.522 Å by its own scope, so the eight bands below it are calculated here rather than taken from anyone: ice Ih structure factors on the oxygen sublattice, kept where they reach 3% of the strongest line, which reproduces the eleven measured positions exactly. The lattice constants are Röttger and co-workers'. K. Röttger, A. Endriss, J. Ihringer, S. Doyle and W. F. Kuhs, "Lattice constants and thermal expansion of H2O and D2O ice Ih between 10 and 265 K" (1994), Acta Cryst. B50, 644-648 doi:10.1107/S0108768194004933.
The twinning L test is Padilla and Yeates's: pairing each acentric reflection with a symmetry-independent neighbour and reading the first and second moments of L = (I1−I2)/(I1+I2) against their untwinned and perfect-twin values. J. E. Padilla and T. O. Yeates, "A statistic for local intensity differences: robustness to anisotropy and pseudo-centering and utility for detecting twinning" (2003), Acta Cryst. D59, 1124-1130 doi:10.1107/S0907444903007947.
Diffraction anisotropy — the description of the overall fall-off by a single anisotropic displacement tensor, its symmetry constraints, and the fact that only its deviatoric part is determined (the isotropic part being degenerate with the overall scale) are Sheriff and Hendrickson's. The estimator fits that tensor to the observed intensity distribution, taking sigma(I) into account, in the sense of Popov and Bourenkov. The directional diffraction limits - <I/sigma(I)> in a cone about each principal direction, and the reporting of the anisotropic deltaB as the range of the principal components - follow AIMLESS. rugnux reports these; it corrects no intensity and removes no reflection on a directional criterion. S. Sheriff and W. A. Hendrickson, "Description of overall anisotropy in diffraction from macromolecular crystals" (1987), Acta Cryst. A43, 118-121 doi:10.1107/S010876738709977X; A. N. Popov and G. P. Bourenkov, "Choice of data-collection parameters based on statistic modelling" (2003), Acta Cryst. D59, 1145-1153 doi:10.1107/S0907444903008163; P. R. Evans and G. N. Murshudov, "How good are my data and what is the resolution?" (2013), Acta Cryst. D69, 1204-1214 doi:10.1107/S0907444913000061.
ANODE — reading the anomalous difference map at the atoms of a supplied model and reporting the strongest sites by name, instead of searching the map for blobs. The map itself is the textbook anomalous difference Fourier; what is taken from ANODE is that reading: A. Thorn and G. M. Sheldrick, "ANODE: anomalous and heavy-atom density calculation" (2011), J. Appl. Cryst. 44, 1285-1287 doi:10.1107/S0021889811041768.
sigma_A map coefficients — the maps written by --model are weighted by a maximum-likelihood
sigma_A estimated per resolution shell, giving 2mFo-DFc and mFo-DFc rather than 2Fo-Fc and Fo-Fc.
What is taken is the formalism itself: the Rice and Woolfson likelihoods of |Fo| given |Fc| and
sigma_A, the figure of merit m and the scale D that follow from it, and the result that the
bias-corrected coefficient is 2mFo-DFc for an acentric reflection and mFo for a centric one.
R. J. Read, "Improved Fourier coefficients for maps using phases from partial structures with
errors" (1986), Acta Cryst. A42, 140-149
doi:10.1107/S0108767386099622.
Data-quality statistics follow the established conventions rather than any one program: R_meas and R_pim, CC1/2 and CC*, and the reporting of I/sigma(I). K. Diederichs and P. A. Karplus, "Improved R-factors for diffraction data analysis in macromolecular crystallography" (1997), Nat. Struct. Biol. 4, 269-275 doi:10.1038/nsb0497-269; P. A. Karplus and K. Diederichs, "Linking crystallographic model and data quality" (2012), Science 336, 1030-1033 doi:10.1126/science.1218231; K. Diederichs and P. A. Karplus, "Better models by discarding data?" (2013), Acta Cryst. D69, 1215-1222 doi:10.1107/S0907444913001121.
Uncertainty conventions follow the IUCr Commission on Crystallographic Nomenclature: D. Schwarzenbach, S. C. Abrahams, H. D. Flack et al., "Statistical descriptors in crystallography: Report of the IUCr Subcommittee on Statistical Descriptors" (1989), Acta Cryst. A45, 63-75 doi:10.1107/S0108767388009596; D. Schwarzenbach, S. C. Abrahams, H. D. Flack, E. Prince and A. J. C. Wilson, "Statistical descriptors in crystallography. II. Report of a Working Group on Expression of Uncertainty in Measurement" (1995), Acta Cryst. A51, 565-569 doi:10.1107/S0108767395002340.