Table of Contents
CCP-EM Suite ... and related tools
CCPEM - Collaborative Computational Project for Electron cryo-Microscopy
CCPEM, Doppio, Relion and other tools are often used together. We are providing these tools in a single, self-contained container runtime environment for the users of our Comet HPC facility. This wiki page demonstrates how to find and use this software on Comet.
- For more details on CCP-EM or Doppio: https://www.ccpem.ac.uk/
Many of the tools included with CCPEM, Doppio and Relion can take advantage of GPU-based acceleration, and you can use the GPU partitions on Comet to run these. Other tools are CPU only and can be run on the standard compute resources.
We refer to all of the applications installed alongside CCP-EM as CCP-EM and tools for simplicity. For the full list of all individual, installed applications, please consult the table below.
Software Packages Included
The CCP-EM Suite container includes the following tools and applications.
Note:
- The Available From column indicates the first release of the container image that the tool was released in.
- CryoDANN and CryoVAE currently have a version mis-match error in the published conda environments which means that they cannot be installed.
Python Modules Included
In addition to the standalone applications listed above, the following Python modules are installed - i.e. they are available if you use python3 installed inside the container to run your Python scripts.
This list only includes modules which have been explicitly installed. The standard Python built-ins are still available: sqlite, json, etc.
Running CCP-EM Suite on Comet
The CCP-EM tools on Comet are provided as an Apptainer container environment - please read the Apptainer guide first. The CCP-EM container images are stored in the following location:
/nobackup/shared/containers/ccpemsuite.2026.03.sif- this is the filesystem image with all of the software contained inside
You do not need to make a copy of this file - it should be left inside the containers directory. You will also find a matching shell script in the same directory:
/nobackup/shared/containers/ccpemsuite.2026.03.sh
To run any of the CCP-EM tools and applications listed on this page, simply source the shell script:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
You can then use a helper command container.run to run commands inside the container, without needing pass huge numbers of parameters to the apptainer exec command - this automatically adds bind arguments to let you access /scratch and /nobackup inside the container, as well as make available Nvidia GPU hardware if you have assigned one via Slurm. For example to run relion from inside the container image:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run relion -h
You only need to source the shell script once, either at the start of your Comet session, if working interactively, or at the top of your sbatch file if submitting a job.
Although we may list a specific version of the container and helper files - such as ccpemsuite.2026.03, you should check the directory to ensure that there is not a newer version available, just run: ls /nobackup/shared/containers and it will show any other versions that we have published since.
ARP/wARP
The use of the web-submission aspects of ARP/wARP is outside the scope of this guide, however the command line tools work as expected. ARP/wARP is installed at /opt/arp_warp_8.0 and all of the tools/commands/scripts in the list below are included:
50_ligands_noH.dat check_refmacversion.sh.bak identify_ligand_multi.sh.bak publ_classic_phases.sh.bak
AAbis.XYZ check_sequence.sh identify_partial.sh publ_ligand.sh
AAbis_extension.XYZ check_sequence.sh.bak identify_partial.sh.bak publ_ligand.sh.bak
ARP_logo_8.0_verysmall216.gif chkdens incl_check_filename.csh publ_loopy.sh
ARP_logo_small.gif chkdens_average_deviations incl_check_filename.csh.bak publ_loopy.sh.bak
M_ligandbuild chkdens_average_deviations_multi lig_M_align.sh publ_nucleotides.sh
NCStry chkseq lig_M_align.sh.bak publ_nucleotides.sh.bak
OpenAstexViewer.jar cif2geo ligaline publ_solvent.sh
PNSext_ncsrestrainer cif2geo_noH ligand_clusters.txt publ_solvent.sh.bak
Restraints.jar cif2pdb ligand_sort read
VERSION cleanup_pdb.pl ligandbuild refine_sort
all_ligands.pdb crlf likelihood_1.tbl resetprotsize.sh
all_ligands_H.pdb density_partial_screen likelihood_2.tbl resetprotsize.sh.bak
all_ligands_features.dat density_shape_screen loop rewrite_pdb
arp_warp dipcheck loopRamachandran.tab sdf2pdb
arpem dipcheck.README loopfit set_1200_2C_my_0.3b_cos_log.llh
arpipc_send dipcheck_example.log mapmod set_1200_2N_my_0.3b_cos_log.llh
arpnavigator dipcheck_example.pdb mapplus smi2sdf
arpnavigator_about.bmp dips_compact.dat mapread snow
arpnavigator_help.txt dips_uniform_compact.dat md5only sse2_detection
arpnavigator_splash.bmp dnarp mengine syminfo.lib
arpwarp_setup_base.bash dnarp_assemble mergefiles symmetry_shift
arpwarp_setup_base.csh editrc.awk mir_atom_count tcsh.macosx
arpwarp_setup_base.csh.bak editsu.awk mmff94.prm warp_albe.sh
arpwarp_setup_base.zsh elal mmxconst.prm warp_albe.sh.bak
auto_albe.sh elal.lib mol2pdb warp_build.sh
auto_albe.sh.bak energy_all mtzlabels warp_build.sh.bak
auto_em.sh extension ncs_arrange warp_fft.sh
auto_ligand.sh find_ccp4.sh pca warp_fft.sh.bak
auto_ligand.sh.bak find_ccp4.sh.bak pdbrenumber warp_freeset.sh
auto_nuce.sh find_fast.sh peaksearch warp_freeset.sh.bak
auto_nuce.sh.bak find_fast.sh.bak pept warp_ligand.sh
auto_solvent.sh find_output_cluster peptcomp warp_ligand.sh.bak
auto_solvent.sh.bak find_output_cluster2 percent_cutter.pl warp_loopy.sh
auto_tracing.sh fragdmat ph_error warp_loopy.sh.bak
auto_tracing.sh.bak gasteiger ph_rms warp_ncsadjust.sh
autopar6.sh gaussm pns_extender.pl warp_ncsadjust.sh.bak
autopar6.sh.bak geometriser po4_peaks.sh warp_nuce.sh
bfactor geometriser.sh po4_peaks.sh.bak warp_nuce.sh.bak
binning geometriser.sh.bak print_warning.sh warp_refmac.sh
calc_charges_prot helix_clean print_warning.sh.bak warp_refmac.sh.bak
check_java.sh hgen_D publ_albe.sh warp_refmac_restraints.sh
check_java.sh.bak hmain publ_albe.sh.bak warp_refmac_restraints.sh.bak
check_programs.sh hmain_dens.dat publ_classic_model.sh warp_solvent.sh
check_programs.sh.bak hmain_dens_dna.dat publ_classic_model.sh.bak warp_solvent.sh.bak
check_prot_remove.sh hmain_table.dat publ_classic_nobuilding.sh warp_tracing.sh
check_prot_remove.sh.bak hmain_table_dna.dat publ_classic_nobuilding.sh.bak warp_tracing.sh.bak
check_refmacversion.sh identify_ligand_multi.sh publ_classic_phases.sh watercomp
Any of the listed commands can be be started using the container.run helper, for example the auto_tracing.sh tool:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run auto_tracing.sh
Usage:
auto_tracing.sh \
datafile {mtzfile} \
[albe {1 to invoke albe, default is 0 for resol < 2.7A, else 1) }] \
[buildingcycles {number_of_building_cycles (default is 10) }] \
[cgr {number_of_NCS_copies (if seqin is provided, default is 1) }] \
[compareto {PDB_file_for_comparison}] \
[fbest {weighted_amplitude_label}] [phibest {phibest_label}] [fom {fom_label}] \
...
...
$
You do not need to initialise ARP/wARP, or source any shell files - the container is already pre-configured with all of ARP/wARP ready to run.
- https://arp-warp.org/ (Note: most links from this page not working as of March 2026)
CCP-EM v1
The older CCP-EM v1 distribution is installed within the container but the applications/tools within are not set up to be used without additional work.
Many of the commands in CCP-EM v1 are also in Doppio/CCP-EM v2, so would clash if they were added to the default runtime $PATH. You may still access the older CCP-EM v1 tools from the following directory inside the container:
/opt/ccpem-20221108/bin
You will need to explicitly run them as follows, for example to run cryoEF:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run /opt/ccpem-20221108/bin/cryoEF
Most users are recommended to move to Doppio or the other tools included in this container, as development/support for CCP-EM v1 ceased in 2022.
CCP-EM v2
See Doppio
CCP4
CCP4 is installed at /opt/ccp4-9 and all of the tools/commands/scripts in the list below are included:
ARCIMBOLDO_BORGES dials.split_experiments modelcraft-contents
ARCIMBOLDO_LITE dials.split_still_data modelcraft-copies
ARCIMBOLDO_SHREDDER dials.spot_counts_per_image molprobity.barbed_wire_analysis
JLigand.jar dials.spot_resolution_shells molprobity.cablam
JLogGraph.jar dials.ssx_index molprobity.cbetadev
LM14.einsle dials.ssx_integrate molprobity.chiral_validation
OpenAstexViewer.jar dials.stereographic_projection molprobity.clashscore
PdbView dials.stills_process molprobity.clashscore2
TMalign dials.symmetry molprobity.kinemage
TMscore dials.two_theta_offset molprobity.molprobity
abs dials.two_theta_refine molprobity.mp_validate_bonds
acedrg dials.unit_cell_histogram molprobity.omegalyze
acorn dials.version molprobity.probe
act diffBragg.completeF molprobity.probescore_ligand
add_heavy diffBragg.errors molprobity.ramalyze
afro diffBragg.estimate_Ncells_Eta molprobity.reduce
aimless diffBragg.filt_refls molprobity.rna_validate
align_DB diffBragg.geometry_refiner molprobity.rna_validate_bonds
almn diffBragg.hopper_process molprobity.rna_validate_puckers
alt_sg_list diffBragg.integrate molprobity.rna_validate_suites
amore diffBragg.make_input_file molprobity.rotalyze
ample diffBragg.pred_offsets molprobity.suitename
angles diffBragg.shoebx molprobity.suitename_old
anisoanl diffBragg.spectra molprobity.undowser2_validation
annlib.python diffBragg.stills_process molprobity.undowser_validation
annlib.show_build_path diffBragg.update_stage1_phil molrep
annlib.show_dist_paths difres mpi_fxs_launch
anode dimple mrbump
areaimol distang mrparse
arpwarp.source-csh distl.find_active_area mtz2cif
arpwarp.source-sh distl.image_viewer mtz2hkl
asc2p84 distl.mp_spotfinder_server_read_file mtz2sca
astex_viewer distl.signal_strength mtz2various
auspex distl.sweep_strength mtzMADmod
avs2ps distl.thin_client mtzdiff
axissearch dm mtzdump
balbes dmmulti mtzfix
balls domain2chain mtzinfo
baverage doser mtzmnf
binsort doubleHelix mtztona4
blend dtrek2mtz mtzutils
bltsh dtrek2scala multicomb
bltsh24 dui2 na4tomtz
bltwish dui2_client nautilus_pipeline
bltwish24 dui2_login ncont
bones2pdb dui2_multi_user_server ncsmask
boost.gcd dui2_old_stable node
boost.lcm dui2_server normal3d
boost.python dxtbx.any2nexus npm
boost.show_build_path dxtbx.depends_on npo
boost.show_dist_paths dxtbx.detector_superpose npx
boost_adaptbx.divide_by_zero dxtbx.display_parallax_correction oasis
boost_adaptbx.double_overflow dxtbx.dlsnxs2cbf omit
boost_adaptbx.inexact dxtbx.image2pickle omptbx.python
boost_adaptbx.int_overflow dxtbx.image_average omptbx.show_build_path
boost_adaptbx.nested_cpp_loops_with_check_signals dxtbx.install_format omptbx.show_dist_paths
boost_adaptbx.segmentation_fault dxtbx.overload othercell
boost_adaptbx.show_environ_usage dxtbx.plot_detector_models overlapmap
boost_adaptbx.show_platform_info dxtbx.print_header p842asc
bp3 dxtbx.print_matching_images pairef
buccaneer_pipeline dxtbx.python pairef-gui
bulking dxtbx.radial_average pandda.analyse
cad dxtbx.saturation pandda.export
caniso dxtbx.show_build_path pandda.inspect
cavenv dxtbx.show_dist_paths pandda.ipython
cbf.dump dxtbx.show_mask_info pandda.setup
cbflib.python dxtbx.show_matching_formats pandemic.adp
cbflib.show_build_path dxtbx.show_registry pcre2-config
cbflib.show_dist_paths dyndom pdb2cif
cbuccaneer ecalc pdb2ins
cbuccaneerNN edstats pdb2s
ccontacts edstats.pl pdb2to3
ccp4-python ens.hopper pdb_extract
ccp4.setup-csh extends pdb_extract_sf
ccp4.setup-csh.bak9350 extract pdb_merge
ccp4.setup-sh f2cif pdb_pack
ccp4.setup-sh.bak9350 f2mtz pdbcur
ccp4.setup-sh.old fable.cout pdbdeposition
ccp4_pipeline_simple fable.fem_include_search_paths pdbdiff
ccp4cloud-cmd fable.insert_write_at_start_of_each_procedure pdbhtf
ccp4cloud-config fable.python pdbset
ccp4cloud-local fable.read peakmax
ccp4cloud-remote fable.show_build_path percent-rank.pl
ccp4i fable.show_calls phaser
ccp4i2 fable.show_dist_paths phaser.MRage
ccp4i2-update fable.split phaser.MRage.solutions
ccp4i_condorsub feckless phaser.abs_convert_hkl_to_mtz
ccp4io.python fffear phaser.abs_extract_shelx_refinement_files
ccp4io.show_build_path ffjoin phaser.abs_merge_shelx_hkl
ccp4io.show_dist_paths fft phaser.abs_reformat_to_sites
ccp4ish fhscal phaser.abs_run_phaser
ccp4iwish find-blobs phaser.abs_split_dataset
ccp4mg findligand phaser.benchmark_basescore_suite
ccp4sm findmysequence phaser.benchmark_basescore_test
ccp4um findncs phaser.benchmark_best_superposition
cctbx.HKLinfo findwaters phaser.benchmark_calculate_mr_statistics
cctbx.HKLviewer fragon phaser.benchmark_create_alignment
cctbx.brehm_diederichs frame.unpickler phaser.benchmark_create_case
cctbx.cod_select_and_pickle freerflag phaser.benchmark_create_model
cctbx.data_viewer fsearch phaser.benchmark_create_test
cctbx.eltbx.show_fp_fdp gcx phaser.benchmark_error_prediction_suite
cctbx.euclidean_model_matching gemmi phaser.benchmark_error_prediction_test
cctbx.image_viewer gensym phaser.benchmark_evaluate_suite
cctbx.integration_pickle_viewer geomcalc phaser.benchmark_evaluate_test
cctbx.loggraph gesamt phaser.benchmark_molecule_replacement
cctbx.make_install_HKLviewer_ChimeraX get-residue-stats.pl phaser.benchmark_score_error_estimate
cctbx.multiplicity_viewer get_chain phaser.benchmark_score_error_estimate_suite
cctbx.patterson_map get_heavy phaser.benchmark_score_suite
cctbx.plot_anomalous_scattering get_structure_DB phaser.benchmark_score_test
cctbx.precession_view get_trans phaser.benchmark_sculpt_model
cctbx.python getax phaser.domain_analysis
cctbx.riso giant.cluster_datasets phaser.ensembler
cctbx.show_build_path giant.consolidate_cifs phaser.ensembler_new
cctbx.show_dist_paths giant.make_restraints phaser.extract_alignments
cctbx.show_systematic_absences giant.mass_refine phaser.famos
cctbx.small_cell_index giant.merge_conformations phaser.filter_anomalous_outliers
cctbx.small_cell_process giant.prepare_datasets phaser.find_alt_orig_sym_mate
cctbx.space_group_info giant.quick_refine phaser.find_anomalous_substructure
cctbx.structure_factor_timings giant.score_model phaser.gyre_and_gimble
cctbx.subgroups giant.split_conformations phaser.nightly_regression_test_report
cctbx.suitename giant.swerp phaser.nma_perturb
cctbx.suitename_old gltbx.python phaser.phassade
cctbx.version gltbx.show_build_path phaser.phil_as_xml
cctbx.visualize_r_factors gltbx.show_dist_paths phaser.process_defaults
cctbx.wavelength_units gltbx.try_imports phaser.proq2_error_estimation
cctbx.web.install gperf phaser.proq2_pdb_to_sculptor_error_file
cctbx.wxpython havecs phaser.python
cctbx.xfel hbond phaser.rigid_body_divide
cctbx.xfel.candidate_cells header2matr phaser.sceds
cctbx.xfel.detector_congruence helixang phaser.sculptor
cctbx.xfel.detector_residuals hgen phaser.sculptor_new
cctbx.xfel.detector_shifts hklplot phaser.show_build_path
cctbx.xfel.drift hklview phaser.show_dist_paths
cctbx.xfel.ensemble_refinement_pipeline hopper phaser.simple_homology_model
cctbx.xfel.experiment_residuals i2run phaser.unit
cctbx.xfel.filter_experiments_by_rmsd icoefl phistats
cctbx.xfel.frame_extractor icu-config pisa
cctbx.xfel.h5_average idiffdisp pixeltool
cctbx.xfel.jungfrau_metrics imosflm platonyzer
cctbx.xfel.merge import_serial pltdev
cctbx.xfel.mpi_integrate iota.double_gauss_visualizer pmf
cctbx.xfel.mpi_submit iotbx.blast_pdb pointless
cctbx.xfel.plot_reflection_stats iotbx.build_phil_cache polarrfn
cctbx.xfel.plot_run_stats_from_experiments iotbx.cbf_read postref
cctbx.xfel.plot_run_stats_from_stats_pickle iotbx.cif.as_miller_arrays prasa
cctbx.xfel.plot_spotfinder_stats_from_stats_pickle iotbx.cif.as_xray_structure prelyscar_ccp4.pl
cctbx.xfel.plot_uc_cloud_from_experiments iotbx.cif.validate prep_bulk
cctbx.xfel.powder_from_spots iotbx.cif_as_mtz prime
cctbx.xfel.preference iotbx.cif_as_pdb prime.change_basis
cctbx.xfel.process iotbx.cns.reflection_reader prime.change_path_indexing_ambiguity
cctbx.xfel.recompute_mosaicity iotbx.cns.sdb_reader prime.cluster_isoform
cctbx.xfel.show_spot_separation iotbx.cns.transfer_crystal_symmetry prime.compare_solution_pickles
cctbx.xfel.small_cell.cake_plot iotbx.crystal_symmetry_from_any prime.comparestats
cctbx.xfel.small_cell.cake_plot_prep iotbx.dano_as_amplitudes prime.explore_twin_operators
cctbx.xfel.small_cell_process iotbx.distance_least_squares prime.frame_extractor
cctbx.xfel.stripe_experiment iotbx.dtrek.to_cns prime.iotacc
cctbx.xfel.trumpet_plots iotbx.emma prime.mpi_run
cctbx.xfel.ui_server iotbx.export_scalepack_unmerged prime.mpi_scale
cctbx.xfel.upload_mtz iotbx.fetch_pdb prime.plot_energy_spectrum
cctbx.xfel.weather iotbx.file_reader prime.plotstats
cctbx.xfel.xtc_dump iotbx.french_wilson prime.postrefine
cctbx.xfel.xtc_process iotbx.get_pubmed_citation prime.print_integration_pickle
cctbx_regression.test_nightly iotbx.lattice_symmetry prime.python
cecalc iotbx.merging_statistics prime.r_mtz_mtz
cextend iotbx.mtz.dump prime.ray_trace
cfft iotbx.pdb.add_conformations prime.run
chainsaw iotbx.pdb.as_xray_structure prime.show_build_path
check_cell_sg iotbx.pdb.box_around_molecule prime.show_dist_paths
check_file_DB iotbx.pdb.flip_symmetric_amino_acids prime.solve_indexing_ambiguity
check_zero_res.py iotbx.pdb.get_citation prime.view_fineslices
checkmysequence iotbx.pdb.insert_scale_records prime.viewstats
chef iotbx.pdb.join_fragment_files privateer
chltofom iotbx.pdb.link_as_geometry_restraints_edits probe
chooch iotbx.pdb.print_sequence probe.probe
cif-diff iotbx.pdb.show_connectivity probe.python
cif-grep iotbx.pdb.sort_atoms probe.show_build_path
cif-merge iotbx.pdb.split_models probe.show_dist_paths
cif-validate iotbx.pdb.superpose_atoms_by_name professs
cif2mtz iotbx.pdb.superpose_centers_of_mass prosmart
cif2pdb iotbx.pdb_as_fasta prosmart_align
cif2xml iotbx.pdb_labels_comparison prosmart_restrain
ciftab iotbx.pdb_labels_diff pybind11-config
cinvfft iotbx.pdb_remediator pyjob
clipper_test iotbx.phil pyqt-bundle
cloudrun iotbx.poscar_as_xray_structure qdistancefieldgenerator
cluster.42 iotbx.python qtpisa
cluster.dials.exec iotbx.r_free_flags_accumulation qtrview
cluster.individual_frame_intensity iotbx.r_free_flags_as_cns qtwaylandscanner
cluster.intensity_statistics iotbx.r_free_flags_completion_simple r500
cluster.unit_cell iotbx.reflection_file_converter r500ccp4
cluster.visualize_orientations iotbx.reflection_file_editor rabdam
cma_es.python iotbx.reflection_file_reader rantan
cma_es.show_build_path iotbx.shelx.as_cif rapper
cma_es.show_dist_paths iotbx.shelx.as_xray_structure rastep
cmakereference iotbx.shelx.dump_as_python rebatch
cmapcoeff iotbx.show_build_path reduce
cmapcut iotbx.show_dist_paths reduce.python
cmaplocal iotbx.show_distances reduce.show_build_path
cmodeltoseq iotbx.simple_map_coefficients reduce.show_dist_paths
cmtzjoin iotbx.split_data_cif refindex
cmtzsplit iotbx.symmetry_search refmac5
cnautilus iotbx.unique_with_biomt refmacat
cncsfrommodel iotbx.xplor.map.show_summary reforigin
combat ipmosflm reindex
comit jligand render
compar jspisa repc
conkit-convert labelit.png revise
conkit-msatool libcheck rfcorr
conkit-plot libg ribbon
conkit-precision libtbx.add_from_future_import_division rings3d
conkit-predict libtbx.analyse_code_comments rods
conkit-validate libtbx.any2unix rotamer
contact libtbx.assert_line_count rotgen
convert2mtz libtbx.assert_not_show_diff rotmat
coord_format libtbx.assert_stdin rsps
coordconv libtbx.assert_stdin_contains_strings rstats
coot libtbx.assert_stdin_does_not_contain_strings rstbx.index
coot-mini-rsr libtbx.assert_stdin_ends_with rstbx.python
cootbx.python libtbx.bash rstbx.show_build_path
cootbx.show_build_path libtbx.bibtex rstbx.show_dist_paths
cootbx.show_dist_paths libtbx.bootstrap rstbx.simage.create
cparrot libtbx.bundle_as_exe rstbx.simage.explore_completeness
cpatterson libtbx.bundle_as_selfx rstbx.simage.solver
cphasecombine libtbx.check_libcpp rstbx.simage.wx_display
cphasematch libtbx.chunk rwcontents
cpirate libtbx.clean_clutter samosa.join
cprodrg libtbx.clear_paths samosa.scale
crank libtbx.configure sapi
create_docs libtbx.create_cctbx_bundle_for_installer save_seg_id
cross_validate libtbx.create_install_script_bat sc
crossec libtbx.create_installer scala
crunch2 libtbx.create_mac_app scaleit
crys3d.model_viewer libtbx.create_selfx scalepack2mtz
crys3d.python libtbx.create_unzipsfx scitbx.apply_tapenade_hints
crys3d.qttbx.view_structure libtbx.create_windows_exe scitbx.generate_error_h
crys3d.show_build_path libtbx.csh scitbx.histogram
crys3d.show_dist_paths libtbx.dtrace scitbx.python
csequins libtbx.easy_qsub scitbx.show_build_path
csfcalc libtbx.env_run scitbx.show_dist_paths
csheetbend libtbx.epydoc_run scitbx.show_exp_times
csigmaa libtbx.exec scitbx.show_sizes
csloop libtbx.extract_code_from_txt scitbx.unicode_examples
csm libtbx.find_clutter search_DB
cspad.cbf_metrology libtbx.find_files seqwt
cspad.circular_gain_mask libtbx.find_in_repositories serialtbx.python
cspad.detector_congruence libtbx.find_old_style_classes serialtbx.show_build_path
cspad.detector_congruence2 libtbx.find_pdb_mmcif_problems serialtbx.show_dist_paths
cspad.detector_shifts libtbx.find_reserved_names servalcat
cspad.detector_statistics libtbx.find_under_build sf_convert
cspad.metrology libtbx.find_untested sfall
cspad.quadrants libtbx.find_unused_imports sfcheck
cspad.quadrants_cbf libtbx.find_unused_imports_crude sftools
csymmatch libtbx.gdb shelx2map
ctruncate libtbx.git_avoid_merges shelxc
cxi.apply_metrology libtbx.help shelxd
cxi.brehm_diederichs libtbx.import_all_ext shelxe
cxi.calc_gain_ratio libtbx.import_all_python shelxl
cxi.calibdir2cbfheader libtbx.install shelxs
cxi.cbfheader2slaccalib libtbx.install_conda shelxt
cxi.cspad_average libtbx.latex shredcif
cxi.cspad_pinwheel libtbx.line_count sigmaa
cxi.detector_format_versions libtbx.list_files simbad
cxi.diff libtbx.list_modules simbad-contaminant
cxi.display_metrology libtbx.list_source_files_sorted_by_size simbad-database
cxi.experiment_json_to_cbf_def libtbx.make_dist simbad-full
cxi.fee_view libtbx.make_sphinx_html simbad-lattice
cxi.find_highres_shots libtbx.nequal simbad-morda
cxi.frame_extractor libtbx.parallel_simple simtbx
cxi.gain_map libtbx.path_utility simtbx.diffBragg.hopper
cxi.gain_map_cbf libtbx.pdflatex simtbx.diffBragg.stage_two
cxi.generate_circular_gain_mask libtbx.phil simtbx.model_img
cxi.get_next_trial_id libtbx.precommit simtbx.python
cxi.hist_finalise libtbx.prime_factors_of simtbx.show_build_path
cxi.image2pickle libtbx.printenv simtbx.show_dist_paths
cxi.image_average libtbx.py_compile_all slicendice
cxi.index libtbx.python smtbx.absolute_structure
cxi.list_db_metadata libtbx.raise_exception_for_testing smtbx.anom_refine
cxi.lsf libtbx.refresh smtbx.benchmark_refinement
cxi.make_dials_mask libtbx.remote_processing smtbx.masks
cxi.make_mar_mask libtbx.remove_tree smtbx.python
cxi.make_mask libtbx.remove_unused_imports smtbx.refine
cxi.merge libtbx.resource_monitor_plot smtbx.show_build_path
cxi.monitor_detectors libtbx.run_pickled_function smtbx.show_dist_paths
cxi.monitor_trials libtbx.run_tests_parallel solomon
cxi.mpi_average libtbx.scons solution_check
cxi.mpi_submit libtbx.seconds_since_the_epoch sortmtz
cxi.optical2cbfheader libtbx.sge_available_slots sortwater
cxi.or_mask libtbx.sge_qstat_counts sphinx.build
cxi.parameters libtbx.sh spicker
cxi.pbs libtbx.show_all_on_path spicker_omp
cxi.pickle2cbf libtbx.show_bin_path spotfinder.python
cxi.pixel_histograms libtbx.show_build_options spotfinder.show_build_path
cxi.plotcv libtbx.show_build_path spotfinder.show_dist_paths
cxi.plotcv_parse libtbx.show_but_no_repr stereo
cxi.postrefine libtbx.show_commands stereo3d
cxi.print_pickle libtbx.show_date_and_time stgrid
cxi.psana libtbx.show_dist_paths stills.merge
cxi.pyana libtbx.show_env stnet
cxi.radial_average libtbx.show_environ_usage superpose
cxi.slaccalib2cbfheader libtbx.show_full_command_path surface
cxi.spots libtbx.show_host_and_user symconv
cxi.stream_to_pickle libtbx.show_include_paths symfit
cxi.trial_stats libtbx.show_lib_path symop2mat
cxi.view libtbx.show_module_dependencies tclsh
cxi.xes_finalise libtbx.show_number_of_processors tclsh8.4
cxi.xmerge libtbx.show_numeric_version tffc
cxi.xtc_process libtbx.show_numpy_version theseus
del_heavy libtbx.show_python_dependencies tlsanl
density-fitness libtbx.show_python_sys_executable tlsextract
detwin libtbx.show_pythonpath tntbx.python
dev.cctbx.xfel.detector_residuals libtbx.show_repository_paths tntbx.show_build_path
dev.dials.generate_tutorial_text libtbx.show_tests tntbx.show_dist_paths
dev.dials.make_sphinx_html libtbx.show_variable_names topdraw
dev.dials.napari_rlv libtbx.start_binary_bundle toplist
dev.dials.show_extensions libtbx.start_process topp
dev.dials.simple_integrate libtbx.tcsh tortoize
dev.dxtbx.debug_memory libtbx.unpack_in_sources tracegen
dev.dxtbx.read_sequence libtbx.valgrind tracer
dev.xia2.check_mosaic libtbx.wait_for_file truncate
dev.xia2.create_mask loggraph unique
dev.xia2.file_statistics logview vecref
dev.xia2.make_sphinx_html lorestr vecsum
dev.xia2.show_mask lsqkab vectors
dev.xia2.show_mtz_cells makedict viewhkl
dials.align_crystal mama2ccp4 volume
dials.anvil_correction map2fs watertidy
dials.apply_mask mapdiff watncs
dials.assign_experiment_identifiers mapdump watpeak
dials.augment_spots mapexchange wilson
dials.background mapmask wish
dials.check_indexing_symmetry mapreplace wish8.4
dials.cluster_unit_cell mapro worms
dials.combine_experiments maprot wxtbx.anomalous_differences_histogram
dials.compare_orientation_matrices mapsig wxtbx.inspect_r_free_flags
dials.complete_full_sphere mapslicer wxtbx.plan_sad_experiment
dials.compute_delta_cchalf maptona4 wxtbx.python
dials.convert_to_cbf mat2symop wxtbx.show_build_path
dials.correlation_matrix matthews_coef wxtbx.show_dist_paths
dials.cosym metalCoord wxtbx.show_file_info
dials.create_profile_model mkdssp wxtbx.xtriage
dials.damage_analysis mlphare xes.histograms
dials.data mmCQL xfel.FEE_average_plot
dials.detect_blanks mmtbx.altloc_remediate xfel.auto_submit
dials.estimate_gain mmtbx.apply_ncs_to_ligand xfel.fee_calibration
dials.estimate_resolution mmtbx.arginine_geometry xfel.get_hit_timestamps
dials.export mmtbx.bfactor_plot xfel.merge
dials.export_best mmtbx.clashscore2 xfel.overlay_spectra
dials.export_bitmaps mmtbx.density_modification xfel.plot_isoform_cloud
dials.filter_reflections mmtbx.development.aev xfel.plot_run_stats
dials.find_bad_pixels mmtbx.development.electrons xfel.plot_uc_cloud
dials.find_hot_pixels mmtbx.development.qscore xfel.print_run_stats
dials.find_rotation_axis mmtbx.development.validate_ligands xfel.python
dials.find_spots mmtbx.development.water_b_factors xfel.show_build_path
dials.find_spots_client mmtbx.distance_difference xfel.show_dist_paths
dials.find_spots_server mmtbx.emringer_score xfel.smooth_spectrum
dials.frame_orientations mmtbx.estimate_bijvoet_ratio xfel.subtract_background
dials.generate_distortion_maps mmtbx.fft xia2
dials.generate_mask mmtbx.find_peaks_holes xia2.add_free_set
dials.geometry_viewer mmtbx.find_residue_in_pdb xia2.cluster_analysis
dials.goniometer_calibration mmtbx.holton_geometry_validation xia2.compare_merging_stats
dials.image_viewer mmtbx.hydrogenate xia2.delta_cc_half
dials.import mmtbx.massage_data xia2.get_image_number
dials.import_xds mmtbx.matthews xia2.html
dials.index mmtbx.model_vs_data xia2.index
dials.indexed_as_integrated mmtbx.mon_lib_cif_triage xia2.integrate
dials.integrate mmtbx.mp_geo xia2.is_doing
dials.merge mmtbx.nonbonded_overlaps xia2.ispyb_json
dials.merge_cbf mmtbx.pdb_interpretation xia2.ispyb_xml
dials.merge_reflection_lists mmtbx.pdb_statistics xia2.merging_statistics
dials.missing_reflections mmtbx.plan_sad_experiment xia2.multiplex
dials.model_background mmtbx.prepare_pdb_deposition xia2.npp
dials.modify_geometry mmtbx.probe2 xia2.overload
dials.plot_Fo_vs_Fc mmtbx.process_predicted_model xia2.plot_multiplicity
dials.plot_reflections mmtbx.prune_model xia2.print
dials.plot_scan_varying_model mmtbx.python xia2.rebatch
dials.plugins mmtbx.quantum_interface xia2.report
dials.powder_calibrate mmtbx.rama_z xia2.rescale
dials.predict mmtbx.rebuild_cablam_cache xia2.setup
dials.python mmtbx.rebuild_rotarama_cache xia2.small_molecule
dials.rbs mmtbx.reduce xia2.ssx
dials.reciprocal_lattice_viewer mmtbx.reduce2 xia2.ssx_reduce
dials.reference_profile_viewer mmtbx.refine_anomalous_substructure xia2.strategy
dials.refine mmtbx.ribbons xia2.table1
dials.refine_bravais_settings mmtbx.rigid_bond_test xia2.to_shelx
dials.refine_error_model mmtbx.ringer xia2.to_shelxcde
dials.reflection_viewer mmtbx.search_pdb_symmetry xmlpatterns
dials.reindex mmtbx.select_best_starting_model xmlpatternsvalidator
dials.report mmtbx.show_build_path xplot84driver
dials.rl_png mmtbx.show_dist_paths xpp.beamcenter
dials.rlv mmtbx.show_r_factors_by_shell xpp.experiment_manager
dials.rs_mapper mmtbx.ss_idealization xpp.isoform
dials.scale mmtbx.super xpp.jobs_sentinel
dials.search_beam_position mmtbx.superpose xpp.progress_bar
dials.sequence_to_stills mmtbx.table_one xpp.progress_detail
dials.shadow_plot mmtbx.undowser2_validation xpp.runs_sentinel
dials.show mmtbx.validation_summary xpp.simulate
dials.show_build_path mmtbx.verify_mon_lib_data zanuda
dials.show_dist_paths mmtbx.xtriage zeroed
dials.slice_sequence mmtbx.xtriage_gui znd_subs
dials.sort_reflections modelcraft
Any of the listed commands can be be started using the container.run helper, for example the pisa tool:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run pisa
Protein Interactions, Surfaces and Assemblies (PISA)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
v2.2.0 built on 15-03-2019
with SSM v1.4.0, SRS v1.0.0, MMDB v2.0.24
(C) Eugene Krissinel 2007-2019
.============================================================.
! Use of this program is subject to citing the following !
! publication: !
! E. Krissinel and K. Henrick (2007). Inference of !
! macromolecular assemblies from crystalline state. !
! J. Mol. Biol. 372, 774-797. !
`============================================================'
USAGE:
~~~~~~
1. Perform PISA analysis of a structure given in PDB or
mmCIF file 'coorfile':
...
...
$
If you run the ccp4i application in an X terminal on the Linux X11 Desktop using our Open OnDemand service instead of from a command line, login node or Slurm batch job, you can also access the gui interface of CCP4:
Now, in the terminal, run ccp4i:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run ccp4i
The CCP4 desktop application window should then appear:
Note: the later ccp4i2 GUI tool does not currently work correctly from within the container environment.
You do not need to initialise CCP4, or source any shell files to run any CCP4 commands - the container is already pre-configured with all of CCP4 ready to run.
- https://www.ccp4.ac.uk/html/ - Documentation on individual CCP4 commands
CheckMySequence / DoubleHelix / FindMySequence
CheckMySequence, DoubleHelix and FindMySequence are installed within a conda environment under /opt/package-ccpem2 within the container, but can be started the same as any other command within the container using container.run.
The following commands are available:
checkmysequencedoubleHelixfindmysequence
For example, to run checkmysequence:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run checkmysequence --help
# checkMySequence (1.5.3): a tool for sequence assignment validation in EM and MX models of macromolecules
findMySequence 1.0.20
doubleHelix 5.6.3
HMMER is available (from CCP4)
INFERNAL is available (from CCP4)
Usage: checkMySequence [options]
Options:
--version show program's version number and exit
-h, --help show this help message and exit
--test a simple test
Required parameters (model, sequences and a map):
--modelin=FILENAME PDB/mmCIF model
--seqin=FILENAME target sequences in FASTA format
--mapin=FILENAME map in CCP4/MRC format
--mtzin=FILENAME input SFs in MTZ format
--labin=FWT,PHWT MTZ file column names
Extra options:
--fraglen=VALUE starting length of test-fragments [default: 10/MX
20/EM]
--max_fraglen=VALUE
maximum length of test-fragments [default: 60]
--plot generate graphical report
--select=STRING model fragment selection string eg. "chain A and resi
10:50" [default: all]
--pdbid=PDB_ID Download and check a model directly from PDB (use
--keep to store the data in curent directory)
--jsonout=FILENAME output filename with the validation results in json
format (machine readable)
$
CryoDRGN
CryoDRGN is installed within a conda environment under /opt/package-ccpem2 within the container, but can be started the same as any other command within the container using container.run.
The following commands are available:
cryodrgncryodrgn_utils
For example, to run cryodrgn:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run cryodrgn --help
usage: cryodrgn [-h] [--version]
{abinit_het,abinit_homo,analyze,analyze_landscape,analyze_landscape_full,backproject_voxel,direct_traversal,downsample,eval_images,eval_vol,filter,graph_traversal,parse_ctf_csparc,parse_ctf_star,parse_pose_csparc,parse_pose_star,pc_traversal,train_nn,train_vae,view_config}
...
Commands installed with cryoDRGN
options:
-h, --help show this help message and exit
--version show program's version number and exit
Choose a command:
{abinit_het,abinit_homo,analyze,analyze_landscape,analyze_landscape_full,backproject_voxel,direct_traversal,downsample,eval_images,eval_vol,filter,graph_traversal,parse_ctf_csparc,parse_ctf_star,parse_pose_csparc,parse_pose_star,pc_traversal,train_nn,train_vae,view_config}
$
CTFFind4
CTFFind4 has been compiled and installed to /opt/bin inside the container. You can run it as follows:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run ctffind help
Usage: ctffind [--old-school-input] [--old-school-input-ctffind4] [--amplitude-spectrum-input] [--filtered-amplitude-spectrum-input] [--fast] [-j <num>] [controller_address] [controller_port] [job_code]
--old-school-input Pretend this is ctffind3 (for compatibility with old scripts and programs)
--old-school-input-ctffind4 Accept parameters from stdin, like ctffind3, but with extra lines for ctffind4-specific options (movie processing and phase shift estimation
--amplitude-spectrum-input The input image is an amplitude spectrum, not a real-space image
--filtered-amplitude-spectrum-input The input image is filtered (background-subtracted) amplitude spectrum
--fast Skip computation of fit statistics as well as spectrum contrast enhancement
-j:<num> Desired number of threads. Overrides interactive user input. Is overriden by env var OMP_NUM_THREADS
$
You can also find ctffind_plot_results.sh installed:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run ctffind_plot_results.sh
Usage: ctffind_plot_results.sh output_from_ctffind_avrot.txt
$
Doppio (aka CCP-EM v2)
Doppio is installed to /opt/doppio_1.5.0 within the container. There are two ways in which the Doppio user interface can be launched; web interface or desktop application.
Desktop Application
To run the Doppio desktop application you must start a Linux X11 Desktop session from our Open OnDemand service. Once you have the Linux desktop running, open the terminal application:
Now, in the terminal, run doppio-desktop:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run doppio-desktop
The desktop application will start within a few seconds:
Web Application
To run the Doppio web interface you must start a Linux X11 Desktop session from our Open OnDemand service. Once you have the Linux desktop running, open the terminal application:
Now, in the terminal, run doppio-web:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run doppio-web
You should see the following output in the terminal window:
Doppio API token: e27d181083116c8c265ca71322449aa054a5205242097d272fa37e5cc32521cb
INFO: Started server process [699731]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://127.0.0.1:44185 (Press CTRL+C to quit)
INFO: Doppio is now running.
Your web browser should open Doppio automatically. If it doesn't, go to this link:
file:///mnt/nfs/home/n1234/.local/share/doppio/doppio-699731-open.html
Or you can access Doppio directly using these details:
Server URL: http://127.0.0.1:44185/
Access token: e27d181083116c8c265ca71322449aa054a5205242097d272fa37e5cc32521cb
Press Ctrl+C to quit this Doppio server
Whilst still on the Linux desktop, you can then either Control+Left Click on the Server URL address in the terminal, or you can start Firefox from the application menu and cut-and-paste the address in.
You will be asked to enter the Access token to verify your access to the running Doppio server.
Remember that if you want to use GPU acceleration within Doppio then you should select a GPU partition and allocate a GPU card at the point you start the Open OnDemand session.
The Doppio server only runs while your Open OnDemand desktop session is running. We do not support running Doppio continuously, nor is there any supported means to expose the Doppio server to the Campus network or the Internet; you must access it while running the desktop session - Comet is not for running network services.
EMDA
EMDA is installed within a conda environment under /opt/package-ccpem2 within the container, but can be started the same as any other command within the container using container.run.
The following commands are included:
emdaemda2
For example, to run emda:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run emda -h
usage: emda [command] [arguments]
positional arguments:
{info,halffsc,fsc,singlemapfsc,ccmask,mapmask,modelmask,lowpass,power,bfac,resol,half2full,map2mtz,map2mtzfull,transform,mtz2map,resample,resamplemap2map,rcc,bfromcc,mmcc,fcc,mapmodelvalidate,mapmodelfsc,overlay,average,diffmap,applymask,scalemap,bestmap,predfsc,refmac,occ,mirror,model2map,composite,magref,com,fetch,axisrefine,pointgroup,symmetrise,rebox,residuecc}
optional arguments:
-h, --help show this help message and exit
--version show program's version number and exit
$
EMDB Validation Analysis
EMDB VA is installed within a conda environment under /opt/package-ccpem2 within the container, but can be started the same as any other command within the container using container.run.
The following commands are included:
va
To run va:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run va -h
ChimeraX will be used to produce for the surface view.
usage: va [-h] [--version] [-f [F [F ...]]] [-pid [PID]] [-hmeven [HMEVEN]] [-hmodd [HMODD]] [-cl [CL]] [-run [RUN [RUN ...]]]
[-met [{tomography,twodcrystal,crystallography,singleparticle,single particle,subtomogramaveraging,subtomogram averaging,helical}]] [-s [S]] [-ms [MS [MS ...]]]
[-mscl [MSCL [MSCL ...]]] [-p [P]] [-i MODELMAP] [--strdlib [STRDLIB]] [--threeddir [THREEDDIR]] [-m [M]] [-d [D]] [-emdid [EMDID]] [-j [J]]
{strudel} ...
$
Locscale
Locscale is installed as a conda environment within the container by package-ccpem2 but can be started like any other command using container.run. The following command are available:
locscale
To run locscale:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run locscale
usage: locscale [-h] [-em EMMAP_PATH | -hm HALFMAP_PATHS HALFMAP_PATHS] [-filter_input] [-ma MASK] [-v] [--print_report] [--report_filename REPORT_FILENAME]
[-op OUTPUT_PROCESSING_FILES] [-fdr FDR_THRESHOLD] [-fdr_w FDR_WINDOW_SIZE] [-avg_filter AVERAGING_FILTER_SIZE] [-fdr_f FDR_FILTER] [-th MASK_THRESHOLD]
[-model_path MODEL_PATH] [--use_low_context_model] [-bs BATCH_SIZE] [-gpus GPU_IDS [GPU_IDS ...]] [-cube_size CUBE_SIZE] [-s STRIDE] [-apply_fsc_filter]
[-o OUTFILE] [-mm MODEL_MAP | -mc MODEL_COORDINATES] [-mres MODEL_RESOLUTION] [-sym SYMMETRY] [-wn WINDOW_SIZE] [-mpi] [-np NUMBER_PROCESSES] [--measure_bfactors]
[-ref_it REFMAC_ITERATIONS] [-res REF_RESOLUTION] [-p APIX] [--add_blur ADD_BLUR] [--refmac5_path REFMAC5_PATH] [--cref_pickle CREF_PICKLE] [-cif_info CIF_INFO]
[--complete_model] [-avg_w AVERAGING_WINDOW] [--build_using_pseudomodel] [-pm PSEUDOMODEL_METHOD] [-pm_it TOTAL_ITERATIONS] [-dst DISTANCE] [-mw MOLECULAR_WEIGHT]
[--activate_pseudomodel] [-smooth SMOOTH_FACTOR] [--boost_secondary_structure BOOST_SECONDARY_STRUCTURE] [--no_reference] [--set_local_bfactor SET_LOCAL_BFACTOR]
[--dev_mode] [--skip_refine]
{feature_enhance,version,test} ...
$
MetalCoord
MetalCoord is installed as a conda environment within the container by package-ccpem2 but can be started like any other command using container.run. The following command are available:
metalCoord
To run metalCoord:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run metalCoord
2026-03-20 10:04:15,008 - MetalCoord - INFO - Logging started. Logging level: 10
usage: metalCoord [-h] [--version] [--no-progress] {update,stats,coord,pdb} ...
MetalCoord: Metal coordination analysis.
positional arguments:
{update,stats,coord,pdb}
update Update a cif file.
stats Distance and angle statistics.
coord List of coordinations.
pdb Get list of PDBs containing the ligand.
optional arguments:
-h, --help show this help message and exit
--version show program's version number and exit
--no-progress Do not show progress bar.
$
Model Angelo
Model Angelo is installed within the container and can be run using the normal container.run helper method.
The installed commands include:
model_angelo buildmodel_angelo build_no_seq
For example:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run model_angelo build --help
usage: model_angelo build [-h] --volume-path VOLUME_PATH --protein-fasta PROTEIN_FASTA [--rna-fasta RNA_FASTA] [--dna-fasta DNA_FASTA] [--output-dir OUTPUT_DIR] [--mask-path MASK_PATH]
[--device DEVICE] [--config-path CONFIG_PATH] [--model-bundle-name MODEL_BUNDLE_NAME] [--model-bundle-path MODEL_BUNDLE_PATH] [--keep-intermediate-results]
-------------------------------------------------------------------------
Welcome to ModelAngelo! Have a look around...
This is the build command, so you need:
1) A cryo-EM map .mrc file, passed to --volume-path/--v/-v
2) A FASTA file with all potential amino acid sequences found in the map,
passed to --fasta-path/--f/-f
You can also provide a mask for the volume using --mask-path/--m/-m
You can also input a custom config file with --config-path/--c/-c
options:
-h, --help show this help message and exit
--rna-fasta RNA_FASTA, --rf RNA_FASTA, -rf RNA_FASTA
The path to the RNA sequence file
--dna-fasta DNA_FASTA, --df DNA_FASTA, -df DNA_FASTA
The path to the DNA sequence file
...
$
- Further documentation is available at: https://github.com/3dem/model-angelo
Modeller
Salilabs' Modeller application is installed from their Debian package (.deb) and is available from /usr/bin. You can run Modeller scripts as follows:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run mod10.8
usage: mod10.8 script [...]
$
- https://salilab.org/modeller/10.8/manual/ - Online documentation
Python
This is a standard version of Python 3.10 as distributed with Ubuntu 22 LTS. It is used to run many of the packages installed inside the container, but you may also choose to use it to run your own scripts, especially if you want to make use of the Python modules which are already installed within the container (torch, model angelo, etc).
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run python3 <myscript.py>
RIBFIND 2
RIBFIND is installed as a conda environment within the container by package-ccpem2 but can be started like any other command using container.run. The following command are available:
ribfind
To run ribfind:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run ribfind
usage: ribfind [-h] --model MODEL [--dssp DSSP] [--rnaml RNAML] [--protein-cutoff-dist PROTEIN_CONTACT_DISTANCE] [--protein-helix-ratio PROTEIN_HELIX_RATIO]
[--protein-dens-cutoff PROTEIN_DENS_CUTOFF] [--protein-minimum-strand-length PROTEIN_MINIMUM_STRAND_LENGTH] [--rna-contact-dist RNA_CONTACT_DISTANCE]
[--rna-minimum-strand-length RNA_MINIMUM_STRAND_LENGTH] [--rna-dens-cutoff RNA_DENS_CUTOFF] [--soft-cutoff] [--output-dir OUTPUT_DIR] [--version]
$
Relion 5
Relion is compiled and installed to /opt/bin. You can find all of the following binaries:
relion relion_localsym relion_python_tomo_get_particle_poses relion_tomo_bin_stack
relion_align_symmetry relion_localsym_mpi relion_python_tomo_import relion_tomo_catalogue_tomos
relion_align_tiltseries relion_manualpick relion_python_tomo_pick relion_tomo_compute_FCC
relion_align_tiltseries_mpi relion_mask_create relion_python_tomo_view relion_tomo_convert_projections
relion_autopick relion_merge_particles relion_python_topaz relion_tomo_dark_erase
relion_autopick_mpi relion_motion_refine relion_qsub.csh relion_tomo_discover_motif
relion_backproject_2d relion_motion_refine_mpi relion_reconstruct relion_tomo_find_fiducials
relion_batchrun relion_movie_reconstruct relion_reconstruct_mpi relion_tomo_find_lattice
relion_batchrun_mpi relion_mrc2vtk relion_refine relion_tomo_fit_bfactors
relion_class_ranker relion_particle_reposition relion_refine_mpi relion_tomo_import_coordinates
relion_convert_star relion_particle_select relion_run_align_tiltseries relion_tomo_local_particle_refine
relion_convert_to_tiff relion_particle_subtract relion_run_align_tiltseries_mpi relion_tomo_make_optimisation_set
relion_convert_to_tiff_mpi relion_particle_subtract_mpi relion_run_ctffind relion_tomo_powspec
relion_ctf_mask_test relion_particle_symmetry_expand relion_run_ctffind_mpi relion_tomo_predict_tilt_series
relion_ctf_refine relion_pipeliner relion_run_motioncorr relion_tomo_reconstruct_particle
relion_ctf_refine_mpi relion_plot_delocalisation relion_run_motioncorr_mpi relion_tomo_reconstruct_particle_mpi
relion_ctf_toolbox relion_postprocess relion_schemegui.py relion_tomo_reconstruct_tomogram
relion_demodulate relion_postprocess_mpi relion_schemer relion_tomo_reconstruct_tomogram_mpi
relion_display relion_prepare_subtomo relion_stack_create relion_tomo_refine_ctf
relion_estimate_gain relion_preprocess relion_star_datablock_ctfdat relion_tomo_refine_ctf_mpi
relion_external_reconstruct relion_preprocess_mpi relion_star_datablock_singlefiles relion_tomo_refine_mag
relion_filament_selection relion_project relion_star_datablock_stack relion_tomo_split_optics
relion_find_tiltpairs relion_pseudotomo_expansion relion_star_handler relion_tomo_subtomo
relion_flex_analyse relion_python_blush relion_star_loopheader relion_tomo_subtomo_mpi
relion_flex_analyse_mpi relion_python_classranker relion_star_plottable relion_tomo_taper
relion_helix_inimodel2d relion_python_dynamight relion_star_printtable relion_tomo_template_pick
relion_helix_toolbox relion_python_fetch_weights relion_suggest_tvalue relion_tomo_test
relion_helix_vote_classes relion_python_modelangelo relion_tiltpair_plot relion_tomo_tomo_ctf
relion_image_handler relion_python_tomo_align_tilt_series relion_tomo_Wiener_divide
relion_import relion_python_tomo_denoise relion_tomo_align
relion_it.py relion_python_tomo_exclude_tilt_images relion_tomo_align_mpi
To run one of the command line tools as an example relion_refine:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run relion_refine
+++ RELION: command line arguments (with defaults for optional ones between parantheses) +++
====== General options =====
--i () : Input particles (in a star-file)
--tomograms () : Star file with the tomograms, in case subtomos are handled as 2D stacks
--trajectories () : Star file with the tomogram motion trajectories
--ios () : Input tomo optimisation set file. It is used to set --i, --tomograms, --ref or --solvent_mask if they are not provided. Updated output optimiser set is created.
--o () : Output rootname
--iter (-1) : Maximum number of iterations to perform
--tau2_fudge (-1) : Regularisation parameter (values higher than 1 give more weight to the data)
--tau2_fudge_scheme () : Tau2 fudge factor updates scheme. Valid values are plain or <deflate>-step. Where <deflate> is the deflate factor during initial stage.
--K (1) : Number of references to be refined
--particle_diameter (-1) : Diameter of the circular mask that will be applied to the experimental images (in Angstroms)
...
$
If you run the main relion application in an X terminal on the Linux X11 Desktop using our Open OnDemand service instead of from a command line, login node or Slurm batch job, you can also access the gui interface of the tool:
Now, in the terminal, run relion:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run relion
The Relion desktop application should appear:
As the relion application interface launches the built-in processing tools, you should ensure that your Open OnDemand session is configured with the necessary resources (CPU cores and RAM). It can also make use of Nvidia GPU cards if launched within a GPU partition and a GPU card is assigned at the point of starting your session.
For most purposes we would still normally recommend that you launch any processing via Slurm sbatch job.
Resmap
Resmap has been compiled and installed to /opt/bin inside the container. You can run it as follows:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run resmap --version
1.1.4
$
If you run resmap from an X terminal from our Open OnDemand Linux X11 Desktop instead of from a command line, login node or Slurm batch job, you can also access the gui interface of the tool:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run resmap
In addition, we have also provided the following convenience symlinks, all of which will start resmap in the same way:
/opt/bin/resmap/opt/bin/resmap.py/opt/bin/ResMap/opt/bin/ResMap.py
TEMPy 2
TEMPy is installed as a conda environment within the container by package-ccpem2 but can be started like any other command using container.run. The following command are available:
TEMPy.smocTEMPy.scccTEMPy.scoresTEMPy.loqfitribfindpdbfixer
For example, to run TEMPy.smoc:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run TEMPy.smoc
usage: Calculate SMOC scores [-h] -m MAP -p MODEL [MODEL ...] [--model-colors MODEL_COLORS [MODEL_COLORS ...]] [--model-names MODEL_NAMES [MODEL_NAMES ...]] -r RESOLUTION
--output-format OUTPUT_FORMATS [OUTPUT_FORMATS ...] [--output-prefix OUTPUT_PREFIX] [--plot-type PLOT_TYPES [PLOT_TYPES ...]]
[--plot-residue-range [PLOT_RESIDUE_RANGE ...]] [--plot-normalize PLOT_NORMALIZE] [--plot-bands [PLOT_BANDS ...]] [--plot-unmodelled PLOT_UNMODELLED]
[--smoc-window SMOC_WINDOW] [--smoc-method SMOC_METHOD]
$
TEMPy-REFF
TEMPy-REFF is installed as a conda environment within the container by package-ccpem2 but can be started like any other command using container.run. The following command are available:
tempy-reffribfindpdbfixerpdb4amber
For example, to run tempy-reff:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run tempy-reff
usage: tempy-reff [-h] --model MODEL --map MAP [--output-dir OUTPUT_DIR] [--overwrite] [--output-cif] [--platform-cpu] [--platform-cuda]
[--platform-cuda-device PLATFORM_CUDA_DEVICE] [--platform-opencl] [--platform-opencl-device PLATFORM_OPENCL_DEVICE] [--platform-precision PLATFORM_PRECISION]
[--forcefield-amber] [--forcefield-charmm] [--forcefield-additions FORCEFIELD_ADDITIONS [FORCEFIELD_ADDITIONS ...]] [--forcefield-no-solvent] [--rescale-bfactors]
[--keep-hydrogens] [--restrain-ribfind] [--restrain-ribfind-dir RESTRAIN_RIBFIND_DIR] [--restrain-rna-angles [RESTRAIN_RNA_ANGLES]]
[--restrain-rna-puckers [RESTRAIN_RNA_PUCKERS]] [--restrain-rna-torsions [RESTRAIN_RNA_TORSIONS]]
[--restrain-rna-backbone-termini [RESTRAIN_RNA_BACKBONE_TERMINI]] [--fitting-density] [--fitting-gmm] [--fitting-density-k FITTING_DENSITY_K]
[--fitting-density-blur FITTING_DENSITY_BLUR] [--fitting-gmm-k FITTING_GMM_K] [--fitting-gmm-init-steps FITTING_GMM_INIT_STEPS]
[--fitting-gmm-bfactor-steps FITTING_GMM_BFACTOR_STEPS] [--fitting-gmm-max-bfactor FITTING_GMM_MAX_BFACTOR] [--simulation-isteps SIMULATION_ISTEPS]
[--convergence-type {variance,patience}] [--convergence-timeout CONVERGENCE_TIMEOUT] [--convergence-runs CONVERGENCE_RUNS]
[--convergence-threshold CONVERGENCE_THRESHOLD] [--convergence-patience CONVERGENCE_PATIENCE] [--metric {ccc,bfactor-ccc,rmsd}] [--resolution RESOLUTION]
$
Topaz
Important!
There are two versions of topaz installed within the container. One version (/usr/local/bin/topaz) is a dependency of Relion, and the other version (/opt/package-ccpem2/setup_ext_env/conda_wrappers/topaz_env) is included by package-ccpem2.
They have different version numbers. Choose the correct one for your purposes.
Topaz as installed as a dependency for Relion can be started as follows:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run topaz --version
TOPAZ 0.2.5a
$
Topaz as installed as a conda environment by package-ccpem2 can be started as follows:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run /opt/package-ccpem2/setup_ext_env/conda_wrappers/topaz_env/topaz --version
0.3.18
$
UCSF Motioncor 3
MotionCor3 has been compiled with Nvidia CUDA GPU support and is installed to /opt/bin inside the container. You can run it as follows:
$ source /nobackup/shared/containers/ccpemsuite.2026.03.sh
$ container.run MotionCor3
-InMrc
-InTiff
-InEer
-InSuffix
-InSkips
-OutMrc
-FmIntFile
-ArcDir
-FullSum
-Gain
-Dark
-DefectFile
...
...
$
In order to use a GPU, you must allocate one in your Slurm job. See our Resources Overview guide for further information.
Accessing Data
As long as you use the container.run method to launch the applications, you will automatically be able to read and write to files in your $HOME, /scratch and /nobackup directories.
If you run any of the applications inside the container manually, without using the container.run helper you will need to use the –bind argument to apptainer to ensure that all relevant directories are exposed within the container.
Do remember that the container filesystem itself cannot be changed - so you won't be able to write or update to /usr/local, /opt, /etc or any other internal folders - keep output directories restricted to the three areas listed above.
Building CCP-EM Suite on Comet
Important!
This section is intended for RSE HPC staff, or users who are interested in how the software is configured. If you only need to use the software, stop reading here.
This table lists the major application dependencies of CCPEM. Most individual libraries are covered by packages included in Ubuntu (the base OS which this container is built on) - the list below is for software outside of the Ubuntu repositories.
| Package | Link | Wanted By | Type | Status | Notes |
|---|---|---|---|---|---|
| Relion | https://relion.readthedocs.io/en/latest/Installation.html | CCP-EM | Mandatory | Installed | The installation of Relion also includes the download of several moderately large data sets which are included in the container (ClassRanker model, Blush model weights, ModelAngelo weights, nucleotides data, nucleotides_no_seq, and a language model.) This adds approximately 12-13GB to the size of the container. Relion is also built with the optional AMD FFTW3 libraries. |
| CCP4 | https://www.ccp4.ac.uk/download/#os=linux | CCP-EM | Mandatory | Installed | |
| ARP WARP | https://www.ccp4.ac.uk/download/#os=linux | Part of CCP4 | Mandatory | Installed | |
| package-ccpem2 | https://gitlab.com/ccpem/package-ccpem2 | CCP-EM | Optional | Installed | Provides pre-configured Conda/Mamba environments of additional key tools; TEMPy2.0, RIBFIND2.0, EMDA, CheckMySequence, findMySequence, doubleHelix, Locscale, EMDB Validation Analysis, MetalCoord, TEMPy-REFF, Topaz, CryoDANN/CryoVAE, CryoDRGN. This adds another 30-35GB to the container. |
| Modeller | https://salilab.org/modeller/registration.html | CCP-EM | Optional | Installed | Free to download, but an academic license key is required. |
| CTFFind4 | https://grigoriefflab.umassmed.edu | Relion | Mandatory | Installed | |
| UCSF MotionCor3 | https://github.com/czimaginginstitute/MotionCor3 | Relion | Optional | Installed | |
| Resmap | https://downloads.sourceforge.net/project/resmap | Relion | Optional | Installed | Downloaded as a pre-built binary from the project website. |
| Miniforge | https://github.com/conda-forge/miniforge | Relion | Optional | Installed | |
| Micromamba | https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html | package-ccpem2 | Mandatory | Installed | This is only installed to allow package–ccpem2 to install and manage environments; it is not intended for general use by users of the container image. |
| PyTorch | https://github.com/pytorch/pytorch | Relion | Mandatory | Installed | A specific version is required (2.0.1) by Relion - this only works with Python ⇐ 3.11. |
| Torch Vision | https://github.com/pytorch/vision | Relion | Mandatory | Installed | A specific version is required (0.15.2) by Relion - this only works with Python ⇐ 3.11. |
| CUDA | https://developer.nvidia.com/cuda-12-8-0-download-archive | Relion, UCSF MotionCor3, PyTorch | Mandatory | Installed | Added from Nvidia repository, as Ubuntu 22 is limited to CUDA 11. |
| CUDNN | https://developer.nvidia.com/cudnn | PyTorch | Mandatory | Installed | Added from Nvidia repository, as Ubuntu 22 is limited to CUDA 11. |
| Doppio | https://www.ccpem.ac.uk/software/downloads/ | Optional | Installed | Doppio is a web interface to many of the CCP-EM v2 tools. The only means of normally launching and interacting with this toolset is if launching it within an Open OnDemand desktop session on a compute node. |
Ubuntu Linux versions
CCPEM and many of its dependencies have requirements for very specific versions of libraries and other runtime components - not too new versions of Python, quite old versions of Torch, even the Nvidia compilers complain if you use too recent a version of GCC etc. For this reason this container was built on an older Ubuntu 22 LTS image, and not Ubuntu 24 LTS.
If you try to build this container with Ubuntu 24 and all of its associated newer libraries many of the components listed above will not build correctly.
Prerequisites & Notes
- You must download the file
ccp4-9.0.014-shelx-arpwarp-linux64.tar.gzbefore running the container build. This file can be downloaded from https://www.ccp4.ac.uk/download/#os=linux - but it is subject to a license agreement, therefore we have decided to not automatically include it in the container build definition. It should be placed in the same directory as the build script and container definition file. It can be removed afterwards. - You must obtain a Academic license to install Doppio. Whilst this does not need to be entered during install in the form of a key or code, the use of the software is dependent upon the acceptance of this license.
- You must obtain a Academic license key for Salilabs Modeller, and paste the key in to the
KEY_MODELLERvariable in the container definition file. - If you are building the container then be aware that
micromambawill attempt to write some environment initialisation code your your$HOME/.bashrc- you can either leave this, or remove it afterwards. This is an artifact of the micromamber installer - the container definition duplicates this by changing the main/etc/bash.bashrcfile within the container itself.
Warning!
At least one of the Conda environments included in package-ccpem2 does not appear to build correctly. The environment in question is CryoDANN and the installation throws this error:
## Creating cryodann_env
warning libmamba Cache file "/opt/micromamba/pkgs/cache/88ec62ec.json" was modified by another program
warning libmamba Cache file "/opt/micromamba/pkgs/cache/eb045dd1.json" was modified by another program
warning libmamba You are using 'pip' as an additional package manager.
Be aware that packages installed with 'pip' are managed independently from 'conda-forge' channel.
ERROR: Ignored the following versions that require a different python version: 1.21.2 Requires-Python >=3.7,<3.11; 1.21.3 Requires-Python >=3.7,<3.11; 1.21.4 Requires-Python >=3.7,<3.11; 1.21.5 Requires-Python >=3.7,<3.11; 1.21.6 Requires-Python >=3.7,<3.11
ERROR: Could not find a version that satisfies the requirement umap (from cryovae) (from versions: none)
ERROR: No matching distribution found for umap
critical libmamba pip failed to install packages
For this reason, CryoDANN is not included in the container image.
Build script:
#!/bin/bash
IMAGE_DATE=`date +%Y.%m`
echo "Loading modules..."
module load apptainer
echo ""
echo "Building container..."
export APPTAINER_TMPDIR=/scratch
# Where downloaded package files live that are NOT installed from the internet
# during building of the container
SW_FOLDER=`pwd`
# The following packages should exist:
#
# ccp4-9.0.014-shelx-arpwarp-linux64.tar.gz - from https://www.ccp4.ac.uk/download/#os=linux
#
if [ -s "$SW_FOLDER/ccp4-9.0.014-shelx-arpwarp-linux64.tar.gz" ]
then
echo "Found $SW_FOLDER/ccp4-9.0.014-shelx-arpwarp-linux64.tar.gz"
else
echo "MISSING: $SW_FOLDER/ccp4-9.0.014-shelx-arpwarp-linux64.tar.gz"
fi
apptainer build --bind $SW_FOLDER:/mnt ccpemsuite.$IMAGE_DATE.sif ccpemsuite.def 2>&1 | tee ccpemsuite.log
Container Definition:
Bootstrap: docker
From: ubuntu:jammy
####################################################################
#
# This is a container environment for CCP-EM and all of its
# supporting software.
#
# For more details:
# https://hpc.researchcomputing.ncl.ac.uk/dokuwiki/doku.php?id=advanced:software:ccpemsuite
#
# This includes the main packages:
# ===============================
# - CCP-EM : https://www.ccpem.ac.uk/software/downloads/
# - Relion : https://relion.readthedocs.io/en/latest/Installation.html
# - CCP4 : https://www.ccp4.ac.uk/download/#os=linux
# - package-ccpem2 : https://gitlab.com/ccpem/package-ccpem2
# - Modeller : https://salilab.org/modeller/registration.html
#
# Dependencies for each package
#
# Relion 5
# ========
# - Essential : C++, MPI, cmake, CUDA SDK, CTFFIND-4.1, Ghostscript, FLTK, X11, FFTW, libtiff, libpng, pbzip2, xz, zstd, conda/miniforge
# - Optional : UCSF MotionCor2, ResMap
#
# CCP4
# ====
# Must be downloaded before building container - license must be accepted.
# 'install script' ran which sets up license agreement and serveral environment variables.
#
# - Essential : tcsh
# - Optional : No listed dependencies
#
# Package-CCPEM2
# ==============
#
# - Essential : micromamba
# - Optional :
#
# Modeller
# ========
#
# - Essential :
# - Optional :
#
# DOPPIO
# ======
#
# - Essential : fuse, libfuse2, fusermount
# - Optional :
%post
# Prevent interactive prompts
export DEBIAN_FRONTEND=noninteractive
export KEY_MODELLER=xxxxxx
# Where we build software
mkdir -p /src
mkdir -p /src/zipped
# Where we install software
mkdir -p /opt
mkdir -p /opt/bin
mkdir -p /opt/lib
mkdir -p /opt/include
mkdir -p /opt/share
# If building *on* Comet, set AMD_ARCH=1
AMD_ARCH=1
if [ "$AMD_ARCH" = "1" ]
then
# Compiling on AMD Epyc - full optimisation (slowest build time = best runtime performance)
export CFLAGS="-O3 -march=native -pipe"
export CXXFLAGS="-O3 -march=native -pipe"
else
# Generic target - no optimisation (fastest build time = slowest runtime performance)
export CFLAGS=""
export CXXFLAGS=""
fi
# Update & install only necessary packages
apt-get update
# Base stuff everything will need
echo ""
echo "INSTALL BASE OS PACKAGES"
echo "========================"
echo ""
apt-get install -y aptitude apt-file wget zip git less vim apt-utils autoconf automake build-essential cmake git less unzip vim wget python3 python3-pip
# Dependencies for CCP4
echo ""
echo "INSTALL OS PACKAGES (CCP4 DEPENDENCIES)"
echo "======================================="
echo ""
apt-get install -y \
tcsh \
expect \
curl \
qtbase5-dev \
qtbase5-dev-tools
# Dependencies for Relion
echo ""
echo "INSTALL OS PACKAGES (RELION DEPENDENCIES)"
echo "========================================="
echo ""
apt-get install -y \
libopenmpi-dev \
cmake \
libfltk1.3-dev \
libfftw3-dev \
libtiff-dev \
libpng-dev \
xz-utils \
pbzip2 \
libzstd-dev \
libxft-dev \
libx11-dev \
libwxgtk3.0-gtk3-dev \
wx-common \
wx3.0-headers \
ghostscript \
libssl-dev \
libcupti-dev \
libopenblas-dev \
libatlas-base-dev \
sphinx \
python3-yaml \
python3-typing-extensions
# Dependencies for DOPPIO
echo ""
echo "INSTALL OS PACKAGES (DOPPIO DEPENDENCIES)"
echo "========================================="
echo ""
apt-get install -y \
fuse \
libfuse2 \
libfuse-dev
# Add on CUDA 12.8 SDK and CUDNN
echo ""
echo "INSTALL NVIDIA CUDA/CUDNN SDK"
echo "============================="
echo ""
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb -O /src/zipped/cuda-keyring_1.1-1_all.deb
dpkg -i /src/zipped/cuda-keyring_1.1-1_all.deb
apt-get update
apt-get -y install cuda-toolkit-12-8
pip install nvidia-pyindex
pip install nvidia-cudnn
cd /usr/local/cuda/lib64/stubs
ln -s libcuda.so libcuda.so.1
# Remove any downloaded package files and any Python cached packages - so they don't remain in the built image
apt-get clean
# Environment variables
export PATH=/opt/bin:$PATH:/opt/miniforge3/bin:/usr/local/cuda/bin
export LD_LIBRARY_PATH=/opt/lib:$LD_LIBRARY_PATH
export CMAKE_POLICY_VERSION_MINIMUM=3.5
export TORCH_HOME=/usr/local/lib/python3.10/dist-packages/torch
export CUDACXX=/usr/local/cuda/bin/nvcc
export SHELL=/bin/bash
###########################################################
#
# !!! Non-OS software package installs below this point !!!
#
###########################################################
#######################################################
# Install CCP4 - and all related tools
#######################################################
echo ""
echo "INSTALL CCP4"
echo "============"
cd /opt
tar -zxf /mnt/ccp4-9.0.014-shelx-arpwarp-linux64.tar.gz
cd /opt/ccp4-9
# Write 'expect' script to handle the CCP4 interactive license prompt
echo "#!/usr/bin/expect" >> /opt/bin/ccp4.exp
echo 'spawn ./BINARY.setup' >> /opt/bin/ccp4.exp
echo 'expect "Do you accept the terms of the license? y/n"' >> /opt/bin/ccp4.exp
echo 'send -- "y\r"' >> /opt/bin/ccp4.exp
echo 'expect eof' >> /opt/bin/ccp4.exp
chmod +x /opt/bin/ccp4.exp
# Run CCP4 installer
/opt/bin/ccp4.exp
# Cannot use aliases in the file, as we are not (always) using bash
cp /opt/ccp4-9/bin/ccp4.setup-sh /opt/ccp4-9/bin/ccp4.setup-sh.old
cat /opt/ccp4-9/bin/ccp4.setup-sh.old | grep -v ^alias > /opt/ccp4-9/bin/ccp4.setup-sh
# Run ARP WARP installer
# Needed to find CCP4 install
export CCP4_MASTER="/opt"
export CCP4=$CCP4_MASTER/"ccp4-9"
cd /opt/arp_warp_8.0
./install.sh
#######################################################
# Install Relion 5 - and all related tools
#######################################################
echo ""
echo "INSTALL RELION"
echo "=============="
echo ""
echo ""
echo "INSTALL RELION (CTFFind4)"
echo "========================="
echo ""
cd /src
wget -q "https://grigoriefflab.umassmed.edu/system/tdf?path=ctffind-4.1.14.tar.gz&file=1" -O /src/zipped/ctffind-4.1.14.tar.gz
cd /src
tar -zxf zipped/ctffind-4.1.14.tar.gz
cd ctffind-4.1.14/
./configure --prefix=/opt
make
make install
echo ""
echo "INSTALL RELION (Motioncor)"
echo "=========================="
echo ""
cd /src
git clone https://github.com/czimaginginstitute/MotionCor3.git
cd MotionCor3
# Patch odd hardcoded CUDA locations in makefile
# Apply our own CFLAGS
# Also fix PIE bug (https://github.com/czimaginginstitute/MotionCor3/issues/11)
cp makefile11 makefile11.old
cat makefile11.old | \
sed 's|NVCC = $(CUDAHOME)/bin/nvcc|NVCC = /usr/local/cuda/bin/nvcc|g' | \
sed 's|CUDAINC = $(CUDAHOME)/include|CUDAINC = /usr/local/cuda/include|g' | \
sed 's|CUDALIB = $(CUDAHOME)/lib64|CUDALIB = /usr/local/cuda/lib|g' | \
sed 's|CFLAG =|CFLAG = ${CFLAGS} |g' | \
sed 's|@$(NVCC) -g -G -m64 $(OBJS)|@$(NVCC) -g -G -m64 -Xcompiler -no-pie $(OBJS)|g' > makefile11
make exe -f makefile11
strip -g MotionCor3
cp -v MotionCor3 /opt/bin
echo ""
echo "INSTALL RELION (Resmap)"
echo "======================="
echo ""
cd /src
wget -q https://downloads.sourceforge.net/project/resmap/ResMap-1.1.4-linux64 -O /src/zipped/ResMap-1.1.4-linux64
cp -v /src/zipped/ResMap-1.1.4-linux64 /opt/bin/ResMap
ln -s /opt/bin/ResMap /opt/bin/ResMap.py
ln -s /opt/bin/ResMap /opt/bin/resmap
ln -s /opt/bin/ResMap /opt/bin/resmap.py
chmod 755 /opt/bin/ResMap
echo ""
echo "INSTALL RELION (Miniforge)"
echo "=========================="
echo ""
wget -q https://github.com/conda-forge/miniforge/releases/download/26.1.0-0/Miniforge3-26.1.0-0-Linux-x86_64.sh -O /src/zipped/Miniforge.sh
chmod +x /src/zipped/Miniforge.sh
/src/zipped/Miniforge.sh -b -p /opt/miniforge3
conda update -n base -c conda-forge conda
# Install Relion python dependencies
# These come from relion/environment.yml and would
# normally be installed in a conda environment... but we're
# already inside a container, so it doesn't make much sense
# to do that here.
#
# Note the following bug for model-angelo (https://github.com/3dem/relion/issues/1322)
echo ""
echo "INSTALL RELION (Python modules)"
echo "==============================="
echo ""
echo "#Relion requirements
torch==2.0.1
torchvision==0.15.2
tqdm==4.65.0
mrcfile==1.4.3
starfile>=0.5.6
loguru==0.7.0
scikit-learn==1.3.0
umap-learn==0.5.3
matplotlib==3.7.2
morphosamplers==0.0.13
pydantic==1.10.18
napari[all]==0.4.18
tsnecuda==3.0.1
PyQt5==5.15.9
typer==0.9.0
click<8.2.0
biopython==1.81
fastcluster==1.2.6
seaborn==0.12.2
dill==0.3.7
numpy==1.26.1
scipy==1.11.2
git+https://github.com/3dem/relion-classranker
git+https://github.com/3dem/relion-blush
git+https://github.com/3dem/DynaMight
git+https://github.com/3dem/topaz
git+https://github.com/3dem/model-angelo@9a44a09cbff805b89220d2fbc89af8123926976c" > /src/relion_requirements.txt
pip install -r /src/relion_requirements.txt
echo ""
echo "INSTALL RELION (compile & build)"
echo "================================"
echo ""
cd /src
git clone https://github.com/3dem/relion.git
cd relion
git checkout ver5.0
mkdir -p build
cd build
if [ "$AMD_ARCH" = "1" ]
then
FFTW_LIB="-DAMDFFTW=ON"
else
FFTW_LIB=""
fi
CMAKE_C_FLAGS="-std=c99 -fopenmp $CFLAGS" CMAKE_CXX_FLAGS="-fPIC -std=c++17 -fopenmp $CXXFLAGS" cmake .. \
$FFTW_LIB -DFORCE_OWN_FFTW=ON \
-DCUDA_ARCH=89 \
-DCMAKE_INSTALL_PREFIX=/opt \
-DPYTHON_EXE_PATH=/usr/bin/python3 \
-DTORCH_HOME_PATH=/usr/local/lib/python3.10/dist-packages/torch
make
make install
#######################################################
# Install package-ccpem2
#######################################################
echo ""
echo "INSTALL PACKAGE-CCPEM2"
echo "======================"
echo ""
# Install micromamba
echo ""
echo "INSTALL PACKAGE-CCPEM2 (MICROMAMBA)"
echo "==================================="
echo ""
# Addeed cert check bypass since this is hosted on a LetsEncrypt host
wget --no-check-certificate -q https://micro.mamba.pm/install.sh -O /src/zipped/micromamba.sh
chmod +x /src/zipped/micromamba.sh
# Write an 'expect' shell script to handle the
# micromamba installer question/answer prompts
echo "#!/usr/bin/expect" >> /opt/bin/micromamba.exp
echo 'spawn /src/zipped/micromamba.sh' >> /opt/bin/micromamba.exp
echo 'expect "Micromamba binary folder?"' >> /opt/bin/micromamba.exp
echo 'send -- "/opt/bin\r"' >> /opt/bin/micromamba.exp
echo 'expect "Init shell (bash)"' >> /opt/bin/micromamba.exp
echo 'send -- "y\r"' >> /opt/bin/micromamba.exp
echo 'expect "Configure conda-forge?"' >> /opt/bin/micromamba.exp
echo 'send -- "y\r"' >> /opt/bin/micromamba.exp
echo 'expect "Prefix location?"' >> /opt/bin/micromamba.exp
echo 'send -- "/opt/micromamba\r"' >> /opt/bin/micromamba.exp
echo 'expect eof' >> /opt/bin/micromamba.exp
chmod +x /opt/bin/micromamba.exp
/opt/bin/micromamba.exp
# Install the ccpem2 packages
echo ""
echo "INSTALL PACKAGE-CCPEM2"
echo "======================"
echo ""
# This is a huge amount of extra software
# installed as conda/mamba environments (and hence loads of duplication...)
cd /opt
git clone https://gitlab.com/ccpem/package-ccpem2.git
cd package-ccpem2/setup_ext_env
./install_doppioext.sh checkmysequence emda tempy tempyreff locscale emdbva metalcoord cryodrgn topaz
########################################################
# Install Modeller
########################################################
echo ""
echo "INSTALL MODELLER"
echo "================"
echo ""
# KEY_MODELLER must be set!
wget -q https://salilab.org/modeller/10.8/modeller_10.8-1_amd64.deb -O /src/zipped/modeller_10.8-1_amd64.deb
dpkg -i /src/zipped/modeller_10.8-1_amd64.deb
########################################################
# Install Doppio
########################################################
echo ""
echo "INSTALL DOPPIO/CCP-EM v2"
echo "========================"
echo ""
wget -q https://www.ccpem.ac.uk/downloads/doppio/doppio-linux-1.5.0.sh -O /src/zipped/doppio-linux-1.5.0.sh
chmod +x /src/zipped/doppio-linux-1.5.0.sh
/src/zipped/doppio-linux-1.5.0.sh -b -m -p /opt/doppio_1.5.0
# Cannot run an .AppImage in a container, as it is not
# allowed to fusermount due to security restrictions.
# Need to extract the appimage and run from the extracted folder
cd /opt/doppio_1.5.0/app
./doppio-desktop.AppImage --appimage-extract
mv -v squashfs-root /opt/doppio_1.5.0/app/doppio-desktop-app
cd /opt/doppio_1.5.0/app/doppio-desktop-app
ln -s AppRun ./doppio-desktop
# Now we can just run 'doppio-desktop' instead
########################################################
# Install CCP-EM v1
########################################################
echo ""
echo "INSTALL CCP-EM v1"
echo "================="
wget -q https://www.ccpem.ac.uk/downloads/ccpem_v1/ccpem-20221108-linux-x86_64.tar.gz -O /src/zipped/ccpem-20221108-linux-x86_64.tar.gz
cd /opt
tar -zxf /src/zipped/ccpem-20221108-linux-x86_64.tar.gz
cd /opt/ccpem-20221108
# Do not do anything else with CCP-EM v1, as it has commands/binaries inside it which are *also*
# included in CCP4 and similar, we do not want to use older binaries from v1!
########################################################
# Create Apptainer runtime environment script
########################################################
echo ""
echo "INJECT APPTAINER INIT FILE"
echo "=========================="
echo ""
# NOTE: Creating /.singularity.d/env entries is *bad*
# ... but... this is the only way to have shell initialisation
# 'stuff' that is in effect both for an 'apptainer shell', as
# well as anything which is ran via 'apptainer exec', as
# /etc/bash.bashrc is *not* executed in 'apptainer exec' mode.
touch /.singularity.d/env/99-ccpem.sh
echo '#!/bin/sh' > /.singularity.d/env/99-ccpem.sh
# Add micromamba to paths
echo 'export MAMBA_EXE="/opt/bin/micromamba"' >> /.singularity.d/env/99-ccpem.sh
echo 'export MAMBA_ROOT_PREFIX="/opt/micromamba"' >> /.singularity.d/env/99-ccpem.sh
# Add CCP4 to paths
echo '. /opt/ccp4-9/bin/ccp4.setup-sh' >> /.singularity.d/env/99-ccpem.sh
# Add ARP/WARP to paths
echo '. /opt/arp_warp_8.0/arpwarp_setup.bash' >> /.singularity.d/env/99-ccpem.sh
# Add micromamba initialisation, but filter out zsh syntax which makes /bin/sh complain
/opt/bin/micromamba shell hook --shell bash --root-prefix "$MAMBA_ROOT_PREFIX" | grep -v COMP_LINE >> /.singularity.d/env/99-ccpem.sh
# This next line doesn't work as the script assumes bash... but Apptainer doesn't use bash to init the container
# See the Package-ccpem2 PATH variable below, as an alternative
#echo '. /opt/package-ccpem2/setup_ext_env/setup_doppioext.sh' >> /.singularity.d/env/99-ccpem.sh
chmod +x /.singularity.d/env/99-ccpem.sh
########################################################
# Final tidyup
########################################################
echo ""
echo "CLEANING UP"
echo "==========="
echo ""
# Remove src tree
rm -rf /src
# Remove pip caches
apt-get clean
# Remove any expect scripts used during install
rm -f /opt/bin/*.exp
# Remove micromamba caches
rm -rf /opt/micromamba/pkgs/cache/*
%environment
# For anything we build after the image is created using the installed compilers
# AMD/Comet
export CFLAGS="-O3 -march=native -pipe"
export CXXFLAGS="-O3 -march=native -pipe"
# Development/others
export CFLAGS=""
export CXXFLAGS=""
export PATH=/opt/bin:$PATH:/opt/miniforge3/bin:/usr/local/cuda/bin
export LD_LIBRARY_PATH=/opt/lib:/usr/local/lib:$LD_LIBRARY_PATH
export TORCH_HOME=/usr/local/lib/python3.10/dist-packages/torch
export CUDACXX=/usr/local/cuda/bin/nvcc
# CCP4
export CCP4_MASTER="/opt"
export CCP4=$CCP4_MASTER/"ccp4-9"
export CCP4I_TCLTK=$CCP4/bin
export BALBES_ROOT=$CCP4/BALBES
export GFORTRAN_UNBUFFERED_PRECONNECTED=Y
export CBIN=$CCP4/bin
export CLIB=$CCP4/lib
export CLIBD=$CCP4/lib/data
export CETC=$CCP4/etc
export CINCL=$CCP4/include
export CHTML=$CCP4/html
export CEXAM=$CCP4/examples
export CCP4I_TOP=$CCP4/share/ccp4i
export MMCIFDIC=$CLIB/ccp4/cif_mmdic.lib
export CRANK=$CCP4I_TOP/crank
export CLIBD_MON=$CCP4/lib/data/monomers/
export CCP4_HELPDIR=$CCP4/help/
export CCP4_SCR="/scratch/ccp4_$USER"
export PDB_EXTRACT=${CCP4}/share
export CCP4_OPEN=UNKNOWN
export PATH=$CCP4/etc:$CCP4/bin:$PATH
export MANPATH=$CCP4/share/man:$MANPATH
# Package-ccpem2
export PATH=$PATH:/opt/package-ccpem2/setup_ext_env/conda_wrappers/cryodrgn_env
export PATH=$PATH:/opt/package-ccpem2/setup_ext_env/conda_wrappers/topaz_env
export PATH=$PATH:/opt/package-ccpem2/setup_ext_env/conda_wrappers/metalcoord_env
export PATH=$PATH:/opt/package-ccpem2/setup_ext_env/conda_wrappers/emdbva_env
export PATH=$PATH:/opt/package-ccpem2/setup_ext_env/conda_wrappers/locscale_env
export PATH=$PATH:/opt/package-ccpem2/setup_ext_env/conda_wrappers/tempyreff_env
export PATH=$PATH:/opt/package-ccpem2/setup_ext_env/conda_wrappers/tempy_env
export PATH=$PATH:/opt/package-ccpem2/setup_ext_env/conda_wrappers/emda_env
export PATH=$PATH:/opt/package-ccpem2/setup_ext_env/conda_wrappers/checkmysequence_env
# ARP WARP
export warpbin=/opt/arp_warp_8.0/bin/bin-x86_64-Linux
export warpdoc=/opt/arp_warp_8.0/manual
export PATH=${warpbin}:$PATH
# Modeller
export KEY_MODELLER=xxxxxx
# Micromamba
export MAMBA_EXE="/opt/bin/micromamba"
export MAMBA_ROOT_PREFIX="/opt/micromamba"
# Doppio
export PATH=/opt/doppio_1.5.0/app:/opt/doppio_1.5.0/app/doppio-desktop-app:$PATH
%runscript
Run Script:
#!/bin/bash
module load apptainer
IMAGE_NAME=/nobackup/shared/containers/ccpemsuite.2026.03.sif
container.run() {
# Run a command inside the container...
# automatically bind the /scratch and /nobackup dirs
# pass through any additional parameters given on the command line
apptainer exec --nv --bind /scratch:/scratch --bind /nobackup:/nobackup ${IMAGE_NAME} $@
}