Packages integrated in Scipion

This is a dynamic list of methods grouped by package that are present in Scipion. Some of them might come from a development environment and soon will be released.


Short-cuts

Appion Atomstructutils Atsas Bamfordlab BSOFT CCP4 Chimerax Cistem Continuousflex Cryoef Cryomethods Cryosparc2 Deepfinder Dials Dynamo Eman2 Emfacilities Empiar Emxlib ESRF Fsc3d Gautomatch gCTF Grigoriefflab Imagic Imod In development ispybmonitor Legacy Localrec Locscale Motioncorr NovaCtf Phenix Pkpd Powerfit_scipion pwed pwem Pyseg pyworkflowtests Relion RelionTomo Resmap Sidesplitter Simple Sphire Spider Tomo Tomoj Topaz Xmipp2 Xmipp3 Xmipptomo

Packages


icon for Appion Appion

Plugin url https://github.com/scipion-em/scipion-em-appion.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

dogpicker (Particle picking)
Protocol to pick particles in a set of micrographs using appion dogpicker.

Contributors:


icon for Atomstructutils Atomstructutils

Plugin url https://github.com/scipion-em/scipion-em-atomstructutils.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

convert_sym
In development
operator
Utilities for handling PDB/mmcif atomic structure files.Current plugin utilities: (A) extract a chain from an atom structure (pdb/cif file),(B) perform union of several atomic structures

Contributors:


icon for Atsas Atsas

Plugin url https://github.com/scipion-em/scipion-em-atsas.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

convert PDB to SAXS curve
Protocol for converting a PDB file (true atoms or pseudoatoms) into a SAXS curve. This is actually a wrapper to the program Crysol from Atsas. See documentation at: http://www.embl-hamburg.de/biosaxs/manuals/crysol.html

Contributors:


icon for Bamfordlab Bamfordlab

Plugin url https://github.com/scipion-em/scipion-em-bamfordlab.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

ethan picker (Particle picking)
ETHAN is a program for automatic detection of spherical particles from electron micrographs. The ETHAN software was written at the Department of Computer Science of University of Helsinki, Finland by Teemu Kivioja.

Contributors:


icon for BSOFT BSOFT

Plugin url https://github.com/scipion-em/scipion-em-bsoft.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

bfilter
Wrapper around bfilter program of BSOFT.
blocres (Local resolution)
Bsoft program: blocres It calculates the local resolution map from to half maps. The method is based on a local measurement inside a mobile window.
particle picking (Particle picking)
Protocol to pick particles in a set of micrographs using bsoft

Contributors:


icon for CCP4 CCP4

Plugin url https://github.com/scipion-em/scipion-em-ccp4.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

CCP4ProtCoot
Description missing, might be a new development.
coot refinement
Coot is an interactive graphical application formacromolecular model building, model completionand validation. IMPORTANT: press "w" in coot to transferthe pdb file from coot to scipion '
refmac
Automatic refinement program in Fourier space of macromolecule structures regarding electron density maps. Generates files for volumes and FSCs to submit structures to EMDB

Contributors:


icon for Chimerax Chimerax

This plugin allows to use chimeraX commands within the Scipion framework.

Plugin url https://github.com/scipion-em/scipion-em-chimera.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

contacts
Identifies interatomic clashes and contacts based on van der Waals radii
map subtraction
Protocol to subtract two volumes. One of these volumes can be derived from an atomic structure. Execute command *scipionwrite #n [prefix stringAddedToFilename]* from command line in order to transfer the generated maps and models to scipion. In addition to maps and models that the protocol saves by default, the user can generate and save some others
model from template
Protocol to model three-dimensional structures of proteins using Modeller. Execute command *scipionwrite #n [prefix stringAddedToFilename] from command line in order to transfer the selected pdb to scipion. Default value is model=#0, model refers to the pdb file.
operate
This protocol provides access to Chimera and allows to save the result in Scipion framework. Execute command *scipionwrite #n [prefix stringAddedToFilename] model refers to the pdb file
restore session
This protocol opens Chimera and restores a session that has been stored each time a 3Dmap or an atomic structure by using `scipionwrite` or `scipionss` commad. Execute command *scipionwrite #n [prefix stringAddedToFilename] model refers to the pdb file
rigid fit
Protocol to perform rigid fit using Chimera. Execute command *scipionwrite #n [prefix stringAddedToFilename] model refers to the pdb file

Contributors:

Missing information!, but be sure someone has done it.

icon for Cistem Cistem

Plugin url https://github.com/scipion-em/scipion-em-cistem.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

classify 2D (2D classification)
Protocol to run 2D classification in cisTEM.
ctffind4 (CTF estimation)
Estimates CTF for a set of micrographs/movies with ctffind4. To find more information about ctffind4 go to: https://grigoriefflab.umassmed.edu/ctffind4
find particles (Particle picking)
Protocol to pick particles in a set of micrographs using cisTEM.
tiltseries ctffind (CTF estimation)
CTF estimation on Tilt-Series using CTFFIND4.
unblur (Movie alignment)
This protocol wraps unblur movie alignment program. More information at https://cistem.org/documentation

Contributors:


icon for Continuousflex Continuousflex

Plugin url https://github.com/scipion-em/scipion-em-continuousflex.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

FlexBatchProtNMAClusterVol
Description missing, might be a new development.
FlexProtAlignmentNMAVol
Description missing, might be a new development.
FlexProtDimredNMAVol
Description missing, might be a new development.
convert a PDB
Convert a PDB file into a volume.
convert to pseudoatoms
Converts an EM volume into pseudoatoms
nma alignment
Protocol for flexible angular alignment.
nma analysis
Flexible angular alignment using normal modes
nma cluster
Protocol executed when a cluster is created from NMA images and theirs deformations.
nma dimred
This protocol will take the images with NMA deformations as points in a N-dimensional space (where N is the number of computed normal modes) and will project them in a reduced spaced (usually with less dimensions).
structure mapping
A quantitive analysis of dissimilarities (distances) among the EM maps that placing the entire set of density maps in to a common space of comparison.The approach is based on statistical analysis of distance among elastically aligned EM maps, and results in visualizing those maps as points in a lower dimensional distance space.

Contributors:


Cryoef

Plugin url https://github.com/scipion-em/scipion-em-cryoef.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

orientation analysis
Protocol for analysing the orientation distribution of single-particle EM data. Find more information at http://www.mrc-lmb.cam.ac.uk/crusso/cryoEF/

Contributors:


Cryomethods

Plugin url https://github.com/mcgill-femr/scipion-em-cryomethods.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

ProtAutoClassifier
Description missing, might be a new development.
2D auto classifier (2D classification)
Description missing, might be a new development.
3D auto classifier (3D classification)
Description missing, might be a new development.
directional_pruning
Analyze 2D classes as assigned to the different directions
volume selector
Protocol to obtain a better initial volume using as input a set of volumes and particles. The protocol uses a small subset (usually 1000/2000) particles per classfrom the input set of particles to estimate a better and reliable volume(s) to use as initial volume in an automatic way.

Contributors:

Missing information!, but be sure someone has done it.

icon for Cryosparc2 Cryosparc2

Plugin url https://github.com/scipion-em/scipion-em-cryosparc2.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

2d classification (2D classification)
Wrapper to CryoSparc 2D clustering program. Classify particles into multiple 2D classes to facilitate stack cleaning and removal of junk particles. Also useful as a sanity check to investigate particle quality.
3D classification (3D classification)
Heterogeneous Refinement simultaneously classifies particles and refines structures from n initial structures, usually obtained following an Ab-Initio Reconstruction. This facilitates the ability to look for small differences between structures which may not be obvious at low resolutions, and also to re-classify particles to aid in sorting.
3D homogeneous refinement (3D refinement)
Protocol to refine a 3D map using cryosparc. Rapidly refine a single homogeneous structure to high-resolution and validate using the gold-standard FSC.
3D non-uniform refinement (3D refinement)
Apply non-uniform refinement to achieve higher resolution and map quality, especially for membrane proteins. Non-uniform refinement iteratively accounts for regions of a structure that have disordered or flexible density causing local loss of resolution. Accounting for these regions and dynamically estimating their locations can significantly improve resolution in other regions as well as overall map quality by impacting the alignment of particles and reducing the tendency for refinement algorithms to over-fit disordered regions.
Local refinement
Signal subtraction protocol of cryoSPARC. Subtract projections of a masked volume from particles.
global ctf refinement(BETA)
Wrapper protocol for the Cryosparc's per-particle Global CTF refinement. Performs per-exposure-group CTF parameter refinement of higher-order aberrations, against a given 3D reference
initial model (Initial model)
Generate a 3D initial model _de novo_ from 2D particles using CryoSparc Stochastic Gradient Descent (SGD) algorithm.
local ctf refinement(BETA)
Wrapper protocol for the Cryosparc's per-particle Local CTF refinement. Performs per-particle defocus estimation for each particle in a dataset, against a given 3D reference structure.
sharppening
Wrapper protocol for the Cryosparc's to calculate the sharpened map.
subtract projection
Signal subtraction protocol of cryoSPARC. Subtract projections of a masked volume from particles.

Contributors:


icon for Deepfinder Deepfinder

Plugin url https://github.com/scipion-em/scipion-em-deepfinder.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

annotate
This protocol allows you to annotate macromolecules in your tomograms, using a visual tool.
cluster
This protocol analyses segmentation maps and outputs particle coordinates and class.
display volume
This protocol allows you to explore tomograms or segmentation maps with ortho-slices. The seegmentation map can be superimposed to the tomogram. Useful for visualising your results.
generate sphere target
This protocol generates segmentation maps from annotations. These segmentation maps will be used as targets to train DeepFinder
segment
This protocol segments tomograms, using a trained neural network.
train
This protocol launches the training procedure

Contributors:

Missing information!, but be sure someone has done it.

icon for Dials Dials

scipion-ed-dials is the Scipion-ED plugin to use programs from DIALS for Electron Diffraction image processing.

Plugin url https://github.com/scipion-ed/scipion-ed-dials.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

DialsProtExport (Export)
Description missing, might be a new development.
DialsProtFindSpots
Description missing, might be a new development.
DialsProtImportDiffractionImages (Import)
Description missing, might be a new development.
DialsProtIndexSpots
Description missing, might be a new development.
DialsProtIntegrateSpots
Description missing, might be a new development.
DialsProtRefineSpots
Description missing, might be a new development.
DialsProtScaling
Description missing, might be a new development.
DialsProtSymmetry
Description missing, might be a new development.

Contributors:

Missing information!, but be sure someone has done it.

icon for Dynamo Dynamo

Plugin url https://github.com/scipion-em/scipion-em-dynamo.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

Subtomogram alignment
This protocol will align subtomograms using Dynamo MRA Subtomogram Averaging
import subtomos from Dynamo (Import)
This protocol imports subtomograms with metadata generated by a Dynamo table. A Dynamo catalogue can be also imported in order to relate subtomograms with their original tomograms.
model manager
Model manger from Dynamo for Mesh creation. After opening the desired Tomogram, a surface model will be created and loaded in Dynamo automatically. Once the points of the Mesh have been defined, close Dynamo to save automatically your data. If a dialog asking to keep the models loaded in memory appears, click on 'Keep it!!' or close the dialog.
vectorial extraction
Extraction of subtomograms using Dynamo
vectorial picking
Manual vectorial picker from Dynamo. After choosing the Tomogram to be picked, the tomo slicer from Dynamo will be direclty loaded with all the models previously saved in the disk (if any). After picking, it is needed to go to: Active Model > Step-by-step workflow for cropping geometry And click -run all- button before closing the window

Contributors:


icon for Eman2 Eman2

Plugin url https://github.com/scipion-em/scipion-em-eman2.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

EmanProtBoxerNew (Particle picking)
Description missing, might be a new development.
EmanProtEvalRefine
In development
EmanProtHelixBoxer (Particle picking)
Description missing, might be a new development.
EmanProtLocScale (Local resolution)
Description missing, might be a new development.
EmanProtTempMatch (Particle picking)
In development, Eman template matching method for tomography
boxer (Particle picking)
Semi-automated particle picker for SPA. Uses EMAN2 e2boxer.py.
boxer auto (Particle picking)
Automated particle picker for SPA. Uses EMAN2 (versions 2.2+) e2boxer.py
ctf auto (CTF estimation)
This protocol wraps *e2ctf_auto.py* EMAN2 program. It automates the CTF fitting and structure factor generation process.
initial model (Initial model)
This protocol wraps *e2initialmodel.py* EMAN2 program. It will take a set of class-averages/projections and build a set of 3-D models suitable for use as initial models in single particle reconstruction. The output set is theoretically sorted in order of quality (best one is numbered 1), though it's best to look at the other answers as well. See more details in: http://blake.bcm.edu/emanwiki/EMAN2/Programs/e2initialmodel
initial model SGD (Initial model)
This protocol wraps *e2initialmodel_sgd.py* EMAN2 program. This program makes initial models using a (kind of) stochastic gradient descent approach. It is recommended that the box size of particles is around 100.
reconstruct (3D reconstruction)
This protocol wraps *e2make3d.py* EMAN2 program. Reconstructs 3D volumes using a set of 2D images. Euler angles are extracted from the 2D image headers and symmetry is imposed. Several reconstruction methods are available. The fourier method is the default and recommended reconstructor.
refine 2D (2D classification)
This protocol wraps *e2refine2d.py* EMAN2 program. This program is used to produce reference-free class averages from a population of mixed, unaligned particle images. These averages can be used to generate initial models or assess the structural variability of the data. They are not normally themselves used as part of the single particle reconstruction refinement process, which uses the raw particles in a reference-based classification approach. However, with a good structure, projections of the final 3-D model should be consistent with the results of this reference-free analysis. This program uses a fully automated iterative alignment/MSA approach. You should normally target a minimum of 10-20 particles per class-average, though more is fine. Default parameters should give a good start, but are likely not optimal for any given system. Note that it does have the --parallel option, but a few steps of the iterative process are not parallellised, so don't be surprised if multiple cores are not always active.
refine 2D bispec (2D classification)
This protocol wraps *e2refine2d_bispec.py* EMAN2 program. This program is used to produce reference-free class averages from a population of mixed, unaligned particle images. These averages can be used to generate initial models or assess the structural variability of the data. They are not normally themselves used as part of the single particle reconstruction refinement process, which uses the raw particles in a reference-based classification approach. However, with a good structure, projections of the final 3-D model should be consistent with the results of this reference-free analysis. This variant of the program uses rotational/translational invariants derived from the bispectrum of each particle.
refine easy (3D refinement)
This protocol wraps *e2refine_easy.py* EMAN2 program.This is the primary single particle refinement program in EMAN2.1+.It replaces earlier programs such as e2refine.py and e2refine_evenodd.py.Major features of this program: * While a range of command-line options still exist. You should not normally specify more than a few basic requirements. The rest will be auto-selected for you. * This program will split your data in half and automatically refine the halves independently to produce a gold standard resolution curve for every step in the refinement. * An HTML report file will be generated as this program runs, telling you exactly what it decided to do and why, as well as giving information about runtime, etc while the job is still running. * The gold standard FSC also permits us to automatically filter the structure at each refinement step. The resolution you specify is a target, NOT the filter resolution.
sparx gaussian picker (Particle picking)
Automated particle picker for SPA. Uses Sparx gaussian picker. For more information see http://sparx-em.org/sparxwiki/e2boxer
subtomogram refinement (3D refinement)
This protocol wraps *e2spt_refine.py* EMAN2 program. Protocol to performs a conventional iterative subtomogram averaging using the full set of particles. It will take a set of subtomograms (particles) and a subtomogram(reference, potentially comming from the initial model protocol) and 3D reconstruct a subtomogram. It also builds a set of subtomograms that contains the original particles plus the score, coverage and align matrix per subtomogram .
template matching
This protocol wraps *e2spt_tempmatch.py* EMAN2 program. It will perform a sweep of an initial volume against a tomogram to find correlation peaks and extract the corresponding subtomogram coordinates
tilt validate
This protocol wraps the *e2tiltvalidate.py* EMAN2 program. It performs tilt validation using the method described in Rosenthal and Henderson, JMB (2003).
tomo boxer (Particle picking)
Manual picker for Tomo. Uses EMAN2 e2spt_boxer.py.
tomo extraction
Extraction for Tomo. Uses EMAN2 e2spt_boxer_old.py.
tomo initial model (Initial model)
This protocol wraps *e2spt_sgd.py* EMAN2 program. It will take a set of subtomograms (particles) and a subtomogram(reference) and build a subtomogram suitable for use as initial models in tomography. It also builds a set of subtomograms that contains the original particles plus the score, coverage and align matrix per subtomogram .
tomo reconstruction (3D reconstruction)
This protocol wraps *e2tomogram.py* EMAN2 program. Alignment of the tilt-series is performed iteratively in conjunction with tomogram reconstruction. Tomograms are not normally reconstructed at full resolution, generally limited to 1k x 1k or 2k x 2k, but the tilt-series are aligned at full resolution. For high resolution subtomogram averaging, the raw tilt-series data is used, based on coordinates from particle picking in the downsampled tomograms. On a typical workstation reconstruction takes about 4-5 minutes per tomogram.

Contributors:


icon for Emfacilities Emfacilities

This plugin allows to use different utils for cryo-EM facilities (like monitors) within the Scipion framework.

Plugin url https://github.com/scipion-em/scipion-em-facilities.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

2d streamer
This protocol will monitor an input set of particles (usually in streaming) and will run/schedule many copies of a given 2D classification protocol but using subsets of the input particles as the 2D classification input.
ctf monitor
check CPU, mem and IO usage.
monitor summary
Provide some summary of the basic steps of the Scipion-Box: - Import movies - Align movies (global and/or local) - CTF estimation - Movie gain estimation.
movie gain monitor
check CPU, mem and IO usage.
system_monitor
check CPU, mem and IO usage.

Contributors:

Missing information!, but be sure someone has done it.

icon for Empiar Empiar

This project is a Scipion plugin to make depositions to https://www.ebi.ac.uk/pdbe/emdb/empiar

Plugin url https://github.com/scipion-em/scipion-em-empiar.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

EmpiarDepositor
Description missing, might be a new development.

Contributors:


icon for Emxlib Emxlib

Plugin url https://github.com/scipion-em/scipion-em-emxlib.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

emx export (Export)
Export micrographs, coordinates or particles to EMX format. EMX is a joint initiative for data exchange format between different EM software packages.

Contributors:


ESRF

ISPyB monitor for facility ESRF

Plugin url https://github.com/scipion-em/scipion-em-esrf.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

ProtMonitorISPyB_ESRF_dev
Description missing, might be a new development.
monitor to ISPyB at the ESRF
Monitor to communicated with ISPyB system at ESRF.

Contributors:


icon for Fsc3d Fsc3d

Plugin url https://github.com/scipion-em/scipion-em-fsc3d.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

estimate resolution (Local resolution)
Protocol to calculate 3D FSC. 3D FSC is software tool for quantifying directional resolution using 3D Fourier shell correlation volumes. Find more information at https://github.com/nysbc/Anisotropy

Contributors:


Gautomatch

Plugin url https://github.com/scipion-em/scipion-em-gautomatch.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

auto-picking (Particle picking)
Automated particle picker for SPA. Gautomatch is a GPU accelerated program for accurate, fast, flexible and fully automatic particle picking from cryo-EM micrographs with or without templates.

Contributors:


gCTF

Plugin url https://github.com/scipion-em/scipion-em-gctf.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

ctf estimation (CTF estimation)
Estimates CTF on a set of micrographs using Gctf. To find more information about Gctf go to: https://www2.mrc-lmb.cam.ac.uk/research/locally-developed-software/zhang-software/#gctf
ctf refinement (CTF estimation)
Refines local CTF of a set of particles using Gctf. To find more information about Gctf go to: https://www2.mrc-lmb.cam.ac.uk/research/locally-developed-software/zhang-software/#gctf
tiltseries gctf (CTF estimation)
CTF estimation on Tilt-Series using GCTF.

Contributors:


icon for Grigoriefflab Grigoriefflab

DEPRECATED! Plugin to use Grigorieff Lab (not CisTEM) programs within the Scipion framework.

Plugin url https://github.com/scipion-em/scipion-em-grigoriefflab.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

ctffind4 (CTF estimation)
Estimates CTF on a set of micrographs using either ctffind3 or ctffind4 program. To find more information about ctffind4 go to: http://grigoriefflab.janelia.org/ctffind4
ctftilt (CTF estimation)
Estimates CTF on a set of tilted micrographs using ctftilt program.
frealign
Protocol to refine a 3D map using Frealign. The algorithms implementedare optimized to perform efficiently the correction for the contrasttransfer function of the microscope and refinement of three-dimensionalreconstructions.
frealign classify (3D classification)
Protocol to classify 3D using Frealign. Frealign employsmaximum likelihood classification for single particle electroncryo-microscopy.Particle alignment parameters are determined by maximizing ajoint likelihood that can include hierarchical priors, whileclassification is performed by expectation maximization of amarginal likelihood.
mag distortion correct
This program automatically corrects anisotropic magnification distortion using previously estimated parameters. It works on a set of movies.
mag distortion correct (coords)
This program automatically corrects anisotropic magnification distortion using previously estimated parameters. It works on a set of coordinates.
mag distortion estimate
This program automatically estimates anisotropic magnification distortion from a set of images of a standard gold shadowed diffraction grating
summovie (Movie alignment)
Summovie generates frame sums that can be used in subsequent image processing steps and optionally applies an exposure-dependent filter to maximize the signal at all resolutions in the frame averages.
unblur (Movie alignment)
Unblur is used to align the frames of movies recorded on an electron microscope to reduce image blurring due to beam-induced motion.

Contributors:


Imagic

This plugin includes two protocols to provide wrappers around Multivariate Statistical Analysis (MSA) module of IMAGIC software suite

Plugin url https://github.com/scipion-em/scipion-em-imagic.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

msa (2D classification)
This protocols wraps MSA-RUN program of IMAGIC. It calculates eigenimages (eigenvectors) and eigenvalues of a set of input aligned images using an iterative eigenvector algorithm optimized for (extremely) large data sets.
msa-classify (2D classification)
This protocols wraps MSA-CLASSIFY program of IMAGIC. It is based on variance-oriented hierarchical ascendant classification program (an enhanced Ward-type algorithm).

Contributors:


Imod

Plugin url https://github.com/scipion-em/scipion-em-imod.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

CTF correction
CTF correction of a set of input tilt-series using the IMOD procedure. More info: https://bio3d.colorado.edu/imod/doc/man/ctfphaseflip.html
CTF estimation (CTF estimation)
CTF estimation of a set of input tilt-series using the IMOD procedure. More info: https://bio3d.colorado.edu/imod/doc/man/ctfplotter.html
apply transformation
Compute the interpolated tilt-series from its transform matrix. More info: https://bio3D.colorado.edu/imod/doc/etomoTutorial.html
etomo interactive
Simple wrapper around etomo to manually reconstruct a Tomogram. More info: https://bio3d.colorado.edu/imod/doc/etomoTutorial.html
fiducial alignment
Construction of a fiducial model and alignment of tilt-series based on the IMOD procedure. More info: https://bio3D.colorado.edu/imod/doc/etomoTutorial.html
tilt-series normalization
Normalize input tilt-series and change its storing formatting. More info: https://bio3D.colorado.edu/imod/doc/etomoTutorial.html
tomo normalization
Normalize input tomogram and change its storing formatting. More info: https://bio3D.colorado.edu/imod/doc/etomoTutorial.html
tomo reconstruction
Tomogram reconstruction procedure based on the IMOD procedure. More info: https://bio3d.colorado.edu/imod/doc/man/tilt.html
xcorr prealignment
Tilt-series' cross correlation alignment based on the IMOD procedure. More info: https://bio3d.colorado.edu/imod/doc/etomoTutorial.html

Contributors:


In development

Methods that are currently in development

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

BatchProtNMAClusterVol
Description missing, might be a new development.
ProtAbInitio3d
Description missing, might be a new development.
ProtCenterSimple
Description missing, might be a new development.
ProtCleanup2D
Description missing, might be a new development.
ProtCluster2D
Description missing, might be a new development.
ProtCluster3D
Description missing, might be a new development.
ProtCNN
Description missing, might be a new development.
ProtDCTF
Description missing, might be a new development.
ProtInit3D
Description missing, might be a new development.
ProtLandscapeNMA
Description missing, might be a new development.
ProtLandscapePCA
Description missing, might be a new development.
ProtLocalizedMultibodyMask
Description missing, might be a new development.
ProtM2PC
Description missing, might be a new development.
ProtMaskSimple (Masking)
Description missing, might be a new development.
ProtMPCA
Description missing, might be a new development.
ProtRef3D
Description missing, might be a new development.
ProtRefi3D
Description missing, might be a new development.
ProtSCNN
Description missing, might be a new development.
ProtSym
Description missing, might be a new development.
ProtTSNE
Description missing, might be a new development.
XmippProtCompareLocalCTF
Description missing, might be a new development.

Contributors:

Missing information!, but be sure someone has done it.

icon for ispybmonitor ispybmonitor

ISPyB monitor for scipion

Plugin url https://github.com/scipion-em/scipion-em-ispyb.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

ProtMonitorISPyB
Description missing, might be a new development.

Contributors:


Legacy

A "bag" for protocols that has been deprecated

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

EMProtocol
Actually comming from tests.
ProtGemPicker (Particle picking)
Description missing, might be a new development.
ProtMicExam
Deprecated
Protocols that were renamed, deprecated or not found in old Scipion versions
Description missing, might be a new development.

Contributors:

Missing information!, but be sure someone has done it.

icon for Localrec Localrec

Plugin url https://github.com/scipion-em/scipion-em-localrec.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

Set origin to subvolume
Set the origin and sampling values assigned to a 3D map so that the subvolume fits the original, larger volume
define subparticles (3D refinement)
This class contains a re-implementation to a method for the localized three-dimensional reconstruction of such subunits. After determining the particle orientations, local areas corresponding to the subunits can be extracted and treated as single particles.
extract subparticles
Extract computed sub-particles from a SetOfParticles.
filter subparticles
This protocol mainly filters output particles from two protocols: extend symmetry and localized subparticles. It can filter the particles (sub-particles) according to spatial distance, view, and angular distance.
particles subset by subparticles
This protocol make a subset of particles for which there is at least one sub-particle.
stitch subvolumes
Generate a full volume from a sub-volume applying a point group symmetry operation. An example of usage is to generate the adenovirus Capsid from its assymetric unit.

Contributors:


icon for Locscale Locscale

Plugin url https://github.com/scipion-em/scipion-em-locscale.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

local sharpening
This protocol computes contrast-enhanced cryo-EM maps by local amplitude scaling using a reference model.

Contributors:


Motioncorr

Plugin url https://github.com/scipion-em/scipion-em-motioncorr.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

movie alignment (Movie alignment)
This protocol wraps motioncor2 movie alignment program developed at UCSF. Motioncor2 performs anisotropic drift correction and dose weighting (written by Shawn Zheng @ David Agard lab)

Contributors:


NovaCtf

Plugin url https://github.com/scipion-em/scipion-em-novaCtf.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

tomo ctf defocus
Defocus estimation of each tilt-image procedure based on the novaCTF procedure. More info: https://github.com/turonova/novaCTF
tomo ctf reconstruction
Tomogram reconstruction with ctf correction procedure based on the novaCTF procedure. More info: https://github.com/turonova/novaCTF
tomo ctf reconstruction
Tomogram reconstruction and ctf correction procedure based on the novaCTF procedure. More info: https://github.com/turonova/novaCTF

Contributors:


icon for Phenix Phenix

Plugin url https://github.com/scipion-em/scipion-em-phenix.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

PhenixProtAutomatedSharpening
Description missing, might be a new development.
dock in map
Docking of a PDB (one or several copies) into a map
emringer
EMRinger is a Phenix application to validate the agreement betweenthe initial map and the derived low-resolution atomic structure. This programsamples the density around Chi1 angles of protein sidechains. Electronicdensity and appropriate rotameric angles must coincide for each residue ifthe atomic structure backbone has been perfectly fitted to the map.
molprobity
MolProbity is a Phenix application to validate the geometry of anatomic structure inferred from an electron density map.
real space refine
Tool for extensive real-space refinement of an atomic structure against the map provided. The map can be derived from X-ray or neutron crystallography, or cryoEM. The program obtains a model that fits the map as well as possible having appropriate geometry. The model should not show validation outliers, such as Ramachandran plot or rotamer outliers.
refinementBase
MolProbity is a Phenix application to validate the geometry of anatomic structure derived from a cryo-EM density map.
superpose pdbs
Superpose two PDBs so that they optimally match
validation_cryoem
MolProbity is a Phenix application to validate the geometry of anatomic structure inferred from an electron density map.

Contributors:


Pkpd

Plugin url https://github.com/cossorzano/scipion-pkpd.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

ProtPKPDImportFromTable (Import)
Description missing, might be a new development.
Exp1 SubGr2 Mean
Compare two means from two subgroups from the same experiment . Protocol created by http://www.kinestatpharma.com
Exp2 SubGr2 Kolmogorov
Check if two distributions come from the same distribution using the Kolmogorov Smirnov test. Protocol created by http://www.kinestatpharma.com
Exp2 SubGr2 Mean
Compare two means from two subgroups from the same experiment . Protocol created by http://www.kinestatpharma.com
Mahalanobis
Experiment 1 defines the mean and covariance for the Mahalanobis distance. Then, the Mahalanobis distance of all elements in Experiment 1 with respect to the mean is calculated If a second experiment is given, then all distances from the second to the mean of the first experiment are also calculated. Protocol created by http://www.kinestatpharma.com
ODE bootstrap
Bootstrap of an ODE protocol
ODE refinement
Refinement of an ODE protocol. The parameters are reestimated with a finer sampling rate.
PK simulate
Simulate a population of ODE parameters. These parameters can be specifically given or from a bootstrap population
absorption rate
Estimation of the absorption rate for a non-intravenous route. The estimation is performed after estimating the elimination rate. The experiment is determined by the Protocol created by http://www.kinestatpharma.com. See the theory at http://www.pharmpress.com/files/docs/Basic%20Pharmacokinetics%20sample.pdf
allometric scaling
Compute the allometric scaling between several spicies. This fits a model of the form Y=k*X^a where X can be any variable (although weight is normally used). The protocol can also leave out some parameters of the input model, and for these parameters, a simple average is calculated.
apply allometric
Apply an allometric scaling previously calculated to an incoming experiment. The labels specified by the allometric scaling model will be rescaled to the target weight. Note that depending on the exponent of the fitting you may want to use a different predictor (weight*maximum lifespan potential, or weight*brain weight) see the rule of exponents (Mahmood and Balian 1996).
average sample
Produce an experiment with a single sample whose value is the average of all the input samples. Protocol created by http://www.kinestatpharma.com
bioavailability nca
Estimate bioavailability as F=(AUCpo/Dpo) / (AUCiv/Div) [Gabrielsson 2010, p. 546], i.e., the ratio between the oral and intravenous AUC normalized by their respective doses. Protocol created by http://www.kinestatpharma.com
change units
Change units of a given variable. Protocol created by http://www.kinestatpharma.com
change via
Change via of administration This protocol may also be used to change the bioavailability or the tlag Protocol created by http://www.kinestatpharma.com
compare experiments
This protocol compares two experiments by plotting a summary of both. Protocol created by http://www.kinestatpharma.com
create experiment
Create experiment. Protocol created by http://www.kinestatpharma.com
create label
Create label by performing calculations on already existing labels. Protocol created by http://www.kinestatpharma.com
create label 2 Exps
Create label by performing calculations on already existing labels from two different experiments. The protocol assumes that the same samples are present in both experiments and calculations are performed only on those samples with the same name in both experiments. Protocol created by http://www.kinestatpharma.com
cumulated dose
Create label with the cumulated dose between two time points. Protocol created by http://www.kinestatpharma.com
deconvolution Loo-Riegelman
Calculate the absorption profile of an in vivo concentration profile using the Loo-Riegelman approach. This is only valid for profiles that have been modelled with a two-compartments PK model. The formula is Fabs(t)=(Cp(t)+Cperipheral(t)+K10*AUC0t(t))/(K10*AUC0inf) where K10=Cl/V and Cperipheral(t_n)=k12*Delta Cp*Delta t/2+k12/k21 * Cp(t_n-1)(1-exp(-k21*Delta t)+Cperipheral(t_n-1)*exp(-k21*Delta t) In this implementation it is assumed that AUC0inf is the last AUC0t observed, meaning that Cp(t) has almost vanished in the last samples. This protocol is much more accurate when the input Cp(t) is reinterpolated to a small time step like 0.5 minutes. Reference: Leon Shargel, Susanna Wu-Pong, Andrew B.C. Yu. Applied Biopharmaceutics & Pharmacokinetics, 6e. McGraw Hill, 1999. Chap. 7
deconvolution Wagner Nelson
Calculate the absorption profile of an in vivo concentration profile using the Wagner-Nelson approach. This is only valid for profiles that have been modelled with a monocompartment PK model. The formula is Fabs(t)=(Cp(t)+Ke*AUC0t(t))/(Ke*AUC0inf) where Ke=Cl/V In this implementation it is assumed that AUC0inf is the last AUC0t observed, meaning that Cp(t) has almost vanished in the last samples
dissol Levy
Calculate the Levy plot between two dissolution experiments. Each experiment may have several profiles and all vs all profiles are calculated
dissol deconv
Deconvolve the drug dissolution from a compartmental model.
dissol deconv Fourier
Deconvolve the drug dissolution from a compartmental model. It does the deconvolution in Fourier so that it only uses the impulse response of the compartmental model. This impulse response only depends on the distribution, metabolism and excretion (DME) part of the ADME properties, meaning that it overcomes the limitations of a poor modelling of the raise of the concentration. On the other side, it has the disadvantage of considering the noise as true fluctuations due to the absorption.
dissol f1 and f2
Calculate the f1 and f2 from two dissolution profiles. The bootstrap confidence interval is bias corrected and accelarated.
dissol ivivc
Calculate the in vitro-in vivo correlation between two experiments. Each experiment may have several profiles and all vs all profiles are calculated. You may scale the time between the two sets of experiments
dissol ivivc generic
Calculate the in vitro-in vivo correlation between two experiments. Each experiment may have several profiles and all vs all profiles are calculated. You may scale the time between the two sets of experiments
dissol ivivc join avg
Join several IVIVCs into a single one. The strategy is to compute the average of all the plots involved in the IVIVC process: 1) tvivo -> tvitro; 2) tvitro -> Adissol; 3) Adissol->FabsPredicted. The plot tvivo-Fabs comes after the IVIVC process, while the plot tvivo-FabsOrig is the observed one in the input files. These two plots need not be exactly the same.
dissol ivivc join recalculate
Join several IVIVCs into a single one. The strategy is to compute the average of all the plots involved in the IVIVC process: 1) tvivo -> tvitro; 2) tvitro -> Adissol; 3) Adissol->FabsPredicted. The plot tvivo-Fabs comes after the IVIVC process, while the plot tvivo-FabsOrig is the observed one in the input files. These two plots need not be exactly the same.
dissol ivivc splines
Calculate the in vitro-in vivo correlation between two experiments. Each experiment may have several profiles and all vs all profiles are calculated. You may scale the time between the two sets of experiments
dose escalation
Given a set of binary responses (toxicity, response/not response, ...), estimate the next dose for a target response Protocol created by http://www.kinestatpharma.com
drop measurements
Filter measurements. Protocol created by http://www.kinestatpharma.com
elimination rate
Fit a single exponential to the input data. Protocol created by http://www.kinestatpharma.com
export to csv (Export)
Export experiment to CSV. Protocol created by http://www.kinestatpharma.com
filter measurements
Filter measurements. Protocol created by http://www.kinestatpharma.com
filter population
Filter a population by some criterion Protocol created by http://www.kinestatpharma.com
filter samples
Filter samples. Protocol created by http://www.kinestatpharma.com
fit bootstrap
Bootstrap estimate of generic fit models. Protocol created by http://www.kinestatpharma.com
fit dissolution
Fit a dissolution model. The observed measurement is modelled as Y=f(t).Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parametersare independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
fit exponentials
Fit a set of exponentials. The observed measurement is modelled as Y=sum_{i=1}^N c_i exp(-lambda_i * X).Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parametersare independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
fit pd generic
Fit a generic model. The observed measurement is modelled as Y=f(X).Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parametersare independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
import experiment (Import)
Protocol to import an PKPD experiment Protocol created by http://www.kinestatpharma.com
import from csv
Import experiment from CSV. You may use WebPlotDigitizer (https://apps.automeris.io/wpd) to generate this CSV, or Excel. Protocol created by http://www.kinestatpharma.com
import from excel (Import)
Import experiment from Excel. Protocol created by http://www.kinestatpharma.com
import from winnonlin (Import)
Import experiment from Winnonlin. Protocol created by http://www.kinestatpharma.com
iv two-compartments
Fit a two-compartmentx model with intravenous absorption to a set of measurements (any arbitrary dosing regimen is allowed) The central compartment is referred to as C, while the peripheral compartment as Cp. The differential equation is V dC/dt = -(Cl+Clp) * C + Clp * Cp + dD/dt, Vp dCp/dt = Clp * C - Clp * Cp where C is the concentration of the central compartment, Cl the clearance, V and Vp the distribution volume of the central and peripheral compartment, Clp is the distribution rate between the central and the peripheral compartments, and D the input dosing regime. Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parameters are independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
ivivc internal validity
This protocol compares the AUC and Cmax predicted from an in vitro-in vivo experiment and the AUC and Cmax of the in vivo population from which the IVIV correlation was estimated. There should not be differences larger than 15%% between the two sets of variables. Protocol created by http://www.kinestatpharma.com
join samples
Join samples. Vias are not prefixed. If they are repeated, those from Experiment 1 prevail. Protocol created by http://www.kinestatpharma.com
merge labels
Merge the labels of Experiment 2 into the samples of Experiment 1. If a label is in Experiment 1 and in Experiment 2, it remains the value from Experiment 1. Protocol created by http://www.kinestatpharma.com
merge populations
Merge two populations. Both populations must have the same labels Protocol created by http://www.kinestatpharma.com
monocompartment urine
Fit a monocompartmental model to a set of plasma and urine (cumulated) measurements ((any arbitrary dosing regimen is allowed) The differential equation is dC/dt = -Cl * C/V + 1/V * dD/dt and dA/dt = fe * Cl * C where C is the concentration, Cl the clearance, V the distribution volume, D the input dosing regime and fe the fraction excreted.Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parametersare independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
nca ev
Non-compartmental analysis of a non-intravenous bolus. Protocol created by http://www.kinestatpharma.com
nca iv exponentials
Non-compartmental analysis based on an exponential fitting. Protocol created by http://www.kinestatpharma.com
nca iv observations
Non-compartmental analysis based on observations. Protocol created by http://www.kinestatpharma.com
nca numeric
Non-compartmental analysis just based on the samples. The results are valid only up to T. It is valid for any kind of via (intravenous, extravascular, ...). Protocol created by http://www.kinestatpharma.com
ode two vias
Simultaneous fit of data obtained by different vias, e.g. IV and PO, but it can be any two vias and any two dosing regimes, dissolution profiles, etc. It is supposed that the PK model in both cases is the same (e.g. two monocompartments, two two-compartments, ...
one-compartment linkpd
Fit a mono-compartment model to a set of plasma and effect measurements (any arbitrary dosing regimen is allowed) The differential equation is dC/dt = -Cl * C/V -Clp *(C-Cp)/V + 1/V * dD/dt, dCb/dt=Cl*C/Vb+Clb*(C-Cb)/Vp and E=E0+a*Cb^b/(Cbm^b+Cb^b) where C is the concentration, Cl the total clearance (metabolic and excretion), V the distribution volume, Clb is the Clearance to the biophase compartment, Vb is the volume of the biophase compartment, D the input dosing regime E is the measured effect, E0 a baseline effect, a and b fitting constants and Cbm the biophase concentration at which half the maximum effect is attained.Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parametersare independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
one-compartment pd
Fit a mono-compartment model to a set of plasma and effect measurements (any arbitrary dosing regimen is allowed) The differential equation is dC/dt = -Cl * C/V + 1/V * dD/dt and E=E0+a*C^b/(Cm^b+C^b) where C is the concentration, Cl the total clearance (metabolic and excretion), V the distribution volume, D the input dosing regime E is the measured effect, E0 a baseline effect, a and b fitting constants and Cm the biophase concentration at which half the maximum effect is attained.Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parametersare independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
operate experiment
Create a new label or measurement to an existing experiment. Protocol created by http://www.kinestatpharma.com
pk monocompartment
Fit a monocompartmental model to a set of measurements obtained by oral doses (any arbitrary dosing regimen is allowed) The differential equation is dC/dt = -Cl * C/V + 1/V * dD/dt where C is the concentration, Cl the clearance, V the distribution volume, and D the input dosing regime.Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parametersare independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
pk monocompartment conv
Fit a monocompartmental model to a set of measurements obtained by oral doses (any arbitrary dosing regimen is allowed) The differential equation is dC/dt = -Cl * C/V + 1/V * dD/dt where C is the concentration, Cl the clearance, V the distribution volume, and D the input dosing regime.Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parametersare independent, which are not. Use Bootstrap estimates instead. The forward model is implemented by convolution instead of by numerical solution of the differential equation. Protocol created by http://www.kinestatpharma.com
pk monocompartment intrinsic
Fit a monocompartmental model to a set of measurements obtained by oral doses (any arbitrary dosing regimen is allowed) The differential equation is dC/dt = -Clint * C/V + 1/V * dD/dt and Clint=Vmax/(Km+C) where C is the concentration, Clint the intrinsic clearance, V the distribution volume, Vmax is the maximum processing capability of the metabolic pathway degrading the drug, Km is the Michaelis-Menten constant, and D the input dosing regime. Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parameters are independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
pk two-compartments
Fit a two-compartmentx model to a set of measurements (any arbitrary dosing regimen is allowed) The central compartment is referred to as C, while the peripheral compartment as Cp. The differential equation is V dC/dt = -(Cl+Clp) * C + Clp * Cp + dD/dt, Vp dCp/dt = Clp * C - Clp * Cp where C is the concentration of the central compartment, Cl the clearance, V and Vp the distribution volume of the central and peripheral compartment, Clp is the distribution rate between the central and the peripheral compartments, and D the input dosing regime. Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parameters are independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
pk two-compartments autoinduction
Fit a two-compartment model to a set of measurements (any arbitrary dosing regimen is allowed) The central compartment is referred to as C, while the peripheral compartment as Cp. The differential equation is V dC/dt = -(Cl+Clp) * C + Clp * Cp + dD/dt, Vp dCp/dt = Clp * C - Clp * Cp being Cl=Cl0-a*Cp where C is the concentration of the central compartment, Cl0 the basal clearance, V and Vp the distribution volume of the central and peripheral compartment, Clp is the distribution rate between the central and the peripheral compartments, and D the input dosing regime. As the concentration in the peripheral compartment increases, the clearance is slowed. Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parameters are independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
pk two-compartments conv
Fit a two-compartmentx model to a set of measurements (any arbitrary dosing regimen is allowed) The central compartment is referred to as C, while the peripheral compartment as Cp. The differential equation is V dC/dt = -(Cl+Clp) * C + Clp * Cp + dD/dt, Vp dCp/dt = Clp * C - Clp * Cp where C is the concentration of the central compartment, Cl the clearance, V and Vp the distribution volume of the central and peripheral compartment, Clp is the distribution rate between the central and the peripheral compartments, and D the input dosing regime. Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parameters are independent, which are not. Use Bootstrap estimates instead. The forward model is implemented by convolution instead of by numerical solution of the differential equation. Protocol created by http://www.kinestatpharma.com
pk two-compartments intrinsic
Fit a two-compartmentx model to a set of measurements (any arbitrary dosing regimen is allowed) The central compartment is referred to as C, while the peripheral compartment as Cp. The differential equation is V dC/dt = -(Clint+Clp) * C + Clp * Cp + dD/dt, Vp dCp/dt = Clp * C - Clp * Cp and Clint=Vmax/(Km+C) where C is the concentration of the central compartment, Cl the clearance, V and Vp the distribution volume of the central and peripheral compartment, Clp is the distribution rate between the central and the peripheral compartments, Vmax is the maximum processing capability of the metabolic pathway degrading the drug, Km is the Michaelis-Menten constant,and D the input dosing regime. Note that the intrinsic clearance occurs at the central volume. Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parameters are independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
pk two-compartments intrinsic, metabolite
Fit a two-compartmentx model to a set of measurements (any arbitrary dosing regimen is allowed) The central compartment is referred to as C, while the peripheral compartment as Cp. The differential equation is V dC/dt = -(Clint+Clp) * C + Clp * Cp + dD/dt, Vp dCp/dt = Clp * C - Clp * Cp, dCm/dt=Clint*C/Vm-Clm*Cm/Vm and Clint=Vmax/(Km+C) where C is the concentration of the central compartment, V, Vp, Vm the distribution volume of the central, peripheral and metabolite compartment, Clp is the distribution rate between the central and the peripheral compartments, Clm is the clearance of metabolite at the metabolite compartments, Vmax is the maximum processing capability of the metabolic pathway degrading the drug into the metabolite, Km is the Michaelis-Menten constant,and D the input dosing regime. Note that the intrinsic clearance occurs at the central volume. Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parameters are independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
regression labels
Perform a regression between two labels Protocol created by http://www.kinestatpharma.com
scale dose
Scale to common dose If the system is linear, then we may scale all measurements as if all individuals had been given the same dose. In this way we may construct a cleaner version of the response (by averaging) and use this cleaner version to find the initial parameters of the rest of samples. This protocol calculates the total dose of each individual and constructs new individuals such that Cnew = Cold * newDose/oldDose The old dose is evaluated in a period (by default, 1 week) that should include all doses Protocol created by http://www.kinestatpharma.com
simulate PK response
This protocol simulates the pharmacokinetic response of an ODE model when it is given a single dose of an drug whose release is modelled by an in vitro fitting and an in vitro-in vivo correlation.
simulate dose escalation
Simulate a dose escalation Protocol created by http://www.kinestatpharma.com
simulate drug interactions
Simulate drug interactions as recommended in EMA CHMP/EWP/560/95 Protocol created by http://www.kinestatpharma.com
simulate generic
Simulate a generic pharmacodynamic response Y=f(X). Protocol created by http://www.kinestatpharma.com
simulate liver flow
Simulate the concentration of a compound (typically an enzyme inhibitor) at liver. Protocol created by http://www.kinestatpharma.com
split/gather experiment
Split an experiment into small pieces, or gather these small pieces into an experiment. Protocol created by http://www.kinestatpharma.com
statistics labels
Calculate statistics of the labels Protocol created by http://www.kinestatpharma.com
two-compartments both
Fit a two-compartments model to a set of plasma and peripheral compartment measurements ((any arbitrary dosing regimen is allowed) The differential equation is dC/dt = -Cl * C/V -Clp *(C-Cp)/V + 1/V * dD/dt, dCp/dt=Cl*C/Vp+Clp*(C-Cp)/Vp where C is the concentration, Cl the total clearance (metabolic and excretion), V the distribution volume, Clp is the Clearance to the peripheric compartment, Vp is the volume of the peripheric compartment, and D the input dosing regime. This protocol assumes that you have measures of both the central and peripheral compartments.Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parametersare independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
two-compartments both pd
Fit a mono-compartment model to a set of plasma and effect measurements (any arbitrary dosing regimen is allowed) The differential equation is dC/dt = -Cl * C/V -Clp *(C-Cp)/V + 1/V * dD/dt, dCp/dt=Cl*C/Vp+Clp*(C-Cp)/Vp and E=E0+a*C^b/(Cm^b+C^b) where C is the concentration, Cl the total clearance (metabolic and excretion), V the distribution volume, Clp is the Clearance to the peripheric compartment, Vp is the volume of the peripheric compartment, and D the input dosing regime. E is the measured effect, E0 a baseline effect, a and b fitting constants and Cm the biophase concentration at which half the maximum effect is attained. This protocol assumes that you have measures of both the central and peripheral compartments.Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parametersare independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com
two-compartments urine
Fit a two-compartments model to a set of plasma and urine (cumulated) measurements ((any arbitrary dosing regimen is allowed) The differential equation is dC/dt = -Cl * C/V -Clp *(C-Cp)/V + 1/V * dD/dt, dCp/dt=Cl*C/Vp+Clp*(C-Cp)/Vp and dA/dt = fe * Cl * C where C is the concentration, Cl the total clearance (metabolic and excretion), V the distribution volume, Clp is the Clearance to the peripheric compartment, Vp is the volume of the peripheric compartment, D the input dosing regime and fe the fraction excreted.Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parametersare independent, which are not. Use Bootstrap estimates instead. Protocol created by http://www.kinestatpharma.com

Contributors:


Powerfit_scipion

Program from Alexandre Bonvin and Gydo van Zundert (Utrecht University) for rigid body fitting of an atomic structure into a 3D volume

Plugin url https://github.com/scipion-em/scipion-em-powerfit.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

powerfit
Protocol for fitting a PDB into a 3D volume This is actually a wrapper to the program Powerfit. See documentation at: http://www.bonvinlab.org/education/powerfit

Contributors:


pwed

scipion-ed is the base plugin defining the Domain for Electron Diffraction image processing

Plugin url https://github.com/scipion-ed/scipion-ed.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

Base class for other ed Import protocols. (Import)
Base class for other ed Import protocols.

Contributors:

Missing information!, but be sure someone has done it.

pwem

Base plugin to deal with EM data. Provides all the import protocols and the model for EM objects (Micrograpths, Movies, Volumes, etc)

Plugin url https://github.com/scipion-em/scipion-em.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

ProtCreateFSC
Description missing, might be a new development.
ProtCreateMask (Masking)
Description missing, might be a new development.
ProtUserSubSet (Tools)
Create subsets from the GUI. This protocol will be executed mainly calling the script 'pw_create_image_subsets.py' from the ShowJ gui. The enabled/disabled changes will be stored in a temporary sqlite file that will be read to create the new subset.
assign Orig & Sampling
Modify the origin and sampling values assigned to a 3D map
assign alignment
Assign a the alignment calculated for a set of particles to another set. This protocol will take into account the differences of pixel size (A/pix) between the two sets and multiply by the right factor the shifts. The particles with the alignment can also be a subset of the other images
assign ctf
This protocol assigns a CTF estimation to a particular set of particles producing a new set.
assign gain to movies
Assign a gain image to a set of movies
average frames
Very simple protocol to align all the frames of a given data collection session. It can be used as a sanity check.
classes consensus
Compare two SetOfClasses
create stream data
create setofXXXX in streaming mode. micrograph -> read a micrograph in memory and writes it nDim times movie -> read a movie in memory and writes it nDim times randomMicrographs -> creates a micrograph with random values and applies a random CTF particles -> read nDim particles in memory and writes it in streaming
export emdb (Export)
generates files for volumes and FSCs to submit structures to EMDB
export emdb/pdb (Export)
generates files for elements to submit structures to EMDB/PDB. Since mmcif/pdb is only partially supported by some software the protocol creates 4 versions of the atomic struct file with the hope that at least one of them will work.
extract coordinates
Extract the coordinates information from a set of particles. This protocol is useful when we want to re-extract the particles (maybe resulting from classification and cleaning) with the original dimensions. It can be also handy to visualize the resulting particles in their location on micrographs.
import atomic structure (Import)
Protocol to import an atomic structure to the project.Format may be PDB or MMCIF
import averages (Import)
Protocol to import a set of averages to the project
import coordinate pairs (Import)
Protocol to import a set of tilt pair coordinates
import coordinates (Import)
Protocol to import a set of coordinates
import ctf (Import)
Common protocol to import a set of ctfs into the project
import mask (Import)
Class for import masks from existing files.
import micrographs (Import)
Protocol to import a set of micrographs to the project
import movies (Import)
Protocol to import a set of movies (from direct detector cameras) to the project.
import particles (Import)
Protocol to import a set of particles to the project
import sequence (Import)
Protocol to import an aminoacid/nucleotide sequence file to the project
import tilted micrographs (Import)
Protocol to import untilted-tilted pairs of micrographs in the project
import volumes (Import)
Protocol to import a set of volumes to the project
invert hand
Modify the transformation matrix of a set of particles So that the handedness changes
join sets (Tools)
Protocol to join two or more sets of images. This protocol allows to select two or more set of images and will produce another set joining all elements of the selected sets. It will validate that all sets are of the same type of elements (Micrographs, Particles or Volumes)
metadata editor
Protocol to edit attributes of all the items of a set using a formula. This could be useful for corrupting your data for testing purposes or editing some values in the set that make sense to do it. Use this with extreme care, you can easily ruin your processing.
parallel test
A parallel test protocol.
particles subset by coordinates
Create a subset of those particles that have a particular set of coordinates
particles subset by micrograph (Tools)
Create a subset of those particles that come from a particular set of micrographs
pdf report
Produce a pdf report from the files in a given directory. Supported file formats: *.tex, *.txt, *.jpg, *.png, *.pdf Files in the directory are sorted by name alphabetically, so if you want them to have the right order a possibility is to name them as 0010-myText.txt 0020-aFigure.png 0030-anotherFigure.jpg 0040-aPaper.pdf 0050-anotherText.tex ... when these files are sorted, they will be sorted by the number in front.
picking difference
Protocol to compute the difference between a reference SetOfParticles and a another set (usually a negative reference). The output will be a SetOfCoordinates with the particles in the reference input that are not close to coordinates in the negative input.
split sets (Tools)
Protocol to split a set in two or more subsets.
stress
stress will stress test a computer system in various selectable ways. Several options require the program stress-ng.
subset (Tools)
Create a set with the elements of an original set that are also referenced in another set. Usually there is a bigger set with all the elements, and a smaller one obtained from classification, cleaning, etc. The desired result is a set with the elements from the original set that are also present somehow in the smaller set (in the smaller set they may be downsampled or processed in some other way). Both sets should be of the same kind (micrographs, particles, volumes) or related (micrographs and CTFs for example).

Contributors:

Missing information!, but be sure someone has done it.

icon for Pyseg Pyseg

Plugin url https://github.com/scipion-em/scipion-em-pyseg.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

import star file PySeg (Import)
This protocol imports subtomograms from a STAR file generated by PySeg

Contributors:


pyworkflowtests

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

test output
Protocol to test scalar output and input linking

Contributors:

Missing information!, but be sure someone has done it.

icon for Relion Relion

Plugin url https://github.com/scipion-em/scipion-em-relion.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

ProtRelionAutopick (Particle picking)
Description missing, might be a new development.
ProtRelionAutopickFom (Particle picking)
Description missing, might be a new development.
2D classification (2D classification)
This protocol runs Relion 2D classification.
3D auto-refine (3D refinement)
Protocol to refine a 3D map using Relion.Relion employs an empirical Bayesian approach to refinementof (multiple) 3D reconstructionsor 2D class averages in electron cryo-microscopy (cryo-EM). Manyparameters of a statistical model are learned from the data,whichleads to objective and high-quality results.
3D classification (3D classification)
Protocol to classify 3D using Relion Bayesian approach. Relion employs an empirical Bayesian approach to refinement of (multiple) 3D reconstructions or 2D class averages in electron cryo-EM. Many parameters of a statistical model are learned from the data, which leads to objective and high-quality results.
3D initial model (Initial model)
This protocols creates a 3D initial model using Relion. Generate a 3D initial model _de novo_ from 2D particles using Relion Stochastic Gradient Descent (SGD) algorithm.
3D multi-body
Relion protocol for multi-body refinement. This approach models flexible complexes as a user-defined number of rigid bodies that move independently from each other. Using separate focused refinements with iteratively improved partial signal subtraction, improved reconstructions are generated for each of the defined bodies. Moreover, using PCA on the relative orientations of the bodies over all particle images in the data set, we generate movies that describe the most important motions in the data.
assign optics group
Assign Optics Group name and related parameters to an input set. Input set can be: movies, micrographs or particles.
auto-picking (Particle picking)
This protocol runs Relion autopicking (version > 3.0). This Relion protocol uses the 'relion_autopick' program to pick particles from micrographs, either using references (2D averages or 3D volumes) The wrapper implementation does not read/write any FOM maps compared to Relion
auto-picking LoG (Particle picking)
This Relion protocol uses 'relion_autopick' program for the Laplacian of Gaussian (LoG) option.
bayesian polishing
Wrapper protocol for the Relion's Bayesian Polishing. As of release 3.0, Relion also implements a new Bayesian approach to beam induced motion correction. This approach aims to optimise a regularised likelihood, which allows us to associate with each hypothetical set of particle trajectories a prior likelihood that favors spatially coherent and temporally smooth motion without imposing any hard constraints. The smoothness prior term requires three parameters that describe the statistics of the observed motion. To estimate the prior that yields the best motion tracks for this particular dataset, we can first run the program in 'training mode'. Once the estimates have been obtained, one can then run the program again to fit tracks for the motion of all particles in the data set and to produce adequately weighted averages of the aligned movie frames.
center averages
Align class averages by their center of mass using *relion_image_handler*. (With *--shift_com* option)
compress movies
Using *relion_convert_to_tiff* to compress a set of movies.
create 3d mask (Masking)
This protocols creates a 3D mask using Relion. The mask is created from a 3d volume or by comparing two input volumes.
ctf refinement
Wrapper protocol for the Relion's CTF refinement.
estimate gain to compress
Using *relion_convert_to_tiff* to estimate the gain that can be used for better compression.
expand symmetry
This protocols wraps relion_particle_symmetry_expand program. Given an input set of particles with angular assignment, expand the set by applying a pseudo-symmetry.
export coordinates (Export)
Export coordinates from Relion to be used outside Scipion.
export ctf (Export)
Export a SetOfCTF to a Relion STAR file.
export particles (Export)
Export particles from Relion to be used outside Scipion.
local resolution (Local resolution)
This protocol does local resolution estimation using Relion. This program basically performs a series of post-processing operations with a small soft, spherical mask that is moved over the entire map, while using phase-randomisation to estimate the convolution effects of that mask.
motion correction (Movie alignment)
Wrapper for the Relion's implementation of motioncor algorithm.
movie particles extraction
Protocol to extract particles from a set of coordinates
particle polishing
This protocols runs particle polishing using Relion. This Relion protocol tracks particle movement in movie frames (from previous movie refinement run), applies a resolution and dose-dependent weighting scheme for each frame and finally sums them together, producing so-called shiny, or polished particles.
particles extraction
Protocol to extract particles using a set of coordinates.
post-processing
Relion post-processing protocol for automated masking, overfitting estimation, MTF-correction and B-factor sharpening.
preprocess particles
This protocol wraps relion_preprocess program. It is used to perform normalisation, filtering or scaling of the particles.
reconstruct (3D reconstruction)
This protocol reconstructs a volume using Relion. Reconstruct a volume from a given set of particles. The alignment parameters will be converted to a Relion star file and used as direction projections to reconstruct.
remove preferential views
Protocol to remove preferential views from a particle set. Inspired by https://github.com/leschzinerlab/Relion
sort particles
Relion particle sorting protocol. It calculates difference images between particles and their aligned (and CTF-convoluted) references, and produces Z-score on the characteristics of these difference images (such as mean, standard deviation, skewness, excess kurtosis and rotational symmetry).
subtract projection
Signal subtraction protocol of Relion. Subtract volume projections from the experimental particles. The particles must have projection alignment in order to properly generate volume projections.
symmetrize volume
Symmetrize a volume using Relion programs: *relion_align_symmetry* and *relion_image_handler*.

Contributors:


icon for RelionTomo RelionTomo

This plugin provide wrappers around several programs of RELION software suite for its use in Tomography.

Plugin url https://github.com/scipion-em/scipion-em-reliontomo.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

ProtRelionEstimateCTF3D (CTF estimation)
Description missing, might be a new development.
ProtRelionImportSubTomograms (Import)
Description missing, might be a new development.
ProtRelionSubtomoClassif3D (3D classification)
Description missing, might be a new development.
ProtRelionSubTomoReconstruct (3D reconstruction)
Description missing, might be a new development.
ProtRelionSubtomoRefine3D (3D refinement)
Description missing, might be a new development.

Contributors:

Missing information!, but be sure someone has done it.

icon for Resmap Resmap

Plugin url https://github.com/scipion-em/scipion-em-resmap.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

local resolution (Local resolution)
ResMap is software tool for computing the local resolution of 3D density maps from electron cryo-microscopy (cryo-EM). Please find the manual at https://sourceforge.net/projects/resmap-latest

Contributors:


Sidesplitter

Plugin url https://github.com/scipion-em/scipion-em-sidesplitter.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

local filter
Protocol for mitigating local over-fitting by filtering. Find more information at https://github.com/StructuralBiology-ICLMedicine/SIDESPLITTER

Contributors:


icon for Simple Simple

This plugin allows to use Simple programs within the Scipion framework.

Plugin url https://github.com/scipion-em/scipion-em-simple.git@python3_migration.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

ProtPrime25
Description missing, might be a new development.
ProtPrime2D (2D classification)
Description missing, might be a new development.
ProtPrime3D (3D refinement)
Description missing, might be a new development.
In development
Description missing, might be a new development.
prime
Produces one or several initial volumes using simple prime

Contributors:


icon for Sphire Sphire

Plugin url https://github.com/scipion-em/scipion-em-sphire.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

ProtISAC (2D classification)
Description missing, might be a new development.
SphireProt2DAlign (2D classification)
Description missing, might be a new development.
cryolo import (Import)
Protocol to import an existing crYOLO training model. The model will be registered as an output of this protocol and it can be used later for further training or for picking.
cryolo picking (Particle picking)
Picks particles in a set of micrographs either manually or in a supervised mode.
cryolo training (Particle picking)
Picks particles in a set of micrographs either manually or in a supervised mode.
janni denoising
Protocol to denoise a set of micrographs in the project.

Contributors:


icon for Spider Spider

Plugin url https://github.com/scipion-em/scipion-em-spider.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

align ap sr (2D alignment)
This protocol wraps SPIDER AP SR command. Reference-free alignment (both translational and rotational) of an image series. See detailed description at: [[http://spider.wadsworth.org/spider_doc/spider/docs/man/apsr.html][SPIDER's AP SR online manual]]
align pairwise (2D alignment)
This protocol wraps SPIDER AP SR command (pairwise alignment). Reference-free alignment (both translational and rotational) of an image series. This alignment scheme aligns a pair of images at a time and then averages them. Then, the averages of each of those pairs are aligned and averaged, and then pairs of those pairs, etc. Compared to [[http://spider.wadsworth.org/spider_doc/spider/docs/man/apsr.html][AP SR]], this alignment scheme appears to be less random, which chooses seed images as alignment references. For more information, see Step 2b at [[http://spider.wadsworth.org/spider_doc/spider/docs/techs/MSA/index.html#pairwise][SPIDER's MDA online manual]]
ca pca (2D classification)
Protocol for Correspondence Analysis (CA) or Principal Component Analysis (PCA). CA is the preferred method of finding inter-image variations. PCA computes the distance between data vectors with Euclidean distances, while CA uses Chi-squared distance. CA is superior because it ignores differences in exposure between images, eliminating the need to rescale between images. In contrast, PCA seems to be more robust: less likely to be trapped in an infinite loop of numerical inaccuracy. For more info see: [[http://spider.wadsworth.org/spider_doc/spider/docs/techs/classification/tutorial.html#CAPCA][SPIDER MDA documentation]]
classify diday (2D classification)
This protocol wraps SPIDER CL CLA command. Performs automatic clustering using Diday's method and Hierarchical Ascendant Classification (HAC) using Ward's criterion on factors produced by CA or PCA.
classify kmeans (2D classification)
This protocol wraps SPIDER CL KM command. Performs automatic K-Means clustering and classification on factors produced by CA or PCA.
classify ward (2D classification)
This protocol wraps SPIDER CL HC command. Finds clusters of images/elements in factor space (or a selected subspace) by using Diday's method of moving centers, and applies hierarchical ascendant classification (HAC) (using Ward's method) to the resulting cluster centers.
create 2d mask (Masking)
This protocol creates a 2D mask using SPIDER. In the step following this one, dimension-reduction, the covariance of the pixels in all images will be computed. Only pixels under a given mask will be analyzed. If this step is performed, a mask that follows closely the contour the particle of interest will be used. Absent a custom-made mask, a circular mask will be used. For non-globular structures, this customized mask will reduce computational demand and the likelihood of numerical inaccuracy in the next dimension-reduction step. On the other hand, given the power of modern computers, this step may be unnecessary.
filter particles
Apply Fourier filters to an image or a volume using Spider FQ or FQ NP. To improve boundary quality the image is padded with the average value to twice the original size during filtration if padding is selected. See more documentation at: [[http://spider.wadsworth.org/spider_doc/spider/docs/man/fq.html][SPIDER's FQ online manual]]
reconstruct fourier (3D reconstruction)
This protocol wraps SPIDER BP 32F command. Simple reconstruction protocol using Fourier back projection. Mainly used for testing conversion of Euler angles.
refine 3D (3D refinement)
Reference-based refinement using SPIDER AP SHC and AP REF commands. Iterative refinement improves the accuracy in the determination of orientations. This improvement is accomplished by successive use of more finely-sampled reference projections. Two different workflows are suggested: with defocus groups or without (gold-standard refinement). For more information, see: [[http://spider.wadsworth.org/spider_doc/spider/docs/techs/recon/mr.html][SPIDER documentation on projection-matching]]

Contributors:


icon for Tomo Tomo

Plugin url https://github.com/scipion-em/scipion-em-tomo.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

Tilt-series assign alignment
Assign the transformation matrices from an input set of tilt-series to a target one.
assign alignment subtomo
Assign the alignment calculated for a set of subtomograms to another set. Both sets should have same pixel size (A/px). The subtomograms with the alignment can also be a subset of a bigger set.
assign tomos to subtomos
This protocol assign tomograms to subtomograms that have been imported before without tomograms. Subtomograms should contain the name of the original tomogram in their own file name.
average tiltseries
Simple protocol to average TiltSeries movies as basic motion correction. It is used mainly for testing purposes.
consensus classes subtomo
Compare several SetOfClassesSubTomograms. Return the intersection of the input classes.
extract 3D coordinates
Extract the coordinates information from a set of subtomograms. This protocol is useful when we want to re-extract the subtomograms (maybe resulting from classification) with the original dimensions. It can be also handy to visualize the resulting subtomograms in their location on the tomograms.
import set of coordinates 3D (Import)
Protocol to import a set of tomograms to the project
import subtomograms (Import)
Protocol to import a set of tomograms to the project
import tilt-series (Import)
Import tilt series
import tilt-series movies (Import)
Import tilt series movies
import tomograms (Import)
Protocol to import a set of tomograms to the project

Contributors:


Tomoj

Plugin url https://github.com/scipion-em/scipion-em-tomoj.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

xcorr prealignment
Tilt-series' cross correlation alignment based on the TomoJ procedure. More info: http://u759.sfbiophys.org/software/update/20140207/Manual_TomoJ_2.24.pdf

Contributors:

Missing information!, but be sure someone has done it.

icon for Topaz Topaz

Plugin url https://github.com/scipion-em/scipion-em-topaz.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

training (Particle picking)
Train the Topaz parameters for a picking

Contributors:


Xmipp2

Plugin url https://github.com/I2PC/scipion-em-xmipp2.git.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

mltomo
Protocol to align subtomograms using MLTomo. It only supports alignment on the axis Y. MLTomo aligns and classifies 3D images with missing data regions in Fourier space, e.g. subtomograms or RCT reconstructions, by a 3D multi-reference refinement based on a maximum-likelihood (ML) target function.

Contributors:

Missing information!, but be sure someone has done it.

icon for Xmipp3 Xmipp3

Plugin to use Xmipp programs within the Scipion framework.

Plugin url https://github.com/i2pc/scipion-em-xmipp.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

Xmipp2ProtSubtomoPaintBack
Description missing, might be a new development.
XmippProtAlignmentNMAVol
Description missing, might be a new development.
XmippProtCombineMasks (Masking)
Description missing, might be a new development.
XmippProtCombinePdb
Description missing, might be a new development.
XmippProtCTFDiscrepancy
Description missing, might be a new development.
XmippProtCTFSelection
Description missing, might be a new development.
XmippProtDeepCones3D
In development, 3D classifier
XmippProtDeepCones3DGT
In development
XmippProtDeepCones3DGT_2
In development
XmippProtDeepCones3DTst
Description missing, might be a new development.
XmippProtDeepSimilarityCones3D (3D classification)
In development, 3D classifier
XmippProtDimredNMAVol
Description missing, might be a new development.
XmippProtDirectionalClasses
Description missing, might be a new development.
XmippProtDirectionalPruning
Description missing, might be a new development.
XmippProtDl2r
Description missing, might be a new development.
XmippProtEliminateFalseParticles
Description missing, might be a new development.
XmippProtExtractMovieParticlesNew
In development
XmippProtExtractSubtomoOrient
In development
XmippProtHelicalSymmetrize
Description missing, might be a new development.
XmippProtMoviePoisson
In development
XmippProtPrepareDeepConsensus
Description missing, might be a new development.
XmippProtProjectZ
In development
XmippProtReAlignClasses
Description missing, might be a new development.
XmippProtScreenDeepLearning1
Description missing, might be a new development.
XmippProtUndoAlignSubtomo
In development
XmippProtVolumeHomogenizer
Description missing, might be a new development.
XmippProtVolumeOccupancy
Description missing, might be a new development.
2D kmeans clustering (2D classification)
Classifies a set of particles using a clustering algorithm to subdivide the original dataset into a given number of classes.
3d bionotes
Protocol for checking annotations This is actually a wrapper to 3D Bionotes. See documentation at: http://3dbionotes.cnb.csic.es
FlexAlign (Movie alignment)
Wrapper protocol to Xmipp Movie Alignment by cross-correlation
XmippProtDeepConsSubSet
Create subsets from the GUI for the Deep Consensus protocol. This protocol will be executed mainly calling the script 'pw_create_image_subsets.py' from the ShowJ gui. The enabled/disabled changes will be stored in a temporary sqlite file that will be read to create the new subset.
add noise particles
Given a set of particles, the protocol will add noise to them The types of noise are Uniform, Student and Gaussian.
add noise volume/s
Given a set of volumes, or a volume the protocol will add noise to them The types of noise are Uniform, Student and Gaussian.
align volume
Aligns a set of volumes using cross correlation or a Fast Fourier method.
align volume and particles
Aligns a volume (inputVolume) using a Fast Fourier method with respect to a reference one (inputReference). The obtained alignment parameters are used to align the set of particles (inputParticles) that generated the input volume.
align volume web
Aligns a set of volumes using cross correlation. Based on Xmipp protocol for aligning volumes, but the parameters are restricted for ease of use.
align with cl2d
Aligns a set of particles using the CL2D algorithm.
analyze local defocus
Assigns to each micrograph a coefficient (R2) which evaluates the result of the local defocus adjustment and displays the local defocus for all the particles in each micrograph.
apply 2d mask (Masking)
Apply mask to a set of particles
apply 3d mask (Masking)
Apply mask to a volume
apply alignment 2d
Apply alignment parameters and produce a new set of images.
apply transformation matrix
Apply transformation matrix of an aligned volume on a set of particles to modify their angular assignment. Note: These particles are practically related to the aligned volume (but before alignment).
assign tiltpairs
From two sets of points (tilted and untilted) the protocol determines the affine transformation between these sets.
auto-picking (step 2) (Particle picking)
Protocol to pick particles automatically in a set of micrographs using previous training
break symmetry
Given an input set of particles with angular assignment, find an equivalent angular assignment for a given symmetry. Be aware that input symmetry values follows Xmipp conventions as described in: http://xmipp.cnb.csic.es/twiki/bin/view/Xmipp/Symmetry
calculate strain
Compare two states of a volume to analyze the local strains and rotations
center particles
Realignment of un-centered particles.
cl2d (2D classification)
Classifies a set of images using a clustering algorithm to subdivide the original dataset into a given number of classes.
compare angles
Compare two sets of angles. The output is a list of all common particles with the angular difference between both assignments. The output is constructed by keeping the information from the Set 1 and adding the shiftDiff and angularDiff.
compare reprojections
Compares a set of classes or averages with the corresponding projections of a reference volume. The set of images must have a 3D angular assignment and the protocol computes the residues (the difference between the experimental images and the reprojections). The zscore of the mean and variance of the residues are computed. Large values of these scores may indicate outliers. The protocol also analyze the covariance matrix of the residual and computes the logarithm of its determinant [Cherian2013]. The extremes of this score (called zScoreResCov), that is values particularly low or high, may indicate outliers.
consensus classes 3D
Compare several SetOfClasses3D. Return the intersection of the input classes.
consensus local defocus
This protocol compares the estimations of local defocus computed by different protocols for a set of particles
convert a PDB
Convert a PDB file into a volume.
convert to pseudoatoms
Converts an EM volume into pseudoatoms
create 2d mask (Masking)
Create a 2D mask. The mask can be created with a given geometrical shape (Circle, Rectangle, Crown...) or it can be obtained from operating on a 2d image or a previuous mask.
create 3d mask (Masking)
Create a 3D mask. The mask can be created with a given geometrical shape (Sphere, Box, Cylinder...) or it can be obtained from operating on a 3d volume or a previous mask.
create gallery
Create a gallery of projections from a volume. This gallery of projections may help to understand the images observed in the microscope.
crop/resize particles
Crop or resize a set of particles
crop/resize volumes
Crop or resize a set of volumes
ctf consensus
Protocol to make a selection of meaningful CTFs in basis of the defocus values, the astigmatism, the resolution, other Xmipp parameters, and the agreement with a secondary CTF for the same set of micrographs.
ctf estimation (CTF estimation)
Protocol to estimate CTF on a set of micrographs using Xmipp.
ctf_correct_wiener2d
Perform CTF correction by Wiener filtering.
deep consensus picking
Protocol to compute a smart consensus between different particle picking algorithms. The protocol takes several Sets of Coordinates calculated by different programs and/or different parameter settings. Let's say: we consider N independent pickings. Then, a neural network is trained using different subset of picked and not picked cooridantes. Finally, a coordinate is considered to be a correct particle according to the neural network predictions.
deep denoising
Description missing, might be a new development.
deep micrograph cleaner
Protocol to remove coordinates in carbon zones or large impurities
deepEMhancer
Given a map the protocol performs automatic deep post-processing to enhance visualization. Usage guide at https://github.com/rsanchezgarc/deepEMhancer
defocus group
Given a set of CTFs group them by defocus value. The output is a metadata file containing a list of defocus values that delimite each defocus group.
denoise particles
Remove particles noise by filtering them. This filtering process is based on a projection over a basis created from some averages (extracted from classes). This filtering is not intended for processing particles. The huge filtering they will be passed through is known to remove part of the signal with the noise. However this is a good method for clearly see which particle are we going to process before it's done.
directional resolution MonoDir
Given a map the protocol assigns local resolutions to each voxel of the map.
eliminate empty classes
Takes a set of classes (or averages) and using statistical methods (variances of sub-parts of input image) eliminates those samples, where there is no object/particle (only noise is presented there). Threshold parameter can be used for fine-tuning the algorithm for type of data. Also discards classes with less population than a given percentage.
eliminate empty particles
Takes a set of particles and using statistical methods (variance of variances of sub-parts of input image) eliminates those samples, where there is no object/particle (only noise is presented there). Threshold parameter can be used for fine-tuning the algorithm for type of data.
enrich
Method to get two volume from different classes (with different conformation) and correcting (deforming) all images of one of the volumes (input volume) with respect to the another one as a reference, using optical flow algorithm. The output is a setOfParticles contaied deformed reference particles.
estimate local defocus (CTF estimation)
Compares a set of particles with the corresponding projections of a reference volume. The set of particles must have a 3D angular assignment. This protocol refines the CTF, computing local defocus change. The maximun allowed defocus is a parameter introduced by the user (advanced). The protocol gives back the input set of particles with the refine local defocus and the defocus change with relation to the global defocus.
extract asymmetric unit
generates files for volumes and FSCs to submit structures to EMDB
extract movie particles
Extract a set of Particles from each frame of a set of Movies.
extract particle pairs
Protocol to extract particles from a set of tilted pairs coordinates
extract particles
Protocol to extract particles from a set of coordinates
filter particles
Apply Fourier filters to a set of particles
filter volumes
Apply Fourier filters to a set of volumes
generate reprojections
Compares a set of classes or averages with the corresponding projections of a reference volume. The set of images must have a 3D angular assignment and the protocol computes the residues (the difference between the experimental images and the reprojections). The zscore of the mean and variance of the residues are computed. Large values of these scores may indicate outliers. The protocol also analyze the covariance matrix of the residual and computes the logarithm of its determinant [Cherian2013]. The extremes of this score (called zScoreResCov), that is values particularly low or high, may indicate outliers.
gl2d (2D classification)
2D alignment using Xmipp GPU Correlation algorithm.
gl2d static
2D alignment in semi streaming using Xmipp GPU Correlation. A previous set of classes must be provided to include the new images in the corresponding class although the representatives will be maintained.
gl2d streaming (2D classification)
2D alignment in full streaming using Xmipp GPU Correlation. The set of classes will be growing whilst new particle images are received.
helical symmetry
Estimate helical parameters and symmetrize. Helical symmetry is defined as V(r,rot,z)=V(r,rot+k*DeltaRot,z+k*Deltaz).
highres (3D refinement)
This is a 3D refinement protocol whose main input is a volume and a set of particles. The set of particles has to be at full size (the finer sampling rate available), but the rest of inputs (reference volume and masks) can be at any downsampling factor. The protocol scales the input images and volumes to a reasonable size depending on the resolution of the previous iteration. The protocol works with any input volume, whichever its resolution, as long as it is a reasonable initial volume for the set of particles. The protocol does not resolve the heterogeneous problem (it assumes an homogeneous population), although it is somewhat tolerant through the use of particle weights in the reconstruction process. It is recommended to perform several global alignment iterations before entering into the local iterations. The switch from global to local should be performed when a substantial percentage of the particles do not move from one iteration to the next. The algorithm reports the cross correlation (global alignment) or cost (local) function per defocus group, so that we can see which was the percentile of each particle in its defocus group. You may want to perform iterations one by one, and remove from one iteration to the next, those particles that worse fit the model.
kerdensom (2D classification)
Classifies a set of images using Kohonen's Self-Organizing Feature Maps (SOM) and Fuzzy c-means clustering technique (FCM) . The kerdenSOM algorithm anneals from an initial high regularization factor to a final lower one, in a user-defined number of steps. KerdenSOM is an excellent tool for classification, especially when using a large number of data and classes and when the transition between the classes is almost continuous, with no clear separation between them. The input images must be previously aligned.
local MonoRes (Local resolution)
Given a map the protocol assigns local resolutions to each voxel of the map.
local MonoTomo
Given a map the protocol assigns local resolutions to each voxel of the map.
local deepRes
Given a map the protocol assigns local resolutions to each voxel of the map.
localdeblur sharpening (3D refinement)
Given a resolution map the protocol calculate the sharpened map.
manual-picking (step 1) (Particle picking)
Picks particles in a set of micrographs either manually or in a supervised mode.
metaprotocol golden highres
Metaprotocol to run golden version of highres
metaprotocol heterogeneity
Metaprotocol to run together all the protocols to discover discrete heterogeneity in a set of particles
metaprotocol heterogeneity output
Metaprotocol to run together all the protocols to discover discrete heterogeneity in a set of particles
metaprotocol heterogeneity subset (Tools)
Metaprotocol to select a set of particles from a 3DClasses and a Volume from a SetOfVolumes
ml2d (2D classification)
Perform (multi-reference) 2D-alignment using a maximum-likelihood ( *ML* ) target function. Initial references can be generated from random subsets of the experimental images or can be provided by the user (this can introduce bias). The output of the protocol consists of the refined 2D classes (weighted averages over all experimental images). The experimental images are not altered at all. Although the calculations can be rather time-consuming (especially for many, large experimental images and a large number of references we strongly recommend to let the calculations converge.
mltomo
Align and classify 3D images with missing data regions in Fourier space, e.g. subtomograms or RCT reconstructions, by a 3D multi-reference refinement based on a maximum-likelihood (ML) target function. See http://xmipp.cnb.csic.es/twiki/bin/view/Xmipp/Ml_tomo_v31 for further documentation
movie average (Movie alignment)
Protocol to average movies
movie gain
Estimate the gain image of a camera, directly analyzing one of its movies.
movie maxshift
Protocol to make an automatic rejection of those movies whose frames move more than a given threshold. Rejection criteria: - *by frame*: Rejects movies with drifts between frames bigger than a certain maximum. - *by whole movie*: Rejects movies with a total travel bigger than a certain maximum. - *by frame and movie*: Rejects movies if both conditions above are met. - *by frame or movie*: Rejects movies if one of the conditions above are met.
movie resize
Resize a set of movies. Only downsampling is allowed.
multiple fscs
Compute the FSCs between a reference volume and a set of input volumes. A mask can be provided and the volumes are aligned by default.
multireference alignability
Performs soft alignment validation of a set of particles confronting them against a given 3DEM map. This protocol produces particle alignment precision and accuracy parameters.
nma alignment
Protocol for flexible angular alignment.
nma analysis
Flexible angular alignment using normal modes
nma cluster
Protocol executed when a cluster is created from NMA images and theirs deformations.
nma dimred
This protocol will take the images with NMA deformations as points in a N-dimensional space (where N is the number of computed normal modes) and will project them in a reduced spaced (usually with less dimensions).
normalize strain
Normalize the local strain and rotations amongst several runs
operate particles
Apply an operation to two sets of particles
operate volumes
Apply an operation to two sets of volumes
optical alignment
Wrapper protocol to Xmipp Movie Alignment by Optical Flow
optical alignment (Movie alignment)
Wrapper protocol to Xmipp Movie Alignment by Optical Flow
particle boxsize
Given a set of micrographs, the protocol estimate the particle box size.
pick noise (Particle picking)
Protocol to pick noise particles
picking consensus
Protocol to estimate the agreement between different particle picking algorithms. The protocol takes several Sets of Coordinates calculated by different programs and/or different parameter settings. Let's say: we consider N independent pickings. Then, a coordinate is considered to be a correct particle if M pickers have selected the same particle (within a radius in pixels specified in the form). If you want to be very strict, then set M=N; that is, a coordinate represents a particle if it has been selected by all particles (this is the default behaviour). Then you may relax this condition by setting M=N-1, N-2, ... If you want to be very flexible, set M=1, in this way it suffices that 1 picker has selected the coordinate to be considered as a particle. Note that in this way, the cleaning of the dataset has to be performed by other means (screen particles, 2D and 3D classification, ...).
preprocess micrographs
Protocol to preprocess a set of micrographs in the project. You can crop borders, remove bad pixels, etc.
preprocess particles
Preprocess a set of particles. You can remove dust, normalize, apply threshold, etc
preprocess volumes
Protocol for Xmipp-based preprocess for volumes
projection matching (3D refinement)
3D reconstruction and classification using multireference projection matching
random conical tilt
Creates initial volumes by using a set of projections/classes from a tilted-pair picking process and using RCT algorithm.
ransac (Initial model)
Computes an initial 3d model from a set of projections/classes using RANSAC algorithm. This method is based on an initial non-lineal dimensionality reduction approach which allows to select representative small sets of class average images capturing the most of the structural information of the particle under study. These reduced sets are then used to generate volumes from random orientation assignments. The best volume is determined from these guesses using a random sample consensus (RANSAC) approach.
reconstruct fourier (3D reconstruction)
Reconstruct a volume using Xmipp_reconstruct_fourier from a given set of particles. The alignment parameters will be converted to a Xmipp xmd file and used as direction projections to reconstruct.
reconstruct significant (Initial model)
This algorithm addresses the initial volume problem in SPA by setting it in a Weighted Least Squares framework and calculating the weights through a statistical approach based on the cumulative density function of different image similarity measures.
remove duplicates
This protocol removes coordinates that are closer than a given threshold. The remaining coordinate is the average of the previous ones.
resolution 3D
Computes resolution by several methods
resolution fso
Given two half maps the protocol estimates Fourier Shell Occupancy to determine the global anisotropy of the map
rotational spectra
Protocol to compute the rotational spectrum of the given particles.
rotational symmetry
Estimate the orientation of a rotational axis and symmetrize. The user should know the order of the axis (two-fold, three-fold, ...) If this is unknown you may try several and see the most consistent results.
screen deep learning
Protocol for screening particles using deep learning.
screen particles
Attach different merit values to every particle in order to prune the set. zScore evaluates the similarity of a particles with an average (lower zScore -> high similarity). SSNR evaluates the signal/noise ration in the Fourier space. Variance evaluates the varaince on the micrographs context where the particle was picked.
significant heterogeneity
3D Reconstruction with heterogeneous datasets
solid angles
Construct image groups based on the angular assignment. All images assigned within a solid angle are assigned to a class. Classes are not exclusive and an image may be assigned to multiple classes
sph angular align
Protocol for flexible angular alignment based on spherical harmonics.
sph struct map
Protocol for structure mapping based on spherical harmonics.
sph volume deform
Protocol for volume deformation based on spherical harmonics.
split frames
Wrapper protocol to Xmipp split Odd Even
split volume
Split volume in two
split volume hierarchical
Construct image groups based on the angular assignment. All images assigned within a solid angle are assigned to a class. Classes are not exclusive and an image may be assigned to multiple classes
structure mapping
A quantitive analysis of dissimilarities (distances) among the EM maps that placing the entire set of density maps in to a common space of comparison.The approach is based on statistical analysis of distance among elastically aligned EM maps, and results in visualizing those maps as points in a lower dimensional distance space.
subtract projection
Subtract volume projections from the experimental particles. The particles must have projection alignment in order to properly generate volume projections. An example of usage is to delete the virus capsid to refine only the genetic material.
swarm consensus
This is a 3D refinement protocol whose main input is a set of volumes and a set of particles. The set of particles has to be at full size (the finer sampling rate available), but the rest of inputs (reference volume and masks) can be at any downsampling factor. The protocol scales the input images and volumes to a size that depends on the target resolution. The input set of volumes is considered to be a swarm of volumes and they try to optimize the correlation between the volumes and the set of particles. This is an stochastic maximization and only a fraction of the particles are used to update the volumes and evaluate them.
tilt pairs particle picking (Particle picking)
Picks particles in a set of untilted-tilted pairs of micrographs.
trigger data
Waits until certain number of images is prepared and then send them to output. It can be done in 3 ways: - If *Send all particles to output?*' is _No_: Once the number of images is reached, a setOfImages is returned and the protocols finished (ending the streaming from this point). - If *Send all particles to output?*' is _Yes_ and: - If *Split particles to multiple sets?* is _Yes_: Multiple closed outputs will be returned as soon as the number of images is reached. - If *Split particles to multiple sets?* is _No_: Only one output is returned and it is growing up in batches of a certain number of images (completely in streaming).
validate fsc-q
The protocol assesses the quality of the fit.
validate overfitting
Check how the resolution changes with the number of projections used for 3D reconstruction. NOTE: Using the output plot, with the reconstruction of aligned gaussian noise, you can assess the validity of the reconstruction from your micrograph images. Practically, if the resolution of reconstruction based on your images is not considerably different from aligned gaussian noise one (for less number of particles),your images may not produce a valid reconstruction. This method has been proposed by: B. Heymann "Validation of 3D EM Reconstructions", 2015. (see References)
validate_nontilt
Ranks a set of volumes according to their alignment reliability obtained from a clusterability test.

Contributors:


Xmipptomo

Plugin url https://github.com/i2pc/scipion-em-xmipptomo.

Available in Linux. Scientific software, Image processing, cryo em

Available methods:

apply alignment subtomo
Apply alignment matrix and produce a new setOfSubtomograms, with each subtomogram aligned to its reference.
apply alignment tilt-series
Compute the interpolated tilt-series from its transform matrix.
cltomo
Averages a set of subtomograms taking into account the missing edge.
connected components
This protocol takes a set of coordinates and identifies connected components among the picked particles.
connected components to ROIs
This protocol adjust a SetOfCoordinates (which usually will come from a connected componnent) to a ROI (region of interest) previously defined
fit vesicles
This protocol adjust a SetOfSubtomograms (with coordinates), to a vesicle (ellipsoid), defining regions of interest (SetOfMeshes) for each vesicle as output.
imagej roi
Tomogram ROI selection in IJ
map back subtomos
This protocol takes a tomogram, a reference subtomogram and a metadata with geometrical parameters (x,y,z) and places the reference subtomogram on the tomogram at the designated locations (map back). It has different representation options.
misalign tilt-series
Introduce misalignment in the transformation matrix of a tilt-series
phantom create subtomo
Create subtomogram phantoms
resize tilt-series
Wrapper protocol to Xmipp image resize applied on tilt-series
split tilt-series
Wrapper protocol to Xmipp split Odd Even on tilt-series
subtomo projection
Project a set of volumes or subtomograms to obtain their X, Y or Z projection of the desired range of slices.
tiltseries FlexAlign
Simple protocol to average TiltSeries movies as basic motion correction. It is used mainly for testing purposes.

Contributors:

Missing information!, but be sure someone has done it.