Categories

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

Movie alignment CTF estimation Initial model Picking 2D classification 3D refinement Local resolution

Movie alignment

Available methods:

motioncor (Relion)
Wrapper for the Relion's implementation of motioncor algorithm.
optical alignment (Xmipp3)
Wrapper protocol to Xmipp Movie Alignment by Optical Flow
movie average (Xmipp3)
Protocol to average movies
correlation alignment (Xmipp3)
Wrapper protocol to Xmipp Movie Alignment by cross-correlation
summovie (Grigoriefflab)
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 (Grigoriefflab)
Unblur is used to align the frames of movies recorded on an electron microscope to reduce image blurring due to beam-induced motion.
movie alignment (Motioncorr)
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)

CTF estimation

Available methods:

ctftilt (Grigoriefflab)
Estimates CTF on a set of tilted micrographs using ctftilt program.
XmippProtLocalCTF (Xmipp3)
Estimates particle CTF
ctf estimation (gCTF)
Estimates CTF on a set of micrographs using Gctf. To find more information about Gctf go to: http://www.mrc-lmb.cam.ac.uk/kzhang
ctf estimation (Xmipp3)
Protocol to estimate CTF on a set of micrographs using Xmipp.
ctffind4 (Grigoriefflab)
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

Initial model

Available methods:

reconstruct significant (Xmipp3)
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.
ransac (Xmipp3)
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.
initial model (Eman2)
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
3D initial model (Relion)
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.

Picking

Available methods:

particle picking (BSOFT)
Protocol to pick particles in a set of micrographs using bsoft
auto-picking LoG (Relion)
This Relion protocol uses 'relion_autopick' program for the Laplacian of Gaussian (LoG) option.
auto-picking (step 2) (Xmipp3)
Protocol to pick particles automatically in a set of micrographs using previous training
boxer auto (Eman2)
Automated particle picker for SPA. Uses EMAN2 (versions 2.2+) e2boxer.py
cryolo picking (Sphire)
Picks particles in a set of micrographs either manually or in a supervised mode.
cryolo training (Sphire)
Picks particles in a set of micrographs either manually or in a supervised mode.
auto-picking (Gautomatch)
Automated particle picker for SPA. Uses Gautomatch. Gautomatch is a GPU accelerated program for accurate, fast, flexible and fully automatic particle picking from cryo-EM micrographs with or without templates.
boxer (Eman2)
Semi-automated particle picker for SPA. Uses EMAN2 e2boxer.py.
ethan picker (Bamfordlab)
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.
sparx gaussian picker (Eman2)
Automated particle picker for SPA. Uses Sparx gaussian picker. For more information see http://sparx-em.org/sparxwiki/e2boxer
dogpicker (Appion)
Protocol to pick particles in a set of micrographs using appion dogpicker.
manual-picking (step 1) (Xmipp3)
Picks particles in a set of micrographs either manually or in a supervised mode.
tilt pairs particle picking (Xmipp3)
Picks particles in a set of untilted-tilted pairs of micrographs.
auto-picking (Relion)
This protocol runs Relion autopicking (version 2.0+). This Relion protocol uses the 'relion_autopick' program to pick particles from micrographs, either using templates or gaussian blobs. The picking with this protocol is divided in three steps: 1) Run with 'Optimize' option for several (less than 30) micrographs. 2) Execute the wizard to refine the picking parameters. 3) Run with 'Pick all' option to pick particles from all micrographs. The first steps will use internally the option '--write-fom-maps' to write to disk the FOM maps. The expensive part of this calculation is to calculate a probability-based figure-of-merit (related to the cross-correlation coefficient between each rotated reference and all positions in the micrographs. That's why it is only done in an small subset of the micrographs, where one should use representative micrographs for the entire data set, e.g. a high and a low-defocus one, and/or with thin or thick ice. Step 2 uses a much cheaper peak-detection algorithm that uses the threshold and minimum distance parameters.

2D classification

Available methods:

ProtCryo2D (CryoSparc)
2D classification (in development)
cl2d (Xmipp3)
Classifies a set of images using a clustering algorithm to subdivide the original dataset into a given number of classes.
gl2d (Xmipp3)
2D alignment using Xmipp GPU Correlation algorithm.
msa-classify (Imagic)
This protocols wraps MSA-CLASSIFY program of IMAGIC. It is based on variance-oriented hierarchical ascendant classification program (an enhanced Ward-type algorithm).
2D classification (Relion)
This protocol runs Relion 2D classification.
gl2d streaming (Xmipp3)
2D alignment in full streaming using Xmipp GPU Correlation. The set of classes will be growing whilst new particle images are received.
classify ward (Spider)
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.
classify kmeans (Spider)
This protocol wraps SPIDER CL KM command. Performs automatic K-Means clustering and classification on factors produced by CA or PCA.
classify diday (Spider)
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.

3D refinement

Available methods:

projection matching (Xmipp3)
3D reconstruction and classification using multireference projection matching
localized subparticles (Localrec)
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.
3D auto-refine (Relion)
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.
localdeblur sharpening (Xmipp3)
Given a resolution map the protocol calculate the sharpened map.
refine 3D (Spider)
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]]
highres (Xmipp3)
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.
refine easy (Eman2)
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.

Local resolution

Available methods:

local resolution (Resmap)
ResMap is software tool for computing the local resolution of 3D density maps studied in structural biology, primarily by cryo-electron microscopy (cryo-EM). Please find the manual at http://resmap.sourceforge.net
blocres (BSOFT)
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.
local MonoRes (Xmipp3)
Given a map the protocol assigns local resolutions to each voxel of the map.
local resolution (Relion)
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.