| || |
|−|There are two volume browserstomoviewand tomoslicerthat load the entire tomograms into memory and allow making annotations to theregions of interest. Tomoview provides fine-tuned visualization along orthogonal directions. Tomoslice has simpler set of controls, and is also more suitable for visualization tasks that require oblique sections through the tomogram. While thevisualization is different, both browsersuse the same toolto keep track of your annotations: a poolof models.Let’s exploreit with the simpler tomoslice.Go to View volume -> load full tomogram into Tomoslice.On the left control panels Sceneturn off the 3d view radiobutton. You can navigate the z-height of the tomogram with a mouse wheel or with a slider in the tomoslicemenu (1 to 256 in this case); you can also change the slice thickness from the default 10pixels. Play with the controls and explore the tomogram.Turn 3d view back on.PICKING PARTICLES IN TOMOGRAMSCoordinates of picked particles are represented by data types called models. In the tomoslicer window go to Model Pool -> Create new model in pool (choose type) -> General. This is the simplest type of model where each clicked/model point corresponds to a single isolated particle. Now you can navigate up and down the tomogram, center the mouse on the particle and click “C” to add a new model point. (Note the Help -> Shortcut options that lists the different the actions of the different keystrokes). Backspace button deletes the last clicked point. Figure 4. Generating a new modelAfter you are done clicking a dozen of particles go to Active model -> Edit active model. This is a summary of the model with control elements for geometric transformations. Models of type generaldon’t allow any geometrical transforms, all you need to do here is to convert the clicked model points into “table points” that will be used for cropping. This is done by clicking the table- looking command icon (4thfrom the right in the top left part of the Editor of model parameterswindow). Alternatively, you could just click on Active model > Update Crop points in the tomoslicewindow. |+|
you a -in from , you can by the ].
|−|Figure 5. Updating crop points and extracting particles from the model editor windowThe model editor also gives you the possibility of changing the name of the model. You may want to give themodels meaningful identifiersbefore storing them into disk.Also, you can open a GUI for extracting your particles (first icon from the right) to extract particles from only this tomogram, however we will do it later for all 3 tomograms. Make sure that the number of clicked points equals to the number of table points in the Informationfield and close the Editorwindow. Importantly, save the model into the catalogue by Active model -> Save active model into catalogue (disk)and close the slicerwindow. Pick particles for tomograms 2 and 3. In total, there are around 40particles in the generated dataset.Figure 6.Remember to save your work: use Update Crop to create table points, and then save the model into the catalogue |+|
|−|Important: when you opena new tomogram, make sure that you delete the pool of modelsfrom memory (model pool > clear current pool from memory). This will have no effect onto the models stored in disk, and it is necessary in order to ensure that you are not mixing models from different tomograms. AN ALTERNATIVE VISUALIZATION:TOMOVIEWThe just described tomoslicerbrowser is one of a visualization option for tomograms. You canuse alternatively the tomoviewbrowser(or skip this section if you are already satisfied with tomoslice). Tomoviewcan be activated through the preview window in the same wayas we activated tomoslice.Tomosliceforces you to move the viewed slice up and down in a tomogram in order to reliably locate its center. In tomoviewyou will click two times on each particle: once on the projection through the tomogram in Z-direction to locate the (x,y) center. Second –in the orthogonal to the projection direction tolocate the Z-height.Click on Picking profile > Isolated particles.This will create a model of type “General” (no need to create it specifically with the [ Model Pool] window), and also will tune the visualization options so that the main window shows a projection of the whole tomogram along z.In the field external viewsactivate the Localradiobutton and type in 200; in the Annotationsfield make sure supportis on. Right (secondary) click on the particle in the tomogram (this is a projection in the Z direction) to pick X-and Y-coordinates. To center the Z-position of your particles left click in the y-slice window. Help on clicking is available under Help.Figure 7tomoview tuned forpickingisolated particles. Right: external viewer: a part of the yz plane including the [x,y] position clicked in the main windowEXTRACTING PARTICLES FROM TOMOGRAMSIn the catalogue managerwindow select the rows for tomograms from which you want to extract particlesfrom. You can either select them one by one with a mouse click holding a CTRL button or click the “Select all” button. |+|
you may a on the of [] in '''', on the for .
|−|Figure 8.Extracting particles with DCM and selectingthe models for particle extractionGo to Crop Particles -> Open Volume List Manager, all the models in the catalogue listed in the bottom of the window. Pick (by checking boxes) the models of type general –the ones that you have just clicked. Increase sidelength to 48-64 pixels. Vll-list is the format in which Dynamo stores directives for particle extraction; you can modify it with your own scripts if you have a lot of | |
|−|tomograms. Now click “Create list” and “Crop particles”. Change output directory name to i.e. “particles” and click “start cropping”.Figure 9.Cropping particles from the set of selected modelsAfter cropping is done explore your cropped particles by clicking ddbrowseunder the output folder name. Ddbrowse (Dynamo Data Browse) is a lightweight browser for particle visualization, just click “show” and make sure your particles are well centeredandfit the box. If not -use pre-existing models to generate the vll-file and re-extract the particles or increase the sidelength.Please note that Dynamo generated a particles/folder with sub-volumes formatted as particle_000XX.em. | |
|−|SUB-TOMOGRAM ALIGNMENT AND AVERAGINGINITIAL MODEL GENERATIONIf you don’t have an initial model it is easy to generateitby manually aligning several particles and summing them up. Dynamo stores information about the particles in dynamo tables: for each particle a table contains shifts and rotations to bring the particle to the average (by default these values are initialized as 0s), particle ID (tag), orientation of the missing wedge and others. For full info type >> dthelpin Matlab. Dynamo catalogue generates aninitial table during particle extraction, its location is particles/crop.tbl. For initial model generation we will use dynamo_galleryto generate initial orientations for some of our particles. Type >> dgallery(‘data’, ‘particles’, ‘table’, ‘particles/crop.tbl’);Load particles into memory by clicking “load” in the Load from diskfield on the left of the window. Display some or all of the particles with a bottom slider in the Shown particlesfield on the top of the window.Toggle between X-Y-and Z-viewsintheView: orientationfield. Now for each particle you can specify the center with a mouse click and the “C” button. Thermosomes are barrel-like particles with a long axis, you therefore may also specify the “top” of the particle by clicking along the long axis of the particle (but still within the box) with a mouse click and click “N” (stands for North). Do it for all the particles alternating the X-Y-and Z-views.Non-aligned particles are marked red, oncethe particle has been left-clicked it adds to the section in memory and turns blue. To remove from selection right click on the box. You can save selected tags and a corresponding dynamo table by clicking the “quick save” button in the Particle Sectionfield on top of the window. It saves a quickbuffer.tbland quickbuffer.tagsto the hard drive that you willuse later. To generate the average you need to “apply”the table on the particles.For this click the “average” button in the Particle selectionfield. It opens dynamo_average_GUIwith a lot ofcontrols, we only need output filename in the Averaged volumefield -my_average.em. Click “compute average” in the bottom of the window; wait till it is done. Right click to the output filename and select the [ view] simple 3d depiction of all slicesoption and examine it from X-Y-and Z-views. If you are not satisfied with the result close the window and refine your manual alignment/ add more particles to the average.ALIGNMENT PROJECTIterative alignment of sub-tomograms to the average is performed by dynamo projectsthat you can run in various high-performance computational environments. To have a project you need particles, an initial reference and a table which you have already generated. Start a WIZARD in the Dynamo command window or type >> dcpin Matlab (dcp stands for dynamo_current_project). Type a project name “drun1”and press Enter, dynamo will generate the auxiliary files in a folder drun1. Provide input in the popup windows: folder with particles (and press OK), initial table particles/crop.tbl. You can also provide the quickbuffer.tblwhich has the initial orientations from manual picking, however remember that this table contains only the selected particles. In cases when you don’t have a table you can generate a blank or a random tables that would be consistent with your particle folder but will lack the metadata i.e. real missing wedge values or source tomograms.Go to “template” and input my_average.em. | |
|−|Go to “Masks”, here you can provide several masks for different purposes, now we are only interested in the alignment mask. Use ellipsoid, you can specify semi-axis or go to “Mask editor” to make more sophisticated masks. Note that Dynamo by default uses Rossman correlation which eliminates the artefacts associated with hard (non-soft) mask. As usual you right click on the mask that you generate in order to open it with dview. For this type of simulated data “use default masks” will work.Numerical parameters menu providesflexibilityon how to run your alignment.You can specify different parameters for different rounds of refinement. You can select any value in the table with a mouse and click the “?” button on the top right of the parameter window, this will give you more info. For more parameters check boxes on the left. The most important parameters to consider are: Number of iterations in each round. Make 2 rounds with 3-4 iterations in each. The first round will be global searchwith a course angular step, the second –constrained search with a finer step.If you decided to use a table with prealigned particles (quickbuffer.tbl) you may skip the first round.Angular search ranges: cone apertureis the scan range for the first two Euler angles with a step of cone samplingstarting at the actual angles stored in the table. 360 is the full scan range. Azimuth rotation rangedefines rotation range around the new vertical axis of the particle. High-and lowpass values in Fourier voxels limit the used frequency range. It can be autotuned (below in this manual), here use some reasonable values like half-Nyquist.Particle dimensions –use the sidelength of your box; if you put lower value the particles will be downsampled for the particular round. This will speed up the process.Refineafter each angular scan the search step reduces refine factortimes, this repeats refinetimes. I.e. if your cone sampling is 10 deg, refineand refine factorand 3 and 2 then 10, 5, 2.5 and 1.25 degrees will be sampled.This is the optimization of angular search space. I typically use 2 and 2.Shift limits limits translation of particles from the center of the box (if shifts limiting wayis 1) or from the previously estimated center (if shifts limiting wayis 2). The previous estimates for the shifts and rotations are taken from input tables and are updated at the end of each iteration.Symmetry –if you know the symmetry of your protein it will speed up the convergence and result in higher resolution. Here we don’t make any assumptions.Threshold parameters and modus specify which particles contribute to the average at the end of each iteration. Ourtypical values are [ 1,2] and [0.5,5] .There are predefined parameter sets for Global and Local searches (Predefined profiles -> Global search / Refinement).Afteryou set up the parameters press OK or close the window. | |
|−|Figure 10. Summary of numerical parameters used for sub tomogram averaging.Go to Computing environmentin Wizard and select GPU (with Matlab). Click “Info (nvidia)” it should output you a table to your Matlabwindow, your GPU identifiers are in the left column of the table. Put those into the GPU identifiers to the ‘identifiers” field. Go back to the Wizardwindow, click “check” and ‘unfold”, this will generate a runnable matlab script drun1.m. If you will modify your project and will want to run it again you need to re-unfold the project before each run.Click “run”. Open another Matlab window, activate dynamo, go to the same folder and monitor the progress of the execution by typing in >> dvstatus drun1. After several iterations you can look at intermediate results by typing >> ddb drun1:a –vor >>ddb drun1:a:ite=* -j c10. For more info on ddb type >> help ddb. In the end of your iterations you can monitor the results in the show resultssection of Wizardandshould get something like this. | |
|−|SUB-TOMOGRAM CLASSIFICATIONFirst generate a dataset which has 2 populations of particles. For this type >> dynamo_tutorial(' data_classification' ,' M', 8,'N',8); in your matlabwindow. It generates two sets of particles, 8 particles in each and stores their real orientations in the directory data_classification.MULTI REFERENCE ALIGNMENT (MRA)The idea behind MRA is:iterative alignment of particles to several references and then each particle assigned to the reference where it fits the best. This wayover iterative refinementsimilar particles will have higher correlation to similar references and will eventually group together. MRA is naturally implemented in Dynamo. For classification into N classes we will need N initial references and N initially identical tables that Dynamo will generate automatically. The references should be slightly different to start particle deconvergence, in Dynamo we add small amount of white noise tothe overall average of all particles.Create a new project by typing “drun2” in the project name of Wizardsetnumber of references to 2, set Swap particles on, put in the particle data. Go to “table” and type data_classification/real.tblinto the “clone this” field and press “copy”, press “OK”. Go to “template” and clone data_classification/original_template.emwith additionof some extra noise with amplitude 1. Initialize masks with the defaultsparameters (large button on the bottom). Go to “numerical parameters” and use Predefined profiles -> refinement. Increase angular search steps to 5, seta thresholing policyand press “OK”. Set computing environment to GPU (under Matlab), specify GPU identifiers.Unfold and run the project, this project will run twice longeras each particle will be aligned to two references.Monitor the progress of your project by >> dvstatus drun2and visualize the results as >> ddb drun2:a:ref=* -j c5. You should get something like this –two averages withparticle in one class larger than in the other.CLASSIFICATION WITH PRINCIPAL COMPONENT ANALYSIS AND K-MEANSAn analytical method is described in a tutorial classification_tutorial.pdf, please refer to it with dhelp.INDEPENDENT HALF-DATASET REFINEMENTAligning all particles to one reference may introduce overfitting resulting in resolution overestimation and a risk of overinterpretation of the final map. This is illustrated by a small exercise in the noise_artifacts.pdftutorial. For the generation of the final map it is therefore highly recommended to separate your particles into two halves and perform independent alignment and | |
|−|averaging. At the end of each iteration you can detect the current resolution by Fourier shell correlation and only use your reliable frequencies for the alignment at the next steps. To perform independent half-dataset refinement load your initial project drun1to Wizard (type in the Project field and press Enter or use Ctrl+L). Go to Multireference -> Adaptive filtering -> Derive a project, Dynamo will generate a new project drun1_eo, load it, inspect/update the parameters and run it. After it is done you can measure your resolution based on fsc plots from Multireference -> Adaptive filtering -> Plotattained resolutionand inspect the average the usual way. | |