Raffaele Coray at 13:16, 2 December 2024

2024-12-02T13:16:08Z

Daniel Castaño: /* Getting the data */

2023-10-26T12:21:01Z

Getting the data

Daniel Castaño: /* Getting the data */

2023-10-26T12:20:41Z

Getting the data

AlmaVivas: Created page with "This walkthrough shows the approach to process proteins that cover spheroidal vesicles following (approximately) a structured lattice. For demonstration, we use a highly binne..."

2023-09-08T16:31:14Z

Created page with "This walkthrough shows the approach to process proteins that cover spheroidal vesicles following (approximately) a structured lattice. For demonstration, we use a highly binne..."

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This walkthrough shows the approach to process proteins that cover spheroidal vesicles following (approximately) a structured lattice. For demonstration, we use a highly binned tomogram with several viral capsides.

== Data ==

The data has been made available by Florian Schur. This single tomogram is part of the data set used for the paper ''An atomic model of HIV-1 capsid-SP1 reveals structures regulating assembly and maturation'' by Schur FK, Obr M, Hagen WJ, Wan W, Jakobi AJ, Kirkpatrick JM, Sachse C, Kräusslich HG and Briggs JA. (2016)

=== Getting the data ===

Open a new local terminal and navigate to the directory where the practices are located:

<tt > d/embo2021/u/emboXX/prac-6

Inside this folder, the file of interest is <code>v17.rec</code>.

=== Viewing the data ===

The tomogram is severely binned, so it will probably fit in memory without major problems. We can just use our typical tool to quick check a tomogram.

<tt> dtmshow v17.rec </tt>

[[ File:HivDtmshow.png |thumb|center|500px| <tt>dtmshow</tt> on the sample tomogram. ]]

== Annotation of a tomogram ==

We are interested in cropping the particles between the two layers of each one of the viruses.

[[ File:HivBox.png |thumb|center|400px| Structure to average]]

We open the tomogram v17.rec with <tt>dtmslice</tt>

<tt>dtmslice v17.rec -c hiv</tt>

Here, the flag <tt>-c hiv</tt> will create a catalogue that will contain all the annotations that we perform on this volume (through the opened <tt>dtmslice</tt> session)

=== DipoleSet models ===

In our approach, we will first use a single model to manually input the approximate centers and radiuses of all the viruses. We will use the model type <tt>dipoleSet</tt>. This model allows to describe each annotated virus as a ''dipole'': we will mark the (approximate) center of the virus as the <tt>center</tt> property of a dipole. The radius of the virus will be described by an annotation of the <tt>north</tt> property of the dipole.

Notice that ''Dynamo'' includes several tools for [[vesicle models]], so you might be surprised that for this particular case we go instead for this (somewhat obscurely named) ''dipoleSet'' type of model. This is just a question of convenience: the <tt>dipoleSet</tt> allows to quickly click a series of centers and radiuses and store them in a ''single'' model. That's all you need to describe vesicular geometry. Additionally, the <tt>dipoleSet</tt> includes some tools for quickly extracting particles from a ''multivesicular'' set, and it also offers a useful on-screen representation in the <tt>dtmslice</tt> tomogram browser.

[[File:HivSelectModel.png|thumb|center|600px| Selecting the ''dipoleSet'' model ]]

When the model is active, we use the key <tt>c</tt> to mark the <tt>center</tt> of the current dipole, and <tt>n</tt> to mark the north. It will create a semitransparent sphere enclosing the completed dipole.

[[File:HivClickDipole.png |thumb|center|600px| Selecting a ''dipole'': click on <tt>C</tt> for the center, then <tt>N</tt> for the north]]

=== Corrections during picking ===

Further clicks on <tt>c</tt> or <tt>n</tt> will just move the <tt>center</tt> or the <tt>north</tt> of the ''same'' dipole. In order to click a further dipole, you need to '''click on <tt>enter</tt>''' and "open" the next dipole for clicking. If you forget to click on <tt>enter</tt> before annotating points on the next vesicle, you'll end up with situation like this:

[[ File:HivForgetDipoleOpening.png |thumb|center|600px| Wrong input: <tt>enter</tt> needed to be clicked between vesicles 2 and 3]]

Here, the user probably clicked on <tt>c</tt> for vesicle 2 in its real center, then clicked on <tt>n</tt> for the north point, and then moved to vesicle 3, clicking on <tt>c</tt> trying to mark the center. As a new dipole was not opened (i.e., the <tt>enter</tt> key was not pressed) when the vesicle 2 was finished, the center of vesicle 2 got misplaced. When this happens, simply press on <tt>h</tt> to hide the spheres and click the center of vesicle 2 back in its position. Then, press on <tt>enter</tt> to start the next vesicle.

=== Corrections after picking ===

It is a good idea to observe your annotation from the ''x'' or ''y''perspectives (for this, press on keys <tt>x</tt> or <tt>y</tt> when the cursor is on the slide). Sometimes you might identify situations like this one:

[[ File:HivWrongOnY.png|thumb|center|600px| Vesicle is too poorly picked]]

In such cases, you want to remove the dipole and click it again. The easiest way to do so is to:
# click on 'h' to hide the spheres, and
# secondary click on the wrongly created dipole (you have to click on the line itself) and select the ''Remove and prepare for reenter'' option in the menu that will popup.

[[ File:HivWrongYReselect.png|thumb|center|600px| Remove dipole and click again]]

When you are finished, don't forget to save the tomogram into the catalogue, using the disk icon (or the ''Active Model'' control)

[[ File:HivFinalSelection.png |thumb|center|600px| Save model into catalogue]]

== Cropping the particles ==

We will describe a protocol to be carried from the command line, explaining each step. In short, we will visit each dipole in the model we just created, define a spherical vesicle centered on the dipole, use it to define a regular distribution of points on each vesicle, define positions pointing outwards and format everything as a [[table]] that can be used directly by ''Dynamo'' to operate an extraction and a subsequent alignment.

=== Table extraction ===

==== In Matlab ====

If you are working in Matlab, you can just proceed with <tt>dcmodels</tt> to find the location of your dipole set.

<nowiki>dcmodels hiv -tc dipoleSet -ws o -gm 1

% assumes that you only have one model file
ds = o.models{1};</nowiki>

===== Create a table for each vesicle =====

Now we can loop on each dipole contained in the dipole set <tt>ds</tt>. The next lines should be copied into a text file (using matlab command <tt>edit</tt>), and executed.

<nowiki>NDipoles = length(ds.dipoles);

for i=1:NDipoles;
v =dmodels.vesicle();

v.center = ds.dipoles{i}.center;

% defines the radius in terms of the clicked dipole
v.radius = norm( ds.dipoles{i}.north - ds.dipoles{i}.center);

% create a separation distance in the vesicle
v.separation = 20;

% This parameter sets the cropping point a number of pixels away from the vesicle
% this distance in pixels is measured in the normal direction.
% here we just use the default value.
v.crop_distance_from_surface =0;

% creates the crop points and respective angles
v.updateCrop();

% extracts a table
tv{i} = v.grepTable();

% marks the table assigning in the column 22 (foreseen for arbitrary use)
% the number of the individual dipole
tv{i}(:,22) = i;

% provides information on the output

disp(sprintf('Dipole %d will provide %d crop points',i,size(v.crop_points,2)));
end </nowiki>

==== In the standalone ====

The standalone can execute matlab scripts, with some restrictions: the scripts need to be self contained: any traffic of variables needs to be carried through writing and reading files. There is no other possibility of transferring workspace variables between your session and the script.

===== Creating a text file =====

Create a text file named <tt>commands.m</tt> (arbitrary name) with any text editor available in your system. In Linux <tt>gedit</tt> is probably available.

<tt>gedit commands.m & </tt>

Now paste the text of the commands excluding comments (# simbols).

<nowiki>ds = dread(<YOUR FILE>);;
NDipoles = length(ds.dipoles);

for i=1:NDipoles;
v =dmodels.vesicle();
v.center = ds.dipoles{i}.center;
v.radius = norm( ds.dipoles{i}.north - ds.dipoles{i}.center);
v.separation = 20;
v.crop_distance_from_surface =0;
v.updateCrop();
tv{i} = v.grepTable();
tv{i}(:,22) = i;
disp(sprintf('Dipole %d will provide %d crop points',i,size(v.crop_points,1)));
end
dwrite(tv,'cellOfTables.mat');
</nowiki>

* Replace <tt><YOUR FILE></tt> with the actual <tt>.omd</tt> file that you created containing the dipoleSet model.
* Notice that we changed the variable input/output, by hardcoding the names of the files that need to be read and created.

===== Executing the text file =====

In a system shell or in ''Dynamo''console, type:

<tt>dynamo commands.m</tt>

===== Getting the information back into the ''Dynamo''console =====
In the ''Dynamo'' console, type.
<tt>tv = dread('cellOfTables.mat'); </tt>

=== Merging the tables ===

Each dipole with number <tt>i</tt> generated a table stored as <tt>tv{i}</tt>, i.e., the entry <tt>i</tt> inside the cell array <tt>t</tt>. We want to generate a single table. Concatenating the table matrixes will not work, as it would have several particles with the same particle tags. We thus ask ''Dynamo'' to operate the table merging by itself, and renaming the particle tags. With the order:

<tt>tAll = dynamo_table_merge(tv,'linear_tags',1);</tt>

we concatenate the tables inside the cell array <tt>tv</tt> and set the particle tags (in column 1) to be named from 1 to the maximal number of particles to be extracted from all the vesicles. You can check the result through

<tt>dtinfo(tAll);</tt>

or graphically through:

<tt>dtplot(tAll,'pf','oriented_positions');axis equal </tt>

[[File:HivDtplotAll.png |thumb|center|600px| <tt>dtplot</tt> graphic of merged table]]

There are several programs to depict tables. In this case we may find <tt>dpktbl.plot.disks</tt> useful, as it allows to depict the particles in a table with an associated extent. This is useful to check how a given sidelength in pixels covers the vesicles for a given particle distribution.

<tt>dpktbl.plots.disks(tAll,'r',10);</tt>

[[ File:HivDisks.png|thumb|center|600px| <tt>dpktbl.plot.disks</tt> graphic of merged table]]

=== Create a data folder ===

We can now create our typical [[data folder]] with dtcrop with a single command.
<nowiki>targetFolder = 'testData';
sidelength = 40;
check = dtcrop(ds.cvolume.file,tAll,targetFolder,sidelength); </nowiki>

Note that that <tt>dtcrop</tt> will create a new file that contains only the particles that were actually cropped from the tomogram. Some particles were probably excluded if they were too close to the boundary of the tomogram. We can read this table file into memory.

<tt>finalTable = dread([targetFolder,'/crop.tbl']);</tt>

Checking the particles can be done with the interactive browsers <tt>ddbrowse</tt> or <tt>dgallery</tt>, but for a quick check we can use the <tt>dslices</tt> command:

<tt>dslices(targetFolder,'projy','*','t',finalTable,'align',1,'tag',1:10:700,'labels','on');</tt>

Here, we selected only one every ten tags to get a general idea on what is going on. The flag <tt>'projy','*'</tt> creates a projection of the aligned particles along ''y'', projecting all the slides, so that we can check that we are indeed greping the capsides.

[[ File:HivDdbrowseInitial.png |thumb|center|600px| <tt>dslices</tt> on a subset of particles, ''y'' projection view]]

We see that there are different problems: we are not hitting the center of the region of interest in many particles, and in some particles the region is out of scope.

=== Create a coarse average ===

We create a "coarse average" by just averaging all the particles with the orientations from the table, i.e, just the normals.

<tt>oa = daverage(targetFolder,'t',finalTable,'fc',1); </tt>

We can quickly check the result through:

<tt>dview(oa)</tt>

[[ File:HivAverageLow.png |thumb|center|600px| <tt>view</tt> of the coarse average]]

In this case, the area of interest is probably too low in the direction 'z': the upper half of the average is occupied by buffer. We want to lift the cropping point of the particles a little bit "downwards", that is, in the negative normal direction.

=== Recropping the particles ===

Well, the protocol that we have described step by step in the previous steps can actually be completed with a single command: the method <tt>getTableFromEnclosingSpheres</tt>

A complete syntax description can be found it:

<tt>help dmodels.dipoleSet.getTableFromEnclosingSpheres</tt>

We just want to extract a data folder where the particles are better centered and have bit more of space, so that particles stamming from spheres that are poorly defined still have a chance to get driven by a posterior alignment to the center of the box. If we initialize some variables:

<nowiki>sep = 20; % for 'separation' flag. This is the separation between particles
cdfs = -12; % for 'crop_distance_from_surface' flag, in pixels
df = 'centeredData'; % for 'dataFolder' flag. It is the data folder were the particles will be created.
sidelength = 64;</nowiki>

the full task can be completed in one step:

<tt>[tnew,og] = ds.getTableFromEnclosingSpheres('sep',sep,'cdfs',cdfs,'df',df,'s', sidelength,'average',1,'mw',2);</tt>

Here the <tt>mw</tt> flag just calls for 2 cores to carry the average. If you don't have a license for the Parallel Toolbox, simply don't use the flag.

Also, notice that two outputs are produced. The first one is just the table, (including also excluded particles!). The second one includes a more detailed output, including a field <tt>averageOutput</tt> that corresponds to the output of the average operation on the newly created data folder <tt>centeredData</tt>. You can view it with the normal <tt>dview</tt> command:

<tt>dview(og.averageOutput.average);</tt>

[[ File:HivAverageCentered.png |thumb|center|600px| <tt>dview</tt> on ''y'' and ''z'' of the newly computed coarse average]]

== Alignment project ==

The particles can now be aligned using the table and template that we just defined. We just need to write them into files

<tt>dwrite(dread([df,'/crop.tbl']),'centered.tbl');</tt>

and the density map hidden in the output:

<tt>dwrite(og.averageOutput.average,'centered.em');</tt>

and create a [[alignment project | project]]

<tt>pr = 'pcnt'; % pr is a variable, "pcnt" is a literal string </tt>

We create an empty project pointing to the right data, template and table:

<tt>dcp.new(pr,'d',df,'t','centered.tbl','template','centered.em','masks','default','show',0);</tt>

Putting the 'show' flag to zero prevented the [[Dcp GUI | <tt>dcp</tt> GUI]] from popping up, as we want to use this walkthrough as an example of how to create and manage projects from the command line. The basic tool is <tt>dvput</tt>, which is used to change one or several project parameters. A list of the parameters and their syntax can be printed with <tt>dvhelp</tt> or consulted through the GUI <tt>dhelp</tt>. For this particular project we suggest the set below:

<nowiki>dvput(pr,'ite_r1',4);
dvput(pr,'dim_r1',32);</nowiki>

In the stage of finding general orientations and coarse centerings, we want to save computing time (without trading accuracy). Thus, we use the <tt>'dim_r1'</tt> parameters tune to 32. As the particle boxes have a sidelength of 64 pixels, here we are asking ''Dynamo'' to bin each particle on the fly, just one time. A neighboorhod of 2x2x2 voxels will collapse into one single voxel, with the averaged intensity. By now, we are just interested in coarsely locating the particles, so that the speed-up in the alignemnt (8x) justifies the loss of accuracy.

Next, we need angular settings reflect that we trust the normals that we have found. This will save computation time and make the alignment more robust.

<nowiki>dvput(pr,'cr_r1',40);
dvput(pr,'cs_r1',20);
dvput(pr,'ir_r1',360);
dvput(pr,'is_r1',40);
dvput(pr,'rf_r1',4);
dvput(pr,'rff_r1',2);</nowiki>

We let the particles shift, but not the the edges of the box:

<nowiki>dvput(pr,'lim_r1',[20,20,20]);
dvput(pr,'limm_r1',1);</nowiki>

Some parameters re provided to control the execution. For projects to be run in parallel under Matlab, we need the <tt>'destination'</tt> parameter tuned to value <tt>'matlab_parfor'</tt>. We also provide a value for the number of cores to be used during the alignments (parameter <tt>'cores'</tt>) and the averaging steps (parameter <tt>matlab_workers_average</tt>).

<tt>dvput(pr,'dst','matlab_parfor','cores',2,'mwa',2);</tt>

=== Run project ===

As we would do the GUI, we have to check, unfold and run the project:

<nowiki>dvcheck pcnt
dvunfold pcnt
dvrun pcnt</nowiki>

==== In scripted protocols ====
''Advanced'': A note for future projects: when your own scripts to run several projects created dynamically, you can put the name of the project into a variable (as we did above) and use the <tt>( ) </tt> notation:
<nowiki>dvcheck(pr);
dvunfold(pr);
dvrun(pr);</nowiki>
When used inside a script, you probably want to use the <tt>;</tt> at the end of each line, to prevent flooding the screen with output.

=== Check results ===

We can check how the particles look like:

<tt>dslices('pcnt:data','projy','*','t','pcnt:rt:ite=3','align',1,'tag',1:10:700,'labels','on');</tt>

Here, <tt>'pcnt:data'</tt> is a database query. it is just a shorthand to tell ''Dynamo'' to use the data folder associated with the project <tt>pcnt</tt>. The same trick is used to extract the refined table (<tt>rt</tt>) at iteration 3. Don't worry if you don't like this notation: you can just input the actual folder and file names.

[[ File:HivDDbrowseCentered.png |thumb|center|600px| <tt> dslices </tt> of a selection of aligned particles]]

The averages can be consulted with <tt>mapview</tt>. We open the averages (<tt>a</tt> item) corresponding to iterations 0 and 4:

<tt>dpkdev.legacy.dynamo_mapview('pcnt:a:ite=[0,4]')</tt>

You can tweak the visualization to see a depiction of how the average evolved from the first template to iteration number 4.

[[ File:HivMapviewEvolution.png |thumb|center|600px| Interactive slice comparison through <tt>mapview</tt> ]]

As a tip, when you know exactly which features you want to test visually, you can use the command line order <tt>dslices</tt> to directly produce the scene of interest:

<tt>dslices pcnt:a:ite=[0,4] -projy 44 -ns 1</tt>

[[ File:HivSlicesEvolution.png|thumb|center|600px| Programatic slice comparison through <tt>dslices</tt> ]]

== Recropping the particles ==

You can create a new table excluding particles that are too close to each other:

<tt>tc = dpktbl.exclusionPerVolume('pcnt:rt:ite=4',15,22);</tt>

where 15 is a distance in pixels. The third parameter denotes the column in the table that denotes particles in the same objects. In this case, we have marked each virus with a different integer in column 22 of the table, and we pass this information to <tt>dpktbl.exclusionPerVolume</tt>.

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	This walkthrough shows the approach to process proteins that cover spheroidal vesicles following (approximately) a structured lattice. For demonstration, we use a highly binned tomogram with several viral capsides.		This walkthrough shows the approach to process proteins that cover spheroidal vesicles following (approximately) a structured lattice. For demonstration, we use a highly binned tomogram with several viral capsides.

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 Open a new local terminal and navigate to the directory where the practices are located:
-  <tt > dynamomo_classics/sphericalGeometries/ </tt>
+  <tt> dynamo_classics/spherical_geometries/ </tt>
 Inside this folder, the file of interest is <code>v17.rec</code>.

Walkthrough for lattices on vesicles (EMBO2023) - Revision history

Raffaele Coray at 13:16, 2 December 2024

Daniel Castaño: /* Getting the data */

Daniel Castaño: /* Getting the data */

AlmaVivas: Created page with "This walkthrough shows the approach to process proteins that cover spheroidal vesicles following (approximately) a structured lattice. For demonstration, we use a highly binne..."