Compact HIV1 walkthrough (PSI 2023) - Revision history

Laura Garcia Cornejo at 12:38, 26 December 2025

2025-12-26T12:38:19Z

2024-12-02T13:15:33Z

2024-03-06T12:29:24Z

Subtomogram averaging

2024-03-05T15:15:55Z

Preparation and management of data

2024-03-05T14:33:01Z

Subtomogram averaging

2024-03-05T14:31:49Z

Dynamo models and particles

2023-08-17T13:28:52Z

Extraction of coarse half maps

2023-08-17T13:28:31Z

Extraction of refined half maps

2023-08-17T13:27:34Z

Computing an average of averages

2023-08-17T13:27:06Z

Computing an average of averages

← Older revision		Revision as of 12:38, 26 December 2025
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		+	__NOINDEX__
	[[Category:WalkthroughsSpecific]]		[[Category:WalkthroughsSpecific]]

← Older revision		Revision as of 13:15, 2 December 2024
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		+	[[Category:WalkthroughsSpecific]]
		+
	= The compact version of the HIV1 walkthrough =		= The compact version of the HIV1 walkthrough =

@@ Line 1,078: / Line 1,078: @@
 stackName{idx} = D{1,2}{idx,1}(end-7:end-4);
 title(['Stack: ' stackName{idx}])</pre>
 Similarly to the subboxing section, is interesting to see how the filtering strategy worked. So:

@@ Line 510: / Line 510: @@
 will help to create a catalogue, as a centralised manager of tomograms.
+The binned version of the tomogram is already computed. The factor selected was 2. In any case, these versions will be saved inside the folder of the tomograms, not inside the catalogue.
 === Particle picking ===

@@ Line 1,033: / Line 1,033: @@
 Open the corresponding script:
-<pre style="color: blue">open coarse_gold_standard_alignment.m</pre>
+<pre style="color: blue">open coarse_gold_standard_alignment_wc.m</pre>
 From the last project, the tables will be gathered. Column 10 stores the CC between each aligned subvolume and the final average. The exclusion process is done by fitting a Gaussian mixture model (GMM) to the such  distribution. The MATLAB function fitgmdist will help to classify them into a combination of two populations. The one with the lower CC score is assumed to be the “bad” class (i.e., particularly bad quality or subvolumes containing pure noise). Therefore, it  needs to be excluded from the processing. The threshold is then defined by taking the minimum between the 2 Gaussian peaks and by adding an empirically defined constant of 0.01.

@@ Line 1,114: / Line 1,114: @@
 Open the corresponding script:
-<pre style="color: blue">open refined_gold_standard_alignment.m </pre>
+<pre style="color: blue">open refined_gold_standard_alignment_wc.m </pre>
 This time, to remove particles of low quality, the dataset is reduced through a normalised, per tomogram, CC filtering. The reason for thresholding  separately is to avoid reducing the coverage of the zeros of the CTF. The risk emerges from different defocus values of the tomograms influencing the overall CC score. With the help of <code>dpksta.filters.byCC</code>, subvolumes with a score lower than 1 standard deviation below the mean are removed. This function performs normalisation by the angle of latitude. This is important, since particles at the poles of the VLPs generally have a lower CC score than particles on the equator due to missing wedge effects. More precisely, this function works by fitting the first 2 terms of a general Fourier series to the data. As usual, after the tables are pruned, a new average is computed that will serve as reference for the last alignment project.

@@ Line 924: / Line 924: @@
 - Centers.</pre>
-The motivation behind this computation is to minimise manual labour in the next step. It will run quickly (near 15 minutes 8 workers and 8 gpus), since it only contains 5 particles. To check the final average,
+The motivation behind this computation is to minimise manual labour in the next step. It will run quickly (near 15 minutes 8 workers and 8 gpus), since it only contains 5 particles.
 <pre style="color: blue">aPath = ddb([pr_a ':as:ite=last']);

@@ Line 913: / Line 913: @@
 To complete this small project, open the corresponding <code>aligment_for_average.m</code> and change the paths inside,
-<pre style="color: blue">open aligment_for_reference.m
+<pre style="color: blue">open aligment_for_reference_wc.m
 Change paths.</pre>
@@ Line 924: / Line 924: @@
 - Centers.</pre>
-The motivation behind this computation is to minimise manual labour in the next step. It will run quickly (near 15 minutes 8 workers), since it only contains 5 particles. To check the final average,
+The motivation behind this computation is to minimise manual labour in the next step. It will run quickly (near 15 minutes 8 workers and 8 gpus), since it only contains 5 particles. To check the final average,
 <pre style="color: blue">aPath = ddb([pr_a ':as:ite=last']);