Difference between revisions of "Adaptive bandpass filtering"

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The "golden standard" methodology is a widespread method to drive the alignment process avoiding overfitting and providing an accepted estimation of the attained [[resolution]].
 
The "golden standard" methodology is a widespread method to drive the alignment process avoiding overfitting and providing an accepted estimation of the attained [[resolution]].
  
It bases on splitting the original data set into two halves that
+
It bases on splitting the original data set into two equally sized halves (called even/odd), and aligning them separately. After each iteration, the attained resolution is estimated by comparing the two separately computed averages  through FSC.  The bandpass filter used in the next iteration will be defined based on this estimation.
  
  
Operationally, an ABF project is just a multireference project that  
+
Operationally, an ABF project is just a multireference project that:
 
* doesn't [[swap particles]] between references, and
 
* doesn't [[swap particles]] between references, and
 
* and that adjust the lowpass filter parameters at each iteration.  
 
* and that adjust the lowpass filter parameters at each iteration.  
 +
 +
The most comfortable way to create an ABF project is by first  designing a '''single reference''' project (i.e {{t|myProject}}), and then letting  ''Dynamo'' derive an ABF project, which generally called {{t|myProject_eo}}.
 +
 +
==The ABF procedure workflow==
 +
  
 
==Parameters in an ABF project==
 
==Parameters in an ABF project==
 +
Several parameters can be chosen by the user.
 +
 +
=== Initial frequency==
 +
 +
=== Alignment of half averages===
 +
 +
=== Resolution estimation===
 +
You can choose:
 +
1) the FSC value that defines the resolution estimation
 +
2)
 +
3)
 +
  
 
==Creating an ABF project in dcp==
 
==Creating an ABF project in dcp==

Revision as of 13:58, 14 April 2016


The "golden standard" methodology is a widespread method to drive the alignment process avoiding overfitting and providing an accepted estimation of the attained resolution.

It bases on splitting the original data set into two equally sized halves (called even/odd), and aligning them separately. After each iteration, the attained resolution is estimated by comparing the two separately computed averages through FSC. The bandpass filter used in the next iteration will be defined based on this estimation.


Operationally, an ABF project is just a multireference project that:

  • doesn't swap particles between references, and
  • and that adjust the lowpass filter parameters at each iteration.

The most comfortable way to create an ABF project is by first designing a single reference project (i.e myProject), and then letting Dynamo derive an ABF project, which generally called myProject_eo.

The ABF procedure workflow

Parameters in an ABF project

Several parameters can be chosen by the user.

= Initial frequency

Alignment of half averages

Resolution estimation

You can choose: 1) the FSC value that defines the resolution estimation 2) 3)


Creating an ABF project in dcp

In general, you would design a single reference project. Then you would derive the

Creating an ABF project in the command line