FRM alignment

Fast Rotational Matching alignment

Overview

This approach achieves basically the same task as stated here, but much faster and more accurate. Some other functionalities are also included, such as resolution determination according to gold-standard FSC and CTF correction using Wiener filter.

This is by no means a comprehensive document about the mathematical details. For that, please refer to the paper: Fast and accurate reference-free alignment of subtomograms, Y. Chen et al., JSB 2013.

If you are a advanced user and for some reason you want to integrate this fast algorithm into your own software, it is also possible and quite straight forward. Check out the underlying library SH Alignment here (written in C and Python) and follow the instruction described below.

Script description

Here we assume you already have the subtomograms to be aligned, either by template matching or any other means. What matters here is the subtomograms on disk and the corresponding particle list file describing all the relevant information (please check here if you already have the subtomograms and want to generate the particle list).

Depending on the purpose, there are four scripts that could be used for the alignment, which are all contained in the module pytom.frm. They can be classified into two categories: with-/without gold standard, with-/without Wiener filtering. You should choose one that fits to your problem. The ways to use all these scripts are similar and they are described below.

Script names and functionalities

	Non gold standard	Gold standard
Non Wiener filter	FRMAlignment.py	GFRMAlignment.py
Wiener filter	WienerFilterAlignment.py	GWienerFilterAlignment.py