Adapt

This gzipped tar archive contains the source required to compile an image classification program based on the work of Ogura et al (see references). It is written in C++ and includes a README file that covers its operation.

The program can also be used in the generation of classes for Random Conical Tilt or Orthogonal Tilt Reconstruction using SPIDER. I've found it to be a very robust, automated and useful piece of software that's essential to my processing of images.

Adapt is a piece of working, in-house software and as such hasn't been extensively tested on many hardware setups and systems. The software is provided 'as is' but I'm commited to supporting it so please email questions, comments and problems to me, . If you find it works well for you, please let me know!

Note: Release 2.01 compiles cleanly under gcc 4.3.x and includes a bugfix for a segfault condition in the previous releasee.

Usage

Required Inputs

- an IMAGIC image stack, most likely the output of a multi-reference alignment (MRA), to be classified into groups.

- various parameters of classification described in the README and Ogura paper.

- the number of nodes in the network (e.g., the number of classes wanted).

Outputs

- an IMAGIC image stack corresponding to 'node images' (see Ogura paper)

- an IMAGIC image stack of class averages

- SPIDER format select files for each class.

References

Ramey VH, Wang HW, Nogales E. (2009) Ab initio reconstruction of helical samples with heterogeneity, disorder and coexisting symmetries. J Struct Biol., E-pub ahead of print.

Ogura T, Iwasaki K, Sato C. (2003) Topology representing network enables highly accurate classification of protein images taken by cryo electron-microscope without masking. J Struct Biol., 143(3), 185-200.

Download

Download Adapt2_01.tar.gz (12k)
View README