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Single Particle Data Analysis Suite

Project description


Arachnid is an open source software package written primarily in Python that processes images of macromolecules captured by cryo-electron microscopy (cryo-EM). Arachnid is focused on automating the single-particle reconstruction workflow and can be thought of as two subpackages:

  1. Arachnid Prime
    A SciPy Toolkit (SciKit) that focuses on every step of the single-particle reconstruction workflow up to orientation assignment and classification. This toolkit also includes a set of application scripts and a workflow manager.
  2. pySPIDER
    This subpackage functions as an interface to the SPIDER package. It includes both a library of SPIDER commands and a set of application scripts to run a set of procedures for every step of single-particle reconstruction including orientation assignment but not classification.

Arachnid Prime currently focuses on automating the pre-processing of the image data captured by cryo-EM. For example, Arachnid has the following highlighted applications handle the particle-picking problem:

  • AutoPicker: Automated reference-free particle selection
  • ViCer: Automated unsupervised particle verification

This software is under development by the Frank Lab and is licensed under GPL 2.0 or later.

For more information, see

Alternatively, HTML documentation can be built locally using python build_sphinx, which assumes you have the prerequisite Python libraries. The documents can be found in build/sphinx/html/.

How to cite

The main reference to cite is:

Langlois, R. E., Ho D. N., Frank, J., 2014. Arachnid: Automated Image-processing for Electron Microscopy. In Preparation.

See CITE for more information and downloadable citations.


The required dependencies to build the software are Python >= 2.6, setuptools, Numpy >= 1.3, SciPy >= 0.7, matplotlib>=1.1.0, mpi4py>=1.2.2, scikit-learn, scikit-image, psutil, sqlalchemy, mysql-python, PIL, basemap, FFTW3 or MKL, and both C/C++ and Fortran compilers.

It is also recommended you install NumPy and SciPy with an optimized Blas library such as MKL, ACML, ATLAS or GOTOBlas.

To build the documentation, Sphinx>=1.0.4 is required.

All of these dependencies can be found in a single free binary package: Anaconda.


The prefered method of installation is to use Anaconda:

# If you do not have Anaconda then run the following (assumes bash shell)

sh -b -p $PWD/anaconda
export PATH=$PWD/anaconda/bin:$PATH

# If you have anaconda or just installed it, then run

conda install -c arachnid


# Install from downloaded source

$ python install –prefix=$HOME

# Using Setup tools

$ easy_install arachnid

# Using PIP

$ pip install arachnid

# Using Anaconda

$ conda install -c arachnid


You can check out the latest source with the command:

git clone

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arachnid-0.1.7.tar.gz (10.2 MB view hashes)

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