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Single Particle Data Analysis Suite

Project Description

Arachnid

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 http://www.arachnid.us.

Alternatively, HTML documentation can be built locally using python setup.py 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.

Dependencies

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.

Install

The prefered method of installation is to use Anaconda:

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

wget http://repo.continuum.io/miniconda/Miniconda-3.0.0-Linux-x86_64.sh
sh Miniconda-3.0.0-Linux-x86_64.sh -b -p $PWD/anaconda
export PATH=$PWD/anaconda/bin:$PATH

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

conda install -c https://conda.binstar.org/ezralanglois arachnid

Alternatives:

# Install from downloaded source

$ python setup.py install –prefix=$HOME

# Using Setup tools

$ easy_install arachnid

# Using PIP

$ pip install arachnid

# Using Anaconda

$ conda install -c https://conda.binstar.org/ezralanglois arachnid

Development

You can check out the latest source with the command:

git clone https://github.com/ezralanglois/arachnid/arachnid.git
Release History

Release History

This version
History Node

0.1.7

History Node

0.1.6

History Node

0.1.5

History Node

0.1.4

Download Files

Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
arachnid-0.1.7.tar.gz (10.2 MB) Copy SHA256 Checksum SHA256 Source Mar 28, 2014

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