Welcome to CoAn

The name CoAn is derived from the purpose of the software package, that is Correlative Analysis.  CoAn was originally designed for correlation-based docking of atomic models into lower-resolution densities generated by electron microscopy and image reconstruction.  The distinguishing factor of the underlying docking methodology is the use of correlation statistics which explicitly accounts for measurement errors through cross-validation and allows the definition of confidence intervals for the rotational and translational parameters, thus defining a solution set of docked models, all of which are compatible with the data within their margin of error.  Since its inception, CoAn has grown significantly in scope and is now more of a design framework into which different software packages can be integrated with a minimum of work.  This allows the end-user great flexibility in data analysis, and allows him or her to focus on the questions at hand, rather than spending time figuring out how to reformat data from package A into something package B can use.  Overall this accelerates the scientific process and in the greatest tradition of well-designed software, gets the software out of the way.  The current version contains several modules that not only target docking of atomic models and related tasks but also interpretation of electron tomograms, in particular segmentation, denoising, and pattern recognition algorithms.  CoAn is in a constant state of ongoing development, including the recasting of all algorithms into the Python world and efforts to parallelize compute extensive tasks.  Modules currently available to the public for beta testing are:

CoFi - Correlation based Fitting This module contains software for fitting atomic models of rigid body domains into density maps of lower resolution such as those obtained by electron microscopy and image reconstruction. Documentation       Download        Publication

CoDiv - Continental Divide Watershed Segmentation This module contains software for semi-automatic segmentation of density maps such as those obtained by electron microscopy and image reconstruction or electron tomography. Documentation       Download        Publication

iMed - iterative Median Filtering This module contains software for automatic noise reduction using iterative median filtering. Documentation        Download        Publication

CoAn is designed and realized by Niels Volkmann and his team at the Burnham Institute for Medical Research