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 for Python, which we call pyCoAn, 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, as more of the original algorithms are being refactored as Python modules, along with continued work on parallelizing compute-intensive tasks.