  Single particle cryo-electron microscopy has become a critical tool in
structural biology over the last decade, able to achieve atomic scale
resolution in three dimensional models from hundreds of thousands of (noisy)
two-dimensional projection views of particles frozen at unknown orientations.
This is accomplished by using a suite of software tools to (i) identify
particles in large micrographs, (ii) obtain low-resolution reconstructions,
(iii) refine those low-resolution structures, and (iv) finally match the
obtained electron scattering density to the constituent atoms that make up the
macromolecule or macromolecular complex of interest.
  Here, we focus on the second stage of the reconstruction pipeline: obtaining
a low resolution model from picked particle images. Our goal is to create an
algorithm that is capable of ab initio reconstruction from small data sets (on
the order of a few thousand selected particles). More precisely, we propose an
algorithm that is robust, automatic, and fast enough that it can potentially be
used to assist in the assessment of particle quality as the data is being
generated during the microscopy experiment.
