Normalizing projection images: a study of image normalizing procedures for single particle three-dimensional electron microscopy.

In the process of three-dimensional reconstruction of single particle biological macromolecules several hundreds, or thousands, of projection images are taken from tens or hundreds of independently digitized micrographs. These different micrographs show differences in the background grey level and particle contrast and, therefore, have to be normalized by scaling their pixel values before entering the reconstruction process. In this work several normalization procedures are studied using a statistical comparison framework. We finally show that the use of the different normalization methods affects the reconstruction quality, providing guidance on the choice of normalization procedures.

Self-organizing tree-growing network for the classification of protein sequences.

The self-organizing tree algorithm (SOTA) was recently introduced to construct phylogenetic trees from biological sequences, based on the principles of Kohonen's self-organizing maps and on Fritzke's growing cell structures. SOTA is designed in such a way that the generation of new nodes can be stopped when the sequences assigned to a node are already above a certain similarity threshold. In this way a phylogenetic tree resolved at a high taxonomic level can be obtained. This capability is especially useful to classify sets of diversified sequences. SOTA was originally designed to analyze pre-aligned sequences. It is now adapted to be able to analyze patterns associated to the frequency of residues along a sequence, such as protein dipeptide composition and other n-gram compositions. In this work we show that the algorithm applied to these data is able to not only successfully construct phylogenetic trees of protein families, such as cytochrome c, triosephophate isomerase, and hemoglobin alpha chains, but also classify very diversified sequence data sets, such as a mixture of interleukins and their receptors.