title: Visualizing sequence similarity of protein families.
abstract: Classification of proteins into families is one of the main goals of functional analysis. Proteins are usually assigned to a family on the basis of the presence of family-specific patterns, domains, or structural elements. Whereas proteins belonging to the same family are generally similar to each other, the extent of similarity varies widely across families. Some families are characterized by short, well-defined motifs, whereas others contain longer, less-specific motifs. We present a simple method for visualizing such differences. We applied our method to the Arabidopsis thaliana families listed at The Arabidopsis Information Resource (TAIR) Web site and for 76% of the nontrivial families (families with more than one member), our method identifies simple similarity measures that are necessary and sufficient to cluster members of the family together. Our visualization method can be used as part of an annotation pipeline to identify potentially incorrectly defined families. We also describe how our method can be extended to identify novel families and to assign unclassified proteins into known families.
authors.affiliation: Department of Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
authors.forename: Vamsi
authors.initials: V
authors.lastname: Veeramachaneni
authors.forename: Wojciech
authors.initials: W
authors.lastname: Makałowski
mesh_terms: D029681:Arabidopsis Proteins / Q000737:chemistry / Q000145:classification*; D016000:Cluster Analysis; D019295:Computational Biology / Q000706:statistics & numerical data; D003196:Computer Graphics / Q000706:statistics & numerical data; D030562:Databases, Protein; D017386:Sequence Homology, Amino Acid; D012984:Software
chemical_list: D029681:Arabidopsis Proteins