On the quality of tree-based protein classification

Motivation: Phylogenetic analysis of protein sequences is widely used in protein function classification and delineation of subfamilies within larger families. In addition, the recent increase in the number of protein sequence entries with controlled vocabulary terms describing function (e.g. the Gene Ontology) suggests that it may be possible to overlay these terms onto phylogenetic trees to automatically locate functional divergence events in protein family evolution. Phylogenetic analysis of large datasets requires fast algorithms; and even 'fast', approximate distance matrix-based phylogenetic algorithms are slow on large datasets since they involve calculating maximum likelihood estimates of pairwise evolutionary distances. There have been many attempts to classify protein sequences on the family and subfamily level without reconstructing phylogenetic trees, but using hierarchical clustering with simpler distance measures, which also produce trees or dendrograms. How can these trees be compared in their ability to accurately classify protein sequences? Results: Given a 'reference classification' or 'group membership labels' for a set of related protein sequences as well as a tree describing their relationships (e.g. a phylogenetic tree), we propose a method for dividing the tree into monophyletic or paraphyletic groups so as to optimize the correspondence between the reference groups and the tree-derived groups. We call the achieved optimal correspondence the 'accuracy of a tree-based classification (TBC)', which measures the ability of a tree to separate proteins of similar function into monophyletic or paraphyletic groups. We apply this measure to compare classical NJ and UPGMA phylogenetic trees with the trees obtained from hierarchical clustering using different protein similarity measures. Our preliminary analysis on a set of expert-curated protein families and alignments suggests that there is no uniformly superior algorithm, and that simple protein similarity measures combined with hierarchical clustering produce trees with reasonable and often the most accurate TBC. We used our measure to help us to design TIPS, a tree-building algorithm, based on agglomerative clustering with a similarity measure derived from profile scoring. TIPS is comparable with phylogenetic algorithms in terms of classification accuracy and is much faster on large protein families. Due to its time scalability and acceptable accuracy, TIPS is being used in the large-scale PANTHER protein classification project. The trees produced by different algorithms for different protein families can be viewed at http://panther.appliedbiosystems.com/pub/tree_quality/trees.jsp. For every tree and every level of classification granularity we provide the optimal TBC along with the reference classification. \