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With the advent of next-generation sequencing and culture-independent methods, we now are accumulating an enormous amount of metagenomic data from microbial communities. These data sets are large, hard to assemble, and might encode rare or novel proteins, posing new computational challenges for protein homology search. This paper presents a novel protein homology search algorithm that combines the salient features of pairwise sequence alignment programs such as Blast and protein family based tools such as Hmmer. It is optimized for protein annotation in metagenomic data sets because: 1) it is fast, 2) it can classify short protein fragments encoded by individual sequence reads, 3) it can find homologs to novel or rare protein families when there is not enough member sequences to build a probabilistic model. Our algorithm builds a new indexing data structure called BlastTree, which can index a large sequence family database because of our effective compression techniques. In addition, BlastTree fully exploits sequence family membership information to improve homology search sensitivity. When the BlastTree Search algorithm is incorporated into Hmmer, it runs in a fraction of the time with comparable quality.