Benchmarking blast accuracy of genus/phyla classification of metagenomic reads.

Metagenomics is the study of environmental samples. Because few tools exist for metagenomic analysis, a natural step has been to utilize the popular homology tool, BLAST, to search for sequence similarity between sample fragments and an administered database. Most biologists use this method today without knowing BLAST's accuracy, especially when a particular taxonomic class is under-represented in the database. The aim of this paper is to benchmark the performance of BLAST for taxonomic classification of metagenomic datasets in a supervised setting; meaning that the database contains microbes of the same class as the 'unknown' query fragments. We examine well- and under-represented genera and phyla in order to study their effect on the accuracy of BLAST. We conclude that on fine-resolution classes, such as genera, the accuracy of BLAST does not degrade very much with under-representation, but in a highly variant class, such as phyla, performance degrades significantly. Our analysis includes five-fold cross validation to substantiate our findings.