Taxonomy based performance metrics for evaluating taxonomic assignment methods

Abstract

Abstract Background Metagenomics experiments often make inferences about microbial communities by sequencing 16S and 18S rRNA, and taxonomic assignment is a fundamental step in such studies. This paper addresses the weaknesses in two types of metrics commonly used by previous studies for measuring the performance of existing taxonomic assignment methods: Sequence count based metrics and Binary error measurement. These metrics made performance evaluation results biased, less informative and mutually incomparable. Results We investigated weaknesses in two types of metrics and proposed new performance metrics including Average Taxonomy Distance (ATD) and ATD_by_Taxa, together with the visualized ATD plot. Conclusions By comparing the evaluation results from four popular taxonomic assignment methods across three test data sets, we found the new metrics more robust, informative and comparable.

Keywords

• Metagenomics
• Classification
• Performance evaluation
• Data analysis