Taxonomy based performance metrics for evaluating taxonomic assignment methods.
BMC Bioinformatics
; 20(1): 310, 2019 Jun 11.
Article
en En
| MEDLINE
| ID: mdl-31185897
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 assignmentmethods:
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.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Clasificación
/
Metagenómica
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Taiwán