Scaling metagenomic classification · Kylepedia

Scaling metagenomic classification


Title: Scalable metagenomic taxonomy classification using a reference genome database

Ames, Sasha K., et al. “Scalable metagenomic taxonomy classification using a reference genome database.” Bioinformatics 29.18 (2013): 2253-2260.


Presents a new taxonomic classification algorithm, with the aim of scaling well to analyze large metagenomic (typically shotgun)datasets. This methods has very high memory requirements (0.5 - 1 terabytes). The authors aim was to classify to the species level when possible.

Tool is a part of the Livermore Metagenomics Analysis Toolkit (LMAT).1

Background Notes

Technical Details


Some Thoughts

This approach could prove to be beneficial for centers that analyze large shotgun metagenomic datasets, on an ongoing basis. In practice,the memory requirements could be prohibitive for most labs. It also would have been nice to see the comparison to other tools be based off of the same reference database.

Here are a few things that I found noteworthy: