Motivation: Metagenomic binning facilitates the reconstruction of genomes and identification of Metagenomic Species Pan-genomes or Metagenomic Assembled Genomes. We propose a method for identifying a set of de novo representative genes, termed signature genes, which can be used to measure the relative abundance and used as markers of each metagenomic species with high accuracy.
Results: An initial set of the 100 genes that correlate with the median gene abundance profile of the entity is selected. A variant of the coupon collector's problem was utilized to evaluate the probability of identifying a certain number of unique genes in a sample. This allows us to reject the abundance measurements of strains exhibiting a significantly skewed gene representation. A rank-based negative binomial model is employed to assess the performance of different gene sets across a large set of samples, facilitating identification of an optimal signature gene set for the entity. When benchmarked the method on a synthetic gene catalog, our optimized signature gene sets estimate relative abundance significantly closer to the true relative abundance compared to the starting gene sets extracted from the metagenomic species. The method was able to replicate results from a study with real data and identify around three times as many metagenomic entities.
Availability and implementation: The code used for the analysis is available on GitHub: https://github.com/trinezac/SG_optimization.
Supplementary information: Supplementary data are available at Bioinformatics Advances online.
© The Author(s) 2023. Published by Oxford University Press.