Estimating the total genome length of a metagenomic sample using k-mers

Abstract

Background: Metagenomic sequencing is a powerful technology for studying the mixture of microbes or the microbiomes on human and in the environment. One basic task of analyzing metagenomic data is to identify the component genomes in the community. This task is challenging due to the complexity of microbiome composition, limited availability of known reference genomes, and usually insufficient sequencing coverage.

Results: As an initial step toward understanding the complete composition of a metagenomic sample, we studied the problem of estimating the total length of all distinct component genomes in a metagenomic sample. We showed that this problem can be solved by estimating the total number of distinct k-mers in all the metagenomic sequencing data. We proposed a method for this estimation based on the sequencing coverage distribution of observed k-mers, and introduced a k-mer redundancy index (KRI) to fill in the gap between the count of distinct k-mers and the total genome length. We showed the effectiveness of the proposed method on a set of carefully designed simulation data corresponding to multiple situations of true metagenomic data. Results on real data indicate that the uncaptured genomic information can vary dramatically across metagenomic samples, with the potential to mislead downstream analyses.

Conclusions: We proposed the question of how long the total genome length of all different species in a microbial community is and introduced a method to answer it.

Keywords: Distinct k-mers; Genome length; Metagenomics; Sequencing coverage.

Fig. 1

Overview of the proposed method. a An illustration of understanding DNA sequence as a collection of k-mers. In this simple case, sequence length L=12, k=6 for the k-mer counting, TKC= L−k+1=7, DKC=5, KRI=TKC/DKC=1.2. b Relationships between metagenome, metagenomic sample and the set of distinct genomes in the metagenome. c Workflow of the proposed method

Fig. 2

Different microbial communities are simulated to test the performance of the proposed method. (a) Results for microbial communities with 10 species. The three histograms on the left show the abundance distributions of different simulated communities. The middle panel shows the estimation results of distinct k-mer count. Each bar represents an estimation result based on a synthetic metagenomic sample and the error bar shows the 95% bootstrap confidence interval of the estimation. The black dash line is the true distinct k-mer count. The right panel shows how the relative error goes as the initial coverage increases (k = 20). (b) The same as (a) except that the species number is 50. (Note that some of the samples with 10 species are not shown in the barplot, see Additional file 1: Figure S1 for all samples with 10 species)

Fig. 3

a Performance on metagenomic data with sequencing errors. b True and estimated K-mer Redundant Index (KRI) in different metagenomics communities. About 60% of the species are randomly chosen as the known species to estimate the KRI of all species. c Results of different selections of K. Simulated metagenomic sample with 50 speices and high complexity of the abundance distribution was used. d Results on HMP Tongue Dorsum datasets

Fig. 4

Results on T2D metagenomic datasets. a Observed and estimated k-mer count. b Histogram and density of the observed distinct k-mer count. c Histogram and density of the predicted distinct k-mer count