HumanMycobiomeScan: a new bioinformatics tool for the characterization of the fungal fraction in metagenomic samples

Abstract

Background: Modern metagenomic analysis of complex microbial communities produces large amounts of sequence data containing information on the microbiome in terms of bacterial, archaeal, viral and eukaryotic composition. The bioinformatics tools available are mainly devoted to profiling the bacterial and viral fractions and only a few software packages consider fungi. As the human fungal microbiome (human mycobiome) can play an important role in the onset and progression of diseases, a comprehensive description of host-microbiota interactions cannot ignore this component.

Results: HumanMycobiomeScan is a bioinformatics tool for the taxonomic profiling of the mycobiome directly from raw data of next-generation sequencing. The tool uses hierarchical databases of fungi in order to unambiguously assign reads to fungal species more accurately and > 10,000 times faster than other comparable approaches. HumanMycobiomeScan was validated using in silico generated synthetic communities and then applied to metagenomic data, to characterize the intestinal fungal components in subjects adhering to different subsistence strategies.

Conclusions: Although blind to unknown species, HumanMycobiomeScan allows the characterization of the fungal fraction of complex microbial ecosystems with good performance in terms of sample denoising from reads belonging to other microorganisms. HumanMycobiomeScan is most appropriate for well-studied microbiomes, for which most of the fungal species have been fully sequenced. This released version is functionally implemented to work with human-associated microbiota samples. In combination with other microbial profiling tools, HumanMycobiomeScan is a frugal and efficient tool for comprehensive characterization of microbial ecosystems through shotgun metagenomics sequencing.

Keywords: Metagenomics; Microbiome; Mycobiome.

Fig. 1

Analysis workflow of HumanMycobiomeScan. a Inputs are single- (.fastq) or paired-end (.fastq or compressed .fastq) reads. b Candidate fungal reads are screened by mapping onto reference fungal genomes contained in a precompiled database. This allows for a first reduction of the sample size, lowering the number of sequences that will be subjected to further steps. c Three filtration steps are carried out to eliminate low quality reads as well as reads belonging to humans and bacteria. d The remaining sequences are realigned onto the fungal genome database for definitive taxonomic assignment of the reads. The results are tabulated as both abundance profiles and read counts, and represented by bar plots

Fig. 2

Comparison of HumanMycobiomeScan with other existing assignment methods. Five synthetic fungal communities were used to compare HumanMycobiomeScan (HMS) with BlastN [32] and MG-RAST [33]. The actual number of misassigned reads, including those under- or over-assigned, is reported at family (a) and species (b) level. The horizontal line in the plots represents the “expected” value, meaning that all reads for a specific taxon were assigned to the correct reference genome. Points below or above the line indicate a lower or higher number of reads assigned to a specific taxon compared to the expected value. c The number of reads processed per second working on a single CPU is shown. d A comparison between the actual relative abundances of a mock community taken as an example and those reconstructed using the various methods of analysis was carried out. The gray portion represents the fraction of misassigned reads

Fig. 3

Characterization of the fungal fraction of the gut microbiome of populations with different subsistence strategies. a Family-level hierarchical Ward-linkage clustering based on the Spearman correlation coefficients of the fungal profiles of 37 metagenomes from Rampelli et al. [34], assigned using HumanMycobiomeScan. The study cohort includes 11 Italian subjects (in blue in the upper phylogenetic tree) and 26 Hadza hunter-gatherers from Tanzania (in orange). b The relative abundances of families are represented below the heatmap along with Simpson’s biodiversity index for each subject (red line)