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. Electronic supplementary material The online version of this article (10.1186/s12864-019-5883-y) contains supplementary material, which is available to authorized users.

The use of taxon-specific reference databases compromises metagenomic classification

“…The frog species Pyxicephalus adspersus and the snake species Pantherophis guttatus were identified in all samples tested. These results are comparable to the findings of Soverini and colleagues, where 65 fungal species were retrieved when using a database containing 66 fungal genomes [5].…”

“…We re-analysed the 38 metagenomes used as a case study in the HumanMycobiomeScan publication [5]. These were obtained from stool samples of Hadza hunter-gatherers from Tanzania (n = 27) and Western subjects from Italy (n = 11) [11].…”

“…A recent article by Soverini and colleagues [5] describes HumanMycobiomeScana new bioinformatics tool to identify fungi in metagenome samples based on mapping sequence reads to a reference database of fungal genomes. The authors applied the pipeline to characterize the gut mycobiome of human populations on different diets (hunter-gatherers and Western populations), resulting in the identification of a range of fungal taxa in 37 of 38 samples analysed.…”

RESCRIPt: Reproducible sequence taxonomy reference database management for the masses

“…Sequence reference databases are often subsetted by investigators to focus on particular clades of interest or to perform additional curation of public datasets. Some researchers have generated environment-specific databases, founded in the belief that such databases increase taxonomic classification accuracy by removing sequences that are genetically related but ecologically distinct from species found in a specific environment [52,[55][56][57][58][59], although this can elevate the risk of false-positive errors [76]. RESCRIPt contains several methods to support and evaluate such filtering decisions, which then become embedded in provenance to facilitate transparent and reproducible use of these databases.…”

“…Additionally, issues with amplicon length and sequence heterogeneity can limit the ability to identify species, especially from short marker-gene sequences or metagenome fragments [51]. Hence, many researchers choose to perform additional curation to focus on type strains [52], quality filtering [14,53], or construct environmentspecific databases that are constrained to contain species found within a given environment [52,[54][55][56][57][58][59]. Database customization is also often performed to add new accessions that are absent in some database releases to increase database coverage [50], or to incorporate outgroups [14].…”