Characterizing species diversity and composition of bacteria hosted by biota is revolutionizing our understanding of the role of symbiotic interactions in ecosystems. Determining microbiomes diversity implies the assignment of individual reads to taxa by comparison to reference databases. Although computational methods aimed at identifying the microbe(s) taxa are available, it is well known that inferences using different methods can vary widely depending on various biases. In this study, we first apply and compare different bioinformatics methods based on 16S ribosomal RNA gene and shotgun sequencing to three mock communities of bacteria, of which the compositions are known. In this ignite talk we will share our view on the need of a novel approach to learn from microbiomes data. In particular, we will propose a taxonomic classification method, named Core-Kaiju, which combines the power of shotgun metagenomics data with a more focused marker gene classification method similar to 16S, but based on emergent statistics of core protein domain families. We have tested [1] the proposed method on various mock communities and we have shown that Core-Kaiju reliably predicts both number of taxa and abundances [1].We have also applied our method on human gut samples [1], showing how Core-Kaiju may give more accurate ecological characterization and a fresh view on real microbiomes.
