Plant-associated microbes are important for the growth and health of their hosts. As a result of numerous prior studies, we know that host genotypes and abiotic factors influence the composition of plant microbiomes. However, the high complexity of these communities challenges detailed studies to define experimentally the mechanisms underlying the dynamics of community assembly and the beneficial effects of such microbiomes on plant hosts. In this work, from the distinctive microbiota assembled by maize roots, through host-mediated selection, we obtained a greatly simplified synthetic bacterial community consisting of seven strains (, , and) representing three of the four most dominant phyla found in maize roots. By using a selective culture-dependent method to track the abundance of each strain, we investigated the role that each plays in community assembly on roots of axenic maize seedlings. Only the removal of led to the complete loss of the community, and took over. This result suggests that plays the role of keystone species in this model ecosystem. and in vitro, this model community inhibited the phytopathogenic fungus , indicating a clear benefit to the host. Thus, combined with the selective culture-dependent quantification method, our synthetic seven-species community representing the root microbiome has the potential to serve as a useful system to explore how bacterial interspecies interactions affect root microbiome assembly and to dissect the beneficial effects of the root microbiota on hosts under laboratory conditions in the future.

We propose a statistical algorithm MethylPurify that uses regions with bisulfite reads showing discordant methylation levels to infer tumor purity from tumor samples alone. MethylPurify can identify differentially methylated regions (DMRs) from individual tumor methylome samples, without genomic variation information or prior knowledge from other datasets. In simulations with mixed bisulfite reads from cancer and normal cell lines, MethylPurify correctly inferred tumor purity and identified over 96% of the DMRs. From patient data, MethylPurify gave satisfactory DMR calls from tumor methylome samples alone, and revealed potential missed DMRs by tumor to normal comparison due to tumor heterogeneity.