The MicrobeAtlas database: Global trends and insights into Earth's microbial ecosystems
Rodrigues, J. R. F. M.; Tackmann, J.; Malfertheiner, L.; Patsch, D.; Perez Molphe Montoya, E.; Napflin, N.; Gaio, D.; Rot, G.; Danaila, M.; Peluso, M. E.; Dmitrijeva, M.; Schmidt, T. S. B.; von Mering, C.
Show abstract
Environmental DNA sequencing has revolutionized our understanding of microbial diversity and ecology. Microbiomes have now been sequenced across the entire planet--from the deep subsurface to the mountain tops--covering a myriad of hosts, biomes, and conditions. Yet, the diversity of sequencing and processing strategies hampers universal insights. MicrobeAtlas unifies more than two million microbiome samples in a single resource, harmonized to facilitate discoveries across technologies. Communities are hierarchically quantified at adjustable SSU rRNA marker gene resolution and feature detailed metadata, including rich geographic information. Connections to genome, phenotype, and ecological resources enable multimodal insights. Microbial lineages can be reliably tracked across environments, including a long tail of rare, uncharacterized species. Recurring community structures and geographic preferences become apparent, and global, taxonomy-specific generalism trends emerge. With MicrobeAtlas (www.microbeatlas.org), both known and newly described species and communities can readily be placed into ecological context, taking full advantage of earlier work.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Genome-scale community modelling reveals conserved metabolic cross-feedings in epipelagic bacterioplankton communities 97%
- Genome-centric analysis of short and long read metagenomes reveals uncharacterized microbiome diversity in Southeast Asians 97%
- Ecology and molecular targets of hypermutation in the global microbiome. 97%
Similar papers in this journal
- MCSPACE: inferring microbiome spatiotemporal dynamics from high-throughput co-localization data 97%
- Deep learning reveals functional archetypes in the adult human gut microbiome that underlie interindividual variability and confound disease signals 96%
- Revealing Interactions between Microbes, Metabolites, and Dietary Compounds using Genome-scale Analysis 96%
Similar papers in this journal
- A human gut metagenome-assembled genome catalogue spanning 41 countries supports genome-scale metabolic models 98%
- Priority effects of heritable seed-borne bacteria drive early assembly of the wheat rhizosphere microbiome 98%
- Bacterial ecology and evolution converge on seasonal and decadal scales 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.