Predictive understanding of ecosystem response to change has become a pressing societal need in the Anthropocene, and requires integration across disciplines, spatial scales, and timeframes. Developing a framework for understanding how different biological systems interact over time is a major challenge in biology. The National Science Foundation-funded EMergent Ecosystem Responses to ChanGE (EMERGE) Biology Integration Institute aims to develop such a framework by integrating research, training, and high-resolution field and laboratory measurements across 15 scientific subdisciplines–including ecology, physiology, genetics, biogeochemistry, remote sensing, and modeling–across 14 institutions, in order to understand ecosystem-climate feedbacks in Stordalen Mire, a thawing permafrost peatland in arctic Sweden. Rapid warming in the Arctic is driving permafrost thaw, and new availability of formerly-frozen soil carbon for cycling and release to the atmosphere, representing a potentially large but poorly constrained accelerant of climate change. This material is based upon work supported by the National Science Foundation under Grant Number 2022070.
Listed below are a number of the tools that members have developed for better understanding and integration of these datasets.
| Tool | Description | Developers | Citation |
|---|---|---|---|
| CoverM | Metagenomic coverage calculator / BAM file generator | Ben Woodcroft (CMR) | |
| Lorikeet | Microbial strain resolver, coverage calculator, variant caller, selective pressure calculator | Rhys Newell (CMR) | |
| Rosella | Metagenomic binning and bin refinement tool | Rhys Newell (CMR) | |
| Aviary (incorporated SlamM) | Microbial genome recovery pipeline with novel methods for long/short read assembly | Rhys Newell (CMR) | |
| Galah | Genome dereplication | Ben Woodcroft (CMR) | |
| Kingfisher | Public sequence and metadata gatherer | Ben Woodcroft (CMR) | |
| SingleM | De-novo OTUs from shotgun metagenomes | Ben Woodcroft (CMR) | |
| GraftM | Meta-omic tool that identifies and classifies marker and functional genes | Ben Woodcroft (CMR) | https://doi.org/10.1093/nar/gky174 |
| DRAM | Annotates MAGs and summarizes metabolic potential | Mikayla Borton (CSU), Mike Shaffer (CSU), Kelly Wrighton | https://doi.org/10.1093/nar/gkaa621 |
| Phylogenetic Null Modeling | partitioning variation in phylogenetic data and attributing to assembly processes | Stacey Doherty (UNH alum), Jessica Ernakovich (based on Stegen et al., 2013) | |
| vConTACT2 | Classifies and clusters viral sequences into approx. genus groups | Ben Bolduc (OSU), Sullivan Lab | https://doi.org/10.1038/s41587-019-0100-8 |
| VirSorter1 | Identifies viral sequences in microbial and viral sequence data | Simon Roux (JGI), Sullivan Lab | https://doi.org/10.7717/peerj.985 |
| VirSorter2 | As VirSorter1, but uses ML and expands viral types detected | Jiarong Guo (OSU), Sullivan Lab | https://doi.org/10.1186/s40168-020-00990-y |
| metaPop | calculates macro- and micro-diversity metrics | Ann Gregory (OSU alum), Sullivan Lab | https://doi.org/10.1186/s40168-022-01231-0 |
| ecosys | site, landscape, and continental-scale land model | Riley, Zhen, Grant | |
| CheckM 2 | Microbial genome Q/A | Alex Chklovski (CMR) | https://doi.org/10.1038/s41592-023-01940-w |
