The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
From cultured to uncultured genome sequences: metagenomics and modeling microbial ecosystems
Open accessAbstract
Microorganisms and the viruses that infect them are the most numerous biological entities on Earth and enclose its greatest biodiversity and genetic reservoir. With strength in their numbers, these microscopic organisms are major players in the cycles of energy and matter that sustain all life. Scientists have only scratched the surface of this vast microbial world through culture-dependent methods. Recent developments in generating metagenomes, large random samples of nucleic acid sequences isolated directly from the environment, are providing comprehensive portraits of the composition, structure, and functioning of microbial communities. Moreover, advances in metagenomic analysis have created the possibility of obtaining complete or nearly complete genome sequences from uncultured microorganisms, providing important means to study their biology, ecology, and evolution. Here we review some of the recent developments in the field of metagenomics, focusing on the discovery of genetic novelty and on methods for obtaining uncultured genome sequences, including through the recycling of previously published datasets. Moreover we discuss how metagenomics has become a core scientific tool to characterize eco-evolutionary patterns of microbial ecosystems, thus allowing us to simultaneously discover new microbes and study their natural communities. We conclude by discussing general guidelines and challenges for modeling the interactions between uncultured microorganisms and viruses based on the information contained in their genome sequences. These models will significantly advance our understanding of the functioning of microbial ecosystems and the roles of microbes in the environment.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Woese, C. R. (1987) · Microbiological Reviews · doi:10.1128/mr.51.2.221-271.1987 · Cited by 5,042
Kraken: ultrafast metagenomic sequence classification using exact alignments
Wood, D. E., & Salzberg, S. L. (2014) · Genome Biology · doi:10.1186/gb-2014-15-3-r46 · Cited by 4,521
Environmental genome shotgun sequencing of the sargasso sea
Venter, J. C., Remington, K., Heidelberg, J. F., Halpern, A. L., Rusch, D., Eisen, J. A., Wu, D., Paulsen, I., Nelson, K. E., Nelson, W., et al. (2004) · Science · doi:10.1126/science.1093857 · Cited by 4,376
Biodiversity and ecosystem productivity in a fluctuating environment: The insurance hypothesis
Yachi, S., & Loreau, M. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.4.1463 · Cited by 3,211
Community structure and metabolism through reconstruction of microbial genomes from the environment
Tyson, G. W., Chapman, J., Hugenholtz, P., Allen, E. E., Ram, R. J., Richardson, P. M., Solovyev, V. V., Rubin, E. M., Rokhsar, D. S., & Banfield, J. F. (2004) · Nature · doi:10.1038/nature02340 · Cited by 2,417
The merging of community ecology and phylogenetic biology
Cavender‐Bares, J., Kozak, K. H., Fine, P. V. A., & Kembel, S. W. (2009) · Ecology Letters · doi:10.1111/j.1461-0248.2009.01314.x · Cited by 2,321
A protocol for generating a high-quality genome-scale metabolic reconstruction
Thiele, I., & Palsson, B. Ø. (2010) · Nature Protocols · doi:10.1038/nprot.2009.203 · Cited by 1,789
Biodiversity enhances ecosystem reliability.
Naeem, S., & Li, S. (1997) · Nature · doi:10.1038/37348 · Cited by 1,438
Complex archaea that bridge the gap between prokaryotes and eukaryotes
Spang, A., Saw, J. H., Jørgensen, S. L., Zaremba-Niedzwiedzka, K., Martijn, J., Lind, A. E., van Eijk, R., Schleper, C., Guy, L., & Ettema, T. J. G. (2015) · Nature · doi:10.1038/nature14447 · Cited by 1,324
A Whole-Cell Computational Model Predicts Phenotype from Genotype
Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V., Jacobs, J. M., Bolival, B., Assad-Garcia, N., Glass, J. I., & Covert, M. W. (2012) · Cell · doi:10.1016/j.cell.2012.05.044 · Cited by 1,300 · cited 1×
“This model describes a single organism and reconstructs several patterns of the bacterial cell cycle that are consistent with measurements in vitro [165].”
Cited by
Colorectal Carcinoma: A General Overview and Future Perspectives in Colorectal Cancer
Mármol, I., Sánchez-de-Diego, C., Pradilla Dieste, A., Cerrada, E., & Rodriguez Yoldi, M. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18010197 · Cited by 1,525
Mechanisms and consequences of intestinal dysbiosis
Weiss, G. A., & Hennet, T. (2017) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-017-2509-x · Cited by 775
Yukgehnaish, K., Kumar, P., Sivachandran, P., Marimuthu, K., Arshad, A., Paray, B. A., & Arockiaraj, J. (2020) · Reviews in Aquaculture · doi:10.1111/raq.12416 · Cited by 470
Felz, S., Vermeulen, P., van Loosdrecht, M. C. M., & Lin, Y. M. (2019) · Water Research · doi:10.1016/j.watres.2019.03.068 · Cited by 365
Origin and Early Evolution of the Eukaryotic Cell
Gabaldón, T. (2021) · Annual Review of Microbiology · doi:10.1146/annurev-micro-090817-062213 · Cited by 121
Metadata
- Authors
- Garza, D. R., & Dutilh, B. E.
- Venue
- Cellular and Molecular Life Sciences
- Published
- 2015-08-09
- Volume
- 72
- Issue
- 22
- Pages
- 4287-4308
- ISSN
- 1420-682X (ISSN-L)
- 1420-9071
- OpenAlex
- W2119474199
- Cited by
- 175
- References
- 22
- FWCI
- 5.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: From cultured to uncultured genome sequences: metagenomics and modeling microbial ecosystems. https://app.cochat.ai/papers/10.1007/s00018-015-2004-1
- The paper
- Garza, D. R., & Dutilh, B. E. (2015). From cultured to uncultured genome sequences: metagenomics and modeling microbial ecosystems. Cellular and Molecular Life Sciences. https://doi.org/10.1007/s00018-015-2004-1
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
