The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
SPINGO: a rapid species-classifier for microbial amplicon sequences
Open accessAbstract
BACKGROUND: Taxonomic classification is a corner stone for the characterisation and comparison of microbial communities. Currently, most existing methods are either slow, restricted to specific communities, highly sensitive to taxonomic inconsistencies, or limited to genus level classification. As crucial microbiota information is hinging on high-level resolution it is imperative to increase taxonomic resolution to species level wherever possible. RESULTS: In response to this need we developed SPINGO, a flexible and stand-alone software dedicated to high-resolution assignment of sequences to species level using partial 16S rRNA gene sequences from any environment. SPINGO compares favourably to other methods in terms of classification accuracy, and is as fast or faster than those that have higher error rates. As a demonstration of its flexibility for other types of target genes we successfully applied SPINGO also on cpn60 amplicon sequences. CONCLUSIONS: SPINGO is an accurate, flexible and fast method for low-level taxonomic assignment. This combination is becoming increasingly important for rapid and accurate processing of amplicon data generated by newer next generation sequencing technologies.
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
Evaluation of 16S rDNA-Based Community Profiling for Human Microbiome Research
Jumpstart Consortium Human Microbiome Project Data Generation Working Group (2012) · PLoS ONE · doi:10.1371/journal.pone.0039315 · Cited by 297 · cited 1×
“Variable regions V1-V3 (6,046 sequences), V3-V5 (5,860) and V6-V9 (5,241) were extracted from the SILVA database using previously described primers [11] with the V-ripper script and subsequently classified using the evaluation database not containing the 9,607 test sequences.”
Method
Cited by
Ghosh, T. S., Rampelli, S., Jeffery, I. B., Santoro, A., Neto, M., Capri, M., Giampieri, E., Jennings, A., Candela, M., Turroni, S., et al. (2020) · Gut · doi:10.1136/gutjnl-2019-319654 · Cited by 991
Self-citationWhole-Virome Analysis Sheds Light on Viral Dark Matter in Inflammatory Bowel Disease
Clooney, A. G., Sutton, T. D. S., Shkoporov, A. N., Holohan, R. K., Daly, K. M., O’Regan, O., Ryan, F. J., Draper, L. A., Plevy, S. E., Ross, R. P., et al. (2019) · Cell Host & Microbe · doi:10.1016/j.chom.2019.10.009 · Cited by 452
Self-citationAccuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences
Edgar, R. C. (2018) · PeerJ · doi:10.7717/peerj.4652 · Cited by 319
Fouhy, F., Clooney, A. G., Stanton, C., Claesson, M. J., & Cotter, P. D. (2016) · BMC Microbiology · doi:10.1186/s12866-016-0738-z · Cited by 310
Self-citationRyan, F. J., Ahern, A. M., Fitzgerald, R. S., Laserna-Mendieta, E. J., Power, E. M., Clooney, A. G., O’Donoghue, K. W., McMurdie, P. J., Iwai, S., Crits-Christoph, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15342-5 · Cited by 285
Self-citation
Metadata
- Authors
- Allard, G., Ryan, F. J., Jeffery, I. B., & Claesson, M. J.
- Venue
- BMC Bioinformatics
- Published
- 2015-10-08
- Volume
- 16
- Issue
- 1
- Pages
- 324-324
- ISSN
- 1471-2105 (ISSN-L)
- OpenAlex
- W1847253990
- Cited by
- 159
- References
- 1
- FWCI
- 4.25
- Type
- journal article
- Language
- en
- Open access
- gold
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: SPINGO: a rapid species-classifier for microbial amplicon sequences. https://app.cochat.ai/papers/10.1186/s12859-015-0747-1
- The paper
- Allard, G., Ryan, F. J., Jeffery, I. B., & Claesson, M. J. (2015). SPINGO: a rapid species-classifier for microbial amplicon sequences. BMC Bioinformatics. https://doi.org/10.1186/s12859-015-0747-1
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.
