The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.
Bioinformatics matters: The accuracy of plant and soil fungal community data is highly dependent on the metabarcoding pipeline
Open accessAbstract
Fungal communities associated with plants and soil influence plant fitness and ecosystem functioning. They are frequently studied by metabarcoding approaches targeting the ribosomal internal transcribed spacer (ITS), but there is no consensus concerning the most appropriate bioinformatic approach for the analysis of these data. We sequenced an artificial fungal community composed of 189 strains covering a wide range of Ascomycota and Basidiomycota , to compare the performance of 360 software and parameter combinations. The most sensitive approaches, based on the USEARCH and VSEARCH clustering algorithms, detected almost all fungal strains but greatly overestimated the total number of strains. By contrast, approaches using DADA2 to detect amplicon sequence variants were the most effective for recovering the richness and composition of the fungal community. Our results suggest that analyzing single forward (R1) sequences with DADA2 and no filter other than the removal of low-quality and chimeric sequences is a good option for fungal community characterization.
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Cites
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics
White, T. J., Bruns, T., Lee, S., & Taylor, J. (1990) · PCR Protocols · doi:10.1016/b978-0-12-372180-8.50042-1 · Cited by 36,648
DADA2: High–resolution sample inference from Illumina amplicon data
Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016) · Nature Methods · doi:10.1038/nmeth.3869 · Cited by 35,698
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
vegan: Community Ecology Package
Oksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O'Hara, R. B., Solymos, P., Stevens, M. H. H., Szoecs, E., et al. (2001) · CRAN: Contributed Packages · doi:10.32614/cran.package.vegan · Cited by 22,615
Search and clustering orders of magnitude faster than BLAST
Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 20,826
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., et al. (2009) · Applied and Environmental Microbiology · doi:10.1128/aem.01541-09 · Cited by 19,549
UCHIME improves sensitivity and speed of chimera detection
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C., & Knight, R. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr381 · Cited by 15,114
VSEARCH: a versatile open source tool for metagenomics
(2016) · doi:10.7717/peerj.2584 · Cited by 10,773
Cited by
Microbiome definition re-visited: old concepts and new challenges
Berg, G., Rybakova, D., Fischer, D., Cernava, T., Vergès, M.-C. C., Charles, T., Chen, X., Cocolin, L., Eversole, K., Corral, G. H., et al. (2020) · Microbiome · doi:10.1186/s40168-020-00875-0 · Cited by 2,275
Metadata
- Authors
- Pauvert, C., Buée, M., Laval, V., Edel-Hermann, V., Fauchery, L., Gautier, A., Lesur, I., Vallance, J., & Vacher, C.
- Venue
- Fungal Ecology
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 41
- Pages
- 23-33
- ISSN
- 1754-5048 (ISSN-L)
- OpenAlex
- W2945514851
- Cited by
- 253
- References
- 84
- FWCI
- 19.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- CoChat. (2026). Citation check: Bioinformatics matters: The accuracy of plant and soil fungal community data is highly dependent on the metabarcoding pipeline. https://app.cochat.ai/papers/10.1016/j.funeco.2019.03.005
- The paper
- Pauvert, C., Buée, M., Laval, V., Edel-Hermann, V., Fauchery, L., Gautier, A., Lesur, I., Vallance, J., & Vacher, C. (2019). Bioinformatics matters: The accuracy of plant and soil fungal community data is highly dependent on the metabarcoding pipeline. Fungal Ecology. https://doi.org/10.1016/j.funeco.2019.03.005
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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