The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
Evolution and comparative genomics of the most common Trichoderma species
Open accessAbstract
BACKGROUND: The growing importance of the ubiquitous fungal genus Trichoderma (Hypocreales, Ascomycota) requires understanding of its biology and evolution. Many Trichoderma species are used as biofertilizers and biofungicides and T. reesei is the model organism for industrial production of cellulolytic enzymes. In addition, some highly opportunistic species devastate mushroom farms and can become pathogens of humans. A comparative analysis of the first three whole genomes revealed mycoparasitism as the innate feature of Trichoderma. However, the evolution of these traits is not yet understood. RESULTS: We selected 12 most commonly occurring Trichoderma species and studied the evolution of their genome sequences. Trichoderma evolved in the time of the Cretaceous-Palaeogene extinction event 66 (±15) mya, but the formation of extant sections (Longibrachiatum, Trichoderma) or clades (Harzianum/Virens) happened in Oligocene. The evolution of the Harzianum clade and section Trichoderma was accompanied by significant gene gain, but the ancestor of section Longibrachiatum experienced rapid gene loss. The highest number of genes gained encoded ankyrins, HET domain proteins and transcription factors. We also identified the Trichoderma core genome, completely curated its annotation, investigated several gene families in detail and compared the results to those of other fungi. Eighty percent of those genes for which a function could be predicted were also found in other fungi, but only 67% of those without a predictable function. CONCLUSIONS: Our study presents a time scaled pattern of genome evolution in 12 Trichoderma species from three phylogenetically distant clades/sections and a comprehensive analysis of their genes. The data offer insights in the evolution of a mycoparasite towards a generalist.
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
MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability
Katoh, K., & Standley, D. M. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst010 · Cited by 46,209
MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets
Kumar, S., Stecher, G., & Tamura, K. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msw054 · Cited by 44,059
BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs
Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V., & Zdobnov, E. M. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv351 · Cited by 13,344
SignalP 4.0: discriminating signal peptides from transmembrane regions
Petersen, T. N., Brunak, S., von Heijne, G., & Nielsen, H. (2011) · Nature Methods · doi:10.1038/nmeth.1701 · Cited by 8,847
BEAST 2: a software platform for Bayesian evolutionary analysis
Bouckaert, R., Heled, J., Kühnert, D., Vaughan, T., Wu, C.-H., Xie, D., Suchard, M. A., Rambaut, A., & Drummond, A. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003537 · Cited by 6,527
The carbohydrate-active enzymes database (CAZy) in 2013
Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M., & Henrissat, B. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1178 · Cited by 6,333
Self-citationThe Pfam protein families database: towards a more sustainable future
Finn, R. D., Coggill, P., Eberhardt, R. Y., Eddy, S. R., Mistry, J., Mitchell, A. L., Potter, S. C., Punta, M., Qureshi, M., Sangrador-Vegas, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1344 · Cited by 5,975
OrthoMCL: Identification of Ortholog Groups for Eukaryotic Genomes
Li, L., Stoeckert, C. J., & Roos, D. S. (2003) · Genome Research · doi:10.1101/gr.1224503 · Cited by 5,707
Talavera, G., & Castresana, J. (2007) · Systematic Biology · doi:10.1080/10635150701472164 · Cited by 5,631
CDD: NCBI's conserved domain database
Marchler-Bauer, A., Derbyshire, M. K., Gonzales, N. R., Lu, S., Chitsaz, F., Geer, L. Y., Geer, R. C., He, J., Gwadz, M., Hurwitz, D. I., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1221 · Cited by 3,433
Cited by
Trichoderma and its role in biological control of plant fungal and nematode disease
Yao, X., Guo, H., Zhang, K., Zhao, M., Ruan, J., & Chen, J. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1160551 · Cited by 448
Trichoderma Species: Our Best Fungal Allies in the Biocontrol of Plant Diseases—A Review
Guzmán-Guzmán, P., Kumar, A., de los Santos-Villalobos, S., Parra-Cota, F. I., Orozco-Mosqueda, M. D. C., Fadiji, A. E., Hyder, S., Babalola, O. O., & Santoyo, G. (2023) · Plants · doi:10.3390/plants12030432 · Cited by 429
Metadata
- Authors
- Kubicek, C. P., Steindorff, A. S., Chenthamara, K., Manganiello, G., Henrissat, B., Zhang, J., Cai, F., Kopchinskiy, A. G., Kubicek, E. M., Kuo, A., Baroncelli, R., Sarrocco, S., Noronha, E. F., Vannacci, G., Shen, Q., Grigoriev, I. V., & Druzhinina, I. S.
- Venue
- BMC Genomics
- Published
- 2019-06-12
- Volume
- 20
- Issue
- 1
- Pages
- 485-485
- ISSN
- 1471-2164 (ISSN-L)
- OpenAlex
- W2949926205
- Cited by
- 299
- References
- 81
- FWCI
- 9.52
- 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: Evolution and comparative genomics of the most common Trichoderma species. https://app.cochat.ai/papers/10.1186/s12864-019-5680-7
- The paper
- Kubicek, C. P., Steindorff, A. S., Chenthamara, K., Manganiello, G., Henrissat, B., Zhang, J., Cai, F., Kopchinskiy, A. G., Kubicek, E. M., Kuo, A., Baroncelli, R., Sarrocco, S., Noronha, E. F., Vannacci, G., Shen, Q., Grigoriev, I. V., & Druzhinina, I. S. (2019). Evolution and comparative genomics of the most common Trichoderma species. BMC Genomics. https://doi.org/10.1186/s12864-019-5680-7
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.