Fungal members of the rhizosphere microbiota of Quercus ilex subsp. ballota

Registros de Dados

Os dados deste recurso de evento de amostragem foram publicados como um Darwin Core Archive (DwC-A), que é o formato padronizado para compartilhamento de dados de biodiversidade como um conjunto de uma ou mais tabelas de dados. A tabela de dados do núcleo contém 506 registros.

Também existem 1 tabelas de dados de extensão. Um registro de extensão fornece informações adicionais sobre um registro do núcleo. O número de registros em cada tabela de dados de extensão é ilustrado abaixo.

This IPT archives the data and thus serves as the data repository. The data and resource metadata are available for download in the downloads section. The versions table lists other versions of the resource that have been made publicly available and allows tracking changes made to the resource over time.

Versões

A tabela abaixo mostra apenas versões de recursos que são publicamente acessíveis.

Como citar

Pesquisadores deveriam citar esta obra da seguinte maneira:

Gomez Aparicio L, García Garrido S (2023). Fungal members of the rhizosphere microbiota of Quercus ilex subsp. ballota. Version 2.1. Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC). Samplingevent dataset. https://doi.org/10.15470/jow7hd

Direitos

Pesquisadores devem respeitar a seguinte declaração de direitos:

O editor e o detentor dos direitos deste trabalho é Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC). This work is licensed under a Creative Commons Attribution (CC-BY 4.0) License.

GBIF Registration

Este recurso foi registrado no GBIF e atribuído ao seguinte GBIF UUID: 0360980c-a656-482a-a689-3c1f1ee3f7d9.  Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC) publica este recurso, e está registrado no GBIF como um publicador de dados aprovado por GBIF Spain.

Palavras-chave

samplingEvent; Biodiversity; Edafic Biodiversity

Contatos

Cobertura Geográfica

Huelva (Spain), Sevilla (Spain) and Córdoba (Spain)

Cobertura Taxonômica

Nenhuma descrição disponível

Dados Sobre o Projeto

The general objective of thE WP7 is to advance our understanding of the impacts of global change drivers - mainly climate change and exotic pathogens - on the above- and below-ground biodiversity of Mediterranean forests and silvopastoral agrosystems, and to use the resulting information to propose management tools aim to improve the resistance and resilience of these forests in scenarios of increasing abiotic and biotic stress.

O pessoal envolvido no projeto:

Métodos de Amostragem

The Illumina paired-end raw sequences were processed using the freely available bioinformatics software QIIME 2 version 2022.2.0 (Bolyen et al., 2019). The sequences from each target (bacteria, fungi or Phytophthora spp.) and each sequencing run were processed equally but separately throughout the analysis. The sequences were trimmed by implementing cutadapt (Martin, 2011) in QIIME 2 with q2-cutadapt plugin and trim-paired command. Chimeric sequences were identified and deleted after quality filtering and de-noising using the Divisive Amplicon Denoising Algorithm 2 (DADA2) pipeline implemented in QIIME 2 with q2-dada2 plugin and denoise-paired command (Callahan et al., 2016). The resulting amplicon sequence variants (ASVs) identified were curated using mumu by removing taxonomically redundant and erroneous ASVs. This involved constructing a database of the ASV sequences using makeblastdb application from the BLAST v.2.9.0 software suite, from which match lists were created using the blastn algorithm (query coverage: 80; percent identity cutoff: 84). These match lists and the ASV feature tables were input into the mumu algorithm to produce curated ASV tables. Singletons were excluded from the analysis. Taxonomic classification of the ASVs for bacteria and fungi was performed with pre-trained Naive Bayes classifiers and the q2-feature-classifier plugin (Bokulich et al., 2018). For the bacterial dataset, the SILVA v.138 database (Quast et al., 2013) was applied using a pre-trained classifier specifically curated for the sequenced 16SV3V4 region. In the case of the fungal dataset, the UNITE dynamic database v.8.3 (Abarenkov et al., 2021), which has been manually curated by experts in these particular fungal lineages, was used. Sequences assigned to mitochondria, chloroplasts and archaea were removed using the q2-taxa plugin in QIIME 2 and a taxonomy-based filtering step using the qiime taxa filter-seqs and qiime taxa filter-table commands.

Descrição dos passos do método:

1. The taxonomic assignment to the ASVs identified in the analysis of Phytohthora species was performed by generating a reference database from a combination of sequences from five different sources: the UNITE dynamic database v.8.3 (consisting of 58,440 eukaryotic sequences), reference sequences from phytophthoradb (http://www.phytophthoradb.org/; 340 sequences), reference sequences from Phytopthora-id (http://Phytophthora-id.org; 270 sequences), 174 sequences of Phytophthora spp. from the database generated in Riddell et al. (2019), and 39,701 sequences from Genbank matching the search "oomycota 'internal transcribed spacer'". In the latter case, the taxonomy for the Genbank accessions was obtained using the taxonomizr package (v 0.10.2) in R v 4.2.2. Finally, the reference sequence database, namely the combined fasta file (98,925 sequences), and the associated taxonomy description file were imported into QIIME 2 and used together with the qiime feature-classifier plugin and the classify-consensus-blast command. As a first step, the sequences of the potential Phytophthora ASVs were aligned against the custom reference database using strict homology parameters (query coverage: 90; percent identity cutoff: 99) to ensure that successful matches belong to a Phytophthora species. The unaligned ASVs were submitted to a second step with relaxed parameters (query coverage: 75; percent identity cutoff: 65). The third step consisted of comparing the unassigned ASVs from the second step to the entire NCBI non-redundant protein database (release-255) using default parameters. In the fourth step, the low confidence ASVs assigned in the second and third steps were concatenated, aligned with MAFFT, and used to construct a maximum likelihood (ML) phylogenetic tree using RAxML (v 8.2.12; Stamatakis, 2014). The tree was inferred employed a general time reversible substitution model with a computational work–around (GTRCAT) without rate heterogeneity with a correction for ascertainment bias. Statistical support was calculated by applying bootstrap runs in an automated approach (autoMRE), where RaxML executes a maximum of 1000 BS replicate searches, although convergence may occur earlier. The best-scoring ML tree of the search analysis was then visualized using the software FIGTREE version 1.4.4 (Rambaut, 2018).

Citações bibliográficas

1. Abarenkov, K., Zirk, A., Piirmann, T., Pöhönen, R., Ivanov, F., Nilsson, R.H., Kõljalg, U., 2021. UNITE QIIME release for Fungi. Version 10.05.2021. [WWW Document]. UNITE Community. URL https://doi.plutof.ut.ee/doi/10.15156/BIO/1264708 (accessed 1.17.23)
2. Bokulich, N.A., Kaehler, B.D., Rideout, J.R., Dillon, M., Bolyen, E., Knight, R., Huttley, G.A., Gregory Caporaso, J., 2018. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90. https://doi.org/10.1186/s40168-018-0470-z
3. Bolyen, E., Rideout, J.R., Dillon, M.R., Bokulich, N.A., Abnet, C.C., Al-Ghalith, G.A., Alexander, H., Alm, E.J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J.E., Bittinger, K., Brejnrod, A., Brislawn, C.J., Brown, C.T., Callahan, B.J., Caraballo-Rodríguez, A.M., Chase, J., Cope, E.K., Da Silva, R., Diener, C., Dorrestein, P.C., Douglas, G.M., Durall, D.M., Duvallet, C., Edwardson, C.F., Ernst, M., Estaki, M., Fouquier, J., Gauglitz, J.M., Gibbons, S.M., Gibson, D.L., Gonzalez, A., Gorlick, K., Guo, J., Hillmann, B., Holmes, S., Holste, H., Huttenhower, C., Huttley, G.A., Janssen, S., Jarmusch, A.K., Jiang, L., Kaehler, B.D., Kang, K.B., Keefe, C.R., Keim, P., Kelley, S.T., Knights, D., Koester, I., Kosciolek, T., Kreps, J., Langille, M.G.I., Lee, J., Ley, R., Liu, Y.-X., Loftfield, E., Lozupone, C., Maher, M., Marotz, C., Martin, B.D., McDonald, D., McIver, L.J., Melnik, A.V., Metcalf, J.L., Morgan, S.C., Morton, J.T., Naimey, A.T., Navas-Molina, J.A., Nothias, L.F., Orchanian, S.B., Pearson, T., Peoples, S.L., Petras, D., Preuss, M.L., Pruesse, E., Rasmussen, L.B., Rivers, A., Robeson, M.S., Rosenthal, P., Segata, N., Shaffer, M., Shiffer, A., Sinha, R., Song, S.J., Spear, J.R., Swafford, A.D., Thompson, L.R., Torres, P.J., Trinh, P., Tripathi, A., Turnbaugh, P.J., Ul-Hasan, S., van der Hooft, J.J.J., Vargas, F., Vázquez-Baeza, Y., Vogtmann, E., von Hippel, M., Walters, W., Wan, Y., Wang, M., Warren, J., Weber, K.C., Williamson, C.H.D., Willis, A.D., Xu, Z.Z., Zaneveld, J.R., Zhang, Y., Zhu, Q., Knight, R., Caporaso, J.G., 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37, 852–857. https://doi.org/10.1038/s41587-019-0209-9
4. Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A., Holmes, S.P., 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869
5. Martin, M., 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12. https://doi.org/10.14806/ej.17.1.200
6. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., Glöckner, F.O., 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41, D590–D596. https://doi.org/10.1093/nar/gks1219
7. Rambaut, A., 2018. FigTree.
8. Riddell, C.E., Frederickson-Matika, D., Armstrong, A.C., Elliot, M., Forster, J., Hedley, P.E., Morris, J., Thorpe, P., Cooke, D.E.L., Pritchard, L., Sharp, P.M., Green, S., 2019. Metabarcoding reveals a high diversity of woody host-associated Phytophthora spp. In soils at public gardens and amenity woodlands in Britain. PeerJ 7, e6931. https://doi.org/10.7717/PEERJ.6931/SUPP-3
9. Stamatakis, A., 2014. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313. https://doi.org/10.1093/bioinformatics/btu033

Metadados Adicionais