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

資料紀錄

此資源sampling event的資料已發佈為達爾文核心集檔案（DwC-A），其以一或多組資料表構成分享生物多樣性資料的標準格式。 核心資料表包含 472 筆紀錄。

亦存在 1 筆延伸集的資料表。延伸集中的紀錄補充核心集中紀錄的額外資訊。 每個延伸集資料表中資料筆數顯示如下。

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版本

以下的表格只顯示可公開存取資源的已發布版本。

如何引用

研究者應依照以下指示引用此資源。:

Gómez Aparicio L, Zamora Ballesteros C, Gil Martinez M, García Garrido S (2023). Phytophthora members of the rhizosphere microbiota of Quercus ilex subsp. ballota. Version 2.4. Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC). Samplingevent dataset. https://doi.org/10.15470/hcvbbv

權利

研究者應尊重以下權利聲明。:

此資料的發布者及權利單位為 Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC)。 This work is licensed under a Creative Commons Attribution (CC-BY 4.0) License.

GBIF 註冊

此資源已向GBIF註冊，並指定以下之GBIF UUID: 3cf6d773-e669-4535-b16a-1f7a3e23d281。  Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC) 發佈此資源，並經由GBIF Spain同意向GBIF註冊成為資料發佈者。

關鍵字

Samplingevent; Biodiversity; Edafic Biodiversity

聯絡資訊

地理涵蓋範圍

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

分類群涵蓋範圍

N/A

計畫資料

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.

取樣方法

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.

方法步驟描述:

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).

引用文獻

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) https://doi.org/10.15470/hcvbbv
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

額外的詮釋資料