diff --git a/joss.06038/10.21105.joss.06038.crossref.xml b/joss.06038/10.21105.joss.06038.crossref.xml new file mode 100644 index 0000000000..f6ed311e5c --- /dev/null +++ b/joss.06038/10.21105.joss.06038.crossref.xml @@ -0,0 +1,473 @@ + + + + 20231219T121851-95eaa0242185a14cd87829bd8407b132fc0dceba + 20231219121851 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 12 + 2023 + + + 8 + + 92 + + + + MiscMetabar: an R package to facilitate visualization +and reproducibility in metabarcoding analysis + + + + Adrien + Taudière + https://orcid.org/0000-0003-1088-1182 + + + + 12 + 19 + 2023 + + + 6038 + + + 10.21105/joss.06038 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10370781 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/6038 + + + + 10.21105/joss.06038 + https://joss.theoj.org/papers/10.21105/joss.06038 + + + https://joss.theoj.org/papers/10.21105/joss.06038.pdf + + + + + + Basic local alignment search +tool + Altschul + Journal of molecular biology + 3 + 215 + 10.32388/rhq6vj + 1990 + Altschul, S. F., Gish, W., Miller, +W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment +search tool. Journal of Molecular Biology, 215(3), 403–410. +https://doi.org/10.32388/rhq6vj + + + microViz: An r package for microbiome data +visualization and statistics + Barnett + Journal of Open Source +Software + 63 + 6 + 10.21105/joss.03201 + 2021 + Barnett, D. J. M., Arts, I. C. W., +& Penders, J. (2021). microViz: An r package for microbiome data +visualization and statistics. Journal of Open Source Software, 6(63), +3201. https://doi.org/10.21105/joss.03201 + + + Reproducible, interactive, scalable and +extensible microbiome data science using QIIME 2 + Bolyen + Nature biotechnology + 8 + 37 + 10.1038/s41587-019-0209-9 + 2019 + 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., & others. (2019). +Reproducible, interactive, scalable and extensible microbiome data +science using QIIME 2. Nature Biotechnology, 37(8), 852–857. +https://doi.org/10.1038/s41587-019-0209-9 + + + Effects of forest urbanization on the +interplay between small mammal communities and their gut +microbiota + Bouilloud + bioRxiv + 10.21105/joss.03201 + 2023 + Bouilloud, M., Galan, M., Pradel, J., +Loiseau, A., FERRERO, J., Gallet, R., Roche, B., & Charbonnel, N. +(2023). Effects of forest urbanization on the interplay between small +mammal communities and their gut microbiota. bioRxiv, 2023–2009. +https://doi.org/10.21105/joss.03201 + + + DADA2: High-resolution sample inference from +illumina amplicon data + Callahan + Nature methods + 7 + 13 + 10.1038/nmeth.3869 + 2016 + 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. +Nature Methods, 13(7), 581–583. +https://doi.org/10.1038/nmeth.3869 + + + Mia: Microbiome analysis + Ernst + 10.18129/B9.bioc.mia + 2023 + Ernst, F. G. M., Shetty, S. A., +Borman, T., & Lahti, L. (2023). Mia: Microbiome analysis. +https://doi.org/10.18129/B9.bioc.mia + + + Algorithm for post-clustering curation of DNA +amplicon data yields reliable biodiversity estimates + Frøslev + Nature communications + 1 + 8 + 10.1038/s41467-017-01312-x + 2017 + Frøslev, T. G., Kjøller, R., Bruun, +H. H., Ejrnæs, R., Brunbjerg, A. K., Pietroni, C., & Hansen, A. J. +(2017). Algorithm for post-clustering curation of DNA amplicon data +yields reliable biodiversity estimates. Nature Communications, 8(1), +1188. https://doi.org/10.1038/s41467-017-01312-x + + + The galaxy platform for accessible, +reproducible and collaborative biomedical analyses: 2020 +update + Jalili + Nucleic acids research + W1 + 48 + 10.1093/nar/gkaa434 + 2020 + Jalili, V., Afgan, E., Gu, Q., +Clements, D., Blankenberg, D., Goecks, J., Taylor, J., & Nekrutenko, +A. (2020). The galaxy platform for accessible, reproducible and +collaborative biomedical analyses: 2020 update. Nucleic Acids Research, +48(W1), W395–W402. +https://doi.org/10.1093/nar/gkaa434 + + + The targets r package: A dynamic make-like +function-oriented pipeline toolkit for reproducibility and +high-performance computing + Landau + Journal of Open Source +Software + 57 + 6 + 10.21105/joss.02959 + 2021 + Landau, W. M. (2021). The targets r +package: A dynamic make-like function-oriented pipeline toolkit for +reproducibility and high-performance computing. Journal of Open Source +Software, 6(57), 2959. +https://doi.org/10.21105/joss.02959 + + + Mumu: Post-clustering curation tool for +metabarcoding data + Mahé + 2023 + Mahé, F. (2023). Mumu: +Post-clustering curation tool for metabarcoding data (Version 1.0.2). +https://github.com/frederic-mahe/mumu/ + + + Meta-analysis of cnidarian microbiomes +reveals insights into the structure, specificity, and fidelity of marine +associations. + McCauley + Research Square + 10.21203/rs.3.rs-2011054/v1 + 2022 + McCauley, M., Goulet, T., Jackson, +C., & Loesgen, S. (2022). Meta-analysis of cnidarian microbiomes +reveals insights into the structure, specificity, and fidelity of marine +associations. Research Square. +https://doi.org/10.21203/rs.3.rs-2011054/v1 + + + Systematic review of cnidarian microbiomes +reveals insights into the structure, specificity, and fidelity of marine +associations + McCauley + Nature Communications + 1 + 14 + 10.21203/rs.3.rs-2011054/v1 + 2023 + McCauley, M., Goulet, T., Jackson, +C., & Loesgen, S. (2023). Systematic review of cnidarian microbiomes +reveals insights into the structure, specificity, and fidelity of marine +associations. Nature Communications, 14(1), 4899. +https://doi.org/10.21203/rs.3.rs-2011054/v1 + + + Mikemc/speedyseq: Speedyseq +v0.2.0 + McLaren + 10.5281/zenodo.3923184 + 2020 + McLaren, M. (2020). Mikemc/speedyseq: +Speedyseq v0.2.0 (Version v0.2.0). Zenodo. +https://doi.org/10.5281/zenodo.3923184 + + + Phyloseq: An r package for reproducible +interactive analysis and graphics of microbiome census +data + McMurdie + PLoS ONE + 4 + 8 + 10.1371/journal.pone.0061217 + 2013 + McMurdie, P. J., & Holmes, S. +(2013). Phyloseq: An r package for reproducible interactive analysis and +graphics of microbiome census data. PLoS ONE, 8(4), e61217. +https://doi.org/10.1371/journal.pone.0061217 + + + Interactive metagenomic visualization in a +web browser + Ondov + BMC bioinformatics + 1 + 12 + 10.1186/1471-2105-12-385 + 2011 + Ondov, B. D., Bergman, N. H., & +Phillippy, A. M. (2011). Interactive metagenomic visualization in a web +browser. BMC Bioinformatics, 12(1), 1–10. +https://doi.org/10.1186/1471-2105-12-385 + + + Bioinformatics matters: The accuracy of plant +and soil fungal community data is highly dependent on the metabarcoding +pipeline + Pauvert + Fungal Ecology + 41 + 10.1016/j.funeco.2019.03.005 + 2019 + 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, 41, 23–33. +https://doi.org/10.1016/j.funeco.2019.03.005 + + + A plant-based diet supplemented with hermetia +illucens alone or in combination with poultry by-product meal: One step +closer to sustainable aquafeeds for european seabass + Pleić + Journal of Animal Science and +Biotechnology + 1 + 13 + 10.1186/s40104-022-00725-z + 2022 + Pleić, I. L., Bušelić, I., Messina, +M., Hrabar, J., Žuvić, L., Talijančić, I., Žužul, I., Pavelin, T., +Anđelić, I., Pleadin, J., Puizina, J., Grubišić, L., Tibaldi, E., & +Šegvić-Bubić, T. (2022). A plant-based diet supplemented with hermetia +illucens alone or in combination with poultry by-product meal: One step +closer to sustainable aquafeeds for european seabass. Journal of Animal +Science and Biotechnology, 13(1), 77. +https://doi.org/10.1186/s40104-022-00725-z + + + R: A language and environment for statistical +computing + R Core Team + 2023 + R Core Team. (2023). R: A language +and environment for statistical computing. R Foundation for Statistical +Computing. https://www.R-project.org/ + + + Reintroducing mothur: 10 years +later + Schloss + Applied and environmental +microbiology + 2 + 86 + 10.1128/aem.02343-19 + 2020 + Schloss, P. D. (2020). Reintroducing +mothur: 10 years later. Applied and Environmental Microbiology, 86(2), +e02343–19. https://doi.org/10.1128/aem.02343-19 + + + Phylosmith: An r-package for reproducible and +efficient microbiome analysis with phyloseq-objects + Smith + Journal of Open Source +Software + 38 + 4 + 10.21105/joss.01442 + 2019 + Smith, S. D. (2019). Phylosmith: An +r-package for reproducible and efficient microbiome analysis with +phyloseq-objects. Journal of Open Source Software, 4(38), 1442. +https://doi.org/10.21105/joss.01442 + + + Environmental dna + Taberlet + Molecular ecology + 21 + 10.1002/(issn)2637-4943 + 2012 + Taberlet, P., Coissac, E., +Hajibabaei, M., & Rieseberg, L. H. (2012). Environmental dna. In +Molecular ecology (No. 8; Vol. 21, pp. 1789–1793). Wiley Online Library. +https://doi.org/10.1002/(issn)2637-4943 + + + Best practices in metabarcoding of fungi: +From experimental design to results + Tedersoo + Molecular ecology + 10 + 31 + 10.22541/au.163430390.04226544/v1 + 2022 + Tedersoo, L., Bahram, M., Zinger, L., +Nilsson, R. H., Kennedy, P. G., Yang, T., Anslan, S., & Mikryukov, +V. (2022). Best practices in metabarcoding of fungi: From experimental +design to results. Molecular Ecology, 31(10), 2769–2795. +https://doi.org/10.22541/au.163430390.04226544/v1 + + + DNA barcoding for ecologists + Valentini + Trends in ecology & +evolution + 2 + 24 + 10.1016/j.tree.2008.09.011 + 2009 + Valentini, A., Pompanon, F., & +Taberlet, P. (2009). DNA barcoding for ecologists. Trends in Ecology +& Evolution, 24(2), 110–117. +https://doi.org/10.1016/j.tree.2008.09.011 + + + Bacterial community patterns in the agaricus +bisporus cultivation system, from compost raw materials to mushroom +caps + Vieira + Microbial Ecology + 1 + 84 + 10.1007/s00248-021-01833-5 + 2021 + Vieira, F. R., & Pecchia, J. A. +(2021). Bacterial community patterns in the agaricus bisporus +cultivation system, from compost raw materials to mushroom caps. +Microbial Ecology, 84(1), 20–32. +https://doi.org/10.1007/s00248-021-01833-5 + + + Manipulating agaricus bisporus developmental +patterns by passaging microbial communities in complex +substrates + Vieira + Microbiology Spectrum + 10.1128/spectrum.01978-23 + 2023 + Vieira, F. R., Di Tomassi, I., +O’Connor, E., Bull, C. T., Pecchia, J. A., & Hockett, K. L. (2023). +Manipulating agaricus bisporus developmental patterns by passaging +microbial communities in complex substrates. Microbiology Spectrum, +e01978–23. +https://doi.org/10.1128/spectrum.01978-23 + + + The best practice for microbiome analysis +using r + Wen + Protein & Cell + 10.1093/procel/pwad024 + 2023 + Wen, T., Niu, G., Chen, T., Shen, Q., +Yuan, J., & Liu, Y.-X. (2023). The best practice for microbiome +analysis using r. Protein & Cell, pwad024. +https://doi.org/10.1093/procel/pwad024 + + + MicrobiotaProcess: A comprehensive r package +for deep mining microbiome + Xu + The Innovation + 2 + 4 + 10.1016/j.xinn.2023.100388 + 2023 + Xu, S., Zhan, L., Tang, W., Wang, Q., +Dai, Z., Zhou, L., Feng, T., Chen, M., Wu, T., Hu, E., & others. +(2023). MicrobiotaProcess: A comprehensive r package for deep mining +microbiome. The Innovation, 4(2). +https://doi.org/10.1016/j.xinn.2023.100388 + + + + + + diff --git a/joss.06038/10.21105.joss.06038.jats b/joss.06038/10.21105.joss.06038.jats new file mode 100644 index 0000000000..b2b55a7689 --- /dev/null +++ b/joss.06038/10.21105.joss.06038.jats @@ -0,0 +1,829 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6038 +10.21105/joss.06038 + +MiscMetabar: an R package to facilitate visualization and +reproducibility in metabarcoding analysis + + + +https://orcid.org/0000-0003-1088-1182 + +Taudière +Adrien + + + + + +IdEst, Saint-Bonnet-de-Salendrinque, 30460 +France + + + + +23 +10 +2023 + +8 +92 +6038 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +R +Bioinformatic +Metagenomics +Barcoding +Reproducibility + + + + + + Summary +

Describing communities of living organisms increasingly relies on + massive DNA sequencing from environmental samples (e-DNA). The + analysis of these large amounts of sequences is well established in + the R ecosystem, especially for metabarcoding, i.e. the massive + sequencing of one or several given DNA regions, called markers. The + MiscMetabar package aims to facilitate the + description, transformation, + exploration, and reproducibility of + metabarcoding analysis. Several tutorials are available + online.

+
+ + Statement of Need +

Biological studies, especially in ecology, health sciences and + taxonomy, need to describe the biological composition of samples. + During the last twenty years, the development of (i) high-throughput + DNA sequencing, (ii) reference databases and (iii) bioinformatics + resources have allowed the description of biological communities + through metabarcoding. Metabarcoding involves the sequencing of + millions (meta-) of short regions of specific DNA + (-barcoding, Valentini et al. + (2009)) + often from environmental samples (eDNA, Taberlet et al. + (2012)) + such as human stomach contents, lake water, soil and air.

+

Several platforms (referenced in Tedersoo et al. + (2022)) + such as QIIME2 + (Bolyen + et al., 2019), mothur + (Schloss, + 2020), and Galaxy + (Jalili + et al., 2020) allow complete analysis from raw fastq sequences + to statistical analysis and visualization. However, the R ecosystem + (R Core + Team, 2023), is very rich (fig. 1) and more flexible than these + platforms.

+

MiscMetabar aims to facilitate the + description, transformation, + exploration and reproducibility of + metabarcoding analysis using R. The development of + MiscMetabar relies heavily on the R packages + dada2, phyloseq and + targets.

+
+ + State of the Field in R +

The metabarcoding ecosystem in the R language is mature, + well-constructed, and relies on a very active community in both the + bioconductor + and + cran + projects. The + bioconductor + even creates specific task views in + Metagenomics + and + Microbiome.

+

R package + dada2 + (Callahan + et al., 2016) provides a highly cited and recommended + clustering method + (Pauvert + et al., 2019). + phyloseq + (McMurdie + & Holmes, 2013) facilitate metagenomics analysis by + providing a way to store data (the phyloseq + class) and provides graphical and statistical functions. + MiscMetabar is based on the + phyloseq class from + phyloseq, the most cited package in + metagenomics + (Wen et + al., 2023). For a description and comparison of other + integrated packages competing with phyloseq, see Wen et al. + (2023). Some + packages already extend the phyloseq packages, in particular + microbiome + package collection + (Ernst + et al., 2023), the speedyseq package + (McLaren, + 2020) and the package + phylosmith + (Smith, + 2019).

+ +

Important functions of MiscMetabar with their equivalent + when available in other R packages: 1. Mia + (Ernst + et al., 2023); 2. microViz + (Barnett + et al., 2021); 3. MicrobiotaProcess + (Xu et + al., 2023); 4 Phylosmith + (Smith, + 2019).

+ +
+

MiscMetabar enriches this R ecosystem by + providing functions to (i) describe your dataset + visually, (ii) transform your data, (iii) + explore biological diversity (alpha, beta, and taxonomic + diversity), and (iv) simplify reproducibility. + MiscMetabar is already used by the scientific + community in several teams + (Bouilloud + et al., 2023; + Mark + McCauley et al., 2022; + M. + McCauley et al., 2023; + Pleić + et al., 2022; + Vieira + et al., 2023; + Vieira + & Pecchia, 2021).

+
+ + Features + + Description +

A quick graphical representation of the phyloseq object is + available using the summary_plot_pq() + function (fig. 2A). This plot provides an information-rich + structural overview of the phyloseq object. The functions + krona() and + tax_datatable() describe the taxonomy of + organisms using krona interactive pie chart + (Ondov + et al., 2011) and + datatable + libraries, respectively.

+
+ + Transformation + + Post-clustering +

Several pipelines use at least two step of clustering. The + function asv2otu(), using either the + DECIPHER::Clusterize() function from R or + the + vsearch + software allow to recluster existing groups such as + ASV (stands for Amplicon Sequence + Variant) obtained by the + dada2::dada() function (see the vignette + reclustering). + Another transformation method is implemented in + lulu_pq(), which uses Frøslev et al. + (2017)’s + method for post-clustering curation of DNA amplicon data. Note + that a fast and robust C++ re-implementation of lulu called + mumu + (Mahé, + 2023) is also available through the function + mumu_pq().

+
+ + Cleaning and filtering +

The function clean_pq() validates a + phyloseq object, mainly by removing empty taxa and samples, and + checking for discrepancies between taxa and sample names in + different slots.

+

The filter functions subset_samples_pq() + and subset_taxa_pq() complement + subset_samples() and + subset_taxa() from the + phyloseq + package, allowing the use of a boolean vector to filter samples or + taxa from a phyloseq object.

+

I also implement a function to filter taxa based on their blast + to a custom database (filter_asv_blast()). + This function uses the blastn software + (Altschul + et al., 1990) to compare ASV sequences to a database and + filter out species that are below a given threshold of e-value + and/or bit-score.

+
+
+ + Exploration +

MiscMetabar provides a large number of + facilities to explore the biological diversity in a phyloseq object. + In most functions, a parameter enables the effect of the number of + reads (sampling depth) to be controlled by rarefaction or other + statistical methods, depending on the function. For example, the + alpha diversity analysis (function hill_pq()) + uses the HSD-Tuckey test on a linear model that includes the square + roots of the number of reads as the first explanatory variable.

+

To illustrate the effect of sample variables on the taxonomy, + MiscMetabar provides the functions + treemap_pq(), + multitax_bar_pq() (fig. 2D) and + heat_tree_pq() (fig. 2E). The effect of an + environmental variable (beta-diversity) on a biological organism can + be explored by upset plot (pset_pq(); + fig. 2F), venn diagram (ggvenn_pq(); + fig. 2G), and circle plot (circle_pq()). This + effect can be tested with PERMANOVA + (adonis_pq()) and the network test + (graph_test_pq()). If only two modalities are + compared, biplot_pq() is very useful + (fig. 2H). Differential abundance analysis can be performed directly + using the plot_deseq2_pq() function + (fig. 2I).

+ +

Some illustrations from MiscMetabar with the tengeler + dataset from mia R package

+ +
+
+ + Reproducibility +

The targets R package + (Landau, + 2021) improves the efficiency and reproducibility of the + pipeline in R. It orchestrates the stage of the pipeline and stores + the objects to skip tasks that are already up to date. Given the + complexity, runtime, and parameter sensitivity of bioinformatic + analysis, the use of targets is particularly relevant for + metabarcoding. I developed functions to list fastq files in a + directory (list_fastq_files()) and to track + the number of sequences, clusters and samples through the pipeline + (track_wkflow()) for a variety of objects. + Function write_pq() save an object of class + phyloseq and read_pq() read a phyloseq object + from files.

+
+
+ + Acknowledgements +

I thank Will Landau, Paul McMurdie, and Benjamin Callahan for their + excellent R packages on which MiscMetabar + rests. I also want to acknowledge Franck Richard, Lise Roy, Élisa + Taschen and the + Mycea + team for the discussion and work around metabarcoding.

+
+ + + + + + + AltschulStephen F + GishWarren + MillerWebb + MyersEugene W + LipmanDavid J + + Basic local alignment search tool + Journal of molecular biology + Elsevier + 1990 + 215 + 3 + 10.32388/rhq6vj + 403 + 410 + + + + + + BarnettDavid J. M. + ArtsIlja C. W. + PendersJohn + + microViz: An r package for microbiome data visualization and statistics + Journal of Open Source Software + The Open Journal + 2021 + 6 + 63 + https://doi.org/10.21105/joss.03201 + 10.21105/joss.03201 + 3201 + + + + + + + BolyenEvan + RideoutJai Ram + DillonMaThew R + BokulichNicholas A + AbnetChristian C + Al-GhalithGabriel A + AlexanderHarriet + AlmEric J + ArumugamManimozhiyan + AsnicarFrancesco + others + + Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 + Nature biotechnology + Nature Publishing Group + 2019 + 37 + 8 + 10.1038/s41587-019-0209-9 + 852 + 857 + + + + + + BouilloudMarie + GalanMaxime + PradelJulien + LoiseauAnne + FERREROJulien + GalletRomain + RocheBenjamin + CharbonnelNathalie + + Effects of forest urbanization on the interplay between small mammal communities and their gut microbiota + bioRxiv + Cold Spring Harbor Laboratory + 2023 + 10.21105/joss.03201 + 2023 + 09 + + + + + + CallahanBenjamin J + McMurdiePaul J + RosenMichael J + HanAndrew W + JohnsonAmy Jo A + HolmesSusan P + + DADA2: High-resolution sample inference from illumina amplicon data + Nature methods + Nature Publishing Group US New York + 2016 + 13 + 7 + 10.1038/nmeth.3869 + 581 + 583 + + + + + + ErnstFelix G. M. + ShettySudarshan A. + BormanTuomas + LahtiLeo + + Mia: Microbiome analysis + 2023 + https://bioconductor.org/packages/mia + 10.18129/B9.bioc.mia + + + + + + FrøslevTobias Guldberg + KjøllerRasmus + BruunHans Henrik + EjrnæsRasmus + BrunbjergAne Kirstine + PietroniCarlotta + HansenAnders Johannes + + Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates + Nature communications + Nature Publishing Group UK London + 2017 + 8 + 1 + 10.1038/s41467-017-01312-x + 1188 + + + + + + + JaliliVahid + AfganEnis + GuQiang + ClementsDave + BlankenbergDaniel + GoecksJeremy + TaylorJames + NekrutenkoAnton + + The galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update + Nucleic acids research + Oxford University Press + 2020 + 48 + W1 + 10.1093/nar/gkaa434 + W395 + W402 + + + + + + LandauWilliam Michael + + The targets r package: A dynamic make-like function-oriented pipeline toolkit for reproducibility and 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in 1 sample: 17722Max nb of sample for one taxa (1726472): 27Nb of taxa present in 1 sample only: 0ADHDControl304050607080Richness (Hill 0)patient_status1020Shannon (Hill 1)4812Simpson (Hill 2)Hill Number 0 Control-ADHDHill Number 1 Control-ADHDHill Number 2 Control-ADHD-10-50510Differences in mean levels (value and confidence intervals at 95%)Tuckey HSD testing for differences in mean Hill numbers531141745312631121341434212111102040Intersection size050100Set sizeControl_Cohort_1ADHD_Cohort_3Control_Cohort_3ADHD_Cohort_2Control_Cohort_2ADHD_Cohort_1patient_status_vs_cohortBacteriaFirmicutesVerrucomicrobiaBacteroidetesProteobacteriaCyanobacteriaClostridiaErysipelotrichiaVerrucomicrobiaeBacteroidiaGammaproteobacteriaMelainabacteriaClostridialesErysipelotrichalesVerrucomicrobialesBacteroidalesOceanospirillalesEnterobacterialesGastranaerophilalesRuminococcaceaeErysipelotrichaceaeLachnospiraceaeVerrucomicrobiaceaeClostridiaceae_1PorphyromonadaceaeBacteroidales_S24-7_groupBacteroidaceaePrevotellaceaeRikenellaceaeHalomonadaceaeEnterobacteriaceaeChristensenellaceaeEubacteriaceae[Eubacterium]_coprostanoligenes_group[Clostridium]_innocuum_groupDielmaEpulopisciumHoldemaniaAkkermansiaErysipelatoclostridiumClostridium_sensu_stricto_1Parabacteroidesuncultured_bacteriumunculturedCoprobacterBacteroidesParaprevotellaBarnesiellaOdoribacterAlistipesHalomonasEscherichia-ShigellaCoprococcus_2FusicatenibacterLachnoclostridium[Ruminococcus]_gnavus_groupHungatellaunculturedEisenbergiella[Eubacterium]_rectale_group[Ruminococcus]_gauvreauii_groupRoseburiaAnaerostipes[Eubacterium]_xylanophilum_group[Eubacterium]_fissicatena_groupCatabacterLachnospiraceae_ND3007_groupRuminococcaceae_UCG-014ButyricicoccusRuminococcaceae_UCG-004unculturedFlavonifractorRuminiclostridium_9AnaerotruncusRuminococcus_2FaecalibacteriumRuminococcus_1SubdoligranulumRuminiclostridium_5Ruminococcaceae_UCG-013EubacteriumBlautia 1.0 14.0 30.4 51.1 77.1110.0151.0 182 16200 58300126000220000340000486000..2..1NodesCohort_1 (10 sam.)Cohort_2 (10 sam.)Cohort_3 (7 sam.)0(0%)0(0%)0(0%)24(16%)2(1%)8(5%)117(77%)-20-100102030BacteroidesFaecalibacteriumParaprevotellaHungatellaRuminococcaceae_UCG-013Ruminococcaceae_UCG-014ParabacteroidesunculturedCatabacter[Ruminococcus]_gauvreauii_groupBlautiataxlog2FoldChangecol_taxBacteroidetesFirmicutesChange in abundance for patient_status (top:ADHD vs down:Control)ADHDControlBacteroidetesCyanobacteriaFirmicutesProteobacteriaVerrucomicrobiaBacteroidiaMelainabacteriaClostridiaErysipelotrichiaGammaproteobacteriaVerrucomicrobiae012345012345BacteroidalesGastranaerophilalesClostridialesErysipelotrichalesEnterobacterialesOceanospirillalesVerrucomicrobialesAbundanceLVL3Control (14 samples)Control (14 samples)ADHD (13 samples)ADHD (13 samples)6625255741276002356822263199871707614726129481034681787192560250404534447142964229387938753743364433623226318731443116304530402693265524872466239223072230195218631732169616631626130711701079107910689388607466465334341726471517264791726472131726471321726471817264740172647221726476117264714517264781726471901726471917264734172647166172647167172647531726471891726472917264748172647271726472517264747172647241726472172647261726473117264711726470Samples-5.0-2.50.02.55.0Abstats::reorder(OTU, Abundance)modalityADHDControl0255075010000200003000040000Number of sequencesNumber of OTUs (with standard error)factA110A111A12A13A14A15A16A17A18A19A21A210A22A23A24A25A26A27A28A29A33A34A35A36A37A38A39