Loading packages
DADA2
You can find the original DADA pipeline at
https://benjjneb.github.io/dada2/
Installing DADA2
Binaries for the current release version of DADA2 (1.32) are available from Bioconductor. Note that you must have R 4.2.0 or newer, and Bioconductor version 3.16, to install the most current release from Bioconductor.
# if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
# BiocManager::install()
#
#
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
# BiocManager::install("dada2")Load DADA2 to your environment
Now, your DADA2 package was installed to your computer. That does not mean it is loaded to your working environment.
To double-check the loaded packages, you can run
sessionInfo().
sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: aarch64-apple-darwin20
## Running under: macOS 15.0.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Asia/Seoul
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] yaml_2.3.10 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
## [5] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
## [9] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 readxl_1.4.3
## [13] phyloseq_1.48.0 pacman_0.5.1
##
## loaded via a namespace (and not attached):
## [1] ade4_1.7-22 tidyselect_1.2.1 Biostrings_2.72.1
## [4] fastmap_1.2.0 digest_0.6.37 timechange_0.3.0
## [7] lifecycle_1.0.4 cluster_2.1.6 survival_3.7-0
## [10] magrittr_2.0.3 compiler_4.4.1 rlang_1.1.4
## [13] sass_0.4.9 tools_4.4.1 igraph_2.1.1
## [16] utf8_1.2.4 data.table_1.16.2 knitr_1.48
## [19] plyr_1.8.9 withr_3.0.1 BiocGenerics_0.50.0
## [22] grid_4.4.1 stats4_4.4.1 fansi_1.0.6
## [25] multtest_2.60.0 biomformat_1.32.0 colorspace_2.1-1
## [28] Rhdf5lib_1.26.0 scales_1.3.0 iterators_1.0.14
## [31] MASS_7.3-61 cli_3.6.3 rmarkdown_2.28
## [34] vegan_2.6-8 crayon_1.5.3 generics_0.1.3
## [37] rstudioapi_0.17.0 tzdb_0.4.0 httr_1.4.7
## [40] reshape2_1.4.4 ape_5.8 cachem_1.1.0
## [43] rhdf5_2.48.0 zlibbioc_1.50.0 splines_4.4.1
## [46] parallel_4.4.1 BiocManager_1.30.25 cellranger_1.1.0
## [49] XVector_0.44.0 rmdformats_1.0.4 vctrs_0.6.5
## [52] Matrix_1.7-1 jsonlite_1.8.9 bookdown_0.41
## [55] hms_1.1.3 IRanges_2.38.1 S4Vectors_0.42.1
## [58] foreach_1.5.2 jquerylib_0.1.4 glue_1.8.0
## [61] codetools_0.2-20 stringi_1.8.4 gtable_0.3.5
## [64] GenomeInfoDb_1.40.1 UCSC.utils_1.0.0 munsell_0.5.1
## [67] pillar_1.9.0 htmltools_0.5.8.1 rhdf5filters_1.16.0
## [70] GenomeInfoDbData_1.2.12 R6_2.5.1 evaluate_1.0.1
## [73] lattice_0.22-6 Biobase_2.64.0 bslib_0.8.0
## [76] Rcpp_1.0.13 nlme_3.1-166 permute_0.9-7
## [79] mgcv_1.9-1 xfun_0.48 pkgconfig_2.0.3As you can see, DADA2 is not loaded. To load package, use
library() function.
library(dada2)## Loading required package: RcppsessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: aarch64-apple-darwin20
## Running under: macOS 15.0.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Asia/Seoul
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] dada2_1.32.0 Rcpp_1.0.13 yaml_2.3.10 lubridate_1.9.3
## [5] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
## [9] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1
## [13] tidyverse_2.0.0 readxl_1.4.3 phyloseq_1.48.0 pacman_0.5.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 deldir_2.0-4
## [3] permute_0.9-7 rlang_1.1.4
## [5] magrittr_2.0.3 ade4_1.7-22
## [7] matrixStats_1.4.1 compiler_4.4.1
## [9] mgcv_1.9-1 png_0.1-8
## [11] vctrs_0.6.5 reshape2_1.4.4
## [13] pwalign_1.0.0 pkgconfig_2.0.3
## [15] crayon_1.5.3 fastmap_1.2.0
## [17] XVector_0.44.0 rmdformats_1.0.4
## [19] utf8_1.2.4 Rsamtools_2.20.0
## [21] rmarkdown_2.28 tzdb_0.4.0
## [23] UCSC.utils_1.0.0 xfun_0.48
## [25] zlibbioc_1.50.0 cachem_1.1.0
## [27] GenomeInfoDb_1.40.1 jsonlite_1.8.9
## [29] biomformat_1.32.0 DelayedArray_0.30.1
## [31] rhdf5filters_1.16.0 BiocParallel_1.38.0
## [33] Rhdf5lib_1.26.0 jpeg_0.1-10
## [35] parallel_4.4.1 cluster_2.1.6
## [37] R6_2.5.1 RColorBrewer_1.1-3
## [39] bslib_0.8.0 stringi_1.8.4
## [41] GenomicRanges_1.56.2 jquerylib_0.1.4
## [43] cellranger_1.1.0 bookdown_0.41
## [45] SummarizedExperiment_1.34.0 iterators_1.0.14
## [47] knitr_1.48 IRanges_2.38.1
## [49] Matrix_1.7-1 splines_4.4.1
## [51] igraph_2.1.1 timechange_0.3.0
## [53] tidyselect_1.2.1 abind_1.4-8
## [55] rstudioapi_0.17.0 vegan_2.6-8
## [57] codetools_0.2-20 hwriter_1.3.2.1
## [59] lattice_0.22-6 plyr_1.8.9
## [61] Biobase_2.64.0 withr_3.0.1
## [63] ShortRead_1.62.0 evaluate_1.0.1
## [65] survival_3.7-0 RcppParallel_5.1.9
## [67] Biostrings_2.72.1 pillar_1.9.0
## [69] BiocManager_1.30.25 MatrixGenerics_1.16.0
## [71] foreach_1.5.2 stats4_4.4.1
## [73] generics_0.1.3 S4Vectors_0.42.1
## [75] hms_1.1.3 munsell_0.5.1
## [77] scales_1.3.0 glue_1.8.0
## [79] tools_4.4.1 interp_1.1-6
## [81] data.table_1.16.2 GenomicAlignments_1.40.0
## [83] rhdf5_2.48.0 grid_4.4.1
## [85] ape_5.8 latticeExtra_0.6-30
## [87] colorspace_2.1-1 nlme_3.1-166
## [89] GenomeInfoDbData_1.2.12 cli_3.6.3
## [91] fansi_1.0.6 S4Arrays_1.4.1
## [93] gtable_0.3.5 sass_0.4.9
## [95] digest_0.6.37 BiocGenerics_0.50.0
## [97] SparseArray_1.4.8 htmltools_0.5.8.1
## [99] multtest_2.60.0 lifecycle_1.0.4
## [101] httr_1.4.7 MASS_7.3-61You can see the dada2 package was loaded.
Opening multiple files for dada2 in R
Without pointing the exact path of a file that you want to play with, R will employ thing at your current working directory.
getwd() will show you the current directory that you are
working on.
getwd()## [1] "/Volumes/macdrive/Dropbox"list.files() function shows all the files in the current
list.
list.files()## [1] "@Lab_Administrative"
## [2] "@minsik"
## [3] "@wet_lab"
## [4] "2024_lailab_tech"
## [5] "Backup"
## [6] "COD_20240828_MGK_SICAS2_kegg_tax_table_validation.Rmd"
## [7] "CV, papers"
## [8] "Database"
## [9] "ETC"
## [10] "Finance"
## [11] "Forms_US"
## [12] "Git"
## [13] "Graduate school data"
## [14] "Icon\r"
## [15] "Inha"
## [16] "KFTP.kaist.ac.kr"
## [17] "KRIBB"
## [18] "Lectures"
## [19] "MGH"
## [20] "Modified.zip"
## [21] "Photos"
## [22] "Pictures"
## [23] "Project_CFB"
## [24] "Project_Freezer"
## [25] "Project_SICAS2_microbiome"
## [26] "Project_Uganda_CAS"
## [27] "Project_Uganda_CAS (view-only conflicts 2024-08-12)"
## [28] "Project_Uganda_CAS (view-only conflicts 2024-08-26)"
## [29] "R"
## [30] "Review"
## [31] "sbp_dataset_korea_2013-2014.csv"
## [32] "scripts"
## [33] "Sequencing_archive"
## [34] "SICAS2_season_git"
## [35] "Summer_Student_Projects"
## [36] "Undergraduate school (2011fall_2014spring)"
## [37] "volume_reduction_data.csv"
## [38] "발표자료 작성방"You can create a directory in R using dir.create("...")
and also set the path of a folder of your interest for
list.files().
#dir.create("dataset")
list.files("IBS7048_dataset/")## character(0)#Else,
list.files("/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/")## [1] "F3D0_S188_L001_R1_001.fastq" "F3D0_S188_L001_R2_001.fastq"
## [3] "F3D1_S189_L001_R1_001.fastq" "F3D1_S189_L001_R2_001.fastq"
## [5] "F3D141_S207_L001_R1_001.fastq" "F3D141_S207_L001_R2_001.fastq"
## [7] "F3D142_S208_L001_R1_001.fastq" "F3D142_S208_L001_R2_001.fastq"
## [9] "F3D143_S209_L001_R1_001.fastq" "F3D143_S209_L001_R2_001.fastq"
## [11] "F3D144_S210_L001_R1_001.fastq" "F3D144_S210_L001_R2_001.fastq"
## [13] "F3D145_S211_L001_R1_001.fastq" "F3D145_S211_L001_R2_001.fastq"
## [15] "F3D146_S212_L001_R1_001.fastq" "F3D146_S212_L001_R2_001.fastq"
## [17] "F3D147_S213_L001_R1_001.fastq" "F3D147_S213_L001_R2_001.fastq"
## [19] "F3D148_S214_L001_R1_001.fastq" "F3D148_S214_L001_R2_001.fastq"
## [21] "F3D149_S215_L001_R1_001.fastq" "F3D149_S215_L001_R2_001.fastq"
## [23] "F3D150_S216_L001_R1_001.fastq" "F3D150_S216_L001_R2_001.fastq"
## [25] "F3D2_S190_L001_R1_001.fastq" "F3D2_S190_L001_R2_001.fastq"
## [27] "F3D3_S191_L001_R1_001.fastq" "F3D3_S191_L001_R2_001.fastq"
## [29] "F3D5_S193_L001_R1_001.fastq" "F3D5_S193_L001_R2_001.fastq"
## [31] "F3D6_S194_L001_R1_001.fastq" "F3D6_S194_L001_R2_001.fastq"
## [33] "F3D7_S195_L001_R1_001.fastq" "F3D7_S195_L001_R2_001.fastq"
## [35] "F3D8_S196_L001_R1_001.fastq" "F3D8_S196_L001_R2_001.fastq"
## [37] "F3D9_S197_L001_R1_001.fastq" "F3D9_S197_L001_R2_001.fastq"
## [39] "filtered" "HMP_MOCK.v35.fasta"
## [41] "Mock_S280_L001_R1_001.fastq" "Mock_S280_L001_R2_001.fastq"
## [43] "mouse.dpw.metadata" "mouse.time.design"
## [45] "silva_nr99_v138.1_train_set.fa" "silva_nr99_v138.1_train_set.fa.zip"
## [47] "stability.batch" "stability.files"This is my directory with the example files.
You can also navigate folders in R. Try typing
list.files("/") and press tab.
Anyway, I can set name this path as path, as it is too
long.
minsik_path = "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset"
# you can also use `<-` instead of `=`.This is my directory with the example files.
As now path is having the information of my location, I
can substitute the long path name with path
list.files(minsik_path)## [1] "F3D0_S188_L001_R1_001.fastq" "F3D0_S188_L001_R2_001.fastq"
## [3] "F3D1_S189_L001_R1_001.fastq" "F3D1_S189_L001_R2_001.fastq"
## [5] "F3D141_S207_L001_R1_001.fastq" "F3D141_S207_L001_R2_001.fastq"
## [7] "F3D142_S208_L001_R1_001.fastq" "F3D142_S208_L001_R2_001.fastq"
## [9] "F3D143_S209_L001_R1_001.fastq" "F3D143_S209_L001_R2_001.fastq"
## [11] "F3D144_S210_L001_R1_001.fastq" "F3D144_S210_L001_R2_001.fastq"
## [13] "F3D145_S211_L001_R1_001.fastq" "F3D145_S211_L001_R2_001.fastq"
## [15] "F3D146_S212_L001_R1_001.fastq" "F3D146_S212_L001_R2_001.fastq"
## [17] "F3D147_S213_L001_R1_001.fastq" "F3D147_S213_L001_R2_001.fastq"
## [19] "F3D148_S214_L001_R1_001.fastq" "F3D148_S214_L001_R2_001.fastq"
## [21] "F3D149_S215_L001_R1_001.fastq" "F3D149_S215_L001_R2_001.fastq"
## [23] "F3D150_S216_L001_R1_001.fastq" "F3D150_S216_L001_R2_001.fastq"
## [25] "F3D2_S190_L001_R1_001.fastq" "F3D2_S190_L001_R2_001.fastq"
## [27] "F3D3_S191_L001_R1_001.fastq" "F3D3_S191_L001_R2_001.fastq"
## [29] "F3D5_S193_L001_R1_001.fastq" "F3D5_S193_L001_R2_001.fastq"
## [31] "F3D6_S194_L001_R1_001.fastq" "F3D6_S194_L001_R2_001.fastq"
## [33] "F3D7_S195_L001_R1_001.fastq" "F3D7_S195_L001_R2_001.fastq"
## [35] "F3D8_S196_L001_R1_001.fastq" "F3D8_S196_L001_R2_001.fastq"
## [37] "F3D9_S197_L001_R1_001.fastq" "F3D9_S197_L001_R2_001.fastq"
## [39] "filtered" "HMP_MOCK.v35.fasta"
## [41] "Mock_S280_L001_R1_001.fastq" "Mock_S280_L001_R2_001.fastq"
## [43] "mouse.dpw.metadata" "mouse.time.design"
## [45] "silva_nr99_v138.1_train_set.fa" "silva_nr99_v138.1_train_set.fa.zip"
## [47] "stability.batch" "stability.files"Downloading files
list.files()## [1] "@Lab_Administrative"
## [2] "@minsik"
## [3] "@wet_lab"
## [4] "2024_lailab_tech"
## [5] "Backup"
## [6] "COD_20240828_MGK_SICAS2_kegg_tax_table_validation.Rmd"
## [7] "CV, papers"
## [8] "Database"
## [9] "ETC"
## [10] "Finance"
## [11] "Forms_US"
## [12] "Git"
## [13] "Graduate school data"
## [14] "Icon\r"
## [15] "Inha"
## [16] "KFTP.kaist.ac.kr"
## [17] "KRIBB"
## [18] "Lectures"
## [19] "MGH"
## [20] "Modified.zip"
## [21] "Photos"
## [22] "Pictures"
## [23] "Project_CFB"
## [24] "Project_Freezer"
## [25] "Project_SICAS2_microbiome"
## [26] "Project_Uganda_CAS"
## [27] "Project_Uganda_CAS (view-only conflicts 2024-08-12)"
## [28] "Project_Uganda_CAS (view-only conflicts 2024-08-26)"
## [29] "R"
## [30] "Review"
## [31] "sbp_dataset_korea_2013-2014.csv"
## [32] "scripts"
## [33] "Sequencing_archive"
## [34] "SICAS2_season_git"
## [35] "Summer_Student_Projects"
## [36] "Undergraduate school (2011fall_2014spring)"
## [37] "volume_reduction_data.csv"
## [38] "발표자료 작성방"Chooing files with pattern
From the list of files, we only want to select .fastq
files. For that, I am going to make a list of files.
Since this file list is having forward and reverse reads per one sample set, we need to separate those files into two groups as well.
list.files(minsik_path, pattern = "_R1_001")## [1] "F3D0_S188_L001_R1_001.fastq" "F3D1_S189_L001_R1_001.fastq"
## [3] "F3D141_S207_L001_R1_001.fastq" "F3D142_S208_L001_R1_001.fastq"
## [5] "F3D143_S209_L001_R1_001.fastq" "F3D144_S210_L001_R1_001.fastq"
## [7] "F3D145_S211_L001_R1_001.fastq" "F3D146_S212_L001_R1_001.fastq"
## [9] "F3D147_S213_L001_R1_001.fastq" "F3D148_S214_L001_R1_001.fastq"
## [11] "F3D149_S215_L001_R1_001.fastq" "F3D150_S216_L001_R1_001.fastq"
## [13] "F3D2_S190_L001_R1_001.fastq" "F3D3_S191_L001_R1_001.fastq"
## [15] "F3D5_S193_L001_R1_001.fastq" "F3D6_S194_L001_R1_001.fastq"
## [17] "F3D7_S195_L001_R1_001.fastq" "F3D8_S196_L001_R1_001.fastq"
## [19] "F3D9_S197_L001_R1_001.fastq" "Mock_S280_L001_R1_001.fastq"Using pattern = argument will make a subset of list of
files including the pattern noted in the quotation mark
"".
With another argument, full.names = TRUE, it will give
you the list of full-path-names of the files with the specified
pattern.
list.files(minsik_path, pattern = "_R1_001", full.names = TRUE)## [1] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D0_S188_L001_R1_001.fastq"
## [2] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D1_S189_L001_R1_001.fastq"
## [3] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D141_S207_L001_R1_001.fastq"
## [4] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D142_S208_L001_R1_001.fastq"
## [5] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D143_S209_L001_R1_001.fastq"
## [6] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D144_S210_L001_R1_001.fastq"
## [7] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D145_S211_L001_R1_001.fastq"
## [8] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D146_S212_L001_R1_001.fastq"
## [9] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D147_S213_L001_R1_001.fastq"
## [10] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D148_S214_L001_R1_001.fastq"
## [11] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D149_S215_L001_R1_001.fastq"
## [12] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D150_S216_L001_R1_001.fastq"
## [13] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D2_S190_L001_R1_001.fastq"
## [14] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D3_S191_L001_R1_001.fastq"
## [15] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D5_S193_L001_R1_001.fastq"
## [16] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D6_S194_L001_R1_001.fastq"
## [17] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D7_S195_L001_R1_001.fastq"
## [18] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D8_S196_L001_R1_001.fastq"
## [19] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D9_S197_L001_R1_001.fastq"
## [20] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/Mock_S280_L001_R1_001.fastq"So, now what?
We are going to store their list of names of files, so that some other function can use that information for loading that data at once.
forward_read_files <- list.files(minsik_path, pattern="_R1_001.fastq", full.names = TRUE)
reverse_read_files <- list.files(minsik_path, pattern="_R2_001.fastq", full.names = TRUE)Meanwhile, they can be having different orders. So it would be helpful if we can sort the data right after loading it.
library(tidyverse)
forward_read_files <- list.files(minsik_path, pattern="_R1_001.fastq", full.names = TRUE) %>% sort()
reverse_read_files <- list.files(minsik_path, pattern="_R2_001.fastq", full.names = TRUE) %>% sort()Manipulating characters
Plus, after processing the data, we need to get
1 sample name per 1 sample. It can be extracted from either
one of the forward and reverse reads.
basename() removes all the higher path of file path.
forward_read_files %>%
basename()## [1] "F3D0_S188_L001_R1_001.fastq" "F3D1_S189_L001_R1_001.fastq"
## [3] "F3D141_S207_L001_R1_001.fastq" "F3D142_S208_L001_R1_001.fastq"
## [5] "F3D143_S209_L001_R1_001.fastq" "F3D144_S210_L001_R1_001.fastq"
## [7] "F3D145_S211_L001_R1_001.fastq" "F3D146_S212_L001_R1_001.fastq"
## [9] "F3D147_S213_L001_R1_001.fastq" "F3D148_S214_L001_R1_001.fastq"
## [11] "F3D149_S215_L001_R1_001.fastq" "F3D150_S216_L001_R1_001.fastq"
## [13] "F3D2_S190_L001_R1_001.fastq" "F3D3_S191_L001_R1_001.fastq"
## [15] "F3D5_S193_L001_R1_001.fastq" "F3D6_S194_L001_R1_001.fastq"
## [17] "F3D7_S195_L001_R1_001.fastq" "F3D8_S196_L001_R1_001.fastq"
## [19] "F3D9_S197_L001_R1_001.fastq" "Mock_S280_L001_R1_001.fastq"str_split_fixed() will separate all the charaters
separated by the pattern specified in quotation mark "" and
generate a matrix of the separated character. The number of separated
output will be set as you set. (you can think of the import wizard in
Excel).
forward_read_files %>%
basename() %>%
str_split_fixed(pattern = "_", n = 5)## [,1] [,2] [,3] [,4] [,5]
## [1,] "F3D0" "S188" "L001" "R1" "001.fastq"
## [2,] "F3D1" "S189" "L001" "R1" "001.fastq"
## [3,] "F3D141" "S207" "L001" "R1" "001.fastq"
## [4,] "F3D142" "S208" "L001" "R1" "001.fastq"
## [5,] "F3D143" "S209" "L001" "R1" "001.fastq"
## [6,] "F3D144" "S210" "L001" "R1" "001.fastq"
## [7,] "F3D145" "S211" "L001" "R1" "001.fastq"
## [8,] "F3D146" "S212" "L001" "R1" "001.fastq"
## [9,] "F3D147" "S213" "L001" "R1" "001.fastq"
## [10,] "F3D148" "S214" "L001" "R1" "001.fastq"
## [11,] "F3D149" "S215" "L001" "R1" "001.fastq"
## [12,] "F3D150" "S216" "L001" "R1" "001.fastq"
## [13,] "F3D2" "S190" "L001" "R1" "001.fastq"
## [14,] "F3D3" "S191" "L001" "R1" "001.fastq"
## [15,] "F3D5" "S193" "L001" "R1" "001.fastq"
## [16,] "F3D6" "S194" "L001" "R1" "001.fastq"
## [17,] "F3D7" "S195" "L001" "R1" "001.fastq"
## [18,] "F3D8" "S196" "L001" "R1" "001.fastq"
## [19,] "F3D9" "S197" "L001" "R1" "001.fastq"
## [20,] "Mock" "S280" "L001" "R1" "001.fastq"if you choose 1st column from the data, using .[,1], it
wlll show the name of files after removing all the characters after the
first underscore _.
forward_read_files %>%
basename() %>%
str_split_fixed("_", 5) %>%
.[,1]## [1] "F3D0" "F3D1" "F3D141" "F3D142" "F3D143" "F3D144" "F3D145" "F3D146"
## [9] "F3D147" "F3D148" "F3D149" "F3D150" "F3D2" "F3D3" "F3D5" "F3D6"
## [17] "F3D7" "F3D8" "F3D9" "Mock"I am going to store this list of file names for future use.
sample.names <- forward_read_files %>%
basename() %>%
str_split_fixed("_", 5) %>%
.[,1]Plotting
DADA2 have a cool function called plotQualityProfile(),
which will automatically plot QC scores by length of all the files that
were listed. Such as,
If I want to plot the QC profile of the f
forward_read_files## [1] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D0_S188_L001_R1_001.fastq"
## [2] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D1_S189_L001_R1_001.fastq"
## [3] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D141_S207_L001_R1_001.fastq"
## [4] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D142_S208_L001_R1_001.fastq"
## [5] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D143_S209_L001_R1_001.fastq"
## [6] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D144_S210_L001_R1_001.fastq"
## [7] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D145_S211_L001_R1_001.fastq"
## [8] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D146_S212_L001_R1_001.fastq"
## [9] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D147_S213_L001_R1_001.fastq"
## [10] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D148_S214_L001_R1_001.fastq"
## [11] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D149_S215_L001_R1_001.fastq"
## [12] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D150_S216_L001_R1_001.fastq"
## [13] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D2_S190_L001_R1_001.fastq"
## [14] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D3_S191_L001_R1_001.fastq"
## [15] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D5_S193_L001_R1_001.fastq"
## [16] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D6_S194_L001_R1_001.fastq"
## [17] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D7_S195_L001_R1_001.fastq"
## [18] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D8_S196_L001_R1_001.fastq"
## [19] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D9_S197_L001_R1_001.fastq"
## [20] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/Mock_S280_L001_R1_001.fastq"forward_read_files[1]## [1] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/F3D0_S188_L001_R1_001.fastq"plotQualityProfile(forward_read_files[1])
It will show the total reads, QC score, and the length of sequencing reads at the same time.
We can do this for multiple samples, by using
plotQualityProfile(
c(forward_read_files[1], forward_read_files[2])
)
or
plotQualityProfile(forward_read_files[1:2])
or
plotQualityProfile(forward_read_files[1:10])
Let’s check reverse reads as well.
plotQualityProfile(reverse_read_files[1:10])
plotQualityProfile(reverse_read_files[1])
As you can see, we have high quality forward reads but low quality reverse reads. These low quality reads need to be removed before mering these files into one complementary read file.
Filter and trim
Before making filtered reads, let’s set an location and names for them.
Using paste() function, we can manipulate charater
variables like ties.
sample.names## [1] "F3D0" "F3D1" "F3D141" "F3D142" "F3D143" "F3D144" "F3D145" "F3D146"
## [9] "F3D147" "F3D148" "F3D149" "F3D150" "F3D2" "F3D3" "F3D5" "F3D6"
## [17] "F3D7" "F3D8" "F3D9" "Mock"paste0(sample.names, "_sample")## [1] "F3D0_sample" "F3D1_sample" "F3D141_sample" "F3D142_sample"
## [5] "F3D143_sample" "F3D144_sample" "F3D145_sample" "F3D146_sample"
## [9] "F3D147_sample" "F3D148_sample" "F3D149_sample" "F3D150_sample"
## [13] "F3D2_sample" "F3D3_sample" "F3D5_sample" "F3D6_sample"
## [17] "F3D7_sample" "F3D8_sample" "F3D9_sample" "Mock_sample"Using paste0, we can create a list of new names at once.
# Place filtered files in filtered/ subdirectory
filtered_forward_reads <- file.path(minsik_path, "filtered", paste0(sample.names, "_F_filt.fastq.gz"))
filtered_reverse_reads <- file.path(minsik_path, "filtered", paste0(sample.names, "_R_filt.fastq.gz"))
minsik_path[1]## [1] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset"filtered_forward_reads[2]## [1] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D1_F_filt.fastq.gz"filtered_forward_reads## [1] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D0_F_filt.fastq.gz"
## [2] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D1_F_filt.fastq.gz"
## [3] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D141_F_filt.fastq.gz"
## [4] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D142_F_filt.fastq.gz"
## [5] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D143_F_filt.fastq.gz"
## [6] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D144_F_filt.fastq.gz"
## [7] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D145_F_filt.fastq.gz"
## [8] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D146_F_filt.fastq.gz"
## [9] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D147_F_filt.fastq.gz"
## [10] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D148_F_filt.fastq.gz"
## [11] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D149_F_filt.fastq.gz"
## [12] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D150_F_filt.fastq.gz"
## [13] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D2_F_filt.fastq.gz"
## [14] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D3_F_filt.fastq.gz"
## [15] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D5_F_filt.fastq.gz"
## [16] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D6_F_filt.fastq.gz"
## [17] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D7_F_filt.fastq.gz"
## [18] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D8_F_filt.fastq.gz"
## [19] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D9_F_filt.fastq.gz"
## [20] "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/Mock_F_filt.fastq.gz"We can also assign names of each element in that list, using
names() function.
names(filtered_forward_reads) <- sample.names
names(filtered_reverse_reads) <- sample.names
filtered_forward_reads## F3D0
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D0_F_filt.fastq.gz"
## F3D1
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D1_F_filt.fastq.gz"
## F3D141
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D141_F_filt.fastq.gz"
## F3D142
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D142_F_filt.fastq.gz"
## F3D143
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D143_F_filt.fastq.gz"
## F3D144
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D144_F_filt.fastq.gz"
## F3D145
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D145_F_filt.fastq.gz"
## F3D146
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D146_F_filt.fastq.gz"
## F3D147
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D147_F_filt.fastq.gz"
## F3D148
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D148_F_filt.fastq.gz"
## F3D149
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D149_F_filt.fastq.gz"
## F3D150
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D150_F_filt.fastq.gz"
## F3D2
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D2_F_filt.fastq.gz"
## F3D3
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D3_F_filt.fastq.gz"
## F3D5
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D5_F_filt.fastq.gz"
## F3D6
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D6_F_filt.fastq.gz"
## F3D7
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D7_F_filt.fastq.gz"
## F3D8
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D8_F_filt.fastq.gz"
## F3D9
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/F3D9_F_filt.fastq.gz"
## Mock
## "/Volumes/macdrive/Dropbox/Inha/5_Lectures/2023/Advanced metagenomics/IBS7048_dataset/filtered/Mock_F_filt.fastq.gz"Now, we are using the dada2 filtering function.
filterAndTrim() have multiple options for filtering.
- the path of original file (forward)
- the path of new file (forward)
- the path of original file (reverse)
- the path of new file (reverse)
- truncLen: Reads after 160th bp showed lower QC scores. Lets remove them, from 160 to 250.
- maxEE: Maximum error after truncation
- compress=TRUE: The output files will be gzipped. 8 multithread=TRUE: Turn on this option if you are using Windows OS
The maxEE parameter sets the maximum number of “expected errors” allowed in a read, which is a better filter than simply averaging quality scores.
out <- filterAndTrim(forward_read_files, #name of forward raw reads
filtered_forward_reads, #name of filtered forward reads
reverse_read_files,
filtered_reverse_reads,
truncLen=c(250,150),# Reads after 160th bp showed lower QC scores. Lets remove them!
compress=TRUE,
#multithread=TRUE, # On Windows set multithread=FALSE
maxEE=c(2,2))
head(out)## reads.in reads.out
## F3D0_S188_L001_R1_001.fastq 7793 7040
## F3D1_S189_L001_R1_001.fastq 5869 5225
## F3D141_S207_L001_R1_001.fastq 5958 5409
## F3D142_S208_L001_R1_001.fastq 3183 2907
## F3D143_S209_L001_R1_001.fastq 3178 2923
## F3D144_S210_L001_R1_001.fastq 4827 4281You can see some reads were removed from the fastq file!
and they are now stored in a new directory.
list.files(path = "/Users/minsikkim/Dropbox (Personal)/Inha/5_Lectures/Advanced metagenomics/IBS7048_dataset/filtered/")## character(0)Error prediction
The DADA2 algorithm makes use of a parametric error model (err) and every amplicon dataset has a different set of error rates. The learnErrors method learns this error model from the data, by alternating estimation of the error rates and inference of sample composition until they converge on a jointly consistent solution. As in many machine-learning problems, the algorithm must begin with an initial guess, for which the maximum possible error rates in this data are used (the error rates if only the most abundant sequence is correct and all the rest are errors).
error_F <- learnErrors(filtered_forward_reads
#, multithread=TRUE #
)## 34607000 total bases in 138428 reads from 20 samples will be used for learning the error rates.error_R <- learnErrors(filtered_reverse_reads
#, multithread=TRUE
)## 20764200 total bases in 138428 reads from 20 samples will be used for learning the error rates.We can double-chekc when the error is ocurring in our sequencing
file, using plotErrors() function.
plotErrors(error_F, nominalQ=TRUE)## Warning in scale_y_log10(): log-10 transformation introduced infinite values.
## log-10 transformation introduced infinite values.
The error rates for each possible transition (A→C, A→G, …) are shown. Points are the observed error rates for each consensus quality score. The black line shows the estimated error rates after convergence of the machine-learning algorithm. The red line shows the error rates expected under the nominal definition of the Q-score. Here the estimated error rates (black line) are a good fit to the observed rates (points), and the error rates drop with increased quality as expected. Everything looks reasonable and we proceed with confidence.
Using this predicted error (which can be also called as
expected error) in adjusting the actuall error happedn in
our data set. In other words, errors that is higher than the expectation
will be strongly filtered.
Use dada() function to remove errors and get
unique sequences!
forward_dada <- dada(filtered_forward_reads, err=error_F)## Sample 1 - 7040 reads in 2162 unique sequences.
## Sample 2 - 5225 reads in 1757 unique sequences.
## Sample 3 - 5409 reads in 1588 unique sequences.
## Sample 4 - 2907 reads in 981 unique sequences.
## Sample 5 - 2923 reads in 1008 unique sequences.
## Sample 6 - 4281 reads in 1374 unique sequences.
## Sample 7 - 6684 reads in 1882 unique sequences.
## Sample 8 - 4513 reads in 1554 unique sequences.
## Sample 9 - 15521 reads in 3916 unique sequences.
## Sample 10 - 11296 reads in 2995 unique sequences.
## Sample 11 - 11901 reads in 3283 unique sequences.
## Sample 12 - 4995 reads in 1689 unique sequences.
## Sample 13 - 17932 reads in 4057 unique sequences.
## Sample 14 - 6202 reads in 1602 unique sequences.
## Sample 15 - 4018 reads in 1308 unique sequences.
## Sample 16 - 7312 reads in 2003 unique sequences.
## Sample 17 - 4731 reads in 1267 unique sequences.
## Sample 18 - 4828 reads in 1498 unique sequences.
## Sample 19 - 6459 reads in 1873 unique sequences.
## Sample 20 - 4251 reads in 941 unique sequences.#multithread=TRUE for windowsreverse_dada <- dada(filtered_reverse_reads, err=error_R)## Sample 1 - 7040 reads in 1437 unique sequences.
## Sample 2 - 5225 reads in 1175 unique sequences.
## Sample 3 - 5409 reads in 1144 unique sequences.
## Sample 4 - 2907 reads in 748 unique sequences.
## Sample 5 - 2923 reads in 772 unique sequences.
## Sample 6 - 4281 reads in 1125 unique sequences.
## Sample 7 - 6684 reads in 1520 unique sequences.
## Sample 8 - 4513 reads in 1109 unique sequences.
## Sample 9 - 15521 reads in 2885 unique sequences.
## Sample 10 - 11296 reads in 2143 unique sequences.
## Sample 11 - 11901 reads in 2387 unique sequences.
## Sample 12 - 4995 reads in 1222 unique sequences.
## Sample 13 - 17932 reads in 2793 unique sequences.
## Sample 14 - 6202 reads in 1193 unique sequences.
## Sample 15 - 4018 reads in 990 unique sequences.
## Sample 16 - 7312 reads in 1444 unique sequences.
## Sample 17 - 4731 reads in 914 unique sequences.
## Sample 18 - 4828 reads in 1036 unique sequences.
## Sample 19 - 6459 reads in 1333 unique sequences.
## Sample 20 - 4251 reads in 669 unique sequences.#multithread=TRUE for windowsforward_dada[[1]]## dada-class: object describing DADA2 denoising results
## 124 sequence variants were inferred from 2162 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16Now, we have sequencing variants after considering
errors.
Mering paired reads
Unique sequences can be merged into single, complementary reads
(forward + reverse) using mergePairs() function.
mergers <- mergePairs(forward_dada,
filtered_forward_reads,
reverse_dada,
filtered_reverse_reads,
verbose=TRUE)## 6507 paired-reads (in 103 unique pairings) successfully merged out of 6806 (in 226 pairings) input.## 4899 paired-reads (in 96 unique pairings) successfully merged out of 5071 (in 169 pairings) input.## 4971 paired-reads (in 81 unique pairings) successfully merged out of 5225 (in 199 pairings) input.## 2546 paired-reads (in 50 unique pairings) successfully merged out of 2739 (in 137 pairings) input.## 2548 paired-reads (in 55 unique pairings) successfully merged out of 2759 (in 128 pairings) input.## 3818 paired-reads (in 60 unique pairings) successfully merged out of 4096 (in 171 pairings) input.## 5981 paired-reads (in 74 unique pairings) successfully merged out of 6423 (in 217 pairings) input.## 3939 paired-reads (in 84 unique pairings) successfully merged out of 4321 (in 210 pairings) input.## 14604 paired-reads (in 145 unique pairings) successfully merged out of 15225 (in 362 pairings) input.## 10407 paired-reads (in 116 unique pairings) successfully merged out of 11005 (in 278 pairings) input.## 11040 paired-reads (in 132 unique pairings) successfully merged out of 11647 (in 324 pairings) input.## 4342 paired-reads (in 78 unique pairings) successfully merged out of 4773 (in 222 pairings) input.## 17222 paired-reads (in 142 unique pairings) successfully merged out of 17663 (in 300 pairings) input.## 5736 paired-reads (in 76 unique pairings) successfully merged out of 6011 (in 181 pairings) input.## 3637 paired-reads (in 79 unique pairings) successfully merged out of 3858 (in 169 pairings) input.## 6762 paired-reads (in 92 unique pairings) successfully merged out of 7137 (in 217 pairings) input.## 4363 paired-reads (in 64 unique pairings) successfully merged out of 4588 (in 145 pairings) input.## 4442 paired-reads (in 93 unique pairings) successfully merged out of 4651 (in 170 pairings) input.## 6048 paired-reads (in 106 unique pairings) successfully merged out of 6257 (in 190 pairings) input.## 4205 paired-reads (in 19 unique pairings) successfully merged out of 4216 (in 26 pairings) input.# Inspect the merger data.frame from the first sample
head(mergers[[1]])## sequence
## 1 TACGGAGGATGCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGCAGGCGGAAGATCAAGTCAGCGGTAAAATTGAGAGGCTCAACCTCTTCGAGCCGTTGAAACTGGTTTTCTTGAGTGAGCGAGAAGTATGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCATACCGGCGCTCAACTGACGCTCATGCACGAAAGTGTGGGTATCGAACAGG
## 2 TACGGAGGATGCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCCTGCCAAGTCAGCGGTAAAATTGCGGGGCTCAACCCCGTACAGCCGTTGAAACTGCCGGGCTCGAGTGGGCGAGAAGTATGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCATACCGGCGCCCTACTGACGCTGAGGCACGAAAGTGCGGGGATCAAACAGG
## 3 TACGGAGGATGCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGGCTGTTAAGTCAGCGGTCAAATGTCGGGGCTCAACCCCGGCCTGCCGTTGAAACTGGCGGCCTCGAGTGGGCGAGAAGTATGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCATACCGGCGCCCGACTGACGCTGAGGCACGAAAGCGTGGGTATCGAACAGG
## 4 TACGGAGGATGCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGGCTTTTAAGTCAGCGGTAAAAATTCGGGGCTCAACCCCGTCCGGCCGTTGAAACTGGGGGCCTTGAGTGGGCGAGAAGAAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTTCCGGCGCCCTACTGACGCTGAGGCACGAAAGTGCGGGGATCGAACAGG
## 5 TACGGAGGATGCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGCAGGCGGACTCTCAAGTCAGCGGTCAAATCGCGGGGCTCAACCCCGTTCCGCCGTTGAAACTGGGAGCCTTGAGTGCGCGAGAAGTAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCCTACCGGCGCGCAACTGACGCTCATGCACGAAAGCGTGGGTATCGAACAGG
## 6 TACGGAGGATGCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGGATGCCAAGTCAGCGGTAAAAAAGCGGTGCTCAACGCCGTCGAGCCGTTGAAACTGGCGTTCTTGAGTGGGCGAGAAGTATGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCATACCGGCGCCCTACTGACGCTGAGGCACGAAAGCGTGGGTATCGAACAGG
## abundance forward reverse nmatch nmismatch nindel prefer accept
## 1 574 1 1 148 0 0 2 TRUE
## 2 472 2 2 148 0 0 2 TRUE
## 3 438 3 4 148 0 0 2 TRUE
## 4 426 4 3 148 0 0 2 TRUE
## 5 347 5 6 148 0 0 2 TRUE
## 6 280 6 5 148 0 0 2 TRUESequence table
Table of sequences
seqtab <- makeSequenceTable(mergers)
dim(seqtab)## [1] 20 29420 files, 291 unique sequencing reads
Length of merged sequences
table(nchar(getSequences(seqtab)))##
## 251 252 253 254 255
## 1 102 183 6 2Remove chimeras
The core dada method corrects substitution and indel errors, but chimeras remain. Fortunately, the accuracy of sequence variants after denoising makes identifying chimeric ASVs simpler than when dealing with fuzzy OTUs. Chimeric sequences are identified if they can be exactly reconstructed by combining a left-segment and a right-segment from two more abundant “parent” sequences.
seqtab.nochim <- removeBimeraDenovo(seqtab, method="consensus", multithread=TRUE, verbose=TRUE)## Identified 70 bimeras out of 294 input sequences.dim(seqtab.nochim)## [1] 20 224291-225 = 66 reads were chimeras.
sum(seqtab.nochim)/sum(seqtab)## [1] 0.96380174% of merged readsa were chimeras.
Track reads through the pipeline
As a final check of our progress, we’ll look at the number of reads that made it through each step in the pipeline:
getN <- function(x) sum(getUniques(x))
track <- cbind(out,
sapply(forward_dada, getN),
sapply(reverse_dada, getN),
sapply(mergers, getN), rowSums(seqtab.nochim))
# If processing a single sample, remove the sapply calls: e.g. replace sapply(dadaFs, getN) with getN(dadaFs)
colnames(track) <- c("input", "filtered", "denoisedF", "denoisedR", "merged", "nonchim")
rownames(track) <- sample.names
head(track)## input filtered denoisedF denoisedR merged nonchim
## F3D0 7793 7040 6878 6930 6507 6485
## F3D1 5869 5225 5088 5197 4899 4888
## F3D141 5958 5409 5261 5335 4971 4838
## F3D142 3183 2907 2778 2843 2546 2472
## F3D143 3178 2923 2787 2871 2548 2510
## F3D144 4827 4281 4141 4200 3818 3625Assign taxonomy
It is common at this point, especially in 16S/18S/ITS amplicon sequencing, to assign taxonomy to the sequence variants. The DADA2 package provides a native implementation of the naive Bayesian classifier method for this purpose. The assignTaxonomy function takes as input a set of sequences to be classified and a training set of reference sequences with known taxonomy, and outputs taxonomic assignments with at least minBoot bootstrap confidence.
We maintain formatted training fastas for the RDP training set, GreenGenes clustered at 97% identity, and the Silva reference database, and additional trainings fastas suitable for protists and certain specific environments have been contributed. For fungal taxonomy, the General Fasta release files from the UNITE ITS database can be used as is. To follow along, download the silva_nr_v132_train_set.fa.gz file, and place it in the directory with the fastq files.
taxa <- assignTaxonomy(seqtab.nochim,
paste0(minsik_path, "/silva_nr99_v138.1_train_set.fa"),
multithread=TRUE)view(taxa)Read count information (to compare their abundances between taxa within sample)
view(seqtab.nochim)Constructing a tree object
# install.packages("phangorn")
library(phangorn)## Loading required package: ape##
## Attaching package: 'ape'## The following object is masked from 'package:dplyr':
##
## where# if (!requireNamespace("BiocManager", quietly=TRUE))
# install.packages("BiocManager")
# BiocManager::install("DECIPHER")
library(DECIPHER)## Loading required package: Biostrings## Loading required package: BiocGenerics##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:lubridate':
##
## intersect, setdiff, union## The following objects are masked from 'package:dplyr':
##
## combine, intersect, setdiff, union## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, aperm, append, as.data.frame, basename, cbind,
## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff, table,
## tapply, union, unique, unsplit, which.max, which.min## Loading required package: S4Vectors## Loading required package: stats4##
## Attaching package: 'S4Vectors'## The following objects are masked from 'package:lubridate':
##
## second, second<-## The following objects are masked from 'package:dplyr':
##
## first, rename## The following object is masked from 'package:tidyr':
##
## expand## The following object is masked from 'package:utils':
##
## findMatches## The following objects are masked from 'package:base':
##
## expand.grid, I, unname## Loading required package: IRanges##
## Attaching package: 'IRanges'## The following object is masked from 'package:lubridate':
##
## %within%## The following objects are masked from 'package:dplyr':
##
## collapse, desc, slice## The following object is masked from 'package:purrr':
##
## reduce## The following object is masked from 'package:phyloseq':
##
## distance## Loading required package: XVector##
## Attaching package: 'XVector'## The following object is masked from 'package:purrr':
##
## compact## Loading required package: GenomeInfoDb##
## Attaching package: 'Biostrings'## The following object is masked from 'package:ape':
##
## complement## The following object is masked from 'package:base':
##
## strsplitseqs <- getSequences(seqtab)
names(seqs) <- seqs # This propagates to the tip labels of the tree
alignment <- AlignSeqs(DNAStringSet(seqs), anchor=NA)## Determining distance matrix based on shared 8-mers:
## ================================================================================
##
## Time difference of 0.43 secs
##
## Clustering into groups by similarity:
## ================================================================================
##
## Time difference of 0.03 secs
##
## Aligning Sequences:
## ================================================================================
##
## Time difference of 0.72 secs
##
## Iteration 1 of 2:
##
## Determining distance matrix based on alignment:
## ================================================================================
##
## Time difference of 0.02 secs
##
## Reclustering into groups by similarity:
## ================================================================================
##
## Time difference of 0.03 secs
##
## Realigning Sequences:
## ================================================================================
##
## Time difference of 0.33 secs
##
## Iteration 2 of 2:
##
## Determining distance matrix based on alignment:
## ================================================================================
##
## Time difference of 0.02 secs
##
## Reclustering into groups by similarity:
## ================================================================================
##
## Time difference of 0.04 secs
##
## Realigning Sequences:
## ================================================================================
##
## Time difference of 0.08 secsphang.align <- phyDat(as(alignment, "matrix"), type="DNA")
dm <- dist.ml(phang.align)
treeNJ <- NJ(dm) # Note, tip order != sequence order
fit = pml(treeNJ, data=phang.align)
## negative edges length changed to 0!
fitGTR <- update(fit, k=4, inv=0.2)
fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE,
rearrangement = "stochastic", control = pml.control(trace = 0))
detach("package:phangorn", unload=TRUE)Making tidy file - phyloseq object
samples.out <- rownames(seqtab.nochim)
subject <- sapply(strsplit(samples.out, "D"), `[`, 1)
gender <- substr(subject,1,1)
subject <- substr(subject,2,999)
day <- as.integer(sapply(strsplit(samples.out, "D"), `[`, 2))
samdf <- data.frame(Subject=subject, Gender=gender, Day=day)
samdf$When <- "Early"
samdf$When[samdf$Day>100] <- "Late"
rownames(samdf) <- samples.out
# The above is your sample data
library(phyloseq)
ps <- phyloseq(otu_table(seqtab.nochim, taxa_are_rows=FALSE),
sample_data(samdf),
tax_table(taxa),
phy_tree(fitGTR$tree))
ps <- prune_samples(sample_names(ps) != "Mock", ps) # Remove mock sample
dna <- Biostrings::DNAStringSet(taxa_names(ps))
names(dna) <- taxa_names(ps)
ps <- merge_phyloseq(ps, dna)
taxa_names(ps) <- paste0("ASV", seq(ntaxa(ps)))
ps %>% sample_data()## Subject Gender Day When
## F3D0 3 F 0 Early
## F3D1 3 F 1 Early
## F3D141 3 F 141 Late
## F3D142 3 F 142 Late
## F3D143 3 F 143 Late
## F3D144 3 F 144 Late
## F3D145 3 F 145 Late
## F3D146 3 F 146 Late
## F3D147 3 F 147 Late
## F3D148 3 F 148 Late
## F3D149 3 F 149 Late
## F3D150 3 F 150 Late
## F3D2 3 F 2 Early
## F3D3 3 F 3 Early
## F3D5 3 F 5 Early
## F3D6 3 F 6 Early
## F3D7 3 F 7 Early
## F3D8 3 F 8 Early
## F3D9 3 F 9 Earlyps %>% otu_table()## OTU Table: [224 taxa and 19 samples]
## taxa are columns
## ASV1 ASV2 ASV3 ASV4 ASV5 ASV6 ASV7 ASV8 ASV9 ASV10 ASV11 ASV12 ASV13
## F3D0 574 347 438 426 153 472 280 183 42 156 16 217 98
## F3D1 403 350 229 70 134 41 95 187 69 102 90 40 46
## F3D141 443 361 328 496 187 322 240 322 160 125 145 140 147
## F3D142 286 303 155 162 175 178 159 89 83 69 41 98 112
## F3D143 231 174 202 228 128 234 153 81 103 62 68 109 63
## F3D144 419 277 293 358 102 356 237 41 154 146 246 145 136
## F3D145 647 493 516 575 301 475 397 125 191 211 292 254 196
## F3D146 316 234 245 390 178 273 212 71 106 81 161 147 96
## F3D147 1501 1216 909 1089 449 1168 853 74 766 252 408 560 455
## F3D148 861 733 576 852 437 871 576 496 399 190 366 430 240
## F3D149 884 781 717 895 413 634 558 510 419 272 428 301 164
## F3D150 316 229 388 463 167 213 223 119 232 138 59 96 74
## F3D2 3491 1579 1165 465 335 114 319 1200 428 608 52 40 141
## F3D3 983 602 462 197 399 25 163 377 305 280 152 0 63
## F3D5 324 268 275 159 151 21 120 205 170 205 53 0 58
## F3D6 1013 674 582 400 473 15 274 260 198 227 39 0 77
## F3D7 645 500 434 308 464 10 192 211 172 256 15 0 62
## F3D8 277 350 347 145 554 0 129 286 109 193 17 0 22
## F3D9 509 422 479 204 594 0 204 437 142 222 26 0 36
## ASV14 ASV15 ASV16 ASV17 ASV18 ASV19 ASV20 ASV21 ASV22 ASV23 ASV24 ASV25
## F3D0 52 105 63 90 78 68 67 41 44 53 69 26
## F3D1 127 30 11 319 0 31 108 52 14 135 67 8
## F3D141 12 64 92 32 103 42 6 45 85 15 7 56
## F3D142 99 63 32 11 52 29 6 0 32 6 6 14
## F3D143 43 59 39 0 40 20 0 12 65 0 5 0
## F3D144 16 82 66 11 112 44 5 44 17 0 5 13
## F3D145 20 120 116 15 122 104 5 39 12 8 5 20
## F3D146 4 59 68 25 35 35 0 16 77 47 19 16
## F3D147 143 289 526 74 305 139 41 118 79 0 0 109
## F3D148 17 197 270 55 269 117 55 110 94 52 12 179
## F3D149 85 162 230 42 175 118 4 145 259 0 8 102
## F3D150 64 69 89 18 29 46 5 19 143 22 0 53
## F3D2 324 105 43 366 17 192 398 30 17 293 333 34
## F3D3 93 57 17 46 24 105 342 120 0 0 44 22
## F3D5 48 35 18 87 35 37 17 10 0 55 57 24
## F3D6 421 106 13 57 0 39 74 29 0 37 66 26
## F3D7 115 73 10 39 0 22 39 38 0 30 13 48
## F3D8 145 64 0 44 6 22 20 111 0 30 57 22
## F3D9 181 72 0 98 0 38 42 24 0 49 44 38
## ASV26 ASV27 ASV28 ASV29 ASV30 ASV31 ASV32 ASV33 ASV34 ASV35 ASV36 ASV37
## F3D0 74 31 58 42 261 60 27 0 73 34 45 0
## F3D1 100 44 140 119 55 25 29 0 71 100 54 12
## F3D141 0 9 0 35 50 12 57 0 22 0 0 30
## F3D142 0 0 0 0 9 21 8 0 6 0 0 0
## F3D143 0 14 0 0 0 22 24 0 13 0 0 13
## F3D144 0 20 0 0 14 0 11 0 16 0 0 14
## F3D145 0 18 0 11 0 0 18 0 12 0 0 11
## F3D146 36 36 0 35 18 0 22 0 23 0 0 12
## F3D147 33 30 14 17 0 51 48 0 12 0 25 44
## F3D148 8 16 0 22 21 57 43 0 9 0 0 20
## F3D149 6 45 23 36 0 44 142 0 43 0 12 59
## F3D150 6 32 0 0 0 0 81 0 30 0 0 53
## F3D2 200 145 281 108 137 59 40 0 82 185 112 94
## F3D3 15 18 20 0 0 22 9 0 8 8 12 17
## F3D5 50 47 34 45 35 76 0 0 40 16 48 33
## F3D6 107 47 40 19 0 42 20 0 46 23 53 30
## F3D7 48 13 0 8 0 0 0 0 7 12 20 8
## F3D8 39 44 0 49 0 61 8 0 30 71 57 24
## F3D9 51 64 56 93 28 52 15 0 40 94 97 32
## ASV38 ASV39 ASV40 ASV41 ASV42 ASV43 ASV44 ASV45 ASV46 ASV47 ASV48 ASV49
## F3D0 54 27 57 18 31 0 50 10 0 5 0 42
## F3D1 0 34 41 20 71 0 63 19 0 72 0 54
## F3D141 0 26 20 13 0 0 18 0 0 8 61 0
## F3D142 0 12 0 0 0 0 0 0 0 0 0 0
## F3D143 0 14 0 11 0 0 8 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0 38 0
## F3D145 76 0 9 6 0 5 0 0 0 0 46 0
## F3D146 0 31 23 23 0 0 20 0 0 11 92 0
## F3D147 152 43 24 38 0 0 12 13 0 11 37 0
## F3D148 82 50 14 21 0 0 14 9 0 15 12 0
## F3D149 63 86 51 44 0 0 45 21 0 33 0 0
## F3D150 0 29 24 40 0 0 25 0 0 16 0 0
## F3D2 54 41 52 82 81 0 89 79 0 54 0 137
## F3D3 0 0 0 7 15 0 28 20 0 10 0 10
## F3D5 0 10 17 18 33 0 7 36 0 52 0 11
## F3D6 0 28 30 21 49 0 0 37 0 25 15 26
## F3D7 0 11 0 10 22 0 0 33 0 8 0 9
## F3D8 0 0 35 21 37 0 9 43 0 16 23 42
## F3D9 0 16 30 31 63 0 0 58 0 29 36 24
## ASV50 ASV51 ASV52 ASV53 ASV54 ASV55 ASV56 ASV57 ASV58 ASV59 ASV60 ASV61
## F3D0 8 33 0 51 18 46 5 49 0 0 83 0
## F3D1 22 45 0 46 34 38 10 25 0 32 48 0
## F3D141 19 0 0 0 30 0 8 0 17 39 0 0
## F3D142 0 0 0 0 0 0 8 6 10 0 0 0
## F3D143 14 0 0 0 0 0 7 8 0 36 0 0
## F3D144 0 0 0 0 4 0 8 14 22 0 0 0
## F3D145 0 0 0 0 0 0 20 12 13 12 3 0
## F3D146 13 0 0 0 16 18 5 20 37 29 0 0
## F3D147 42 11 0 0 29 23 36 30 89 17 26 0
## F3D148 33 0 0 0 22 0 13 23 62 43 0 0
## F3D149 63 7 0 8 27 0 10 35 43 28 0 0
## F3D150 20 0 0 0 17 14 8 15 41 19 0 0
## F3D2 73 151 0 107 48 54 55 38 0 0 81 0
## F3D3 0 4 0 0 11 0 43 0 0 0 15 0
## F3D5 6 9 0 53 19 21 6 21 0 20 0 0
## F3D6 7 44 0 11 22 61 23 18 0 0 6 0
## F3D7 7 12 0 0 0 0 26 0 0 0 19 0
## F3D8 11 13 0 35 23 25 21 7 0 26 11 0
## F3D9 17 23 0 33 23 41 26 16 0 31 30 0
## ASV62 ASV63 ASV64 ASV65 ASV66 ASV67 ASV68 ASV69 ASV70 ASV71 ASV72 ASV73
## F3D0 0 0 0 0 60 0 92 23 19 19 0 0
## F3D1 21 0 0 0 58 39 12 0 52 19 0 0
## F3D141 0 30 0 0 0 6 22 16 0 13 0 45
## F3D142 0 23 0 0 0 0 0 28 0 0 0 0
## F3D143 0 25 0 0 0 6 20 10 0 4 0 22
## F3D144 0 17 0 2 0 0 13 21 0 0 0 0
## F3D145 0 18 5 4 0 10 0 7 0 5 0 0
## F3D146 0 5 0 0 0 13 0 3 0 15 0 11
## F3D147 18 55 0 0 0 0 0 28 0 13 0 17
## F3D148 0 94 0 0 0 10 10 71 0 18 0 18
## F3D149 0 34 0 0 0 13 10 28 0 59 0 82
## F3D150 0 14 0 0 0 12 0 19 0 23 0 46
## F3D2 44 0 0 0 71 81 37 4 79 47 0 0
## F3D3 0 0 0 0 12 10 0 16 0 5 0 0
## F3D5 22 0 0 0 33 18 10 0 24 0 0 0
## F3D6 29 0 0 0 34 12 15 0 41 9 0 0
## F3D7 20 0 0 0 20 0 9 0 0 0 0 0
## F3D8 56 0 0 0 0 31 17 0 36 12 0 0
## F3D9 105 0 0 0 0 27 15 0 20 5 0 0
## ASV74 ASV75 ASV76 ASV77 ASV78 ASV79 ASV80 ASV81 ASV82 ASV83 ASV84 ASV85
## F3D0 41 128 0 0 0 0 24 0 14 0 0 5
## F3D1 41 0 0 11 13 0 3 0 0 2 15 20
## F3D141 10 6 30 0 16 0 15 0 0 0 7 12
## F3D142 0 4 0 0 0 0 0 0 0 0 0 0
## F3D143 0 0 13 0 13 11 0 0 0 0 0 8
## F3D144 0 6 0 0 0 10 0 0 0 0 0 0
## F3D145 0 14 0 5 0 0 0 0 0 0 9 0
## F3D146 0 0 9 12 12 0 6 0 0 0 19 8
## F3D147 10 18 25 0 0 45 11 0 10 5 18 39
## F3D148 0 31 21 0 14 12 16 0 23 0 0 0
## F3D149 19 13 73 8 66 0 14 0 19 0 13 25
## F3D150 0 6 51 0 18 30 0 0 0 0 11 11
## F3D2 99 0 0 20 32 0 37 0 29 5 37 12
## F3D3 17 0 0 21 0 0 19 0 0 0 0 5
## F3D5 0 0 0 14 0 0 11 0 23 4 13 10
## F3D6 0 0 0 30 15 0 9 0 18 0 17 11
## F3D7 0 0 0 14 0 0 19 0 10 2 0 5
## F3D8 0 0 0 38 7 41 8 0 17 3 13 12
## F3D9 0 0 0 48 12 64 17 0 38 0 25 11
## ASV86 ASV87 ASV88 ASV89 ASV90 ASV91 ASV92 ASV93 ASV94 ASV95 ASV96 ASV97
## F3D0 10 24 0 0 0 50 0 0 6 0 17 0
## F3D1 11 10 0 0 19 48 0 30 0 14 0 43
## F3D141 0 0 0 0 0 0 0 5 13 9 0 0
## F3D142 0 0 0 0 15 0 0 0 5 13 0 0
## F3D143 0 0 0 0 0 0 0 0 11 11 14 0
## F3D144 0 0 0 0 14 0 0 0 0 0 0 0
## F3D145 0 0 0 21 0 0 0 3 13 17 9 0
## F3D146 0 13 34 0 26 0 0 3 13 0 0 0
## F3D147 64 9 20 0 33 0 0 17 0 33 24 0
## F3D148 18 0 16 37 13 0 0 17 32 20 0 0
## F3D149 24 0 14 0 17 0 0 7 31 17 0 0
## F3D150 0 0 0 0 8 0 0 0 26 0 0 0
## F3D2 16 46 0 38 38 61 0 5 9 22 42 76
## F3D3 0 0 0 0 0 0 0 15 0 0 0 0
## F3D5 0 12 0 55 0 20 0 18 10 0 13 0
## F3D6 38 31 0 34 0 0 0 16 0 11 11 0
## F3D7 0 0 6 0 0 0 0 18 0 0 0 6
## F3D8 0 21 46 0 0 0 0 13 0 0 15 35
## F3D9 12 24 52 0 0 0 0 9 6 8 27 0
## ASV98 ASV99 ASV100 ASV101 ASV102 ASV103 ASV104 ASV105 ASV106 ASV107
## F3D0 0 0 0 0 0 0 3 0 0 0
## F3D1 0 0 0 0 4 0 7 0 0 0
## F3D141 0 12 10 0 8 0 0 19 0 14
## F3D142 0 0 6 0 0 0 0 0 0 0
## F3D143 0 6 0 0 0 0 0 6 0 5
## F3D144 0 10 0 0 0 0 0 6 0 10
## F3D145 0 13 0 0 5 0 0 0 2 0
## F3D146 0 14 13 0 14 0 0 15 0 17
## F3D147 0 0 14 0 11 0 0 32 0 0
## F3D148 0 23 19 0 8 0 0 14 0 0
## F3D149 0 42 59 0 23 0 0 0 0 39
## F3D150 0 12 23 0 11 0 0 0 0 17
## F3D2 0 0 0 0 19 0 31 22 0 8
## F3D3 0 0 0 0 6 0 2 0 0 0
## F3D5 0 6 0 0 6 0 0 0 0 0
## F3D6 0 10 0 0 5 0 19 0 0 0
## F3D7 0 0 0 0 0 0 8 0 0 0
## F3D8 0 0 0 0 9 0 25 0 0 0
## F3D9 0 0 0 0 8 0 19 0 0 0
## ASV108 ASV109 ASV110 ASV111 ASV112 ASV113 ASV114 ASV115 ASV116 ASV117
## F3D0 0 23 0 0 19 6 0 0 0 0
## F3D1 0 0 8 0 23 24 0 0 0 0
## F3D141 14 0 0 0 0 9 0 0 0 10
## F3D142 5 0 0 0 0 0 11 0 0 0
## F3D143 6 0 0 0 0 0 0 0 0 0
## F3D144 6 0 0 0 0 0 12 0 0 0
## F3D145 10 0 0 0 0 0 16 4 0 0
## F3D146 13 0 0 0 0 0 11 0 12 11
## F3D147 0 0 0 0 0 0 11 0 15 8
## F3D148 15 0 0 0 0 0 12 0 0 9
## F3D149 24 5 0 0 0 19 0 0 41 32
## F3D150 10 0 0 0 0 8 9 14 23 18
## F3D2 0 15 26 0 32 21 0 0 0 0
## F3D3 0 0 5 0 0 0 0 0 0 0
## F3D5 6 0 11 0 0 0 0 44 0 0
## F3D6 0 24 29 0 0 0 0 29 0 0
## F3D7 0 0 7 0 11 0 0 0 0 0
## F3D8 0 14 11 0 10 7 11 0 0 0
## F3D9 0 24 8 0 0 0 0 0 0 0
## ASV118 ASV119 ASV120 ASV121 ASV122 ASV123 ASV124 ASV125 ASV126 ASV127
## F3D0 34 0 10 16 10 9 9 0 0 18
## F3D1 14 0 0 15 6 7 17 0 0 17
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 0 7 0 0 0 0 0 0 0
## F3D143 0 0 6 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 6 0 0 0 0 0 0 0
## F3D146 0 16 16 0 0 0 0 0 0 7
## F3D147 0 0 13 10 13 0 0 17 0 0
## F3D148 0 0 13 0 0 0 0 0 0 0
## F3D149 0 0 9 17 10 0 0 0 8 0
## F3D150 0 0 5 10 9 0 0 0 0 0
## F3D2 39 20 0 0 12 35 46 65 37 30
## F3D3 0 0 0 0 0 12 0 0 21 0
## F3D5 0 0 0 0 0 0 0 0 16 0
## F3D6 0 20 0 0 8 0 0 0 0 0
## F3D7 0 0 0 0 0 0 0 0 0 0
## F3D8 0 0 0 16 9 9 11 0 0 0
## F3D9 0 31 0 0 7 11 0 0 0 0
## ASV128 ASV129 ASV130 ASV131 ASV132 ASV133 ASV134 ASV135 ASV136 ASV137
## F3D0 0 14 20 0 0 0 0 0 0 17
## F3D1 0 0 7 0 0 0 0 0 0 11
## F3D141 13 0 0 0 12 0 0 6 0 0
## F3D142 0 0 0 0 0 0 0 0 3 0
## F3D143 0 0 0 0 0 0 0 0 10 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 8 0 0 0 0 0 0 15 0
## F3D146 0 0 11 0 0 12 0 0 0 0
## F3D147 0 15 0 0 9 13 0 0 15 0
## F3D148 0 0 0 0 0 11 21 11 11 0
## F3D149 0 0 0 0 26 12 41 17 4 0
## F3D150 0 0 0 0 15 0 0 0 0 0
## F3D2 13 31 10 0 0 0 0 0 0 21
## F3D3 0 0 0 5 0 0 0 0 0 0
## F3D5 0 0 9 0 0 0 0 0 0 0
## F3D6 0 0 0 17 0 14 0 0 0 8
## F3D7 0 0 0 16 0 0 0 0 0 0
## F3D8 19 0 0 13 0 0 0 10 0 0
## F3D9 24 0 7 12 0 0 0 15 0 0
## ASV138 ASV139 ASV140 ASV141 ASV142 ASV143 ASV144 ASV145 ASV146 ASV147
## F3D0 0 56 55 10 7 16 8 0 0 0
## F3D1 7 0 0 0 0 0 7 0 0 0
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 0 0 0 0 0 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 2 8 0 0
## F3D146 0 0 0 0 0 0 0 0 0 0
## F3D147 0 0 0 0 0 0 0 0 0 25
## F3D148 0 0 0 0 0 0 5 0 0 0
## F3D149 10 0 0 0 0 0 0 21 0 0
## F3D150 0 0 0 0 0 0 0 23 0 0
## F3D2 7 0 0 11 39 14 14 0 0 0
## F3D3 0 0 0 12 0 0 0 0 0 0
## F3D5 8 0 0 0 9 0 2 0 0 0
## F3D6 0 0 0 0 0 0 5 0 0 24
## F3D7 0 0 0 5 0 0 0 0 0 0
## F3D8 16 0 0 8 0 11 5 0 0 0
## F3D9 9 0 0 9 0 13 6 0 0 0
## ASV148 ASV149 ASV150 ASV151 ASV152 ASV153 ASV154 ASV155 ASV156 ASV157
## F3D0 0 0 0 0 19 18 0 0 21 10
## F3D1 0 0 0 0 0 7 0 0 0 6
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 0 0 0 0 0 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 0 0 0 0
## F3D146 0 0 0 0 0 0 20 0 0 11
## F3D147 0 0 0 0 0 0 0 0 0 0
## F3D148 0 0 0 17 0 0 0 0 10 0
## F3D149 0 0 0 24 0 0 0 0 4 0
## F3D150 0 10 0 0 0 0 0 0 0 0
## F3D2 8 9 0 0 0 7 17 37 0 8
## F3D3 0 0 0 0 0 0 0 0 0 0
## F3D5 8 0 0 0 0 0 0 0 0 0
## F3D6 9 13 0 0 0 0 0 0 0 0
## F3D7 0 0 0 0 0 0 0 0 0 0
## F3D8 0 11 0 0 4 0 0 0 0 0
## F3D9 19 0 43 0 17 7 0 0 0 0
## ASV158 ASV159 ASV160 ASV161 ASV162 ASV163 ASV164 ASV165 ASV166 ASV167
## F3D0 0 0 0 0 9 0 0 0 0 0
## F3D1 0 0 0 0 0 3 0 0 9 0
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 0 0 0 0 0 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 0 0 0 0
## F3D146 0 0 0 0 0 0 0 0 0 0
## F3D147 0 0 13 11 0 0 6 0 4 0
## F3D148 0 0 0 5 0 8 15 0 0 0
## F3D149 23 14 6 0 12 0 9 0 7 0
## F3D150 0 0 0 0 9 0 0 0 0 0
## F3D2 0 20 0 8 0 10 0 20 9 20
## F3D3 12 0 10 0 0 0 0 0 0 0
## F3D5 0 0 3 0 0 4 0 0 0 0
## F3D6 0 0 0 0 0 0 0 0 0 9
## F3D7 0 0 0 0 0 2 0 0 0 0
## F3D8 0 0 0 0 0 3 0 0 0 0
## F3D9 0 0 0 8 0 0 0 10 0 0
## ASV168 ASV169 ASV170 ASV171 ASV172 ASV173 ASV174 ASV175 ASV176 ASV177
## F3D0 11 0 0 0 0 0 0 3 0 0
## F3D1 0 0 0 0 0 0 0 0 11 0
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 0 0 0 0 0 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 0 0 0 0
## F3D146 0 0 0 0 0 0 6 0 0 0
## F3D147 0 0 15 12 7 5 0 0 0 0
## F3D148 0 0 0 7 0 9 6 0 0 0
## F3D149 0 0 12 6 0 8 9 5 0 20
## F3D150 0 0 0 0 0 0 0 0 0 0
## F3D2 0 28 0 0 0 0 0 3 0 0
## F3D3 0 0 0 0 0 0 0 0 0 0
## F3D5 0 0 0 0 0 0 0 0 0 0
## F3D6 7 0 0 0 7 0 0 9 0 0
## F3D7 0 0 0 0 0 0 0 0 0 0
## F3D8 0 0 0 0 0 0 0 0 9 0
## F3D9 10 0 0 0 10 0 0 0 0 0
## ASV178 ASV179 ASV180 ASV181 ASV182 ASV183 ASV184 ASV185 ASV186 ASV187
## F3D0 7 0 0 0 10 0 0 0 0 0
## F3D1 0 0 0 0 0 0 0 0 0 0
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 0 0 0 0 5 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 0 0 0 0
## F3D146 0 0 0 0 0 0 0 0 0 0
## F3D147 0 7 0 0 0 0 0 0 0 0
## F3D148 0 0 0 0 0 0 0 9 0 0
## F3D149 6 0 0 0 0 0 0 0 0 0
## F3D150 0 0 0 0 0 0 0 0 0 0
## F3D2 5 6 10 0 7 0 0 0 7 0
## F3D3 0 0 0 0 0 0 0 7 0 0
## F3D5 0 0 0 0 0 0 0 0 0 0
## F3D6 0 0 0 0 0 0 0 0 0 0
## F3D7 0 0 0 0 0 0 0 0 0 0
## F3D8 0 0 4 0 0 12 0 0 0 0
## F3D9 0 5 4 18 0 0 17 0 9 16
## ASV188 ASV189 ASV190 ASV191 ASV192 ASV193 ASV194 ASV195 ASV196 ASV197
## F3D0 0 0 0 0 0 12 0 0 0 10
## F3D1 0 3 0 0 0 0 0 0 0 0
## F3D141 0 0 0 3 0 0 0 0 0 0
## F3D142 0 0 0 0 0 0 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 0 0 0 0
## F3D146 0 3 0 0 0 0 0 0 0 0
## F3D147 0 0 14 0 0 0 0 11 0 0
## F3D148 0 0 0 4 0 0 0 0 0 0
## F3D149 0 0 0 6 0 0 0 0 0 0
## F3D150 0 0 0 0 0 0 0 0 0 0
## F3D2 0 0 0 0 13 0 12 0 5 0
## F3D3 0 0 0 0 0 0 0 0 0 0
## F3D5 0 0 0 0 0 0 0 0 0 0
## F3D6 0 0 0 0 0 0 0 0 0 0
## F3D7 0 0 0 0 0 0 0 0 0 0
## F3D8 0 3 0 0 0 0 0 0 0 0
## F3D9 0 5 0 0 0 0 0 0 6 0
## ASV198 ASV199 ASV200 ASV201 ASV202 ASV203 ASV204 ASV205 ASV206 ASV207
## F3D0 5 0 0 0 0 0 0 0 0 0
## F3D1 5 0 0 0 8 0 0 0 0 0
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 5 0 0 0 2 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 9 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 0 0 0 0
## F3D146 0 0 0 0 0 0 2 0 0 0
## F3D147 0 0 0 0 0 4 3 0 0 0
## F3D148 0 5 0 0 0 2 0 0 0 0
## F3D149 0 0 0 0 0 0 0 8 0 0
## F3D150 0 0 0 0 0 0 0 0 0 0
## F3D2 0 0 0 0 0 0 0 0 0 0
## F3D3 0 0 0 0 0 0 0 0 0 0
## F3D5 0 0 0 0 0 0 0 0 0 0
## F3D6 0 0 0 0 0 0 0 0 0 0
## F3D7 0 0 0 0 0 0 0 0 0 0
## F3D8 0 0 0 0 0 0 0 0 8 0
## F3D9 0 0 0 9 0 0 3 0 0 8
## ASV208 ASV209 ASV210 ASV211 ASV212 ASV213 ASV214 ASV215 ASV216 ASV217
## F3D0 0 0 0 0 0 0 0 0 4 4
## F3D1 0 0 0 0 0 0 0 0 0 0
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 0 0 0 0 0 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 0 0 0 0
## F3D146 0 0 6 0 0 0 0 0 0 0
## F3D147 0 0 0 6 0 0 0 0 0 0
## F3D148 0 0 0 0 6 0 0 0 0 0
## F3D149 7 0 0 0 0 0 0 0 0 0
## F3D150 0 0 0 0 0 0 0 5 0 0
## F3D2 0 0 0 0 0 6 0 0 0 0
## F3D3 0 7 0 0 0 0 0 0 0 0
## F3D5 0 0 0 0 0 0 0 0 0 0
## F3D6 0 0 0 0 0 0 0 0 0 0
## F3D7 0 0 0 0 0 0 0 0 0 0
## F3D8 0 0 0 0 0 0 0 0 0 0
## F3D9 0 0 0 0 0 0 6 0 0 0
## ASV218 ASV219 ASV220 ASV221 ASV222 ASV223 ASV224
## F3D0 0 0 0 0 0 0 0
## F3D1 0 0 0 0 0 0 0
## F3D141 4 0 0 0 0 0 0
## F3D142 0 0 0 0 0 0 0
## F3D143 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 3
## F3D145 0 0 0 0 0 0 0
## F3D146 0 0 0 0 0 0 0
## F3D147 0 4 0 0 0 0 0
## F3D148 0 0 0 0 0 0 0
## F3D149 0 0 0 0 0 0 0
## F3D150 0 0 0 0 0 0 0
## F3D2 0 0 4 4 0 0 0
## F3D3 0 0 0 0 4 0 0
## F3D5 0 0 0 0 0 0 0
## F3D6 0 0 0 0 0 0 0
## F3D7 0 0 0 0 0 0 0
## F3D8 0 0 0 0 0 0 0
## F3D9 0 0 0 0 0 4 0ps %>% tax_table()## Taxonomy Table: [224 taxa by 6 taxonomic ranks]:
## Kingdom Phylum Class
## ASV1 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV2 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV3 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV4 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV5 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV6 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV7 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV8 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV9 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV10 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV11 "Bacteria" "Firmicutes" "Bacilli"
## ASV12 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV13 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV14 "Bacteria" "Firmicutes" "Bacilli"
## ASV15 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV16 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV17 "Bacteria" "Firmicutes" "Clostridia"
## ASV18 "Bacteria" "Firmicutes" "Bacilli"
## ASV19 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV20 "Bacteria" "Firmicutes" "Bacilli"
## ASV21 "Bacteria" "Firmicutes" "Bacilli"
## ASV22 "Bacteria" "Firmicutes" "Clostridia"
## ASV23 "Bacteria" "Firmicutes" "Clostridia"
## ASV24 "Bacteria" "Firmicutes" "Clostridia"
## ASV25 "Bacteria" "Firmicutes" "Clostridia"
## ASV26 "Bacteria" "Firmicutes" "Clostridia"
## ASV27 "Bacteria" "Firmicutes" "Clostridia"
## ASV28 "Bacteria" "Firmicutes" "Clostridia"
## ASV29 "Bacteria" "Firmicutes" "Clostridia"
## ASV30 "Bacteria" "Firmicutes" "Clostridia"
## ASV31 "Bacteria" "Firmicutes" "Clostridia"
## ASV32 "Bacteria" "Firmicutes" "Clostridia"
## ASV33 "Bacteria" "Firmicutes" "Bacilli"
## ASV34 "Bacteria" "Firmicutes" "Clostridia"
## ASV35 "Bacteria" "Firmicutes" "Clostridia"
## ASV36 "Bacteria" "Firmicutes" "Clostridia"
## ASV37 "Bacteria" "Firmicutes" "Clostridia"
## ASV38 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV39 "Bacteria" "Firmicutes" "Clostridia"
## ASV40 "Bacteria" "Firmicutes" "Clostridia"
## ASV41 "Bacteria" "Firmicutes" "Clostridia"
## ASV42 "Bacteria" "Firmicutes" "Clostridia"
## ASV43 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV44 "Bacteria" "Firmicutes" "Clostridia"
## ASV45 "Bacteria" "Firmicutes" "Clostridia"
## ASV46 "Bacteria" "Firmicutes" "Bacilli"
## ASV47 "Bacteria" "Firmicutes" "Clostridia"
## ASV48 "Bacteria" "Firmicutes" "Clostridia"
## ASV49 "Bacteria" "Firmicutes" "Clostridia"
## ASV50 "Bacteria" "Firmicutes" "Clostridia"
## ASV51 "Bacteria" "Firmicutes" "Clostridia"
## ASV52 "Bacteria" "Campylobacterota" "Campylobacteria"
## ASV53 "Bacteria" "Firmicutes" "Clostridia"
## ASV54 "Bacteria" "Firmicutes" "Clostridia"
## ASV55 "Bacteria" "Firmicutes" "Clostridia"
## ASV56 "Bacteria" "Firmicutes" "Clostridia"
## ASV57 "Bacteria" "Firmicutes" "Clostridia"
## ASV58 "Bacteria" "Firmicutes" "Clostridia"
## ASV59 "Bacteria" "Firmicutes" "Clostridia"
## ASV60 "Bacteria" "Firmicutes" "Bacilli"
## ASV61 "Bacteria" "Actinobacteriota" "Actinobacteria"
## ASV62 "Bacteria" "Firmicutes" "Clostridia"
## ASV63 "Bacteria" "Firmicutes" "Bacilli"
## ASV64 "Bacteria" "Firmicutes" "Clostridia"
## ASV65 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV66 "Bacteria" "Firmicutes" "Clostridia"
## ASV67 "Bacteria" "Firmicutes" "Clostridia"
## ASV68 "Bacteria" "Firmicutes" "Clostridia"
## ASV69 "Bacteria" "Actinobacteriota" "Actinobacteria"
## ASV70 "Bacteria" "Firmicutes" "Clostridia"
## ASV71 "Bacteria" "Firmicutes" "Clostridia"
## ASV72 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV73 "Bacteria" "Firmicutes" "Clostridia"
## ASV74 "Bacteria" "Firmicutes" "Clostridia"
## ASV75 "Bacteria" "Firmicutes" "Clostridia"
## ASV76 "Bacteria" "Firmicutes" "Clostridia"
## ASV77 "Bacteria" "Firmicutes" "Clostridia"
## ASV78 "Bacteria" "Firmicutes" "Clostridia"
## ASV79 "Bacteria" "Firmicutes" "Clostridia"
## ASV80 "Bacteria" "Firmicutes" "Clostridia"
## ASV81 "Bacteria" "Firmicutes" "Bacilli"
## ASV82 "Bacteria" "Firmicutes" "Clostridia"
## ASV83 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV84 "Bacteria" "Firmicutes" "Clostridia"
## ASV85 "Bacteria" "Firmicutes" "Clostridia"
## ASV86 "Bacteria" "Firmicutes" "Clostridia"
## ASV87 "Bacteria" "Firmicutes" "Clostridia"
## ASV88 "Bacteria" "Firmicutes" "Clostridia"
## ASV89 "Bacteria" "Firmicutes" "Clostridia"
## ASV90 "Bacteria" "Firmicutes" "Clostridia"
## ASV91 "Bacteria" "Firmicutes" "Clostridia"
## ASV92 "Bacteria" "Firmicutes" "Bacilli"
## ASV93 "Bacteria" "Patescibacteria" "Saccharimonadia"
## ASV94 "Bacteria" "Firmicutes" "Clostridia"
## ASV95 "Bacteria" "Firmicutes" "Clostridia"
## ASV96 "Bacteria" "Firmicutes" "Clostridia"
## ASV97 "Bacteria" "Firmicutes" "Clostridia"
## ASV98 "Bacteria" "Firmicutes" "Bacilli"
## ASV99 "Bacteria" "Firmicutes" "Clostridia"
## ASV100 "Bacteria" "Firmicutes" "Clostridia"
## ASV101 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV102 "Bacteria" "Firmicutes" "Clostridia"
## ASV103 "Bacteria" "Firmicutes" "Bacilli"
## ASV104 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV105 "Bacteria" "Firmicutes" "Clostridia"
## ASV106 "Bacteria" "Deinococcota" "Deinococci"
## ASV107 "Bacteria" "Firmicutes" "Clostridia"
## ASV108 "Bacteria" "Firmicutes" "Clostridia"
## ASV109 "Bacteria" "Firmicutes" "Clostridia"
## ASV110 "Bacteria" "Firmicutes" "Clostridia"
## ASV111 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV112 "Bacteria" "Firmicutes" "Clostridia"
## ASV113 "Bacteria" "Firmicutes" "Clostridia"
## ASV114 "Bacteria" "Firmicutes" "Clostridia"
## ASV115 "Bacteria" "Firmicutes" "Clostridia"
## ASV116 "Bacteria" "Firmicutes" "Clostridia"
## ASV117 "Bacteria" "Firmicutes" "Clostridia"
## ASV118 "Bacteria" "Firmicutes" "Clostridia"
## ASV119 "Bacteria" "Firmicutes" "Clostridia"
## ASV120 "Bacteria" "Firmicutes" "Clostridia"
## ASV121 "Bacteria" "Firmicutes" "Clostridia"
## ASV122 "Bacteria" "Firmicutes" "Clostridia"
## ASV123 "Bacteria" "Firmicutes" "Clostridia"
## ASV124 "Bacteria" "Firmicutes" "Clostridia"
## ASV125 "Bacteria" "Firmicutes" "Clostridia"
## ASV126 "Bacteria" "Firmicutes" "Clostridia"
## ASV127 "Bacteria" "Firmicutes" "Clostridia"
## ASV128 "Bacteria" "Firmicutes" "Clostridia"
## ASV129 "Bacteria" "Firmicutes" "Clostridia"
## ASV130 "Bacteria" "Firmicutes" "Clostridia"
## ASV131 "Bacteria" "Firmicutes" "Clostridia"
## ASV132 "Bacteria" "Firmicutes" "Clostridia"
## ASV133 "Bacteria" "Firmicutes" "Clostridia"
## ASV134 "Bacteria" "Firmicutes" "Clostridia"
## ASV135 "Bacteria" "Firmicutes" "Clostridia"
## ASV136 "Bacteria" "Firmicutes" "Bacilli"
## ASV137 "Bacteria" "Firmicutes" "Clostridia"
## ASV138 "Bacteria" "Firmicutes" "Clostridia"
## ASV139 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV140 "Bacteria" "Bacteroidota" "Bacteroidia"
## ASV141 "Bacteria" "Firmicutes" "Clostridia"
## ASV142 "Bacteria" "Firmicutes" "Clostridia"
## ASV143 "Bacteria" "Firmicutes" "Clostridia"
## ASV144 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV145 "Bacteria" "Firmicutes" "Clostridia"
## ASV146 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV147 "Bacteria" "Firmicutes" "Clostridia"
## ASV148 "Bacteria" "Firmicutes" "Clostridia"
## ASV149 "Bacteria" "Firmicutes" "Clostridia"
## ASV150 "Bacteria" "Firmicutes" "Clostridia"
## ASV151 "Bacteria" "Firmicutes" "Clostridia"
## ASV152 "Bacteria" "Firmicutes" "Clostridia"
## ASV153 "Bacteria" "Firmicutes" "Clostridia"
## ASV154 "Bacteria" "Firmicutes" "Clostridia"
## ASV155 "Bacteria" "Firmicutes" "Clostridia"
## ASV156 "Bacteria" "Firmicutes" "Clostridia"
## ASV157 "Bacteria" "Firmicutes" "Clostridia"
## ASV158 "Bacteria" "Firmicutes" "Clostridia"
## ASV159 "Bacteria" "Firmicutes" "Clostridia"
## ASV160 "Bacteria" "Firmicutes" "Clostridia"
## ASV161 "Bacteria" "Firmicutes" "Clostridia"
## ASV162 "Bacteria" "Firmicutes" "Clostridia"
## ASV163 "Bacteria" "Actinobacteriota" "Coriobacteriia"
## ASV164 "Bacteria" "Firmicutes" "Clostridia"
## ASV165 "Bacteria" "Firmicutes" "Clostridia"
## ASV166 "Bacteria" "Firmicutes" "Clostridia"
## ASV167 "Bacteria" "Firmicutes" "Clostridia"
## ASV168 "Bacteria" "Firmicutes" "Clostridia"
## ASV169 "Bacteria" "Firmicutes" "Clostridia"
## ASV170 "Bacteria" "Firmicutes" "Clostridia"
## ASV171 "Bacteria" "Firmicutes" "Clostridia"
## ASV172 "Bacteria" "Firmicutes" "Clostridia"
## ASV173 "Bacteria" "Firmicutes" "Clostridia"
## ASV174 "Bacteria" "Firmicutes" "Clostridia"
## ASV175 "Bacteria" "Firmicutes" "Clostridia"
## ASV176 "Bacteria" "Firmicutes" "Clostridia"
## ASV177 "Bacteria" "Firmicutes" "Clostridia"
## ASV178 "Bacteria" "Firmicutes" "Clostridia"
## ASV179 "Bacteria" "Firmicutes" "Clostridia"
## ASV180 "Bacteria" "Firmicutes" "Clostridia"
## ASV181 "Bacteria" "Firmicutes" "Clostridia"
## ASV182 "Bacteria" "Verrucomicrobiota" "Verrucomicrobiae"
## ASV183 "Bacteria" "Cyanobacteria" "Cyanobacteriia"
## ASV184 "Bacteria" "Firmicutes" "Clostridia"
## ASV185 "Bacteria" "Firmicutes" "Clostridia"
## ASV186 "Bacteria" "Firmicutes" "Clostridia"
## ASV187 "Bacteria" "Firmicutes" "Clostridia"
## ASV188 "Bacteria" "Firmicutes" "Bacilli"
## ASV189 "Bacteria" "Firmicutes" "Clostridia"
## ASV190 "Bacteria" "Firmicutes" "Clostridia"
## ASV191 "Bacteria" "Firmicutes" "Clostridia"
## ASV192 "Bacteria" "Firmicutes" "Clostridia"
## ASV193 "Bacteria" "Firmicutes" "Clostridia"
## ASV194 "Bacteria" "Firmicutes" "Clostridia"
## ASV195 "Bacteria" "Actinobacteriota" "Coriobacteriia"
## ASV196 "Bacteria" "Cyanobacteria" "Cyanobacteriia"
## ASV197 "Bacteria" "Firmicutes" "Clostridia"
## ASV198 "Bacteria" "Firmicutes" "Bacilli"
## ASV199 "Bacteria" "Firmicutes" "Clostridia"
## ASV200 "Bacteria" "Patescibacteria" "Saccharimonadia"
## ASV201 "Bacteria" "Firmicutes" "Clostridia"
## ASV202 "Bacteria" "Firmicutes" "Clostridia"
## ASV203 "Bacteria" "Firmicutes" "Bacilli"
## ASV204 "Bacteria" "Actinobacteriota" "Coriobacteriia"
## ASV205 "Bacteria" "Firmicutes" "Clostridia"
## ASV206 "Bacteria" "Firmicutes" "Clostridia"
## ASV207 "Bacteria" "Firmicutes" "Clostridia"
## ASV208 "Bacteria" "Firmicutes" "Clostridia"
## ASV209 "Bacteria" "Firmicutes" "Bacilli"
## ASV210 "Bacteria" "Firmicutes" "Clostridia"
## ASV211 "Bacteria" "Firmicutes" "Clostridia"
## ASV212 "Bacteria" "Firmicutes" "Clostridia"
## ASV213 "Bacteria" "Firmicutes" "Bacilli"
## ASV214 "Bacteria" "Firmicutes" "Clostridia"
## ASV215 "Bacteria" "Actinobacteriota" "Coriobacteriia"
## ASV216 "Bacteria" "Firmicutes" "Clostridia"
## ASV217 "Bacteria" "Firmicutes" "Clostridia"
## ASV218 "Bacteria" "Firmicutes" "Clostridia"
## ASV219 "Bacteria" "Actinobacteriota" "Coriobacteriia"
## ASV220 "Bacteria" "Firmicutes" "Clostridia"
## ASV221 "Bacteria" "Firmicutes" "Clostridia"
## ASV222 "Bacteria" "Cyanobacteria" "Cyanobacteriia"
## ASV223 "Bacteria" "Firmicutes" "Clostridia"
## ASV224 "Bacteria" "Actinobacteriota" "Coriobacteriia"
## Order
## ASV1 "Bacteroidales"
## ASV2 "Bacteroidales"
## ASV3 "Bacteroidales"
## ASV4 "Bacteroidales"
## ASV5 "Bacteroidales"
## ASV6 "Bacteroidales"
## ASV7 "Bacteroidales"
## ASV8 "Bacteroidales"
## ASV9 "Bacteroidales"
## ASV10 "Bacteroidales"
## ASV11 "Lactobacillales"
## ASV12 "Bacteroidales"
## ASV13 "Bacteroidales"
## ASV14 "Lactobacillales"
## ASV15 "Bacteroidales"
## ASV16 "Bacteroidales"
## ASV17 "Lachnospirales"
## ASV18 "Erysipelotrichales"
## ASV19 "Bacteroidales"
## ASV20 "RF39"
## ASV21 "Lactobacillales"
## ASV22 "Lachnospirales"
## ASV23 "Lachnospirales"
## ASV24 "Lachnospirales"
## ASV25 "Peptococcales"
## ASV26 "Oscillospirales"
## ASV27 "Oscillospirales"
## ASV28 "Lachnospirales"
## ASV29 "Lachnospirales"
## ASV30 "Lachnospirales"
## ASV31 "Lachnospirales"
## ASV32 "Lachnospirales"
## ASV33 "Staphylococcales"
## ASV34 "Lachnospirales"
## ASV35 "Lachnospirales"
## ASV36 "Lachnospirales"
## ASV37 "Lachnospirales"
## ASV38 "Bacteroidales"
## ASV39 "Lachnospirales"
## ASV40 "Lachnospirales"
## ASV41 "Lachnospirales"
## ASV42 "Lachnospirales"
## ASV43 "Pseudomonadales"
## ASV44 "Lachnospirales"
## ASV45 "Lachnospirales"
## ASV46 "Bacillales"
## ASV47 "Lachnospirales"
## ASV48 "Lachnospirales"
## ASV49 "Lachnospirales"
## ASV50 "Lachnospirales"
## ASV51 "Oscillospirales"
## ASV52 "Campylobacterales"
## ASV53 "Lachnospirales"
## ASV54 "Lachnospirales"
## ASV55 "Lachnospirales"
## ASV56 "Peptostreptococcales-Tissierellales"
## ASV57 "Oscillospirales"
## ASV58 "Lachnospirales"
## ASV59 "Lachnospirales"
## ASV60 "Acholeplasmatales"
## ASV61 "Actinomycetales"
## ASV62 "Lachnospirales"
## ASV63 "RF39"
## ASV64 "Clostridiales"
## ASV65 "Burkholderiales"
## ASV66 "Oscillospirales"
## ASV67 "Oscillospirales"
## ASV68 "Lachnospirales"
## ASV69 "Bifidobacteriales"
## ASV70 "Lachnospirales"
## ASV71 "Lachnospirales"
## ASV72 "Bacteroidales"
## ASV73 "Lachnospirales"
## ASV74 "Lachnospirales"
## ASV75 "Clostridiales"
## ASV76 "Lachnospirales"
## ASV77 "Lachnospirales"
## ASV78 "Lachnospirales"
## ASV79 "Oscillospirales"
## ASV80 "Clostridia vadinBB60 group"
## ASV81 "Lactobacillales"
## ASV82 "Lachnospirales"
## ASV83 "Enterobacterales"
## ASV84 "Oscillospirales"
## ASV85 "Lachnospirales"
## ASV86 "Lachnospirales"
## ASV87 "Lachnospirales"
## ASV88 "Lachnospirales"
## ASV89 "Lachnospirales"
## ASV90 "Oscillospirales"
## ASV91 "Lachnospirales"
## ASV92 "Lactobacillales"
## ASV93 "Saccharimonadales"
## ASV94 "Lachnospirales"
## ASV95 "Lachnospirales"
## ASV96 "Oscillospirales"
## ASV97 "Lachnospirales"
## ASV98 "Lactobacillales"
## ASV99 "Lachnospirales"
## ASV100 "Lachnospirales"
## ASV101 "Pseudomonadales"
## ASV102 "Peptococcales"
## ASV103 "Lactobacillales"
## ASV104 "Rickettsiales"
## ASV105 "Lachnospirales"
## ASV106 "Deinococcales"
## ASV107 "Oscillospirales"
## ASV108 "Clostridia vadinBB60 group"
## ASV109 "Lachnospirales"
## ASV110 "Lachnospirales"
## ASV111 "Bacteroidales"
## ASV112 "Oscillospirales"
## ASV113 "Lachnospirales"
## ASV114 "Clostridia UCG-014"
## ASV115 "Lachnospirales"
## ASV116 "Lachnospirales"
## ASV117 "Lachnospirales"
## ASV118 "Lachnospirales"
## ASV119 "Lachnospirales"
## ASV120 "Lachnospirales"
## ASV121 "Lachnospirales"
## ASV122 "Lachnospirales"
## ASV123 "Lachnospirales"
## ASV124 "Lachnospirales"
## ASV125 "Lachnospirales"
## ASV126 "Lachnospirales"
## ASV127 "Lachnospirales"
## ASV128 "Lachnospirales"
## ASV129 "Oscillospirales"
## ASV130 "Lachnospirales"
## ASV131 "Lachnospirales"
## ASV132 "Lachnospirales"
## ASV133 "Lachnospirales"
## ASV134 "Lachnospirales"
## ASV135 "Lachnospirales"
## ASV136 "RF39"
## ASV137 "Lachnospirales"
## ASV138 "Lachnospirales"
## ASV139 "Bacteroidales"
## ASV140 "Bacteroidales"
## ASV141 "Lachnospirales"
## ASV142 "Lachnospirales"
## ASV143 "Lachnospirales"
## ASV144 "Pseudomonadales"
## ASV145 "Lachnospirales"
## ASV146 "Rhodobacterales"
## ASV147 "Lachnospirales"
## ASV148 "Lachnospirales"
## ASV149 "Lachnospirales"
## ASV150 "Lachnospirales"
## ASV151 "Lachnospirales"
## ASV152 "Clostridia vadinBB60 group"
## ASV153 "Clostridia vadinBB60 group"
## ASV154 "Lachnospirales"
## ASV155 "Lachnospirales"
## ASV156 "Oscillospirales"
## ASV157 "Oscillospirales"
## ASV158 "Lachnospirales"
## ASV159 "Lachnospirales"
## ASV160 "Clostridia UCG-014"
## ASV161 "Lachnospirales"
## ASV162 "Lachnospirales"
## ASV163 "Coriobacteriales"
## ASV164 "Lachnospirales"
## ASV165 "Lachnospirales"
## ASV166 "Peptostreptococcales-Tissierellales"
## ASV167 "Oscillospirales"
## ASV168 "Lachnospirales"
## ASV169 "Oscillospirales"
## ASV170 "Lachnospirales"
## ASV171 "Lachnospirales"
## ASV172 "Lachnospirales"
## ASV173 "Lachnospirales"
## ASV174 "Lachnospirales"
## ASV175 "Oscillospirales"
## ASV176 "Lachnospirales"
## ASV177 "Lachnospirales"
## ASV178 "Christensenellales"
## ASV179 "Lachnospirales"
## ASV180 "Lachnospirales"
## ASV181 "Lachnospirales"
## ASV182 "Verrucomicrobiales"
## ASV183 "Chloroplast"
## ASV184 "Lachnospirales"
## ASV185 "Oscillospirales"
## ASV186 "Oscillospirales"
## ASV187 "Lachnospirales"
## ASV188 "Lactobacillales"
## ASV189 "Lachnospirales"
## ASV190 "Clostridia UCG-014"
## ASV191 "Oscillospirales"
## ASV192 "Oscillospirales"
## ASV193 "Oscillospirales"
## ASV194 "Lachnospirales"
## ASV195 "Coriobacteriales"
## ASV196 "Chloroplast"
## ASV197 "Clostridia vadinBB60 group"
## ASV198 "Lactobacillales"
## ASV199 "Clostridiales"
## ASV200 "Saccharimonadales"
## ASV201 "Lachnospirales"
## ASV202 "Lachnospirales"
## ASV203 "Erysipelotrichales"
## ASV204 "Coriobacteriales"
## ASV205 "Lachnospirales"
## ASV206 "Oscillospirales"
## ASV207 "Clostridia UCG-014"
## ASV208 "Lachnospirales"
## ASV209 "Lactobacillales"
## ASV210 "Lachnospirales"
## ASV211 "Oscillospirales"
## ASV212 "Lachnospirales"
## ASV213 "Lactobacillales"
## ASV214 "Lachnospirales"
## ASV215 "Coriobacteriales"
## ASV216 "Lachnospirales"
## ASV217 "Clostridia vadinBB60 group"
## ASV218 "Clostridia UCG-014"
## ASV219 "Coriobacteriales"
## ASV220 "Lachnospirales"
## ASV221 "Clostridia vadinBB60 group"
## ASV222 "Chloroplast"
## ASV223 "Clostridia UCG-014"
## ASV224 "Coriobacteriales"
## Family
## ASV1 "Muribaculaceae"
## ASV2 "Muribaculaceae"
## ASV3 "Muribaculaceae"
## ASV4 "Muribaculaceae"
## ASV5 "Bacteroidaceae"
## ASV6 "Muribaculaceae"
## ASV7 "Muribaculaceae"
## ASV8 "Rikenellaceae"
## ASV9 "Muribaculaceae"
## ASV10 "Muribaculaceae"
## ASV11 "Lactobacillaceae"
## ASV12 "Muribaculaceae"
## ASV13 "Muribaculaceae"
## ASV14 "Lactobacillaceae"
## ASV15 "Muribaculaceae"
## ASV16 "Muribaculaceae"
## ASV17 "Lachnospiraceae"
## ASV18 "Erysipelotrichaceae"
## ASV19 "Muribaculaceae"
## ASV20 NA
## ASV21 "Lactobacillaceae"
## ASV22 "Lachnospiraceae"
## ASV23 "Lachnospiraceae"
## ASV24 "Lachnospiraceae"
## ASV25 "Peptococcaceae"
## ASV26 "Oscillospiraceae"
## ASV27 "Oscillospiraceae"
## ASV28 "Lachnospiraceae"
## ASV29 "Lachnospiraceae"
## ASV30 "Lachnospiraceae"
## ASV31 "Lachnospiraceae"
## ASV32 "Lachnospiraceae"
## ASV33 "Staphylococcaceae"
## ASV34 "Lachnospiraceae"
## ASV35 "Lachnospiraceae"
## ASV36 "Lachnospiraceae"
## ASV37 "Lachnospiraceae"
## ASV38 "Muribaculaceae"
## ASV39 "Lachnospiraceae"
## ASV40 "Lachnospiraceae"
## ASV41 "Lachnospiraceae"
## ASV42 "Lachnospiraceae"
## ASV43 "Moraxellaceae"
## ASV44 "Lachnospiraceae"
## ASV45 "Lachnospiraceae"
## ASV46 "Bacillaceae"
## ASV47 "Lachnospiraceae"
## ASV48 "Lachnospiraceae"
## ASV49 "Lachnospiraceae"
## ASV50 "Lachnospiraceae"
## ASV51 "Ruminococcaceae"
## ASV52 "Helicobacteraceae"
## ASV53 "Lachnospiraceae"
## ASV54 "Lachnospiraceae"
## ASV55 "Lachnospiraceae"
## ASV56 "Anaerovoracaceae"
## ASV57 "Ruminococcaceae"
## ASV58 "Lachnospiraceae"
## ASV59 "Lachnospiraceae"
## ASV60 "Acholeplasmataceae"
## ASV61 "Actinomycetaceae"
## ASV62 "Lachnospiraceae"
## ASV63 NA
## ASV64 "Clostridiaceae"
## ASV65 "Neisseriaceae"
## ASV66 "Oscillospiraceae"
## ASV67 "Oscillospiraceae"
## ASV68 "Lachnospiraceae"
## ASV69 "Bifidobacteriaceae"
## ASV70 "Lachnospiraceae"
## ASV71 "Lachnospiraceae"
## ASV72 "Bacteroidaceae"
## ASV73 "Lachnospiraceae"
## ASV74 "Lachnospiraceae"
## ASV75 "Clostridiaceae"
## ASV76 "Lachnospiraceae"
## ASV77 "Lachnospiraceae"
## ASV78 "Lachnospiraceae"
## ASV79 "Oscillospiraceae"
## ASV80 NA
## ASV81 "Streptococcaceae"
## ASV82 "Lachnospiraceae"
## ASV83 "Enterobacteriaceae"
## ASV84 "Oscillospiraceae"
## ASV85 "Lachnospiraceae"
## ASV86 "Lachnospiraceae"
## ASV87 "Lachnospiraceae"
## ASV88 "Lachnospiraceae"
## ASV89 "Lachnospiraceae"
## ASV90 "Oscillospiraceae"
## ASV91 "Lachnospiraceae"
## ASV92 "Enterococcaceae"
## ASV93 "Saccharimonadaceae"
## ASV94 "Lachnospiraceae"
## ASV95 "Lachnospiraceae"
## ASV96 "Oscillospiraceae"
## ASV97 "Lachnospiraceae"
## ASV98 "Listeriaceae"
## ASV99 "Lachnospiraceae"
## ASV100 "Lachnospiraceae"
## ASV101 "Pseudomonadaceae"
## ASV102 "Peptococcaceae"
## ASV103 "Streptococcaceae"
## ASV104 "Mitochondria"
## ASV105 "Lachnospiraceae"
## ASV106 "Deinococcaceae"
## ASV107 "Ruminococcaceae"
## ASV108 NA
## ASV109 "Lachnospiraceae"
## ASV110 "Lachnospiraceae"
## ASV111 "Porphyromonadaceae"
## ASV112 "Oscillospiraceae"
## ASV113 "Lachnospiraceae"
## ASV114 NA
## ASV115 "Lachnospiraceae"
## ASV116 "Lachnospiraceae"
## ASV117 "Lachnospiraceae"
## ASV118 "Lachnospiraceae"
## ASV119 "Lachnospiraceae"
## ASV120 "Lachnospiraceae"
## ASV121 "Lachnospiraceae"
## ASV122 "Lachnospiraceae"
## ASV123 "Lachnospiraceae"
## ASV124 "Lachnospiraceae"
## ASV125 "Lachnospiraceae"
## ASV126 "Lachnospiraceae"
## ASV127 "Lachnospiraceae"
## ASV128 "Lachnospiraceae"
## ASV129 "Oscillospiraceae"
## ASV130 "Lachnospiraceae"
## ASV131 "Lachnospiraceae"
## ASV132 "Lachnospiraceae"
## ASV133 "Lachnospiraceae"
## ASV134 "Lachnospiraceae"
## ASV135 "Lachnospiraceae"
## ASV136 NA
## ASV137 "Lachnospiraceae"
## ASV138 "Lachnospiraceae"
## ASV139 "Muribaculaceae"
## ASV140 "Muribaculaceae"
## ASV141 "Lachnospiraceae"
## ASV142 "Lachnospiraceae"
## ASV143 "Lachnospiraceae"
## ASV144 "Pseudomonadaceae"
## ASV145 "Lachnospiraceae"
## ASV146 "Rhodobacteraceae"
## ASV147 "Lachnospiraceae"
## ASV148 "Lachnospiraceae"
## ASV149 "Lachnospiraceae"
## ASV150 "Lachnospiraceae"
## ASV151 "Lachnospiraceae"
## ASV152 NA
## ASV153 NA
## ASV154 "Lachnospiraceae"
## ASV155 "Lachnospiraceae"
## ASV156 "[Eubacterium] coprostanoligenes group"
## ASV157 "Oscillospiraceae"
## ASV158 "Lachnospiraceae"
## ASV159 "Lachnospiraceae"
## ASV160 NA
## ASV161 "Lachnospiraceae"
## ASV162 "Lachnospiraceae"
## ASV163 "Eggerthellaceae"
## ASV164 "Lachnospiraceae"
## ASV165 "Lachnospiraceae"
## ASV166 "Anaerovoracaceae"
## ASV167 "Oscillospiraceae"
## ASV168 "Lachnospiraceae"
## ASV169 "Ruminococcaceae"
## ASV170 "Lachnospiraceae"
## ASV171 "Lachnospiraceae"
## ASV172 "Lachnospiraceae"
## ASV173 "Lachnospiraceae"
## ASV174 "Lachnospiraceae"
## ASV175 "Ruminococcaceae"
## ASV176 "Lachnospiraceae"
## ASV177 "Lachnospiraceae"
## ASV178 "Christensenellaceae"
## ASV179 "Lachnospiraceae"
## ASV180 "Lachnospiraceae"
## ASV181 "Lachnospiraceae"
## ASV182 "Akkermansiaceae"
## ASV183 NA
## ASV184 "Lachnospiraceae"
## ASV185 "Oscillospiraceae"
## ASV186 "Butyricicoccaceae"
## ASV187 "Lachnospiraceae"
## ASV188 "Streptococcaceae"
## ASV189 "Lachnospiraceae"
## ASV190 NA
## ASV191 "Ruminococcaceae"
## ASV192 "Ruminococcaceae"
## ASV193 "Ruminococcaceae"
## ASV194 "Lachnospiraceae"
## ASV195 "Eggerthellaceae"
## ASV196 NA
## ASV197 NA
## ASV198 "Streptococcaceae"
## ASV199 "Clostridiaceae"
## ASV200 "Saccharimonadaceae"
## ASV201 "Lachnospiraceae"
## ASV202 "Lachnospiraceae"
## ASV203 "Erysipelatoclostridiaceae"
## ASV204 "Eggerthellaceae"
## ASV205 "Lachnospiraceae"
## ASV206 "Ruminococcaceae"
## ASV207 NA
## ASV208 "Lachnospiraceae"
## ASV209 "Streptococcaceae"
## ASV210 "Lachnospiraceae"
## ASV211 "Oscillospiraceae"
## ASV212 "Lachnospiraceae"
## ASV213 "Streptococcaceae"
## ASV214 "Lachnospiraceae"
## ASV215 "Eggerthellaceae"
## ASV216 "Lachnospiraceae"
## ASV217 NA
## ASV218 NA
## ASV219 "Atopobiaceae"
## ASV220 "Lachnospiraceae"
## ASV221 NA
## ASV222 NA
## ASV223 NA
## ASV224 "Eggerthellaceae"
## Genus
## ASV1 NA
## ASV2 NA
## ASV3 NA
## ASV4 NA
## ASV5 "Bacteroides"
## ASV6 NA
## ASV7 NA
## ASV8 "Alistipes"
## ASV9 NA
## ASV10 NA
## ASV11 "Lactobacillus"
## ASV12 NA
## ASV13 NA
## ASV14 "Ligilactobacillus"
## ASV15 NA
## ASV16 NA
## ASV17 "Lachnospiraceae NK4A136 group"
## ASV18 "Turicibacter"
## ASV19 NA
## ASV20 NA
## ASV21 "HT002"
## ASV22 NA
## ASV23 "Lachnospiraceae NK4A136 group"
## ASV24 NA
## ASV25 NA
## ASV26 "Oscillibacter"
## ASV27 "Oscillibacter"
## ASV28 "Lachnospiraceae NK4A136 group"
## ASV29 NA
## ASV30 "Lachnospiraceae NK4A136 group"
## ASV31 "Lachnospiraceae NK4A136 group"
## ASV32 NA
## ASV33 "Staphylococcus"
## ASV34 "Lachnospiraceae NK4A136 group"
## ASV35 NA
## ASV36 "Lachnospiraceae NK4A136 group"
## ASV37 "Lachnospiraceae NK4A136 group"
## ASV38 NA
## ASV39 NA
## ASV40 "Lachnospiraceae NK4A136 group"
## ASV41 NA
## ASV42 "Lachnospiraceae UCG-001"
## ASV43 "Acinetobacter"
## ASV44 "Roseburia"
## ASV45 "Lachnoclostridium"
## ASV46 "Bacillus"
## ASV47 NA
## ASV48 "A2"
## ASV49 NA
## ASV50 NA
## ASV51 "Incertae Sedis"
## ASV52 "Helicobacter"
## ASV53 "Lachnospiraceae UCG-001"
## ASV54 "Lachnoclostridium"
## ASV55 "Lachnospiraceae NK4A136 group"
## ASV56 "[Eubacterium] nodatum group"
## ASV57 "Incertae Sedis"
## ASV58 "Lachnospiraceae NK4A136 group"
## ASV59 NA
## ASV60 "Anaeroplasma"
## ASV61 "Actinomyces"
## ASV62 "Lachnospiraceae NK4A136 group"
## ASV63 NA
## ASV64 "Clostridium sensu stricto 1"
## ASV65 "Neisseria"
## ASV66 NA
## ASV67 "Intestinimonas"
## ASV68 "Lachnospiraceae NK4A136 group"
## ASV69 "Bifidobacterium"
## ASV70 NA
## ASV71 NA
## ASV72 "Bacteroides"
## ASV73 NA
## ASV74 "Roseburia"
## ASV75 "Clostridium sensu stricto 1"
## ASV76 "Lachnoclostridium"
## ASV77 "Roseburia"
## ASV78 "Lachnospiraceae UCG-004"
## ASV79 NA
## ASV80 NA
## ASV81 "Streptococcus"
## ASV82 "Lachnospiraceae NK4A136 group"
## ASV83 "Escherichia-Shigella"
## ASV84 NA
## ASV85 NA
## ASV86 "Lachnospiraceae FCS020 group"
## ASV87 "Roseburia"
## ASV88 "Lachnospiraceae NK4A136 group"
## ASV89 "Lachnospiraceae NK4A136 group"
## ASV90 "Colidextribacter"
## ASV91 "Lachnoclostridium"
## ASV92 "Enterococcus"
## ASV93 "Candidatus Saccharimonas"
## ASV94 NA
## ASV95 NA
## ASV96 "Colidextribacter"
## ASV97 NA
## ASV98 "Listeria"
## ASV99 "Lachnospiraceae UCG-006"
## ASV100 NA
## ASV101 "Pseudomonas"
## ASV102 NA
## ASV103 "Streptococcus"
## ASV104 NA
## ASV105 "A2"
## ASV106 "Deinococcus"
## ASV107 "Anaerotruncus"
## ASV108 NA
## ASV109 "A2"
## ASV110 NA
## ASV111 "Porphyromonas"
## ASV112 "Colidextribacter"
## ASV113 NA
## ASV114 NA
## ASV115 "Lachnospiraceae NK4A136 group"
## ASV116 "Roseburia"
## ASV117 "Roseburia"
## ASV118 "Roseburia"
## ASV119 "A2"
## ASV120 NA
## ASV121 "A2"
## ASV122 "Lachnospiraceae FCS020 group"
## ASV123 "Lachnospiraceae UCG-006"
## ASV124 NA
## ASV125 "Lachnospiraceae UCG-001"
## ASV126 "[Eubacterium] xylanophilum group"
## ASV127 "Roseburia"
## ASV128 "Lachnospiraceae NK4A136 group"
## ASV129 "Colidextribacter"
## ASV130 "GCA-900066575"
## ASV131 NA
## ASV132 NA
## ASV133 NA
## ASV134 "Lachnospiraceae NK4A136 group"
## ASV135 NA
## ASV136 NA
## ASV137 "ASF356"
## ASV138 NA
## ASV139 NA
## ASV140 NA
## ASV141 "ASF356"
## ASV142 "Lachnospiraceae UCG-001"
## ASV143 NA
## ASV144 "Pseudomonas"
## ASV145 NA
## ASV146 "Rhodobacter"
## ASV147 "Lachnoclostridium"
## ASV148 NA
## ASV149 "Acetatifactor"
## ASV150 NA
## ASV151 "Roseburia"
## ASV152 NA
## ASV153 NA
## ASV154 NA
## ASV155 "Lachnospiraceae NK4A136 group"
## ASV156 NA
## ASV157 "Oscillibacter"
## ASV158 NA
## ASV159 NA
## ASV160 NA
## ASV161 "GCA-900066575"
## ASV162 NA
## ASV163 "Enterorhabdus"
## ASV164 NA
## ASV165 NA
## ASV166 "Family XIII UCG-001"
## ASV167 NA
## ASV168 "Tyzzerella"
## ASV169 NA
## ASV170 "Acetatifactor"
## ASV171 NA
## ASV172 "GCA-900066575"
## ASV173 NA
## ASV174 "Lachnospiraceae NK4A136 group"
## ASV175 NA
## ASV176 "Roseburia"
## ASV177 NA
## ASV178 NA
## ASV179 NA
## ASV180 "Roseburia"
## ASV181 "Lachnoclostridium"
## ASV182 "Akkermansia"
## ASV183 NA
## ASV184 NA
## ASV185 NA
## ASV186 "Butyricicoccus"
## ASV187 "Acetatifactor"
## ASV188 "Streptococcus"
## ASV189 NA
## ASV190 NA
## ASV191 NA
## ASV192 NA
## ASV193 NA
## ASV194 "Lachnospiraceae NK4B4 group"
## ASV195 "Enterorhabdus"
## ASV196 NA
## ASV197 NA
## ASV198 "Streptococcus"
## ASV199 "Candidatus Arthromitus"
## ASV200 "Candidatus Saccharimonas"
## ASV201 NA
## ASV202 NA
## ASV203 "Candidatus Stoquefichus"
## ASV204 NA
## ASV205 "Lachnoclostridium"
## ASV206 "Anaerotruncus"
## ASV207 NA
## ASV208 "Roseburia"
## ASV209 "Streptococcus"
## ASV210 "GCA-900066575"
## ASV211 "Intestinimonas"
## ASV212 "Lachnospiraceae FCS020 group"
## ASV213 "Streptococcus"
## ASV214 NA
## ASV215 "Enterorhabdus"
## ASV216 "Lachnospiraceae UCG-006"
## ASV217 NA
## ASV218 NA
## ASV219 "Coriobacteriaceae UCG-002"
## ASV220 "GCA-900066575"
## ASV221 NA
## ASV222 NA
## ASV223 NA
## ASV224 "Enterorhabdus"ps %>% refseq()## DNAStringSet object of length 224:
## width seq names
## [1] 252 TACGGAGGATGCGAGCGTTATC...GAAAGTGTGGGTATCGAACAGG ASV1
## [2] 252 TACGGAGGATGCGAGCGTTATC...GAAAGCGTGGGTATCGAACAGG ASV2
## [3] 252 TACGGAGGATGCGAGCGTTATC...GAAAGCGTGGGTATCGAACAGG ASV3
## [4] 252 TACGGAGGATGCGAGCGTTATC...GAAAGTGCGGGGATCGAACAGG ASV4
## [5] 253 TACGGAGGATCCGAGCGTTATC...GAAAGTGTGGGTATCAAACAGG ASV5
## ... ... ...
## [220] 253 TACGTAGGGGGCAAGCGTTATC...GAAAGCGTGGGGAGCAAACAGG ASV220
## [221] 253 TACGTAGGAGGCAAGCGTTATC...GAAAGCGTGGGGAGCAAACAGG ASV221
## [222] 253 GACAGAGGATGCAAGCGTTATC...GAAAGCTAGGGGAGCGAATGGG ASV222
## [223] 253 TACGTAGGGAGCGAGCGTTATC...GAAAGTGTGGGGAGCAAACAGG ASV223
## [224] 252 TACGTAGGGAGCGAGCGTTATC...GAAAGCTGGGGGAGCGAACAGG ASV224Saving data
# saveRDS(ps, "/Users/minsikkim/Dropbox (Personal)/Inha/5_Lectures/Advanced metagenomics/scripts/IBS7048_Advanced_metagenomics/phyloseq_example_tree.rds")
sample_data(ps)## Subject Gender Day When
## F3D0 3 F 0 Early
## F3D1 3 F 1 Early
## F3D141 3 F 141 Late
## F3D142 3 F 142 Late
## F3D143 3 F 143 Late
## F3D144 3 F 144 Late
## F3D145 3 F 145 Late
## F3D146 3 F 146 Late
## F3D147 3 F 147 Late
## F3D148 3 F 148 Late
## F3D149 3 F 149 Late
## F3D150 3 F 150 Late
## F3D2 3 F 2 Early
## F3D3 3 F 3 Early
## F3D5 3 F 5 Early
## F3D6 3 F 6 Early
## F3D7 3 F 7 Early
## F3D8 3 F 8 Early
## F3D9 3 F 9 Earlyotu_table(ps)## OTU Table: [224 taxa and 19 samples]
## taxa are columns
## ASV1 ASV2 ASV3 ASV4 ASV5 ASV6 ASV7 ASV8 ASV9 ASV10 ASV11 ASV12 ASV13
## F3D0 574 347 438 426 153 472 280 183 42 156 16 217 98
## F3D1 403 350 229 70 134 41 95 187 69 102 90 40 46
## F3D141 443 361 328 496 187 322 240 322 160 125 145 140 147
## F3D142 286 303 155 162 175 178 159 89 83 69 41 98 112
## F3D143 231 174 202 228 128 234 153 81 103 62 68 109 63
## F3D144 419 277 293 358 102 356 237 41 154 146 246 145 136
## F3D145 647 493 516 575 301 475 397 125 191 211 292 254 196
## F3D146 316 234 245 390 178 273 212 71 106 81 161 147 96
## F3D147 1501 1216 909 1089 449 1168 853 74 766 252 408 560 455
## F3D148 861 733 576 852 437 871 576 496 399 190 366 430 240
## F3D149 884 781 717 895 413 634 558 510 419 272 428 301 164
## F3D150 316 229 388 463 167 213 223 119 232 138 59 96 74
## F3D2 3491 1579 1165 465 335 114 319 1200 428 608 52 40 141
## F3D3 983 602 462 197 399 25 163 377 305 280 152 0 63
## F3D5 324 268 275 159 151 21 120 205 170 205 53 0 58
## F3D6 1013 674 582 400 473 15 274 260 198 227 39 0 77
## F3D7 645 500 434 308 464 10 192 211 172 256 15 0 62
## F3D8 277 350 347 145 554 0 129 286 109 193 17 0 22
## F3D9 509 422 479 204 594 0 204 437 142 222 26 0 36
## ASV14 ASV15 ASV16 ASV17 ASV18 ASV19 ASV20 ASV21 ASV22 ASV23 ASV24 ASV25
## F3D0 52 105 63 90 78 68 67 41 44 53 69 26
## F3D1 127 30 11 319 0 31 108 52 14 135 67 8
## F3D141 12 64 92 32 103 42 6 45 85 15 7 56
## F3D142 99 63 32 11 52 29 6 0 32 6 6 14
## F3D143 43 59 39 0 40 20 0 12 65 0 5 0
## F3D144 16 82 66 11 112 44 5 44 17 0 5 13
## F3D145 20 120 116 15 122 104 5 39 12 8 5 20
## F3D146 4 59 68 25 35 35 0 16 77 47 19 16
## F3D147 143 289 526 74 305 139 41 118 79 0 0 109
## F3D148 17 197 270 55 269 117 55 110 94 52 12 179
## F3D149 85 162 230 42 175 118 4 145 259 0 8 102
## F3D150 64 69 89 18 29 46 5 19 143 22 0 53
## F3D2 324 105 43 366 17 192 398 30 17 293 333 34
## F3D3 93 57 17 46 24 105 342 120 0 0 44 22
## F3D5 48 35 18 87 35 37 17 10 0 55 57 24
## F3D6 421 106 13 57 0 39 74 29 0 37 66 26
## F3D7 115 73 10 39 0 22 39 38 0 30 13 48
## F3D8 145 64 0 44 6 22 20 111 0 30 57 22
## F3D9 181 72 0 98 0 38 42 24 0 49 44 38
## ASV26 ASV27 ASV28 ASV29 ASV30 ASV31 ASV32 ASV33 ASV34 ASV35 ASV36 ASV37
## F3D0 74 31 58 42 261 60 27 0 73 34 45 0
## F3D1 100 44 140 119 55 25 29 0 71 100 54 12
## F3D141 0 9 0 35 50 12 57 0 22 0 0 30
## F3D142 0 0 0 0 9 21 8 0 6 0 0 0
## F3D143 0 14 0 0 0 22 24 0 13 0 0 13
## F3D144 0 20 0 0 14 0 11 0 16 0 0 14
## F3D145 0 18 0 11 0 0 18 0 12 0 0 11
## F3D146 36 36 0 35 18 0 22 0 23 0 0 12
## F3D147 33 30 14 17 0 51 48 0 12 0 25 44
## F3D148 8 16 0 22 21 57 43 0 9 0 0 20
## F3D149 6 45 23 36 0 44 142 0 43 0 12 59
## F3D150 6 32 0 0 0 0 81 0 30 0 0 53
## F3D2 200 145 281 108 137 59 40 0 82 185 112 94
## F3D3 15 18 20 0 0 22 9 0 8 8 12 17
## F3D5 50 47 34 45 35 76 0 0 40 16 48 33
## F3D6 107 47 40 19 0 42 20 0 46 23 53 30
## F3D7 48 13 0 8 0 0 0 0 7 12 20 8
## F3D8 39 44 0 49 0 61 8 0 30 71 57 24
## F3D9 51 64 56 93 28 52 15 0 40 94 97 32
## ASV38 ASV39 ASV40 ASV41 ASV42 ASV43 ASV44 ASV45 ASV46 ASV47 ASV48 ASV49
## F3D0 54 27 57 18 31 0 50 10 0 5 0 42
## F3D1 0 34 41 20 71 0 63 19 0 72 0 54
## F3D141 0 26 20 13 0 0 18 0 0 8 61 0
## F3D142 0 12 0 0 0 0 0 0 0 0 0 0
## F3D143 0 14 0 11 0 0 8 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0 38 0
## F3D145 76 0 9 6 0 5 0 0 0 0 46 0
## F3D146 0 31 23 23 0 0 20 0 0 11 92 0
## F3D147 152 43 24 38 0 0 12 13 0 11 37 0
## F3D148 82 50 14 21 0 0 14 9 0 15 12 0
## F3D149 63 86 51 44 0 0 45 21 0 33 0 0
## F3D150 0 29 24 40 0 0 25 0 0 16 0 0
## F3D2 54 41 52 82 81 0 89 79 0 54 0 137
## F3D3 0 0 0 7 15 0 28 20 0 10 0 10
## F3D5 0 10 17 18 33 0 7 36 0 52 0 11
## F3D6 0 28 30 21 49 0 0 37 0 25 15 26
## F3D7 0 11 0 10 22 0 0 33 0 8 0 9
## F3D8 0 0 35 21 37 0 9 43 0 16 23 42
## F3D9 0 16 30 31 63 0 0 58 0 29 36 24
## ASV50 ASV51 ASV52 ASV53 ASV54 ASV55 ASV56 ASV57 ASV58 ASV59 ASV60 ASV61
## F3D0 8 33 0 51 18 46 5 49 0 0 83 0
## F3D1 22 45 0 46 34 38 10 25 0 32 48 0
## F3D141 19 0 0 0 30 0 8 0 17 39 0 0
## F3D142 0 0 0 0 0 0 8 6 10 0 0 0
## F3D143 14 0 0 0 0 0 7 8 0 36 0 0
## F3D144 0 0 0 0 4 0 8 14 22 0 0 0
## F3D145 0 0 0 0 0 0 20 12 13 12 3 0
## F3D146 13 0 0 0 16 18 5 20 37 29 0 0
## F3D147 42 11 0 0 29 23 36 30 89 17 26 0
## F3D148 33 0 0 0 22 0 13 23 62 43 0 0
## F3D149 63 7 0 8 27 0 10 35 43 28 0 0
## F3D150 20 0 0 0 17 14 8 15 41 19 0 0
## F3D2 73 151 0 107 48 54 55 38 0 0 81 0
## F3D3 0 4 0 0 11 0 43 0 0 0 15 0
## F3D5 6 9 0 53 19 21 6 21 0 20 0 0
## F3D6 7 44 0 11 22 61 23 18 0 0 6 0
## F3D7 7 12 0 0 0 0 26 0 0 0 19 0
## F3D8 11 13 0 35 23 25 21 7 0 26 11 0
## F3D9 17 23 0 33 23 41 26 16 0 31 30 0
## ASV62 ASV63 ASV64 ASV65 ASV66 ASV67 ASV68 ASV69 ASV70 ASV71 ASV72 ASV73
## F3D0 0 0 0 0 60 0 92 23 19 19 0 0
## F3D1 21 0 0 0 58 39 12 0 52 19 0 0
## F3D141 0 30 0 0 0 6 22 16 0 13 0 45
## F3D142 0 23 0 0 0 0 0 28 0 0 0 0
## F3D143 0 25 0 0 0 6 20 10 0 4 0 22
## F3D144 0 17 0 2 0 0 13 21 0 0 0 0
## F3D145 0 18 5 4 0 10 0 7 0 5 0 0
## F3D146 0 5 0 0 0 13 0 3 0 15 0 11
## F3D147 18 55 0 0 0 0 0 28 0 13 0 17
## F3D148 0 94 0 0 0 10 10 71 0 18 0 18
## F3D149 0 34 0 0 0 13 10 28 0 59 0 82
## F3D150 0 14 0 0 0 12 0 19 0 23 0 46
## F3D2 44 0 0 0 71 81 37 4 79 47 0 0
## F3D3 0 0 0 0 12 10 0 16 0 5 0 0
## F3D5 22 0 0 0 33 18 10 0 24 0 0 0
## F3D6 29 0 0 0 34 12 15 0 41 9 0 0
## F3D7 20 0 0 0 20 0 9 0 0 0 0 0
## F3D8 56 0 0 0 0 31 17 0 36 12 0 0
## F3D9 105 0 0 0 0 27 15 0 20 5 0 0
## ASV74 ASV75 ASV76 ASV77 ASV78 ASV79 ASV80 ASV81 ASV82 ASV83 ASV84 ASV85
## F3D0 41 128 0 0 0 0 24 0 14 0 0 5
## F3D1 41 0 0 11 13 0 3 0 0 2 15 20
## F3D141 10 6 30 0 16 0 15 0 0 0 7 12
## F3D142 0 4 0 0 0 0 0 0 0 0 0 0
## F3D143 0 0 13 0 13 11 0 0 0 0 0 8
## F3D144 0 6 0 0 0 10 0 0 0 0 0 0
## F3D145 0 14 0 5 0 0 0 0 0 0 9 0
## F3D146 0 0 9 12 12 0 6 0 0 0 19 8
## F3D147 10 18 25 0 0 45 11 0 10 5 18 39
## F3D148 0 31 21 0 14 12 16 0 23 0 0 0
## F3D149 19 13 73 8 66 0 14 0 19 0 13 25
## F3D150 0 6 51 0 18 30 0 0 0 0 11 11
## F3D2 99 0 0 20 32 0 37 0 29 5 37 12
## F3D3 17 0 0 21 0 0 19 0 0 0 0 5
## F3D5 0 0 0 14 0 0 11 0 23 4 13 10
## F3D6 0 0 0 30 15 0 9 0 18 0 17 11
## F3D7 0 0 0 14 0 0 19 0 10 2 0 5
## F3D8 0 0 0 38 7 41 8 0 17 3 13 12
## F3D9 0 0 0 48 12 64 17 0 38 0 25 11
## ASV86 ASV87 ASV88 ASV89 ASV90 ASV91 ASV92 ASV93 ASV94 ASV95 ASV96 ASV97
## F3D0 10 24 0 0 0 50 0 0 6 0 17 0
## F3D1 11 10 0 0 19 48 0 30 0 14 0 43
## F3D141 0 0 0 0 0 0 0 5 13 9 0 0
## F3D142 0 0 0 0 15 0 0 0 5 13 0 0
## F3D143 0 0 0 0 0 0 0 0 11 11 14 0
## F3D144 0 0 0 0 14 0 0 0 0 0 0 0
## F3D145 0 0 0 21 0 0 0 3 13 17 9 0
## F3D146 0 13 34 0 26 0 0 3 13 0 0 0
## F3D147 64 9 20 0 33 0 0 17 0 33 24 0
## F3D148 18 0 16 37 13 0 0 17 32 20 0 0
## F3D149 24 0 14 0 17 0 0 7 31 17 0 0
## F3D150 0 0 0 0 8 0 0 0 26 0 0 0
## F3D2 16 46 0 38 38 61 0 5 9 22 42 76
## F3D3 0 0 0 0 0 0 0 15 0 0 0 0
## F3D5 0 12 0 55 0 20 0 18 10 0 13 0
## F3D6 38 31 0 34 0 0 0 16 0 11 11 0
## F3D7 0 0 6 0 0 0 0 18 0 0 0 6
## F3D8 0 21 46 0 0 0 0 13 0 0 15 35
## F3D9 12 24 52 0 0 0 0 9 6 8 27 0
## ASV98 ASV99 ASV100 ASV101 ASV102 ASV103 ASV104 ASV105 ASV106 ASV107
## F3D0 0 0 0 0 0 0 3 0 0 0
## F3D1 0 0 0 0 4 0 7 0 0 0
## F3D141 0 12 10 0 8 0 0 19 0 14
## F3D142 0 0 6 0 0 0 0 0 0 0
## F3D143 0 6 0 0 0 0 0 6 0 5
## F3D144 0 10 0 0 0 0 0 6 0 10
## F3D145 0 13 0 0 5 0 0 0 2 0
## F3D146 0 14 13 0 14 0 0 15 0 17
## F3D147 0 0 14 0 11 0 0 32 0 0
## F3D148 0 23 19 0 8 0 0 14 0 0
## F3D149 0 42 59 0 23 0 0 0 0 39
## F3D150 0 12 23 0 11 0 0 0 0 17
## F3D2 0 0 0 0 19 0 31 22 0 8
## F3D3 0 0 0 0 6 0 2 0 0 0
## F3D5 0 6 0 0 6 0 0 0 0 0
## F3D6 0 10 0 0 5 0 19 0 0 0
## F3D7 0 0 0 0 0 0 8 0 0 0
## F3D8 0 0 0 0 9 0 25 0 0 0
## F3D9 0 0 0 0 8 0 19 0 0 0
## ASV108 ASV109 ASV110 ASV111 ASV112 ASV113 ASV114 ASV115 ASV116 ASV117
## F3D0 0 23 0 0 19 6 0 0 0 0
## F3D1 0 0 8 0 23 24 0 0 0 0
## F3D141 14 0 0 0 0 9 0 0 0 10
## F3D142 5 0 0 0 0 0 11 0 0 0
## F3D143 6 0 0 0 0 0 0 0 0 0
## F3D144 6 0 0 0 0 0 12 0 0 0
## F3D145 10 0 0 0 0 0 16 4 0 0
## F3D146 13 0 0 0 0 0 11 0 12 11
## F3D147 0 0 0 0 0 0 11 0 15 8
## F3D148 15 0 0 0 0 0 12 0 0 9
## F3D149 24 5 0 0 0 19 0 0 41 32
## F3D150 10 0 0 0 0 8 9 14 23 18
## F3D2 0 15 26 0 32 21 0 0 0 0
## F3D3 0 0 5 0 0 0 0 0 0 0
## F3D5 6 0 11 0 0 0 0 44 0 0
## F3D6 0 24 29 0 0 0 0 29 0 0
## F3D7 0 0 7 0 11 0 0 0 0 0
## F3D8 0 14 11 0 10 7 11 0 0 0
## F3D9 0 24 8 0 0 0 0 0 0 0
## ASV118 ASV119 ASV120 ASV121 ASV122 ASV123 ASV124 ASV125 ASV126 ASV127
## F3D0 34 0 10 16 10 9 9 0 0 18
## F3D1 14 0 0 15 6 7 17 0 0 17
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 0 7 0 0 0 0 0 0 0
## F3D143 0 0 6 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 6 0 0 0 0 0 0 0
## F3D146 0 16 16 0 0 0 0 0 0 7
## F3D147 0 0 13 10 13 0 0 17 0 0
## F3D148 0 0 13 0 0 0 0 0 0 0
## F3D149 0 0 9 17 10 0 0 0 8 0
## F3D150 0 0 5 10 9 0 0 0 0 0
## F3D2 39 20 0 0 12 35 46 65 37 30
## F3D3 0 0 0 0 0 12 0 0 21 0
## F3D5 0 0 0 0 0 0 0 0 16 0
## F3D6 0 20 0 0 8 0 0 0 0 0
## F3D7 0 0 0 0 0 0 0 0 0 0
## F3D8 0 0 0 16 9 9 11 0 0 0
## F3D9 0 31 0 0 7 11 0 0 0 0
## ASV128 ASV129 ASV130 ASV131 ASV132 ASV133 ASV134 ASV135 ASV136 ASV137
## F3D0 0 14 20 0 0 0 0 0 0 17
## F3D1 0 0 7 0 0 0 0 0 0 11
## F3D141 13 0 0 0 12 0 0 6 0 0
## F3D142 0 0 0 0 0 0 0 0 3 0
## F3D143 0 0 0 0 0 0 0 0 10 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 8 0 0 0 0 0 0 15 0
## F3D146 0 0 11 0 0 12 0 0 0 0
## F3D147 0 15 0 0 9 13 0 0 15 0
## F3D148 0 0 0 0 0 11 21 11 11 0
## F3D149 0 0 0 0 26 12 41 17 4 0
## F3D150 0 0 0 0 15 0 0 0 0 0
## F3D2 13 31 10 0 0 0 0 0 0 21
## F3D3 0 0 0 5 0 0 0 0 0 0
## F3D5 0 0 9 0 0 0 0 0 0 0
## F3D6 0 0 0 17 0 14 0 0 0 8
## F3D7 0 0 0 16 0 0 0 0 0 0
## F3D8 19 0 0 13 0 0 0 10 0 0
## F3D9 24 0 7 12 0 0 0 15 0 0
## ASV138 ASV139 ASV140 ASV141 ASV142 ASV143 ASV144 ASV145 ASV146 ASV147
## F3D0 0 56 55 10 7 16 8 0 0 0
## F3D1 7 0 0 0 0 0 7 0 0 0
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 0 0 0 0 0 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 2 8 0 0
## F3D146 0 0 0 0 0 0 0 0 0 0
## F3D147 0 0 0 0 0 0 0 0 0 25
## F3D148 0 0 0 0 0 0 5 0 0 0
## F3D149 10 0 0 0 0 0 0 21 0 0
## F3D150 0 0 0 0 0 0 0 23 0 0
## F3D2 7 0 0 11 39 14 14 0 0 0
## F3D3 0 0 0 12 0 0 0 0 0 0
## F3D5 8 0 0 0 9 0 2 0 0 0
## F3D6 0 0 0 0 0 0 5 0 0 24
## F3D7 0 0 0 5 0 0 0 0 0 0
## F3D8 16 0 0 8 0 11 5 0 0 0
## F3D9 9 0 0 9 0 13 6 0 0 0
## ASV148 ASV149 ASV150 ASV151 ASV152 ASV153 ASV154 ASV155 ASV156 ASV157
## F3D0 0 0 0 0 19 18 0 0 21 10
## F3D1 0 0 0 0 0 7 0 0 0 6
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 0 0 0 0 0 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 0 0 0 0
## F3D146 0 0 0 0 0 0 20 0 0 11
## F3D147 0 0 0 0 0 0 0 0 0 0
## F3D148 0 0 0 17 0 0 0 0 10 0
## F3D149 0 0 0 24 0 0 0 0 4 0
## F3D150 0 10 0 0 0 0 0 0 0 0
## F3D2 8 9 0 0 0 7 17 37 0 8
## F3D3 0 0 0 0 0 0 0 0 0 0
## F3D5 8 0 0 0 0 0 0 0 0 0
## F3D6 9 13 0 0 0 0 0 0 0 0
## F3D7 0 0 0 0 0 0 0 0 0 0
## F3D8 0 11 0 0 4 0 0 0 0 0
## F3D9 19 0 43 0 17 7 0 0 0 0
## ASV158 ASV159 ASV160 ASV161 ASV162 ASV163 ASV164 ASV165 ASV166 ASV167
## F3D0 0 0 0 0 9 0 0 0 0 0
## F3D1 0 0 0 0 0 3 0 0 9 0
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 0 0 0 0 0 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 0 0 0 0
## F3D146 0 0 0 0 0 0 0 0 0 0
## F3D147 0 0 13 11 0 0 6 0 4 0
## F3D148 0 0 0 5 0 8 15 0 0 0
## F3D149 23 14 6 0 12 0 9 0 7 0
## F3D150 0 0 0 0 9 0 0 0 0 0
## F3D2 0 20 0 8 0 10 0 20 9 20
## F3D3 12 0 10 0 0 0 0 0 0 0
## F3D5 0 0 3 0 0 4 0 0 0 0
## F3D6 0 0 0 0 0 0 0 0 0 9
## F3D7 0 0 0 0 0 2 0 0 0 0
## F3D8 0 0 0 0 0 3 0 0 0 0
## F3D9 0 0 0 8 0 0 0 10 0 0
## ASV168 ASV169 ASV170 ASV171 ASV172 ASV173 ASV174 ASV175 ASV176 ASV177
## F3D0 11 0 0 0 0 0 0 3 0 0
## F3D1 0 0 0 0 0 0 0 0 11 0
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 0 0 0 0 0 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 0 0 0 0
## F3D146 0 0 0 0 0 0 6 0 0 0
## F3D147 0 0 15 12 7 5 0 0 0 0
## F3D148 0 0 0 7 0 9 6 0 0 0
## F3D149 0 0 12 6 0 8 9 5 0 20
## F3D150 0 0 0 0 0 0 0 0 0 0
## F3D2 0 28 0 0 0 0 0 3 0 0
## F3D3 0 0 0 0 0 0 0 0 0 0
## F3D5 0 0 0 0 0 0 0 0 0 0
## F3D6 7 0 0 0 7 0 0 9 0 0
## F3D7 0 0 0 0 0 0 0 0 0 0
## F3D8 0 0 0 0 0 0 0 0 9 0
## F3D9 10 0 0 0 10 0 0 0 0 0
## ASV178 ASV179 ASV180 ASV181 ASV182 ASV183 ASV184 ASV185 ASV186 ASV187
## F3D0 7 0 0 0 10 0 0 0 0 0
## F3D1 0 0 0 0 0 0 0 0 0 0
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 0 0 0 0 5 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 0 0 0 0
## F3D146 0 0 0 0 0 0 0 0 0 0
## F3D147 0 7 0 0 0 0 0 0 0 0
## F3D148 0 0 0 0 0 0 0 9 0 0
## F3D149 6 0 0 0 0 0 0 0 0 0
## F3D150 0 0 0 0 0 0 0 0 0 0
## F3D2 5 6 10 0 7 0 0 0 7 0
## F3D3 0 0 0 0 0 0 0 7 0 0
## F3D5 0 0 0 0 0 0 0 0 0 0
## F3D6 0 0 0 0 0 0 0 0 0 0
## F3D7 0 0 0 0 0 0 0 0 0 0
## F3D8 0 0 4 0 0 12 0 0 0 0
## F3D9 0 5 4 18 0 0 17 0 9 16
## ASV188 ASV189 ASV190 ASV191 ASV192 ASV193 ASV194 ASV195 ASV196 ASV197
## F3D0 0 0 0 0 0 12 0 0 0 10
## F3D1 0 3 0 0 0 0 0 0 0 0
## F3D141 0 0 0 3 0 0 0 0 0 0
## F3D142 0 0 0 0 0 0 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 0 0 0 0
## F3D146 0 3 0 0 0 0 0 0 0 0
## F3D147 0 0 14 0 0 0 0 11 0 0
## F3D148 0 0 0 4 0 0 0 0 0 0
## F3D149 0 0 0 6 0 0 0 0 0 0
## F3D150 0 0 0 0 0 0 0 0 0 0
## F3D2 0 0 0 0 13 0 12 0 5 0
## F3D3 0 0 0 0 0 0 0 0 0 0
## F3D5 0 0 0 0 0 0 0 0 0 0
## F3D6 0 0 0 0 0 0 0 0 0 0
## F3D7 0 0 0 0 0 0 0 0 0 0
## F3D8 0 3 0 0 0 0 0 0 0 0
## F3D9 0 5 0 0 0 0 0 0 6 0
## ASV198 ASV199 ASV200 ASV201 ASV202 ASV203 ASV204 ASV205 ASV206 ASV207
## F3D0 5 0 0 0 0 0 0 0 0 0
## F3D1 5 0 0 0 8 0 0 0 0 0
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 5 0 0 0 2 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 9 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 0 0 0 0
## F3D146 0 0 0 0 0 0 2 0 0 0
## F3D147 0 0 0 0 0 4 3 0 0 0
## F3D148 0 5 0 0 0 2 0 0 0 0
## F3D149 0 0 0 0 0 0 0 8 0 0
## F3D150 0 0 0 0 0 0 0 0 0 0
## F3D2 0 0 0 0 0 0 0 0 0 0
## F3D3 0 0 0 0 0 0 0 0 0 0
## F3D5 0 0 0 0 0 0 0 0 0 0
## F3D6 0 0 0 0 0 0 0 0 0 0
## F3D7 0 0 0 0 0 0 0 0 0 0
## F3D8 0 0 0 0 0 0 0 0 8 0
## F3D9 0 0 0 9 0 0 3 0 0 8
## ASV208 ASV209 ASV210 ASV211 ASV212 ASV213 ASV214 ASV215 ASV216 ASV217
## F3D0 0 0 0 0 0 0 0 0 4 4
## F3D1 0 0 0 0 0 0 0 0 0 0
## F3D141 0 0 0 0 0 0 0 0 0 0
## F3D142 0 0 0 0 0 0 0 0 0 0
## F3D143 0 0 0 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 0 0 0 0
## F3D145 0 0 0 0 0 0 0 0 0 0
## F3D146 0 0 6 0 0 0 0 0 0 0
## F3D147 0 0 0 6 0 0 0 0 0 0
## F3D148 0 0 0 0 6 0 0 0 0 0
## F3D149 7 0 0 0 0 0 0 0 0 0
## F3D150 0 0 0 0 0 0 0 5 0 0
## F3D2 0 0 0 0 0 6 0 0 0 0
## F3D3 0 7 0 0 0 0 0 0 0 0
## F3D5 0 0 0 0 0 0 0 0 0 0
## F3D6 0 0 0 0 0 0 0 0 0 0
## F3D7 0 0 0 0 0 0 0 0 0 0
## F3D8 0 0 0 0 0 0 0 0 0 0
## F3D9 0 0 0 0 0 0 6 0 0 0
## ASV218 ASV219 ASV220 ASV221 ASV222 ASV223 ASV224
## F3D0 0 0 0 0 0 0 0
## F3D1 0 0 0 0 0 0 0
## F3D141 4 0 0 0 0 0 0
## F3D142 0 0 0 0 0 0 0
## F3D143 0 0 0 0 0 0 0
## F3D144 0 0 0 0 0 0 3
## F3D145 0 0 0 0 0 0 0
## F3D146 0 0 0 0 0 0 0
## F3D147 0 4 0 0 0 0 0
## F3D148 0 0 0 0 0 0 0
## F3D149 0 0 0 0 0 0 0
## F3D150 0 0 0 0 0 0 0
## F3D2 0 0 4 4 0 0 0
## F3D3 0 0 0 0 4 0 0
## F3D5 0 0 0 0 0 0 0
## F3D6 0 0 0 0 0 0 0
## F3D7 0 0 0 0 0 0 0
## F3D8 0 0 0 0 0 0 0
## F3D9 0 0 0 0 0 4 0Bibliography
## Computing. R Foundation for Statistical Computing, Vienna, Austria. <https://www.R-project.org/>. We have invested a lot of time and effort in creating R, please cite it when using it for data analysis. See also 'citation("pkgname")' for citing R packages.
## version 1.4.3, <https://CRAN.R-project.org/package=readxl>.
## graphics of microbiome census data. Paul J. McMurdie and Susan Holmes (2013) PLoS ONE 8(4):e61217.
## Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). "Welcome to the tidyverse." Journal of Open Source Software_, *4*(43), 1686. doi:10.21105/joss.01686 <https://doi.org/10.21105/joss.01686>.
## R. version 0.5.0. Buffalo, New York. http://github.com/trinker/pacman
## J, reikoch, Beasley W, O'Connor B, Warnes GR, Quinn M, Kamvar ZN, Gao C (2024). yaml: Methods to Convert R Data to YAML and Back_. R package version 2.3.10, <https://CRAN.R-project.org/package=yaml>. ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see 'help("citation")'.