Hi,
I am trying to install using mamba and run this software but I am having some trouble with the phlyloseq package and R. The packages seem to be present but I get an error.

[13:14:24] Input directory "path_to/trial_reads/data/microbial/toy/example-output1/" created with Dadaist2 1.2.5 dadaist2-phyloseqMake:55
MicrobiomeAnalyst directory found: path_to/data/microbial/toy/example-output1/MicrobiomeAnalyst dadaist2-phyloseqMake:56
ERROR: R script failed with exit code 1
R version 4.0.5 (2021-03-31)

• Input: path_to/trial_reads/data/microbial/toy/example-output1/
  Error: package or namespace load failed for ‘phyloseq’ in dyn.load(file, DLLpath = DLLpath, ...):
  unable to load shared object 'path_to/miniconda3/envs/dadaist/lib/R/library/rhdf5/libs/rhdf5.so':
  libcrypto.so.1.1: cannot open shared object file: No such file or directory
  Execution halted

#################################################
#install
conda install -y -c conda-forge mamba
mamba install -y -c conda-forge -c bioconda dadaist2

dadaist2 --version
dadaist2 v1.2.5

mamba list

Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
_r-mutex 1.0.1 anacondar_1 conda-forge
argtable2 2.13 h14c3975_1001 conda-forge
binutils_impl_linux-64 2.36.1 h193b22a_2 conda-forge
bioconductor-biobase 2.50.0 r40hd029910_1 bioconda
bioconductor-biocgenerics 0.36.0 r40hdfd78af_1 bioconda
bioconductor-biocparallel 1.24.1 r40h399db7b_0 bioconda
bioconductor-biomformat 1.18.0 r40hdfd78af_1 bioconda
bioconductor-biostrings 2.58.0 r40hd029910_1 bioconda
bioconductor-dada2 1.18.0 r40h399db7b_1 bioconda
bioconductor-decipher 2.18.1 r40hd029910_0 bioconda
bioconductor-delayedarray 0.16.3 r40hd029910_0 bioconda
bioconductor-genomeinfodb 1.26.4 r40hdfd78af_0 bioconda
bioconductor-genomeinfodbdata 1.2.4 r40hdfd78af_2 bioconda
bioconductor-genomicalignments 1.26.0 r40hd029910_1 bioconda
bioconductor-genomicranges 1.42.0 r40hd029910_1 bioconda
bioconductor-iranges 2.24.1 r40hd029910_0 bioconda
bioconductor-matrixgenerics 1.2.1 r40hdfd78af_0 bioconda
bioconductor-microbiome 1.12.0 r40hdfd78af_1 bioconda
bioconductor-multtest 2.46.0 r40hd029910_1 bioconda
bioconductor-phyloseq 1.34.0 r40hdfd78af_1 bioconda
bioconductor-rhdf5 2.34.0 r40h399db7b_1 bioconda
bioconductor-rhdf5filters 1.2.0 r40h399db7b_1 bioconda
bioconductor-rhdf5lib 1.12.1 r40hd029910_0 bioconda
bioconductor-rhtslib 1.22.0 r40hd029910_1 bioconda
bioconductor-rsamtools 2.6.0 r40h399db7b_1 bioconda
bioconductor-s4vectors 0.28.1 r40hd029910_0 bioconda
bioconductor-shortread 1.48.0 r40h399db7b_1 bioconda
bioconductor-summarizedexperiment 1.20.0 r40hdfd78af_1 bioconda
bioconductor-xvector 0.30.0 r40hd029910_1 bioconda
bioconductor-zlibbioc 1.36.0 r40hd029910_1 bioconda
biom-format 2.1.10 py310h96516ba_1 conda-forge
bwidget 1.9.14 ha770c72_1 conda-forge
bzip2 1.0.8 h7f98852_4 conda-forge
c-ares 1.18.1 h7f98852_0 conda-forge
ca-certificates 2022.6.15 ha878542_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
cairo 1.16.0 ha61ee94_1011 conda-forge
click 8.1.3 py310hff52083_0 conda-forge
clustalo 1.2.4 h87f3376_5 bioconda
commonmark 0.9.1 py_0 conda-forge
curl 7.83.1 h2283fc2_0 conda-forge
cutadapt 4.1 py310h1425a21_1 bioconda
dadaist2 1.2.5 hdfd78af_0 bioconda
dataclasses 0.8 pyhc8e2a94_3 conda-forge
dnaio 0.9.1 py310h1425a21_1 bioconda
expat 2.4.8 h27087fc_0 conda-forge
fastp 0.23.2 hb7a2d85_2 bioconda
fasttree 2.1.11 hec16e2b_1 bioconda
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge
font-ttf-inconsolata 3.000 h77eed37_0 conda-forge
font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge
font-ttf-ubuntu 0.83 hab24e00_0 conda-forge
fontconfig 2.14.0 h8e229c2_0 conda-forge
fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
freetype 2.10.4 hca18f0e_2 conda-forge
fribidi 1.0.10 h36c2ea0_0 conda-forge
future 0.18.2 py310hff52083_5 conda-forge
gcc_impl_linux-64 12.1.0 hea43390_16 conda-forge
gettext 0.19.8.1 h73d1719_1008 conda-forge
gfortran_impl_linux-64 12.1.0 h1db8e46_16 conda-forge
glpk 5.0 h445213a_0 conda-forge
gmp 6.2.1 h58526e2_0 conda-forge
graphite2 1.3.13 h58526e2_1001 conda-forge
gsl 2.7 he838d99_0 conda-forge
gxx_impl_linux-64 12.1.0 hea43390_16 conda-forge
h5py 3.7.0 nompi_py310h06dffec_100 conda-forge
harfbuzz 5.1.0 hf9f4e7c_0 conda-forge
hdf5 1.12.1 nompi_h4df4325_104 conda-forge
icu 70.1 h27087fc_0 conda-forge
isa-l 2.30.0 ha770c72_4 conda-forge
jpeg 9e h166bdaf_2 conda-forge
kernel-headers_linux-64 2.6.32 he073ed8_15 conda-forge
keyutils 1.6.1 h166bdaf_0 conda-forge
krb5 1.19.3 h08a2579_0 conda-forge
ld_impl_linux-64 2.36.1 hea4e1c9_2 conda-forge
lerc 3.0 h9c3ff4c_0 conda-forge
libblas 3.9.0 16_linux64_openblas conda-forge
libcblas 3.9.0 16_linux64_openblas conda-forge
libcurl 7.83.1 h2283fc2_0 conda-forge
libdeflate 1.10 h7f98852_0 conda-forge
libedit 3.1.20191231 he28a2e2_2 conda-forge
libev 4.33 h516909a_1 conda-forge
libffi 3.4.2 h7f98852_5 conda-forge
libgcc-devel_linux-64 12.1.0 h1ec3361_16 conda-forge
libgcc-ng 12.1.0 h8d9b700_16 conda-forge
libgfortran-ng 12.1.0 h69a702a_16 conda-forge
libgfortran5 12.1.0 hdcd56e2_16 conda-forge
libglib 2.72.1 h2d90d5f_0 conda-forge
libgomp 12.1.0 h8d9b700_16 conda-forge
libiconv 1.16 h516909a_0 conda-forge
liblapack 3.9.0 16_linux64_openblas conda-forge
libnghttp2 1.47.0 he49606f_0 conda-forge
libnsl 2.0.0 h7f98852_0 conda-forge
libopenblas 0.3.21 pthreads_h78a6416_0 conda-forge
libpng 1.6.37 h753d276_3 conda-forge
libsanitizer 12.1.0 ha89aaad_16 conda-forge
libssh2 1.10.0 ha35d2d1_2 conda-forge
libstdcxx-devel_linux-64 12.1.0 h1ec3361_16 conda-forge
libstdcxx-ng 12.1.0 ha89aaad_16 conda-forge
libtiff 4.4.0 h0fcbabc_0 conda-forge
libuuid 2.32.1 h7f98852_1000 conda-forge
libwebp-base 1.2.4 h166bdaf_0 conda-forge
libxcb 1.13 h7f98852_1004 conda-forge
libxml2 2.9.14 h22db469_3 conda-forge
libzip 1.9.2 hc929e4a_0 conda-forge
libzlib 1.2.12 h166bdaf_2 conda-forge
lz4-c 1.9.3 h9c3ff4c_1 conda-forge
make 4.3 hd18ef5c_1 conda-forge
ncurses 6.3 h27087fc_1 conda-forge
numpy 1.23.1 py310h53a5b5f_0 conda-forge
openssl 3.0.5 h166bdaf_1 conda-forge
pandas 1.4.3 py310h769672d_0 conda-forge
pango 1.50.8 hc4f8a73_1 conda-forge
pbzip2 1.1.13 0 conda-forge
pcre 8.45 h9c3ff4c_0 conda-forge
pcre2 10.37 h032f7d1_0 conda-forge
perl 5.32.1 2_h7f98852_perl5 conda-forge
perl-capture-tiny 0.48 pl5321ha770c72_1 conda-forge
perl-carp 1.50 pl5321hd8ed1ab_0 conda-forge
perl-exporter 5.74 pl5321hd8ed1ab_0 conda-forge
perl-extutils-makemaker 7.64 pl5321hd8ed1ab_0 conda-forge
perl-fastx-reader 1.5.0 pl5321hdfd78af_0 bioconda
pigz 2.6 h27826a3_0 conda-forge
pip 22.2.2 pyhd8ed1ab_0 conda-forge
pixman 0.40.0 h36c2ea0_0 conda-forge
pthread-stubs 0.4 h36c2ea0_1001 conda-forge
pygments 2.12.0 pyhd8ed1ab_0 conda-forge
python 3.10.5 ha86cf86_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-isal 1.0.1 py310h5764c6d_0 conda-forge
python_abi 3.10 2_cp310 conda-forge
pytz 2022.1 pyhd8ed1ab_0 conda-forge
qax 0.9.6 hac521b0_1 bioconda
r-ade4 1.7_19 r40h0154571_0 conda-forge
r-ape 5.6_2 r40h43535f1_0 conda-forge
r-assertthat 0.2.1 r40hc72bb7e_2 conda-forge
r-backports 1.4.1 r40hcfec24a_0 conda-forge
r-base 4.0.5 ha8c3e7c_7 conda-forge
r-bh 1.78.0_0 r40hc72bb7e_0 conda-forge
r-bit 4.0.4 r40hcfec24a_0 conda-forge
r-bit64 4.0.5 r40hcfec24a_0 conda-forge
r-bitops 1.0_7 r40h06615bd_0 conda-forge
r-blob 1.2.3 r40hc72bb7e_0 conda-forge
r-brio 1.1.3 r40hcfec24a_0 conda-forge
r-cachem 1.0.6 r40hcfec24a_0 conda-forge
r-callr 3.7.1 r40hc72bb7e_0 conda-forge
r-cli 3.3.0 r40h7525677_0 conda-forge
r-cluster 2.1.3 r40h8da6f51_0 conda-forge
r-codetools 0.2_18 r40hc72bb7e_0 conda-forge
r-colorspace 2.0_3 r40h06615bd_0 conda-forge
r-crayon 1.5.1 r40hc72bb7e_0 conda-forge
r-data.table 1.14.2 r40hcfec24a_0 conda-forge
r-dbi 1.1.3 r40hc72bb7e_0 conda-forge
r-deldir 1.0_6 r40h8da6f51_0 conda-forge
r-desc 1.4.1 r40hc72bb7e_0 conda-forge
r-diffobj 0.3.5 r40hcfec24a_0 conda-forge
r-digest 0.6.29 r40h03ef668_0 conda-forge
r-dplyr 1.0.9 r40h7525677_0 conda-forge
r-ellipsis 0.3.2 r40hcfec24a_0 conda-forge
r-evaluate 0.16 r40hc72bb7e_0 conda-forge
r-fansi 1.0.3 r40h06615bd_0 conda-forge
r-farver 2.1.1 r40h7525677_0 conda-forge
r-fastmap 1.1.0 r40h03ef668_0 conda-forge
r-foreach 1.5.2 r40hc72bb7e_0 conda-forge
r-formatr 1.12 r40hc72bb7e_0 conda-forge
r-fs 1.5.2 r40h7525677_1 conda-forge
r-futile.logger 1.4.3 r40hc72bb7e_1003 conda-forge
r-futile.options 1.0.1 r40hc72bb7e_1002 conda-forge
r-generics 0.1.3 r40hc72bb7e_0 conda-forge
r-ggplot2 3.3.6 r40hc72bb7e_0 conda-forge
r-glue 1.6.2 r40h06615bd_0 conda-forge
r-gtable 0.3.0 r40hc72bb7e_3 conda-forge
r-hms 1.1.1 r40hc72bb7e_0 conda-forge
r-hwriter 1.3.2.1 r40hc72bb7e_0 conda-forge
r-igraph 1.3.4 r40hb34fc8a_0 conda-forge
r-interp 1.1_3 r40h7525677_0 conda-forge
r-isoband 0.2.5 r40h03ef668_0 conda-forge
r-iterators 1.0.14 r40hc72bb7e_0 conda-forge
r-jpeg 0.1_9 r40hcfec24a_0 conda-forge
r-jsonlite 1.8.0 r40h06615bd_0 conda-forge
r-labeling 0.4.2 r40hc72bb7e_1 conda-forge
r-lambda.r 1.2.4 r40hc72bb7e_1 conda-forge
r-lattice 0.20_45 r40hcfec24a_0 conda-forge
r-latticeextra 0.6_30 r40hc72bb7e_0 conda-forge
r-lifecycle 1.0.1 r40hc72bb7e_0 conda-forge
r-magrittr 2.0.3 r40h06615bd_0 conda-forge
r-mass 7.3_58.1 r40h06615bd_0 conda-forge
r-matrix 1.3_2 r40he454529_0 conda-forge
r-matrixstats 0.62.0 r40h06615bd_0 conda-forge
r-memoise 2.0.1 r40hc72bb7e_0 conda-forge
r-mgcv 1.8_40 r40h0154571_0 conda-forge
r-munsell 0.5.0 r40hc72bb7e_1004 conda-forge
r-nlme 3.1_159 r40h8da6f51_0 conda-forge
r-permute 0.9_7 r40hc72bb7e_0 conda-forge
r-pillar 1.8.0 r40hc72bb7e_0 conda-forge
r-pixmap 0.4_12 r40hc72bb7e_0 conda-forge
r-pkgconfig 2.0.3 r40hc72bb7e_1 conda-forge
r-pkgload 1.3.0 r40hc72bb7e_0 conda-forge
r-plogr 0.2.0 r40hc72bb7e_1003 conda-forge
r-plyr 1.8.7 r40h7525677_0 conda-forge
r-png 0.1_7 r40hcfec24a_1004 conda-forge
r-praise 1.0.0 r40hc72bb7e_1005 conda-forge
r-prettyunits 1.1.1 r40hc72bb7e_1 conda-forge
r-processx 3.7.0 r40h06615bd_0 conda-forge
r-progress 1.2.2 r40hc72bb7e_2 conda-forge
r-ps 1.7.1 r40h06615bd_0 conda-forge
r-purrr 0.3.4 r40hcfec24a_1 conda-forge
r-r6 2.5.1 r40hc72bb7e_0 conda-forge
r-rcolorbrewer 1.1_3 r40h785f33e_0 conda-forge
r-rcpp 1.0.9 r40h7525677_0 conda-forge
r-rcppeigen 0.3.3.9.2 r40h43535f1_0 conda-forge
r-rcppparallel 5.1.5 r40h7525677_0 conda-forge
r-rcurl 1.98_1.8 r40h06615bd_0 conda-forge
r-rematch2 2.1.2 r40hc72bb7e_1 conda-forge
r-reshape2 1.4.4 r40h03ef668_1 conda-forge
r-rlang 1.0.4 r40h7525677_0 conda-forge
r-rprojroot 2.0.3 r40hc72bb7e_0 conda-forge
r-rsqlite 2.2.8 r40h03ef668_0 conda-forge
r-rtsne 0.16 r40h37cf8d7_0 conda-forge
r-scales 1.2.0 r40hc72bb7e_0 conda-forge
r-snow 0.4_4 r40hc72bb7e_0 conda-forge
r-sp 1.5_0 r40h06615bd_0 conda-forge
r-stringi 1.7.8 r40h30a9eb7_0 conda-forge
r-stringr 1.4.0 r40hc72bb7e_2 conda-forge
r-survival 3.4_0 r40h06615bd_0 conda-forge
r-testthat 3.1.4 r40h7525677_0 conda-forge
r-tibble 3.1.8 r40h06615bd_0 conda-forge
r-tidyr 1.2.0 r40h03ef668_0 conda-forge
r-tidyselect 1.1.2 r40hc72bb7e_0 conda-forge
r-utf8 1.2.2 r40hcfec24a_0 conda-forge
r-vctrs 0.4.1 r40h7525677_0 conda-forge
r-vegan 2.6_2 r40he5c027b_0 conda-forge
r-viridislite 0.4.0 r40hc72bb7e_0 conda-forge
r-waldo 0.4.0 r40hc72bb7e_0 conda-forge
r-withr 2.5.0 r40hc72bb7e_0 conda-forge
readline 8.1.2 h0f457ee_0 conda-forge
rich 12.5.1 pyhd8ed1ab_0 conda-forge
scipy 1.9.0 py310hdfbd76f_0 conda-forge
sed 4.8 he412f7d_0 conda-forge
seqfu 1.14.0 hbd632db_1 bioconda
setuptools 63.4.3 py310hff52083_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
sqlite 3.39.2 h4ff8645_0 conda-forge
sysroot_linux-64 2.12 he073ed8_15 conda-forge
tk 8.6.12 h27826a3_0 conda-forge
tktable 2.10 hb7b940f_3 conda-forge
typing_extensions 4.3.0 pyha770c72_0 conda-forge
tzdata 2022a h191b570_0 conda-forge
wheel 0.37.1 pyhd8ed1ab_0 conda-forge
xopen 1.5.0 py310hff52083_0 conda-forge
xorg-kbproto 1.0.7 h7f98852_1002 conda-forge
xorg-libice 1.0.10 h7f98852_0 conda-forge
xorg-libsm 1.2.3 hd9c2040_1000 conda-forge
xorg-libx11 1.7.2 h7f98852_0 conda-forge
xorg-libxau 1.0.9 h7f98852_0 conda-forge
xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge
xorg-libxext 1.3.4 h7f98852_1 conda-forge
xorg-libxrender 0.9.10 h7f98852_1003 conda-forge
xorg-libxt 1.2.1 h7f98852_2 conda-forge
xorg-renderproto 0.11.1 h7f98852_1002 conda-forge
xorg-xextproto 7.3.0 h7f98852_1002 conda-forge
xorg-xproto 7.0.31 h7f98852_1007 conda-forge
xz 5.2.5 h516909a_1 conda-forge
zip 3.0 h7f98852_1 conda-forge
zlib 1.2.12 h166bdaf_2 conda-forge
zstd 1.5.2 h8a70e8d_3 conda-forge

running the command

dadaist2 -i data/16S/ -o example-output1/ -m metadata.tsv -d silva_nr_v138_train_set.fa.gz --verbose -trunc-len-1 255 -trunc-len-2 224 -maxee1 2 -maxee1 2 -s1 16 -s2 24 --max-loss 0.05 --save-rds

Error

2022-08-12 13:14:22] DADA2 Finished.
[2022-08-12 13:14:22] Converting dada2 taxonomy output: /tmp/dadaist2_S5cnK8/taxonomy.tsv
[2022-08-12 13:14:22] 38 representative sequences found.
[2022-08-12 13:14:22] DADA2 filtered 28.8127% from total 16104 to 4640
[2022-08-12 13:14:22] Multiple sequence alignment and tree generation
[2022-08-12 13:14:23] Feature tree generated
[2022-08-12 13:14:23] Exporting MicrobiomeAnalyst
[2022-08-12 13:14:29] PhyloSeq file not generated: path_to/trial_reads/data/microbial/toy/example-output1/R/phyloseq.rds
[2022-08-12 13:14:29] Diagnostics: ╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ dadaist2-phyloseqMake 1.2.5 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─────────────────────────────────────────────────── Dadaist2: path_to/data/microbial/toy/example-output1 ───────────────────────────────────────────────────╮
│ │
│ path: path_totrial_reads/data/microbial/toy/example-output1 │
│ version: 1.2.5 │
│ valid: Yes │
│ num_otus: 38 │
│ num_samples: 3 │
│ base_files: rep-seqs.fasta, feature-table.tsv, metadata.tsv, rep-seqs.tree │
│ has_phyloseq: - │
│ has_rhea: - │
│ has_analyst: Yes path_to/trial_reads/data/microbial/toy/example-output1/MicrobiomeAnalyst │
│ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[13:14:24] Input directory "path_to/trial_reads/data/microbial/toy/example-output1/" created with Dadaist2 1.2.5 dadaist2-phyloseqMake:55
MicrobiomeAnalyst directory found: path_to/data/microbial/toy/example-output1/MicrobiomeAnalyst dadaist2-phyloseqMake:56
ERROR: R script failed with exit code 1
R version 4.0.5 (2021-03-31)

• Input: path_to/trial_reads/data/microbial/toy/example-output1/
  Error: package or namespace load failed for ‘phyloseq’ in dyn.load(file, DLLpath = DLLpath, ...):
  unable to load shared object 'path_to/miniconda3/envs/dadaist/lib/R/library/rhdf5/libs/rhdf5.so':
  libcrypto.so.1.1: cannot open shared object file: No such file or directory
  Execution halted

use File::Spec-> to ensure paths are correctly crafted

re-add taxonomy to fasta file

Thank you for such a great tool. Question: I may have missed something obvious in the manual, but can the clustalo/tree inference steps be disabled?

This is not a problem of dadaist2 per se, but I cannot figure out which samples (and why) are causing the following error (there are several of these:

Error in (function (fn, fout, maxN = c(0, 0), truncQ = c(2, 2), truncLen = c(0,  :
  Mismatched forward and reverse sequence files: 3811, 11495.

That said, I wonder if dadaist filters the forward and reverse reads independently, resulting in mismatched filtered fastq files.

My command was dadaist2 -i ./ -o output_folder --maxee1 2 --maxee2 2 -t 8 -d ~/tools/dadaist2/refs/silva_nr_v138_train_set.fa.gz

Describe the bug
wget to be added to allow getdb to work on new releases

I am very interested in using this program, but I was wondering if you are able to customize any DADA2 commands within the wrapper (sorry in advance if this is stated in the documentation and I missed it). For example, if you wanted to modify the pooling option in the dada() core denoising command or change any parameters at the filtering step. I normally make these kinds of modifications via the RStudio DADA2 package, but are you only able to run it with default parameters with dadaist2?

Describe the solution you'd like
If this isn't available already, being able to manually tweak any of the DADA2 command, like you can do when running the program on RStudio.

Additional context
Here's an example of parameters that I have modified on RStudio, where singletons are maintained and pooling is set to pseudo:

setDadaOpt(DETECT_SINGLETONS=TRUE)

dada_F_bp <- dada(derep_F_bp, err=errors_F, multithread=TRUE, pool = "pseudo")

Thank you in advance!

check Rccp and RccpParallel at startup

If a sample fails QC (i.e. cannot determine get_qualified_positions() output) the sample should be removed (maybe from the metadata as well).

Dear dadaist2 team,

I am trying to install dadaist2 in a conda environment. The computing cluster of my university does not allow installing 'mamba', so I skipped it and now trying to install dadaist2 directly:

conda create -n dadaist
conda activate dadaist
conda install -c bioconda dadaist2

However, I am at the 'solving environment' stage for more than 20 min. Is it possible to install it this way?

Best,
Gyuhyon

Hello,
I recently installed dadaist2 via conda and after adjusting some parameters, the pipeline finished. However, I did get an error for the generation of the phyloseq file.

ERROR: R script failed with exit code 1
R version 4.0.5 (2021-03-31)

• Input: /cephfs/abteilung4/Projects_NGS/16S/Veganerstudie/dadaist2/Singlets_raw_data/dadaist2_out/
• Loading feature table
• Taxonomy loaded
• Tree loaded
• Metadata loaded
• PhyloSeq: Feature table done
• PhyloSeq: Taxonomy table done
• PhyloSeq: adding tree
  Error in validObject(.Object) : invalid class “phyloseq” object:
  Component sample names do not match.
  Try sample_names()
  Calls: phyloseq ... do.call -> new -> initialize -> initialize -> validObject
  Execution halted

Is this issue related to the sample naming?
e.g. 22-SQ00181-0 and VE-17-0109-007-2

Thank you for your help with this problem!
Best,
Josephine

Describe the bug
hello,
sorry to disturb you again.
I tried the dadaist2 on my data after i cleaned it using trimmomatic tool.
first i checked the data using seqfu:

then i made the metadata using the command dadaist2-metadata:

I run it as usual:

dadaist2  --max-loss 0.05 -i metagenome/16S/ -o water -m metadata.tsv -d ~/refs/silva_nr_v138_train_set.fa.gz
    ____            __      _      __ ___
   / __ \____ _____/ /___ _(_)____/ /|__ \
  / / / / __ `/ __  / __ `/ / ___/ __/_/ /
 / /_/ / /_/ / /_/ / /_/ / (__  ) /_/ __/
/_____/\__,_/\__,_/\__,_/_/____/\__/____/

1.2.5

[WARNING] Output directory found.
 This is a warning but in future releases this might require to specify --force to proceed.
[2022-08-02 14:13:48] Ready to log in /home/najib/water/dadaist.log
[2022-08-02 14:13:48] dadaist2 1.2.5
[2022-08-02 14:13:48] Taxonomy database found: /home/najib/refs/silva_nr_v138_train_set.fa.gz
[2022-08-02 14:13:48] Parameter: taxonomy-type: dada2
[2022-08-02 14:13:48] Parameter: taxonomy-db: /home/najib/refs/silva_nr_v138_train_set.fa.gz
 * Input directory: metagenome/16S/
 * Output directory: /home/najib/water/
 * Metadata: metadata.tsv
 * Reference database: /home/najib/refs/silva_nr_v138_train_set.fa.gz
 * Threads: 6
 * Temporary directory: /tmp/dadaist2_sJ798a
 * QC strategy: skip
[2022-08-02 14:13:48] QC: Checking quality profile with SeqFu
[2022-08-02 14:13:48] SeqFu quality truncation at (trunc-len-1 and trunc-len-2): 290 - 231
[2022-08-02 14:13:48] Checking dependencies
 * RScript: R scripting front-end version 4.0.5 (2021-03-31)
 * Taxonomy: dadaist2-assigntax 1.1.3
 * assign-taxonomy: dadaist2-assigntax 1.1.3
 * clustalo: 1.2.4
 * dada2 (lib): <pass>
 * exporter: dadaist2-exporter 1.4.0
 * fastp: fastp 0.23.2
 * fasttree: FastTree version 2.1.11 Double precision (No SSE3):
 * fu-primers: fu-primers 1.12.0
[2022-08-02 14:13:54] Temporary directory: /tmp/dadaist2_sJ798a
[2022-08-02 14:13:54] Threads: 6
[2022-08-02 14:13:54] Output directory: /home/najib/water/
[2022-08-02 14:13:54] Checked metadata for autumn
[2022-08-02 14:13:54] Checked metadata for spirng
[2022-08-02 14:13:54] Checked metadata for summer
[2022-08-02 14:13:54] Checked metadata for winter
[2022-08-02 14:13:54] Input directory "metagenome/16S/": 4 found (paired-end)
[2022-08-02 14:13:54] (1/4) Processing autumn: skip
[2022-08-02 14:13:54] Copying input reads for DADA2
[2022-08-02 14:13:54] (2/4) Processing spirng: skip
[2022-08-02 14:13:54] Copying input reads for DADA2
[2022-08-02 14:13:54] (3/4) Processing summer: skip
[2022-08-02 14:13:54] Copying input reads for DADA2
[2022-08-02 14:13:54] (4/4) Processing winter: skip
[2022-08-02 14:13:54] Copying input reads for DADA2
[2022-08-02 14:13:54] Running DADA2...
[2022-08-02 14:13:54] Dada2 script parameters:
 * [1] forward_reads: /tmp/dadaist2_sJ798a/for
 * [2] reverse_reads: /tmp/dadaist2_sJ798a/rev
 * [3] feature_table_output: /tmp/dadaist2_sJ798a/dada2/dada2.tsv
 * [4] stats_output: /tmp/dadaist2_sJ798a/dada2/stats.tsv
 * [5] filt_forward: /tmp/dadaist2_sJ798a/for/filtered
 * [6] filt_reverse: /tmp/dadaist2_sJ798a/rev/filtered
 * [7] truncLenF: 290
 * [8] truncLenR: 231
 * [9] trimLeftF: 0
 * [10] trimLeftR: 0
 * [11] maxEEF: 1
 * [12] maxEER: 1.5
 * [13] truncQ: 10
 * [14] chimeraMethod: consensus
 * [15] minFold: 1
 * [16] threads: 6
 * [17] nreads_learn: 0
 * [18] baseDir: /tmp/dadaist2_sJ798a
 * [19] doPlots: do_plots
 * [20] taxonomyDb: /home/najib/refs/silva_nr_v138_train_set.fa.gz
 * [21] saveRDS: no
 * [22] noMerge: 0
 * [23] processPool: 0
[2022-08-02 14:22:48] DADA2 Finished.
[2022-08-02 14:22:48] Converting dada2 taxonomy output: /tmp/dadaist2_sJ798a/taxonomy.tsv
[2022-08-02 14:22:48] 922 representative sequences found.
DADA2 ERROR:
[2022-08-02 14:22:48] DADA2 filtered too many reads: 4.7926% from total 486266 to 23305
[2022-08-02 14:22:48] Multiple sequence alignment and tree generation
[2022-08-02 14:23:30] Feature tree generated
[2022-08-02 14:23:30] Dadaist finished, output files saved:
 * dada-taxonomy-table: /home/najib/water/taxonomy.txt
 * feature-table: /home/najib/water/feature-table.tsv
 * features-tree: /home/najib/water/rep-seqs.tree
 * multiple-alignment: /home/najib/water/rep-seqs.msa
 * rep-seqs: /home/najib/water/rep-seqs.fasta

as u can see, there was DADA2 error and the tool didn't generate MicrobiomeAnalyst files.
how is it possible to fix it, so i can get the files for microbiomeanalyst and then makde the phyloseq object?
thank you and sorry for the trouble again.

Hi!

Thank you for developing this tool! It solves a lot of my issues to be honest and gives me consistently better results.

I've been trying out dadaist2 since the past few days and I had some issues with running the same; since I've upgraded to v0.73.

I have in the mean-time pinned the version 0.4 since I'm running in the qiime2 environment for the time-being, though I should be upgrading shortly.

1. Using the new parameter: --skip-qc: The pipeline seems to not detect that the folder structure (whilst running with Fastp) is not getting created; thus giving the following errors:

[2021-03-06 09:47:51] (1/3) Processing sample1: skip

[2021-03-06 09:47:51] Skipping QC:sample1

[2021-03-06 09:47:52] (2/3) Processing sample2: skip

[2021-03-06 09:47:52] Skipping QC:sample2

[2021-03-06 09:47:53] (3/3) Processing dadaist2.log: skip

[2021-03-06 09:47:53] Skipping QC:dadaist2.log

Use of uninitialized value $from in string eq at /data/miniconda3/lib/5.26.2/File/Copy.pm line 64.

Use of uninitialized value $from in -d at /data/miniconda3/lib/5.26.2/File/Copy.pm line 96.

Use of uninitialized value $_[0] in substitution (s///) at /data/miniconda3/lib/5.26.2/File/Basename.pm line 341.

fileparse(): need a valid pathname at /data/miniconda3/lib/5.26.2/File/Copy.pm line 51.

Dadaist2 execution finished (16.00s)

2. Trimming Primers: The seqfu based trimmer is somewhat slow, I've been currently handing off data from cutadapt for better runtimes (even under R). I believe that this is due to cutadapt under python3 does multi-threading based on spamming htop usage during the run, whereas fu-primers is using a single thread for some reason on my ThinkPad.

3. Enhancement for Error Detection: Instead of picking the first sample and using nreads.learn to learn error rates, why not subsample reads from all the samples totaling nreads.learn? This ideally would be more robust in the aspect that we can capture a better error profile.

4. Enhancement for Taxonomy assignments: Give the option to GTDB database for dada2's classifier? I checked the 0.73 and it's available for DECIPHER which is super lightweight (I don't even hit swap on my 8GB RAM), but dada2 classifier is somewhat a bit lenient so it's able to assign a fair-bit more number of sequences (though it's expected to hit swap).

Ensure to check .fastq.gz extension (or rename) when skipping qc

PhyloSeq creation failed. at /root/miniconda/bin/dadaist2-phyloseqMake line 96.
I ran three samples together for my analysis, but got phyloseq error while using just concat (-j) option!

To Reproduce
Following is the command:
mkdir -p output/ && dadaist2 --max-loss 0.01 -i input/ -o output/ -d ref/bacterial16S.fasta.gz --trim-primer-for 0 --trim-primer-rev 0 -no-trunc -j -t 16 -m metadata.tsv --verbose

Error Log

Dadaist2
[2022-10-27 09:38:53] Ready to log in /mnt/output_04/dadaist.log
[2022-10-27 09:38:53] dadaist2 1.0.2
[2022-10-27 09:38:53] Taxonomy database found: ref/zymo.bacterial16S.cleaned.fasta.gz
 * Input directory: input/
 * Output directory: /mnt/output_04/
 * Metadata: metadata.tsv
 * Reference database: ref/zymo.bacterial16S.cleaned.fasta.gz
 * Threads: 16
 * Temporary directory: /tmp/dadaist2_HYjKoX
 * QC strategy: fastp
[2022-10-27 09:38:53] Checking dependencies
 * RScript: R scripting front-end version 4.0.5 (2021-03-31)
 * Taxonomy: dadaist2-assigntax 1.1.3
 * assign-taxonomy: dadaist2-assigntax 1.1.3
 * clustalo: 1.2.4
 * dada2 (lib): <pass>
 * exporter: dadaist2-exporter 1.4.0
 * fastp: fastp 0.20.1
 * fasttree: FastTree version 2.1.10 Double precision (No SSE3):
 * fu-primers: fu-primers 1.6.0
[2022-10-27 09:39:02] Temporary directory: /tmp/dadaist2_HYjKoX
[2022-10-27 09:39:02] Threads: 16
[2022-10-27 09:39:02] Output directory: /mnt/output_04/
[2022-10-27 09:39:02] Input directory "input/": 3 found (paired-end)
[2022-10-27 09:39:02] (1/3) Processing V1V3aZYMO: fastp
[2022-10-27 09:39:24] 152268/152270 (100%) reads kept.
 * Average insert size: 382 bp
 * Q30: 0.732603
 * Qualified region: [0 - 271]/301, [0 - 191]/301
[2022-10-27 09:39:24] (2/3) Processing V3V4aZYMO: fastp
[2022-10-27 09:39:48] 208536/208536 (100%) reads kept.
 * Average insert size: 458 bp
 * Q30: 0.837994
 * Qualified region: [0 - 296]/301, [0 - 220]/301
[2022-10-27 09:39:48] (3/3) Processing V3V5bZYMO: fastp
[2022-10-27 09:40:11] 175472/175472 (100%) reads kept.
 * Average insert size: 420 bp
 * Q30: 0.758107
 * Qualified region: [0 - 287]/301, [0 - 187]/301
[2022-10-27 09:40:11] Skipping truncation
[2022-10-27 09:40:11] Running DADA2...
[2022-10-27 09:42:29] DADA2 Finished.
[2022-10-27 09:42:29] Converting dada2 taxonomy output: /tmp/dadaist2_HYjKoX/taxonomy.tsv
[2022-10-27 09:42:29] 97 representative sequences found.
[2022-10-27 09:42:29] DADA2 filtered 56.0409% from total 268138 to 150267
readline() on closed filehandle $I at /root/miniconda/bin/dadaist2 line 935.
[2022-10-27 09:42:29] Multiple sequence alignment and tree generation
[2022-10-27 09:43:18] Feature tree generated
[2022-10-27 09:43:18] Exporting MicrobiomeAnalyst
[2022-10-27 09:43:23] PhyloSeq file not generated: /mnt/output_04/R/phyloseq.rds
[2022-10-27 09:43:23] Diagnostics:
 DADAIST2 Import to PhyloSeq
R version 4.0.5 (2021-03-31)
 * Input:  /mnt/output_04/
 * Loading feature table
 * Taxonomy loaded
 * Tree loaded
 * Metadata loaded
 * PhyloSeq: Feature table done
Error in dimnames(x) <- dn :
  length of 'dimnames' [1] not equal to array extent
Calls: tax_table -> tax_table -> .local -> rownames<- -> rownames<-
Execution halted

PhyloSeq creation failed. at /root/miniconda/bin/dadaist2-phyloseqMake line 96.
[2022-10-27 09:43:26] Rhea normalization/alpha finished.
[2022-10-27 09:43:26] Dadaist finished, output files saved:
 * dada-taxonomy-table: /mnt/output_04/taxonomy.txt
 * feature-table: /mnt/output_04/feature-table.tsv
 * features-tree: /mnt/output_04/rep-seqs.tree
 * mba-files: /mnt/output_04/MicrobiomeAnalyst
 * multiple-alignment: /mnt/output_04/rep-seqs.msa
 * rep-seqs: /mnt/output_04/rep-seqs.fasta
 * rhea: /mnt/output_04/Rhea

[2022-10-27 09:43:26] Cleaning up

Environment:

• OS: [e.g. Ubuntu 18.04]
• Dadaist2 version: 1.0.2

like: "sample_counter/total_samples processed..."

Great toolkit! I was wondering whether dadaist 2 can handle different regions of the 16S gene, e.g. the V1-V3 region? I'm asking because many other tools and tutorials are often optimized to work with the V4 region

Describe the bug
hello, I installed dadaist2 on my computer and i used the tutorial to learn how to use it.
so it working fine. just while reading the tutorial, there was subdirectories:

• MicrobiomeAnalyst
• Rhea
• R directory.

I check the generated subdirectories but there was only R subdirectory and it was empty.
code used for the tutorial:

git clone https://github.com/quadram-institute-bioscience/dadaist2
Cloning into 'dadaist2'...
remote: Enumerating objects: 1599, done.
remote: Counting objects: 100% (279/279), done.
remote: Compressing objects: 100% (212/212), done.
remote: Total 1599 (delta 134), reused 175 (delta 60), pack-reused 1320
Receiving objects: 100% (1599/1599), 40.00 MiB | 11.14 MiB/s, done.
Resolving deltas: 100% (936/936), done.
cd dadaist2
seqfu count --basename data/16S/*.gz
A01_S0_L001_R1_001.fastq.gz     6137    Paired
F99_S0_L001_R1_001.fastq.gz     4553    Paired
A02_S0_L001_R1_001.fastq.gz     5414    Paired
conda activate dadaist2
 mkdir -p refs
dadaist2-getdb -d decipher-silva-138 -o ./refs
dadaist2-metadata -i data/16S > metadata.tsv
dadaist2 -i data/16S/ -o example-output -d refs/SILVA_SSU_r138_2019.RData -t 8 -m metadata.tsv

>  Dadaist2
[2022-03-22 11:13:10] Ready to log in /home/najib/dadaist2/example-output/dadaist.log
[2022-03-22 11:13:10] dadaist2 1.1.0
[2022-03-22 11:13:10] DECIPHER Taxonomy database found: refs/SILVA_SSU_r138_2019.RData
 * Input directory: data/16S/
 * Output directory: /home/najib/dadaist2/example-output/
 * Metadata: metadata.tsv
 * Reference database: skip
 * Threads: 8
 * Temporary directory: /tmp/dadaist2_L39RRA
 * QC strategy: skip
[2022-03-22 11:13:11] SeqFu quality truncation at: 254 - 209
[2022-03-22 11:13:11] Checking dependencies
 * DECIPHER: <pass>
 * RScript: R scripting front-end version 4.0.5 (2021-03-31)
 * assign-taxonomy: dadaist2-assigntax 1.1.3
 * clustalo: 1.2.4
 * dada2 (lib): <pass>
 * exporter: dadaist2-exporter 1.4.0
 * fastp: fastp 0.20.1
 * fasttree: FastTree version 2.1.10 Double precision (No SSE3):
 * fu-primers: fu-primers 1.6.0
[2022-03-22 11:13:18] Temporary directory: /tmp/dadaist2_L39RRA
[2022-03-22 11:13:18] Threads: 8
[2022-03-22 11:13:18] Output directory: /home/najib/dadaist2/example-output/
[2022-03-22 11:13:18] Input directory "data/16S/": 3 found (paired-end)
[2022-03-22 11:13:18] (1/3) Processing A01: skip
[2022-03-22 11:13:18] (2/3) Processing A02: skip
[2022-03-22 11:13:18] (3/3) Processing F99: skip
[2022-03-22 11:13:18] Running DADA2...
[2022-03-22 11:13:18] Dada2 script parameters:
[2022-03-22 11:13:33] DADA2 Finished.
[2022-03-22 11:13:33] 13 representative sequences found.
DADA2 ERROR:
[2022-03-22 11:13:33] DADA2 filtered too many reads: 3.5209% from total 16104 to 567
[2022-03-22 11:13:33] Assigning taxonomy using DECIPHER: refs/SILVA_SSU_r138_2019.RData
[2022-03-22 11:13:56] Converting decipher taxonomy output: /tmp/dadaist2_L39RRA/taxonomy.tsv
[2022-03-22 11:13:56] Multiple sequence alignment and tree generation
[2022-03-22 11:13:56] Feature tree generated
[2022-03-22 11:13:56] Dadaist finished, output files saved:
 * decipher-taxonomy-table: /home/najib/dadaist2/example-output/taxonomy.txt
 * feature-table: /home/najib/dadaist2/example-output/feature-table.tsv
 * features-tree: /home/najib/dadaist2/example-output/rep-seqs.tree
 * multiple-alignment: /home/najib/dadaist2/example-output/rep-seqs.msa
 * rep-seqs: /home/najib/dadaist2/example-output/rep-seqs.fasta

[2022-03-22 11:13:56] Cleaning up

Environment:

• OS: Ubuntu 20.04.3 LTS
• Dadaist2 version: dadaist2 v1.1.0

Sample names beginning with numbers can be problematic with R.

Add a prefix both to the filename and eventually to the metafile, if supplied.