[2023-04-01 13:28:10,462 - INFO] Nephele, developed by BCBB/OCICB/NIAID/NIH version: 2.23.4, tag: Nephele_2023_March_28, commit: d9e1ff4 [2023-04-01 13:28:10,462 - INFO] Python version: 3.8.13 [2023-04-01 13:28:10,462 - INFO] Current time: 2023-04-01 13:28 [2023-04-01 13:28:10,462 - INFO] Pipeline name: DADA2 ITS [2023-04-01 13:28:10,990 - INFO] Job Description: E23-ITSdefault [2023-04-01 13:28:10,990 - INFO] Job parameters job_id: c919f2eeb5a0 inputs_dir: None outputs_dir: None map_file: <_io.TextIOWrapper name='/nephele_data/inputs/E23-ITS_fastq_single_end_mapping_template_qiime_corrected.txt' mode='r' encoding='UTF-8'> dbs_json: /usr/local/src/nephele2/pipelines/DADA2_ITS/dbs.json data_type: ITS_SE fwd_primer: ACCTGCGGARGGATCA rev_primer: GAGATCCRTTGYTRAAAGTT cutadapt_path: cutadapt min_len: 50 wurlitzer_stdout: file wurlitzer_stderr: file maxee: 5 truncq: 4 just_concatenate: False maxmismatch: 0 trim_overhang: False chimera: True ref_db: unite sampling_depth: None [2023-04-01 13:28:10,990 - INFO] Checking Mapfile for Gzipped inputs. [2023-04-01 13:28:10,990 - INFO] Gzipped files listed in map file, attempting to rm .gz extension. [2023-04-01 13:28:10,991 - INFO] Done. Attempting file decompression. [2023-04-01 13:28:14,012 - INFO] Finished decompression. [2023-04-01 13:28:14,012 - INFO] Skipping FASTQ file validation [2023-04-01 13:28:14,012 - INFO] Using reference DB: UNITE ITS general FASTA release (unite); version: 8.3; source: https://files.plutof.ut.ee/public/orig/7B/23/7B235835FAF5C85D7B01E40FEF17F687914CB81A182554C5BD95E3168328E604.tgz [2023-04-01 13:28:14,539 - INFO] Reference DB checksum: 4b28de370fa31b122a76f99367fafd9f [2023-04-01 13:28:14,539 - INFO] Loading R module: DADA2_ITS/ITSdada2nephele. [2023-04-01 13:28:43,680 - INFO] Running DADA2 ITS R version 4.1.3 (2022-03-10) Platform: x86_64-conda-linux-gnu (64-bit) Running under: Debian GNU/Linux 11 (bullseye) Matrix products: default BLAS/LAPACK: /usr/local/bin/miniconda3/envs/qiime2-2022.2/lib/libopenblasp-r0.3.20.so locale: [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] tools stats graphics grDevices utils datasets methods [8] base other attached packages: [1] ITSdada2nephele_0.0.1 loaded via a namespace (and not attached): [1] Rcpp_1.0.8.3 lattice_0.20-45 [3] png_0.1-7 Rsamtools_2.10.0 [5] Biostrings_2.62.0 foreach_1.5.2 [7] digest_0.6.29 utf8_1.2.2 [9] R6_2.5.1 GenomeInfoDb_1.30.0 [11] plyr_1.8.7 ShortRead_1.52.0 [13] stats4_4.1.3 ggplot2_3.3.6 [15] pillar_1.7.0 zlibbioc_1.40.0 [17] rlang_1.0.2 S4Vectors_0.32.3 [19] Matrix_1.4-1 BiocParallel_1.28.3 [21] stringr_1.4.0 dada2_1.22.0 [23] RCurl_1.98-1.6 munsell_0.5.0 [25] DelayedArray_0.20.0 compiler_4.1.3 [27] pkgconfig_2.0.3 BiocGenerics_0.40.0 [29] biomformat_1.22.0 tidyselect_1.1.2 [31] SummarizedExperiment_1.24.0 tibble_3.1.7 [33] GenomeInfoDbData_1.2.7 codetools_0.2-18 [35] IRanges_2.28.0 matrixStats_0.62.0 [37] fansi_1.0.3 crayon_1.5.1 [39] dplyr_1.0.9 GenomicAlignments_1.30.0 [41] bitops_1.0-7 rhdf5filters_1.6.0 [43] grid_4.1.3 jsonlite_1.8.0 [45] gtable_0.3.0 lifecycle_1.0.1 [47] magrittr_2.0.3 scales_1.2.0 [49] RcppParallel_5.1.5 cli_3.3.0 [51] stringi_1.7.6 XVector_0.34.0 [53] hwriter_1.3.2.1 reshape2_1.4.4 [55] latticeExtra_0.6-29 ellipsis_0.3.2 [57] generics_0.1.2 vctrs_0.4.1 [59] Rhdf5lib_1.16.0 RColorBrewer_1.1-3 [61] iterators_1.0.14 Biobase_2.54.0 [63] glue_1.6.2 purrr_0.3.4 [65] MatrixGenerics_1.6.0 jpeg_0.1-9 [67] parallel_4.1.3 colorspace_2.0-3 [69] rhdf5_2.38.0 GenomicRanges_1.46.1 [2023-04-01 13:28:51.155] Taxonomic Reference Database: /mnt/EFS/dbs/ITSdada2_unite_v8.3/ITSdada2_unite_v8.3.fa [2023-04-01 13:28:51.156] Reading in map file: /nephele_data/outputs/E23-ITS_fastq_single_end_mapping_template_qiime_corrected.txt.no_gz [2023-04-01 13:28:51.157] Printing dada algorithm options. BAND_SIZE DETECT_SINGLETONS GAP_PENALTY 16 FALSE -8 GAPLESS GREEDY HOMOPOLYMER_GAP_PENALTY TRUE TRUE NULL KDIST_CUTOFF MATCH MAX_CLUST 0.42 5 0 MAX_CONSIST MIN_ABUNDANCE MIN_FOLD 10 1 1 MIN_HAMMING MISMATCH OMEGA_A 1 -4 1e-40 OMEGA_C OMEGA_P PSEUDO_ABUNDANCE 1e-40 1e-04 Inf PSEUDO_PREVALENCE SSE USE_KMERS 2 2 TRUE USE_QUALS VECTORIZED_ALIGNMENT TRUE TRUE [2023-04-01 13:28:51.160] Data type: Single End [2023-04-01 13:28:51.160] filterAndTrim( fwd=forwardPaths, filt=forwardPathsFiltN, multithread=FALSE) [2023-04-01 13:30:02.871] Checking the primers validity for this data set [2023-04-01 13:30:02.871] checkResults = checkPrimers( fwdPrimer=fwdPrimer, revPrimer=revPrimer, forwardReadPaths=forwardPathsFiltN[[1]]) [2023-04-01 13:30:02.928] Checking if cutadapt, an external tool for primer removal, is callable. [2023-04-01 13:30:02.928] cutadapt --version: [2023-04-01 13:30:03.293] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-1.fastq /nephele_data/outputs//filtN/E23-ITS-1.fastq [2023-04-01 13:30:03.418] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-2.fastq /nephele_data/outputs//filtN/E23-ITS-2.fastq [2023-04-01 13:30:03.662] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-3.fastq /nephele_data/outputs//filtN/E23-ITS-3.fastq [2023-04-01 13:30:03.902] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-4.fastq /nephele_data/outputs//filtN/E23-ITS-4.fastq [2023-04-01 13:30:04.154] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-5.fastq /nephele_data/outputs//filtN/E23-ITS-5.fastq [2023-04-01 13:30:04.405] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-6.fastq /nephele_data/outputs//filtN/E23-ITS-6.fastq [2023-04-01 13:30:04.685] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-7.fastq /nephele_data/outputs//filtN/E23-ITS-7.fastq [2023-04-01 13:30:04.935] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-8.fastq /nephele_data/outputs//filtN/E23-ITS-8.fastq [2023-04-01 13:30:05.053] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-9.fastq /nephele_data/outputs//filtN/E23-ITS-9.fastq [2023-04-01 13:30:05.172] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-10.fastq /nephele_data/outputs//filtN/E23-ITS-10.fastq [2023-04-01 13:30:05.433] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-11.fastq /nephele_data/outputs//filtN/E23-ITS-11.fastq [2023-04-01 13:30:05.710] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-12.fastq /nephele_data/outputs//filtN/E23-ITS-12.fastq [2023-04-01 13:30:05.982] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-13.fastq /nephele_data/outputs//filtN/E23-ITS-13.fastq [2023-04-01 13:30:06.225] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-14.fastq /nephele_data/outputs//filtN/E23-ITS-14.fastq [2023-04-01 13:30:06.497] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-15.fastq /nephele_data/outputs//filtN/E23-ITS-15.fastq [2023-04-01 13:30:06.743] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-16.fastq /nephele_data/outputs//filtN/E23-ITS-16.fastq [2023-04-01 13:30:06.930] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-17.fastq /nephele_data/outputs//filtN/E23-ITS-17.fastq [2023-04-01 13:30:07.047] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-18.fastq /nephele_data/outputs//filtN/E23-ITS-18.fastq [2023-04-01 13:30:07.321] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-19.fastq /nephele_data/outputs//filtN/E23-ITS-19.fastq [2023-04-01 13:30:07.540] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-20.fastq /nephele_data/outputs//filtN/E23-ITS-20.fastq [2023-04-01 13:30:07.800] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-21.fastq /nephele_data/outputs//filtN/E23-ITS-21.fastq [2023-04-01 13:30:08.045] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-22.fastq /nephele_data/outputs//filtN/E23-ITS-22.fastq [2023-04-01 13:30:08.311] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-23.fastq /nephele_data/outputs//filtN/E23-ITS-23.fastq [2023-04-01 13:30:08.565] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-24.fastq /nephele_data/outputs//filtN/E23-ITS-24.fastq [2023-04-01 13:30:08.794] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-25.fastq /nephele_data/outputs//filtN/E23-ITS-25.fastq [2023-04-01 13:30:08.912] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-26.fastq /nephele_data/outputs//filtN/E23-ITS-26.fastq [2023-04-01 13:30:09.214] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-27.fastq /nephele_data/outputs//filtN/E23-ITS-27.fastq [2023-04-01 13:30:09.460] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-28.fastq /nephele_data/outputs//filtN/E23-ITS-28.fastq [2023-04-01 13:30:09.736] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-29.fastq /nephele_data/outputs//filtN/E23-ITS-29.fastq [2023-04-01 13:30:09.982] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-30.fastq /nephele_data/outputs//filtN/E23-ITS-30.fastq [2023-04-01 13:30:10.267] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-31.fastq /nephele_data/outputs//filtN/E23-ITS-31.fastq [2023-04-01 13:30:10.509] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-32.fastq /nephele_data/outputs//filtN/E23-ITS-32.fastq [2023-04-01 13:30:10.691] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-33.fastq /nephele_data/outputs//filtN/E23-ITS-33.fastq [2023-04-01 13:30:10.808] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-34.fastq /nephele_data/outputs//filtN/E23-ITS-34.fastq [2023-04-01 13:30:10.957] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-35.fastq /nephele_data/outputs//filtN/E23-ITS-35.fastq [2023-04-01 13:30:11.227] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-36.fastq /nephele_data/outputs//filtN/E23-ITS-36.fastq [2023-04-01 13:30:11.515] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-37.fastq /nephele_data/outputs//filtN/E23-ITS-37.fastq [2023-04-01 13:30:11.782] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-38.fastq /nephele_data/outputs//filtN/E23-ITS-38.fastq [2023-04-01 13:30:12.116] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-NTC1.fastq /nephele_data/outputs//filtN/E23-ITS-NTC1.fastq [2023-04-01 13:30:12.472] cutadapt -g ACCTGCGGARGGATCA -a AACTTTYARCAAYGGATCTC --cores 0 -n 2 --output /nephele_data/outputs//cutadapt/E23-ITS-NTC2.fastq /nephele_data/outputs//filtN/E23-ITS-NTC2.fastq [2023-04-01 13:30:12.743] Checking whether all primers have been removed from the reads. [2023-04-01 13:30:12.743] checkResults = checkPrimers( postCut=TRUE, fwdPrimer=fwdPrimer, revPrimer=revPrimer, forwardReadPaths=forwardPathsCutadapt[[1]]) [2023-04-01 13:30:12.799] pqp = lapply(allPathsCutadapt, FUN = function(x) { ppp = plotQualityProfile(x); ppp$facet$params$ncol = 4; ppp }) [2023-04-01 13:30:34.505] Saving quality profile plots to quality_Profile_R*.pdf [2023-04-01 13:30:37.116] out = filterAndTrim(fwd=forwardPathsCutadapt, filt=file.path(filt.dir,trimlist$R1), maxN = 0, maxEE = maxEE, truncQ = truncQ, minLen = minLen, rm.phix = TRUE, compress = TRUE, multithread = FALSE) reads.in reads.out E23-ITS-1.fastq 6 6 E23-ITS-2.fastq 22429 22238 E23-ITS-3.fastq 20428 20203 E23-ITS-4.fastq 26411 26128 E23-ITS-5.fastq 25286 25073 E23-ITS-6.fastq 44017 43536 E23-ITS-7.fastq 23036 22835 E23-ITS-8.fastq 3 3 E23-ITS-9.fastq 398 392 E23-ITS-10.fastq 25753 25432 E23-ITS-11.fastq 21167 20997 E23-ITS-12.fastq 29240 29035 E23-ITS-13.fastq 19757 19453 E23-ITS-14.fastq 32930 32677 E23-ITS-15.fastq 21183 21039 E23-ITS-16.fastq 5606 5554 E23-ITS-17.fastq 203 201 E23-ITS-18.fastq 18960 18815 E23-ITS-19.fastq 11949 11878 E23-ITS-20.fastq 30612 30347 E23-ITS-21.fastq 23409 23063 E23-ITS-22.fastq 35704 35271 E23-ITS-23.fastq 26064 25814 E23-ITS-24.fastq 15088 14943 E23-ITS-25.fastq 30 30 E23-ITS-26.fastq 57453 56688 E23-ITS-27.fastq 22790 22513 E23-ITS-28.fastq 28497 28288 E23-ITS-29.fastq 25169 24984 E23-ITS-30.fastq 44712 44394 E23-ITS-31.fastq 21534 21355 E23-ITS-32.fastq 5047 5014 E23-ITS-33.fastq 454 450 E23-ITS-34.fastq 3442 3397 E23-ITS-35.fastq 33952 33712 E23-ITS-36.fastq 48950 48386 E23-ITS-37.fastq 39713 38985 E23-ITS-38.fastq 80063 79275 E23-ITS-NTC1.fastq 83238 82384 E23-ITS-NTC2.fastq 41176 40708 [2023-04-01 13:31:57.556] Checking that trimmed files exist. [2023-04-01 13:31:57.557] list2env(checkTrimFiles(sampleAnnotation, filt.dir, trimlist), envir = environment()) [2023-04-01 13:31:57.557] err = lapply(trimlist, function(x) learnErrors(x, multithread=nThread, nbases=100000000,randomize=FALSE)) 100925306 total bases in 434176 reads from 22 samples will be used for learning the error rates. [2023-04-01 13:37:27.919] pe = lapply(err, function(x) plotErrors(x, nominalQ=TRUE)) [2023-04-01 13:37:28.568] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:28.586] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 1 sequence variants were inferred from 6 input unique sequences. [2023-04-01 13:37:28.610] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:28.946] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 12 sequence variants were inferred from 20210 input unique sequences. [2023-04-01 13:37:30.542] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:30.856] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 7 sequence variants were inferred from 18885 input unique sequences. [2023-04-01 13:37:32.604] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:32.973] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 17 sequence variants were inferred from 23472 input unique sequences. [2023-04-01 13:37:34.827] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:35.213] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 12 sequence variants were inferred from 22852 input unique sequences. [2023-04-01 13:37:36.929] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:37.603] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 15 sequence variants were inferred from 39849 input unique sequences. [2023-04-01 13:37:41.167] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:41.489] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 12 sequence variants were inferred from 20621 input unique sequences. [2023-04-01 13:37:42.943] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:42.960] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 1 sequence variants were inferred from 3 input unique sequences. [2023-04-01 13:37:42.983] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:43.024] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 1 sequence variants were inferred from 391 input unique sequences. [2023-04-01 13:37:43.077] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:43.428] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 10 sequence variants were inferred from 23517 input unique sequences. [2023-04-01 13:37:45.602] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:45.923] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 16 sequence variants were inferred from 19180 input unique sequences. [2023-04-01 13:37:47.557] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:47.973] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 16 sequence variants were inferred from 25560 input unique sequences. [2023-04-01 13:37:50.243] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:50.518] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 13 sequence variants were inferred from 18136 input unique sequences. [2023-04-01 13:37:51.803] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:52.280] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 25 sequence variants were inferred from 27781 input unique sequences. [2023-04-01 13:37:54.762] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:55.094] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 28 sequence variants were inferred from 17476 input unique sequences. [2023-04-01 13:37:56.859] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:57.016] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 4 sequence variants were inferred from 5325 input unique sequences. [2023-04-01 13:37:57.493] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:57.513] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 1 sequence variants were inferred from 200 input unique sequences. [2023-04-01 13:37:57.551] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:57.805] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 14 sequence variants were inferred from 16513 input unique sequences. [2023-04-01 13:37:59.191] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:37:59.351] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 11 sequence variants were inferred from 9926 input unique sequences. [2023-04-01 13:38:00.185] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:00.630] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 17 sequence variants were inferred from 27280 input unique sequences. [2023-04-01 13:38:02.949] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:03.273] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 9 sequence variants were inferred from 21317 input unique sequences. [2023-04-01 13:38:05.187] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:05.667] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 16 sequence variants were inferred from 32137 input unique sequences. [2023-04-01 13:38:08.402] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:08.756] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 15 sequence variants were inferred from 23729 input unique sequences. [2023-04-01 13:38:10.437] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:10.646] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 7 sequence variants were inferred from 13932 input unique sequences. [2023-04-01 13:38:11.823] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:11.841] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 1 sequence variants were inferred from 30 input unique sequences. [2023-04-01 13:38:11.866] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:12.712] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 16 sequence variants were inferred from 52009 input unique sequences. [2023-04-01 13:38:16.821] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:17.271] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 10 sequence variants were inferred from 20943 input unique sequences. [2023-04-01 13:38:18.977] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:19.418] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 9 sequence variants were inferred from 25495 input unique sequences. [2023-04-01 13:38:21.670] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:22.058] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 22 sequence variants were inferred from 21723 input unique sequences. [2023-04-01 13:38:24.026] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:24.675] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 23 sequence variants were inferred from 35775 input unique sequences. [2023-04-01 13:38:27.814] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:28.156] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 15 sequence variants were inferred from 19028 input unique sequences. [2023-04-01 13:38:29.804] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:29.922] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 7 sequence variants were inferred from 4349 input unique sequences. [2023-04-01 13:38:30.305] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:30.414] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 1 sequence variants were inferred from 447 input unique sequences. [2023-04-01 13:38:30.471] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:30.528] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 3 sequence variants were inferred from 3278 input unique sequences. [2023-04-01 13:38:30.811] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:31.301] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 25 sequence variants were inferred from 26447 input unique sequences. [2023-04-01 13:38:34.047] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:34.744] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 21 sequence variants were inferred from 43304 input unique sequences. [2023-04-01 13:38:38.442] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:39.012] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 13 sequence variants were inferred from 36139 input unique sequences. [2023-04-01 13:38:41.816] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:43.537] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 26 sequence variants were inferred from 67376 input unique sequences. [2023-04-01 13:38:49.745] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:50.951] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 32 sequence variants were inferred from 71754 input unique sequences. [2023-04-01 13:38:57.944] derep = lapply(trimlist, function(x) derepFastq(x[sample], verbose=TRUE)) [2023-04-01 13:38:58.524] dd = sapply(nameslist, function(x) dada(derep[[x]], err=err[[x]], multithread=nThread, verbose=F), USE.NAMES=TRUE, simplify=FALSE) R1: 10 sequence variants were inferred from 37093 input unique sequences. [2023-04-01 13:39:01.894] seqtab = makeSequenceTable(sampleVariants$sv) [2023-04-01 13:39:01.902] seqtabNoChimera = removeBimeraDenovo(seqtab, verbose=TRUE, multithread=nThread) % Reads remaining after chimera removal: 98.8758824322907 [2023-04-01 13:39:01.917] seqtab = seqtabNoChimera [2023-04-01 13:39:01.918] Track Reads denoisedF denoisedR merged filter nochim EN.MWC13 6 NA NA 6 6 EN.MWCB18 22136 NA NA 22136 22136 EN.MWCUN 19968 NA NA 19968 19968 EC.MWCB13 25980 NA NA 25980 25787 EC.MWCB18 24872 NA NA 24872 24872 EC.MWCUN 43447 NA NA 43447 43447 ES.MWCB13 22767 NA NA 22767 22767 ES.MWCB18 3 NA NA 3 3 ES.MWCUN 335 NA NA 335 335 EN.CB13 25183 NA NA 25183 25183 EN.CB18 20939 NA NA 20939 20939 EN.CUN 28808 NA NA 28808 28808 EC.CB13 19289 NA NA 19289 19289 EC.CB18 32477 NA NA 32477 29800 EC.CUN 20928 NA NA 20928 20490 ES.CB13 5531 NA NA 5531 5531 ES.CB18 173 NA NA 173 173 ES.CUN 18634 NA NA 18634 18634 EN.OB13 11747 NA NA 11747 11582 EN.OB18 30214 NA NA 30214 30214 EN.OUN 22840 NA NA 22840 22840 EC.OB13 35170 NA NA 35170 35170 EC.OB18 25711 NA NA 25711 25711 EC.OUN 14766 NA NA 14766 14766 ES.OB13 25 NA NA 25 25 ES.OB18 56615 NA NA 56615 56615 ES.OUN 22445 NA NA 22445 22445 EN.Buf1 28143 NA NA 28143 28143 EN.Buf2 24880 NA NA 24880 24355 EC.Buf1 44169 NA NA 44169 43596 EC.Buf2 21228 NA NA 21228 21228 ES.Buf1 4999 NA NA 4999 4999 ES.Buf2 373 NA NA 373 373 E23.ITS.34 3362 NA NA 3362 3362 E23.ITS.35 33598 NA NA 33598 32310 E23.ITS.36 48324 NA NA 48324 48324 E23.ITS.37 38910 NA NA 38910 38910 E23.ITS.38 79148 NA NA 79148 78181 E23.ITS.NTC1 82128 NA NA 82128 77704 E23.ITS.NTC2 40514 NA NA 40514 40514 [2023-04-01 13:39:01.918] rep_seq_names = makeSeqNames(seqtab) [2023-04-01 13:39:01.919] writeFasta(seqs, file=file.path(outDirPath, "seq.fasta")) [2023-04-01 13:39:01.921] Taxonomic assignment with UNITE ITS UNITE fungal taxonomic reference detected. Finished processing reference fasta. [2023-04-01 13:39:38.541] otu_tab = seqtab; colnames(otu_tab) = replaceNames(colnames(otu_tab), rep_seq_names) [2023-04-01 13:39:38.542] row.names(taxa) = replaceNames(row.names(taxa), rep_seq_names) [2023-04-01 13:39:38.542] writeBiom(dada2biom(otu_tab,taxa, metadata = A), file.path(outDirPath, "taxa.biom")) [2023-04-01 13:39:38.696] dada2text(otu_tab, taxa, file.path(outDirPath, "OTU_table.txt")) [2023-04-01 13:39:38.703] dada2taxonomy(taxa, file.path(outDirPath, "taxonomy_table.txt")) [2023-04-01 13:39:38.706] Removing intermediate FASTQ files in directories: /nephele_data/outputs//filtN, /nephele_data/outputs//cutadapt [2023-04-01 13:39:40,057 - INFO] Summarizing biom file to /nephele_data/outputs/OTU_summary_table.txt. [2023-04-01 13:39:40,072 - INFO] Creating a phylogenetic tree with 12 threads [2023-04-01 13:39:40,072 - INFO] phylogeny version 2022.2.0. This QIIME 2 plugin supports generating and manipulating phylogenetic trees. [2023-04-01 13:39:40,072 - INFO] Artifact.import_data(type='FeatureData[Sequence]', view=/nephele_data/outputs/seq.fasta) [2023-04-01 13:39:40,114 - INFO] align_to_tree_mafft_fasttree(sequences=seqs, n_threads=num_threads) [2023-04-01 13:39:46,364 - INFO] Saving trees to /nephele_data/outputs/phylo [2023-04-01 13:39:46,364 - INFO] Checking output file from dada2 pipeline required by data visualization pipeline. [2023-04-01 13:39:46,373 - INFO] Loading R module: datavis16s. [2023-04-01 13:39:52,391 - INFO] Running data visualization pipeline. R version 4.1.3 (2022-03-10) Platform: x86_64-conda-linux-gnu (64-bit) Running under: Debian GNU/Linux 11 (bullseye) Matrix products: default BLAS/LAPACK: /usr/local/bin/miniconda3/envs/qiime2-2022.2/lib/libopenblasp-r0.3.20.so locale: [1] C.UTF-8 attached base packages: [1] tools stats graphics grDevices utils datasets methods [8] base other attached packages: [1] datavis16s_0.1.3 ITSdada2nephele_0.0.1 loaded via a namespace (and not attached): [1] MatrixGenerics_1.6.0 Biobase_2.54.0 [3] httr_1.4.3 tidyr_1.2.0 [5] splines_4.1.3 viridisLite_0.4.0 [7] jsonlite_1.8.0 foreach_1.5.2 [9] morpheus_0.1.1.1 RcppParallel_5.1.5 [11] stats4_4.1.3 latticeExtra_0.6-29 [13] GenomeInfoDbData_1.2.7 Rsamtools_2.10.0 [15] ggrepel_0.9.1 pillar_1.7.0 [17] lattice_0.20-45 glue_1.6.2 [19] digest_0.6.29 GenomicRanges_1.46.1 [21] RColorBrewer_1.1-3 XVector_0.34.0 [23] colorspace_2.0-3 htmltools_0.5.2 [25] Matrix_1.4-1 plyr_1.8.7 [27] pkgconfig_2.0.3 ShortRead_1.52.0 [29] zlibbioc_1.40.0 purrr_0.3.4 [31] scales_1.2.0 jpeg_0.1-9 [33] BiocParallel_1.28.3 tibble_3.1.7 [35] mgcv_1.8-40 generics_0.1.2 [37] farver_2.1.0 IRanges_2.28.0 [39] ggplot2_3.3.6 ellipsis_0.3.2 [41] SummarizedExperiment_1.24.0 lazyeval_0.2.2 [43] BiocGenerics_0.40.0 cli_3.3.0 [45] magrittr_2.0.3 crayon_1.5.1 [47] ampvis2_2.7.4 dada2_1.22.0 [49] fansi_1.0.3 MASS_7.3-57 [51] nlme_3.1-157 hwriter_1.3.2.1 [53] vegan_2.6-2 data.table_1.14.2 [55] lifecycle_1.0.1 matrixStats_0.62.0 [57] stringr_1.4.0 plotly_4.10.0 [59] Rhdf5lib_1.16.0 S4Vectors_0.32.3 [61] munsell_0.5.0 cluster_2.1.3 [63] DelayedArray_0.20.0 Biostrings_2.62.0 [65] compiler_4.1.3 GenomeInfoDb_1.30.0 [67] rlang_1.0.2 rhdf5_2.38.0 [69] grid_4.1.3 RCurl_1.98-1.6 [71] iterators_1.0.14 rhdf5filters_1.6.0 [73] biomformat_1.22.0 htmlwidgets_1.5.4 [75] bitops_1.0-7 labeling_0.4.2 [77] gtable_0.3.0 codetools_0.2-18 [79] reshape2_1.4.4 R6_2.5.1 [81] GenomicAlignments_1.30.0 dplyr_1.0.9 [83] fastmap_1.1.0 utf8_1.2.2 [85] permute_0.9-7 ape_5.6-2 [87] stringi_1.7.6 parallel_4.1.3 [89] Rcpp_1.0.8.3 vctrs_0.4.1 [91] png_0.1-7 tidyselect_1.1.2 [2023-04-01 13:39:52.693] "allgraphs"(datafile="/nephele_data/outputs/OTU_table.txt", outdir="/nephele_data/outputs//graphs", mapfile="/nephele_data/outputs/E23-ITS_fastq_single_end_mapping_template_qiime_corrected.txt.no_gz",tsvfile=TRUE, ...) [2023-04-01 13:39:52.693] Reading in map file /nephele_data/outputs/E23-ITS_fastq_single_end_mapping_template_qiime_corrected.txt.no_gz [2023-04-01 13:39:52.694] Reading in OTU file /nephele_data/outputs/OTU_table.txt [2023-04-01 13:39:52.694] otu <- read.delim(datafile, check.names = FALSE, na.strings = '', row.names = 1) [2023-04-01 13:39:52.698] tax <- otu[,!names(otu) %in% map$SampleID] [2023-04-01 13:39:52.698] otu <- otu[, names(otu) %in% map$SampleID, drop=F] [2023-04-01 13:39:52.698] otu <- cbind(otu, tax) [2023-04-01 13:39:52.699] amp <- amp_load(otu, map) ampvis2 object with 3 elements. Summary of OTU table: Samples OTUs Total#Reads Min#Reads Max#Reads Median#Reads 40 492 989535 3 78181 22803.5 Avg#Reads 24738.38 Assigned taxonomy: Kingdom Phylum Class Order Family Genus 492(100%) 143(29.07%) 39(7.93%) 0(0%) 0(0%) 0(0%) Species 0(0%) Metadata variables: 5 SampleID, ForwardFastqFile, TreatmentGroup, ID2, Description [2023-04-01 13:39:52.724] Rarefaction curve [2023-04-01 13:39:52.724] rarefactioncurve(outdir = outdir, amp = amp, colors = allcols) [2023-04-01 13:39:53.157] Saving plot to /nephele_data/outputs/graphs/rarecurve.html [2023-04-01 13:39:53.270] Saving rarefaction curve table to /nephele_data/outputs//graphs/rarecurve.txt [2023-04-01 13:39:53.273] Relative abundance heatmaps [2023-04-01 13:39:53.273] morphheatmap(outdir = outdir, amp = amp, colors=allcols, filter_level = 5) [2023-04-01 13:39:53.273] Filter taxa below 5 counts/abundance. [2023-04-01 13:39:53.273] amp <- filterlowabund(amp, level = 5, abs=T) [2023-04-01 13:39:53.278] Calculate relative abundance. [2023-04-01 13:39:53.278] amp <- subsetamp(amp, sampdepth = NULL, rarefy=FALSE, normalise = TRUE, printsummary = FALSE) [2023-04-01 13:39:53.294] makeheatmap("seq", amp) [2023-04-01 13:39:53.515] heatmap <- morpheus(mat, columns=columns, columnAnnotations = amptax$metadata, columnColorModel = list(type=as.list(colors)), colorScheme = list(scalingMode="fixed", values=values, colors=hmapcolors, stepped=FALSE), rowAnnotations = amptax$tax, rows = rows, dendrogram="none") [2023-04-01 13:39:57.236] Saving plot to /nephele_data/outputs/graphs/seq_heatmap.html [2023-04-01 13:39:57.297] Sampling depth: 11582 [2023-04-01 13:39:57.297] Filter samples below 11582 counts. [2023-04-01 13:39:57.297] amp <- amp_subset_samples(amp, minreads = 11582, ...) [2023-04-01 13:39:57.304] Saving excluded sample ids to /nephele_data/outputs//graphs/samples_being_ignored.txt ampvis2 object with 3 elements. Summary of OTU table: Samples OTUs Total#Reads Min#Reads Max#Reads Median#Reads 31 472 974728 11582 78181 25711 Avg#Reads 31442.84 Assigned taxonomy: Kingdom Phylum Class Order Family Genus 472(100%) 133(28.18%) 37(7.84%) 0(0%) 0(0%) 0(0%) Species 0(0%) Metadata variables: 5 SampleID, ForwardFastqFile, TreatmentGroup, ID2, Description [2023-04-01 13:39:57.306] PCoA plot with binomial distance [2023-04-01 13:39:57.306] pcoaplot(outdir = outdir, amp = ampsub, distm = "binomial", colors = allcols) [2023-04-01 13:39:57.306] pcoa <- amp_ordinate(amp, filter_species =0.1,type="PCOA", distmeasure ="binomial",sample_color_by = "TreatmentGroup", detailed_output = TRUE, transform="none") [2023-04-01 13:39:57.487] Saving plot to /nephele_data/outputs/graphs/pcoa_binomial.html [2023-04-01 13:39:57.547] Saving binomial PCoA table to /nephele_data/outputs//graphs/pcoa_binomial.txt [2023-04-01 13:39:57.549] Rarefying OTU Table to 11582 reads. [2023-04-01 13:39:57.549] set.seed(500) [2023-04-01 13:39:57.549] otu <- rrarefy(t(amp$abund), sampdepth) [2023-04-01 13:39:57.576] amp <- amp_subset_samples(amp, minreads = 11582, ...) ampvis2 object with 3 elements. Summary of OTU table: Samples OTUs Total#Reads Min#Reads Max#Reads Median#Reads 31 472 359042 11582 11582 11582 Avg#Reads 11582 Assigned taxonomy: Kingdom Phylum Class Order Family Genus 472(100%) 133(28.18%) 37(7.84%) 0(0%) 0(0%) 0(0%) Species 0(0%) Metadata variables: 5 SampleID, ForwardFastqFile, TreatmentGroup, ID2, Description [2023-04-01 13:39:57.583] Saving rarefied OTU Table to /nephele_data/outputs//graphs/rarefied_OTU_table_11582.txt [2023-04-01 13:39:57.590] Making heatmap from rarefied counts. [2023-04-01 13:39:57.590] morphheatmap(outdir = outdir, amp = amprare, colors=allcols, filter_level = 5, filesuffix = "_rarefied") [2023-04-01 13:39:57.590] Filter taxa below 5 counts/abundance. [2023-04-01 13:39:57.590] amp <- filterlowabund(amp, level = 5, abs=T) [2023-04-01 13:39:57.594] Calculate relative abundance. [2023-04-01 13:39:57.594] amp <- subsetamp(amp, sampdepth = NULL, rarefy=FALSE, normalise = TRUE, printsummary = FALSE) [2023-04-01 13:39:57.604] makeheatmap("seq", amp) [2023-04-01 13:39:57.794] heatmap <- morpheus(mat, columns=columns, columnAnnotations = amptax$metadata, columnColorModel = list(type=as.list(colors)), colorScheme = list(scalingMode="fixed", values=values, colors=hmapcolors, stepped=FALSE), rowAnnotations = amptax$tax, rows = rows, dendrogram="none") [2023-04-01 13:40:01.056] Saving plot to /nephele_data/outputs/graphs/seq_heatmap_rarefied.html [2023-04-01 13:40:01.106] Normalizing rarefied OTU table to 100 for Bray-Curtis distance. [2023-04-01 13:40:01.116] pcoaplot(outdir = outdir, amp = ampbc, distm = "bray", colors = allcols, filesuffix="_rarefied") [2023-04-01 13:40:01.116] pcoa <- amp_ordinate(amp, filter_species =0.1,type="PCOA", distmeasure ="bray",sample_color_by = "TreatmentGroup", detailed_output = TRUE, transform="none") [2023-04-01 13:40:01.267] Saving plot to /nephele_data/outputs/graphs/pcoa_bray_rarefied.html [2023-04-01 13:40:01.334] Saving bray PCoA table to /nephele_data/outputs//graphs/pcoa_bray_rarefied.txt [2023-04-01 13:40:01.336] Alpha diversity boxplot [2023-04-01 13:40:01.336] adivboxplot(outdir = outdir, amp = amprare, sampdepth = sampdepth, colors = allcols) [2023-04-01 13:40:01.336] alphadiv <- amp_alphadiv(amp, measure="shannon", richness = TRUE, rarefy = 11582) [2023-04-01 13:40:01.357] Saving alpha diversity table to /nephele_data/outputs//graphs/alphadiv.txt [2023-04-01 13:40:02.502] Saving plot to /nephele_data/outputs/graphs/alphadiv.html [2023-04-01 13:40:02.958] "allgraphs" complete. [2023-04-01 13:40:03,519 - INFO] DADA2 pipeline complete. [2023-04-01 13:40:03,521 - INFO] 0