tidydragen turns raw Illumina DRAGEN output directories into consistently structured, versioned, analysis-ready tables/files. This vignette walks through the core usage patterns.
Test data
Example input files ship in inst/extdata/, one subdirectory per tool. Larger real-world DRAGEN results are tracked separately via DVC and can be downloaded with either:
-
dvc pull: requires dvc installation and cloned tidydragen source repo.
-
?nemo::dvc_download_all(): no additional requirements, uses download.file() internally.
No credentials are required since the remote is a public Cloudflare R2 bucket.
Example DRAGEN results (one subdirectory per tool):
View input files
indir <- system.file("extdata", package = "tidydragen")
dir_tree(indir)
#> /home/runner/miniconda3/envs/pkgdown_env/lib/R/library/tidydragen/extdata
#> ├── dragencov
#> │ ├── sampleA.exon_coverage_metrics.csv
#> │ ├── sampleA.exon_coverage_metrics.csv.dvc
#> │ ├── sampleA.qc-coverage-region-umccr_cov_report.bed
#> │ ├── sampleA.qc-coverage-region-umccr_cov_report.bed.dvc
#> │ ├── sampleA.qc-coverage-region-umccr_read_cov_report.bed
#> │ ├── sampleA.qc-coverage-region-umccr_read_cov_report.bed.dvc
#> │ ├── sampleA.target_bed_coverage_metrics.csv
#> │ ├── sampleA.target_bed_coverage_metrics.csv.dvc
#> │ ├── sampleA.wgs_contig_mean_cov.csv
#> │ ├── sampleA.wgs_contig_mean_cov.csv.dvc
#> │ ├── sampleA.wgs_contig_mean_cov_normal.csv
#> │ ├── sampleA.wgs_contig_mean_cov_normal.csv.dvc
#> │ ├── sampleA.wgs_contig_mean_cov_tumor.csv
#> │ ├── sampleA.wgs_contig_mean_cov_tumor.csv.dvc
#> │ ├── sampleA.wgs_coverage_metrics.csv
#> │ ├── sampleA.wgs_coverage_metrics.csv.dvc
#> │ ├── sampleA.wgs_fine_hist.csv
#> │ └── sampleA.wgs_fine_hist.csv.dvc
#> ├── dragenfqc
#> │ ├── sampleA.fastqc_metrics.csv
#> │ └── sampleA.fastqc_metrics.csv.dvc
#> ├── dragenmap
#> │ ├── sampleA.fragment_length_hist.csv
#> │ ├── sampleA.fragment_length_hist.csv.dvc
#> │ ├── sampleA.gc_metrics.csv
#> │ ├── sampleA.gc_metrics.csv.dvc
#> │ ├── sampleA.mapping_metrics.csv
#> │ ├── sampleA.mapping_metrics.csv.dvc
#> │ ├── sampleA.time_metrics.csv
#> │ ├── sampleA.time_metrics.csv.dvc
#> │ ├── sampleA.trimmer_metrics.csv
#> │ ├── sampleA.trimmer_metrics.csv.dvc
#> │ ├── sampleA.umi_metrics.csv
#> │ └── sampleA.umi_metrics.csv.dvc
#> ├── dragenrna
#> │ ├── sampleA.fusion_metrics.csv
#> │ ├── sampleA.fusion_metrics.csv.dvc
#> │ ├── sampleA.quant_metrics.csv
#> │ └── sampleA.quant_metrics.csv.dvc
#> ├── dragentso
#> │ ├── sampleA.exon_cov_report.tsv
#> │ ├── sampleA.exon_cov_report.tsv.dvc
#> │ ├── sampleA.gene_cov_report.tsv
#> │ ├── sampleA.gene_cov_report.tsv.dvc
#> │ ├── sampleA.tmb.msaf.csv
#> │ ├── sampleA.tmb.msaf.csv.dvc
#> │ ├── sampleA.tmb.trace.tsv
#> │ ├── sampleA.tmb.trace.tsv.dvc
#> │ ├── sampleA_CombinedVariantOutput.tsv
#> │ ├── sampleA_CombinedVariantOutput.tsv.dvc
#> │ ├── sampleA_Fusions.csv
#> │ ├── sampleA_Fusions.csv.dvc
#> │ ├── sampleA_SampleAnalysisResults.json
#> │ └── sampleA_SampleAnalysisResults.json.dvc
#> └── dragenvar
#> ├── sampleA.allele_transition_noise_metrics.csv
#> ├── sampleA.allele_transition_noise_metrics.csv.dvc
#> ├── sampleA.cnv_metrics.csv
#> ├── sampleA.cnv_metrics.csv.dvc
#> ├── sampleA.contamination.json
#> ├── sampleA.contamination.json.dvc
#> ├── sampleA.gvcf_metrics.csv
#> ├── sampleA.gvcf_metrics.csv.dvc
#> ├── sampleA.hrdscore.csv
#> ├── sampleA.hrdscore.csv.dvc
#> ├── sampleA.microsat_output.json
#> ├── sampleA.microsat_output.json.dvc
#> ├── sampleA.ploidy.vcf.gz
#> ├── sampleA.ploidy.vcf.gz.dvc
#> ├── sampleA.ploidy_estimation_metrics.csv
#> ├── sampleA.ploidy_estimation_metrics.csv.dvc
#> ├── sampleA.sv_metrics.csv
#> ├── sampleA.sv_metrics.csv.dvc
#> ├── sampleA.tmb.metrics.csv
#> ├── sampleA.tmb.metrics.csv.dvc
#> ├── sampleA.vc_hethom_ratio_metrics.csv
#> ├── sampleA.vc_hethom_ratio_metrics.csv.dvc
#> ├── sampleA.vc_metrics.csv
#> ├── sampleA.vc_metrics.csv.dvc
#> ├── sampleB.cnv_metrics.csv
#> ├── sampleB.cnv_metrics.csv.dvc
#> ├── sampleB.ploidy_estimation_metrics.csv
#> ├── sampleB.ploidy_estimation_metrics.csv.dvc
#> ├── sampleB.vc_metrics.csv
#> └── sampleB.vc_metrics.csv.dvc
Each tool has its own R6 class. Construct it with the tool’s output directory, then call run() to parse, tidy, and write in one step:
outdir <- file.path(tempdir(), "qs_map")
DragenMap$new(file.path(indir, "dragenmap"))$run(
output_dir = outdir,
format = "parquet",
input_id = "sampleA_id"
)
list.files(outdir, pattern = "\\.parquet$")
#> [1] "metadata_dragenmap.parquet" "sampleA_dragenmap_fraglenhist.parquet"
#> [3] "sampleA_dragenmap_gcbias.parquet" "sampleA_dragenmap_gcmain.parquet"
#> [5] "sampleA_dragenmap_metrics.parquet" "sampleA_dragenmap_time.parquet"
#> [7] "sampleA_dragenmap_trimmer.parquet" "sampleA_dragenmap_umihist.parquet"
#> [9] "sampleA_dragenmap_umimain.parquet"
File naming
Output files follow the pattern {prefix}_{tool}_{table}.parquet, where prefix is derived from the input filenames (here sampleA). A single input file can fan out into several tables (e.g. the mapping outputs yield metrics, time, fraglenhist, and more). The metadata.parquet file is always written alongside the data files and records input/output paths and package versions.
Reading a table back
m <- read_parquet(file.path(outdir, "sampleA_dragenmap_metrics.parquet"))
m |> str()
#> tibble [4 × 131] (S3: tbl_df/tbl/data.frame)
#> $ input_id : chr [1:4] "sampleA_id" "sampleA_id" "sampleA_id" "sampleA_id"
#> $ section : chr [1:4] "TUMOR" "NORMAL" "TUMOR" "NORMAL"
#> $ rg : chr [1:4] "Total" "Total" "CAAGCTAG+CGCTATGT.4.260813_A01052_0316_AHTYNTDSXF" "CAGTAGGC+ATTCGTCA.3.260813_A01052_0316_AHTYNTDSXF"
#> $ reads_tot_input : num [1:4] 2.64e+09 9.39e+08 NA NA
#> $ reads_tot_input_pct : num [1:4] 100 100 NA NA
#> $ reads_num_dupmarked : num [1:4] 5.24e+08 1.31e+08 5.24e+08 1.31e+08
#> $ reads_num_dupmarked_pct : num [1:4] 19.9 13.9 19.9 13.9
#> $ reads_num_dupmarked_mate_reads_removed : num [1:4] NA NA NA NA
#> $ reads_num_uniq : num [1:4] 2.11e+09 8.08e+08 2.11e+09 8.08e+08
#> $ reads_num_uniq_pct : num [1:4] 80.1 86.1 80.1 86.1
#> $ reads_w_mate_seq : num [1:4] 2.64e+09 9.39e+08 2.64e+09 9.39e+08
#> $ reads_w_mate_seq_pct : num [1:4] 100 100 100 100
#> $ reads_wo_mate_seq : num [1:4] 0 0 0 0
#> $ reads_wo_mate_seq_pct : num [1:4] 0 0 0 0
#> $ reads_qcfail : num [1:4] 0 0 0 0
#> $ reads_qcfail_pct : num [1:4] 0 0 0 0
#> $ reads_mapped : num [1:4] 2.57e+09 9.19e+08 2.57e+09 9.19e+08
#> $ reads_mapped_pct : num [1:4] 97.6 97.9 97.6 97.9
#> $ reads_mapped_r1 : num [1:4] 1.29e+09 4.61e+08 1.29e+09 4.61e+08
#> $ reads_mapped_r1_pct : num [1:4] 98.1 98.2 98.1 98.2
#> $ reads_mapped_r2 : num [1:4] 1.28e+09 4.58e+08 1.28e+09 4.58e+08
#> $ reads_mapped_r2_pct : num [1:4] 97.2 97.6 97.2 97.6
#> $ reads_mapped_pai : num [1:4] 0 0 0 0
#> $ reads_mapped_pai_pct : num [1:4] 0 0 0 0
#> $ reads_mapped_nrd : num [1:4] 45892511 16234720 45892511 16234720
#> $ reads_mapped_nrd_pct : num [1:4] 1.74 1.73 1.74 1.73
#> $ reads_mapped_pai_nrd : num [1:4] 45892511 16234720 45892511 16234720
#> $ reads_mapped_pai_nrd_pct : num [1:4] 1.74 1.73 1.74 1.73
#> $ reads_mapped_rna_rrna_filt : num [1:4] NA NA NA NA
#> $ reads_mapped_rna_chrm_excl : num [1:4] NA NA NA NA
#> $ reads_mapped_incl_refext_filt_excl : num [1:4] 2.62e+09 9.35e+08 2.62e+09 9.35e+08
#> $ reads_mapped_incl_refext_filt_excl_pct : num [1:4] 99.4 99.6 99.4 99.6
#> $ reads_unmapped : num [1:4] 62134158 19917093 62134158 19917093
#> $ reads_unmapped_pct : num [1:4] 2.36 2.12 2.36 2.12
#> $ reads_unmapped_minus_refext : num [1:4] 16241647 3682373 16241647 3682373
#> $ reads_unmapped_minus_refext_pct : num [1:4] 0.62 0.39 0.62 0.39
#> $ reads_unmapped_minus_filt : num [1:4] 62134158 19917093 62134158 19917093
#> $ reads_unmapped_minus_filt_pct : num [1:4] 2.36 2.12 2.36 2.12
#> $ reads_unmapped_minus_excl : num [1:4] 62134158 19917093 62134158 19917093
#> $ reads_unmapped_minus_excl_pct : num [1:4] 2.36 2.12 2.36 2.12
#> $ reads_unmapped_minus_refext_filt_excl : num [1:4] 16241647 3682373 16241647 3682373
#> $ reads_unmapped_minus_refext_filt_excl_pct: num [1:4] 0.62 0.39 0.62 0.39
#> $ reads_singleton : num [1:4] 14289628 3295781 14289628 3295781
#> $ reads_singleton_pct : num [1:4] 0.54 0.35 0.54 0.35
#> $ reads_paired : num [1:4] 2.56e+09 9.16e+08 2.56e+09 9.16e+08
#> $ reads_paired_pct : num [1:4] 97.1 97.5 97.1 97.5
#> $ reads_paired_proper : num [1:4] 2.55e+09 9.12e+08 2.55e+09 9.12e+08
#> $ reads_paired_proper_pct : num [1:4] 96.7 97.1 96.7 97.1
#> $ reads_discordant : num [1:4] 11520728 3939608 11520728 3939608
#> $ reads_discordant_pct : num [1:4] 0.44 0.42 0.44 0.42
#> $ reads_paired_mapped_diff_chrom : num [1:4] 3992370 1220540 3992370 1220540
#> $ reads_paired_mapped_diff_chrom_pct : num [1:4] 0.16 0.13 0.16 0.13
#> $ reads_paired_mapped_diff_chrom_mapq10 : num [1:4] 2933157 846318 2933157 846318
#> $ reads_paired_mapped_diff_chrom_mapq10_pct: num [1:4] 0.11 0.09 0.11 0.09
#> $ reads_map_multiloc : num [1:4] 1.10e+08 3.84e+07 1.10e+08 3.84e+07
#> $ reads_map_multiloc_pct : num [1:4] 4.16 4.09 4.16 4.09
#> $ reads_mapq_40_inf : num [1:4] 2.40e+09 8.57e+08 2.40e+09 8.57e+08
#> $ reads_mapq_40_inf_pct : num [1:4] 90.9 91.3 90.9 91.3
#> $ reads_mapq_30_40 : num [1:4] 7124864 2469532 7124864 2469532
#> $ reads_mapq_30_40_pct : num [1:4] 0.27 0.26 0.27 0.26
#> $ reads_mapq_20_30 : num [1:4] 17744796 6147077 17744796 6147077
#> $ reads_mapq_20_30_pct : num [1:4] 0.67 0.65 0.67 0.65
#> $ reads_mapq_10_20 : num [1:4] 28433795 10003292 28433795 10003292
#> $ reads_mapq_10_20_pct : num [1:4] 1.08 1.07 1.08 1.07
#> $ reads_mapq_0_10 : num [1:4] 1.24e+08 4.32e+07 1.24e+08 4.32e+07
#> $ reads_mapq_0_10_pct : num [1:4] 4.7 4.6 4.7 4.6
#> $ reads_mapq_na_unmapped : num [1:4] 62134158 19917093 62134158 19917093
#> $ reads_mapq_na_unmapped_pct : num [1:4] 2.36 2.12 2.36 2.12
#> $ reads_indel_r1 : num [1:4] 40110680 14217155 40110680 14217155
#> $ reads_indel_r1_pct : num [1:4] 3.1 3.08 3.1 3.08
#> $ reads_indel_r2 : num [1:4] 37398760 13618249 37398760 13618249
#> $ reads_indel_r2_pct : num [1:4] 2.92 2.97 2.92 2.97
#> $ reads_splicejunc : num [1:4] NA NA NA NA
#> $ bases_tot : num [1:4] 3.98e+11 1.42e+11 3.98e+11 1.42e+11
#> $ bases_tot_r1 : num [1:4] 1.99e+11 7.09e+10 1.99e+11 7.09e+10
#> $ bases_tot_r2 : num [1:4] 1.99e+11 7.09e+10 1.99e+11 7.09e+10
#> $ bases_mapped : num [1:4] 3.89e+11 1.39e+11 3.89e+11 1.39e+11
#> $ bases_mapped_r1 : num [1:4] 1.95e+11 6.96e+10 1.95e+11 6.96e+10
#> $ bases_mapped_r2 : num [1:4] 1.93e+11 6.92e+10 1.93e+11 6.92e+10
#> $ bases_softclip : num [1:4] 4.57e+09 1.28e+09 4.57e+09 1.28e+09
#> $ bases_softclip_pct : num [1:4] 1.18 0.93 1.18 0.93
#> $ bases_softclip_r1 : num [1:4] 1.49e+09 4.82e+08 1.49e+09 4.82e+08
#> $ bases_softclip_r1_pct : num [1:4] 0.76 0.69 0.76 0.69
#> $ bases_softclip_r2 : num [1:4] 3.08e+09 8.03e+08 3.08e+09 8.03e+08
#> $ bases_softclip_r2_pct : num [1:4] 1.59 1.16 1.59 1.16
#> $ bases_hardclip : num [1:4] 0 0 0 0
#> $ bases_hardclip_pct : num [1:4] 0 0 0 0
#> $ bases_hardclip_r1 : num [1:4] 0 0 0 0
#> $ bases_hardclip_r1_pct : num [1:4] 0 0 0 0
#> $ bases_hardclip_r2 : num [1:4] 0 0 0 0
#> $ bases_hardclip_r2_pct : num [1:4] 0 0 0 0
#> $ bases_mismatched_r1 : num [1:4] 8.57e+08 3.16e+08 8.57e+08 3.16e+08
#> $ bases_mismatched_r1_pct : num [1:4] 0.44 0.45 0.44 0.45
#> $ bases_mismatched_r2 : num [1:4] 1.83e+09 4.77e+08 1.83e+09 4.77e+08
#> $ bases_mismatched_r2_pct : num [1:4] 0.94 0.69 0.94 0.69
#> $ bases_mismatched_r1_noindels : num [1:4] 7.35e+08 2.72e+08 7.35e+08 2.72e+08
#> $ bases_mismatched_r1_noindels_pct : num [1:4] 0.38 0.39 0.38 0.39
#> $ bases_mismatched_r2_noindels : num [1:4] 1.72e+09 4.37e+08 1.72e+09 4.37e+08
#> $ bases_mismatched_r2_noindels_pct : num [1:4] 0.89 0.63 0.89 0.63
#> [list output truncated]
Output - Full DRAGEN run
Dragen processes all supported tools in one call. Point it at a directory containing DRAGEN results; tools whose files are absent simply contribute nothing, so the same call works across e.g. the germline, somatic tumor-normal, RNA and ctTSO500 pipelines:
outdir_d <- file.path(tempdir(), "qs_dragen")
d <- Dragen$new(indir)
d$run(
output_dir = outdir_d,
format = "parquet"
)
list.files(outdir_d, pattern = "\\.parquet$") |> sort() |> str()
#> chr [1:64] "metadata.parquet" "sampleA_dragenfqc_posbasecontent.parquet" ...
ID columns
Three optional columns can be prepended to every written table. All are off by default:
input_id = "x" |
input_id |
sample or run identifier you supply |
output_id = "x" |
output_id |
processing run identifier you supply |
prefix_include = TRUE |
input_prefix |
filename prefix extracted from input files |
These are useful when loading results from multiple samples into the same database table or combined data frame:
outdir_id <- file.path(tempdir(), "qs_map_id")
DragenMap$new(file.path(indir, "dragenmap"))$run(
output_dir = outdir_id,
format = "parquet",
input_id = "sampleA",
output_id = "out1",
prefix_include = TRUE
)
read_parquet(file.path(outdir_id, "sampleA_dragenmap_metrics.parquet")) |>
str()
#> tibble [4 × 133] (S3: tbl_df/tbl/data.frame)
#> $ input_id : chr [1:4] "sampleA" "sampleA" "sampleA" "sampleA"
#> $ input_prefix : chr [1:4] "sampleA" "sampleA" "sampleA" "sampleA"
#> $ output_id : chr [1:4] "out1" "out1" "out1" "out1"
#> $ section : chr [1:4] "TUMOR" "NORMAL" "TUMOR" "NORMAL"
#> $ rg : chr [1:4] "Total" "Total" "CAAGCTAG+CGCTATGT.4.260813_A01052_0316_AHTYNTDSXF" "CAGTAGGC+ATTCGTCA.3.260813_A01052_0316_AHTYNTDSXF"
#> $ reads_tot_input : num [1:4] 2.64e+09 9.39e+08 NA NA
#> $ reads_tot_input_pct : num [1:4] 100 100 NA NA
#> $ reads_num_dupmarked : num [1:4] 5.24e+08 1.31e+08 5.24e+08 1.31e+08
#> $ reads_num_dupmarked_pct : num [1:4] 19.9 13.9 19.9 13.9
#> $ reads_num_dupmarked_mate_reads_removed : num [1:4] NA NA NA NA
#> $ reads_num_uniq : num [1:4] 2.11e+09 8.08e+08 2.11e+09 8.08e+08
#> $ reads_num_uniq_pct : num [1:4] 80.1 86.1 80.1 86.1
#> $ reads_w_mate_seq : num [1:4] 2.64e+09 9.39e+08 2.64e+09 9.39e+08
#> $ reads_w_mate_seq_pct : num [1:4] 100 100 100 100
#> $ reads_wo_mate_seq : num [1:4] 0 0 0 0
#> $ reads_wo_mate_seq_pct : num [1:4] 0 0 0 0
#> $ reads_qcfail : num [1:4] 0 0 0 0
#> $ reads_qcfail_pct : num [1:4] 0 0 0 0
#> $ reads_mapped : num [1:4] 2.57e+09 9.19e+08 2.57e+09 9.19e+08
#> $ reads_mapped_pct : num [1:4] 97.6 97.9 97.6 97.9
#> $ reads_mapped_r1 : num [1:4] 1.29e+09 4.61e+08 1.29e+09 4.61e+08
#> $ reads_mapped_r1_pct : num [1:4] 98.1 98.2 98.1 98.2
#> $ reads_mapped_r2 : num [1:4] 1.28e+09 4.58e+08 1.28e+09 4.58e+08
#> $ reads_mapped_r2_pct : num [1:4] 97.2 97.6 97.2 97.6
#> $ reads_mapped_pai : num [1:4] 0 0 0 0
#> $ reads_mapped_pai_pct : num [1:4] 0 0 0 0
#> $ reads_mapped_nrd : num [1:4] 45892511 16234720 45892511 16234720
#> $ reads_mapped_nrd_pct : num [1:4] 1.74 1.73 1.74 1.73
#> $ reads_mapped_pai_nrd : num [1:4] 45892511 16234720 45892511 16234720
#> $ reads_mapped_pai_nrd_pct : num [1:4] 1.74 1.73 1.74 1.73
#> $ reads_mapped_rna_rrna_filt : num [1:4] NA NA NA NA
#> $ reads_mapped_rna_chrm_excl : num [1:4] NA NA NA NA
#> $ reads_mapped_incl_refext_filt_excl : num [1:4] 2.62e+09 9.35e+08 2.62e+09 9.35e+08
#> $ reads_mapped_incl_refext_filt_excl_pct : num [1:4] 99.4 99.6 99.4 99.6
#> $ reads_unmapped : num [1:4] 62134158 19917093 62134158 19917093
#> $ reads_unmapped_pct : num [1:4] 2.36 2.12 2.36 2.12
#> $ reads_unmapped_minus_refext : num [1:4] 16241647 3682373 16241647 3682373
#> $ reads_unmapped_minus_refext_pct : num [1:4] 0.62 0.39 0.62 0.39
#> $ reads_unmapped_minus_filt : num [1:4] 62134158 19917093 62134158 19917093
#> $ reads_unmapped_minus_filt_pct : num [1:4] 2.36 2.12 2.36 2.12
#> $ reads_unmapped_minus_excl : num [1:4] 62134158 19917093 62134158 19917093
#> $ reads_unmapped_minus_excl_pct : num [1:4] 2.36 2.12 2.36 2.12
#> $ reads_unmapped_minus_refext_filt_excl : num [1:4] 16241647 3682373 16241647 3682373
#> $ reads_unmapped_minus_refext_filt_excl_pct: num [1:4] 0.62 0.39 0.62 0.39
#> $ reads_singleton : num [1:4] 14289628 3295781 14289628 3295781
#> $ reads_singleton_pct : num [1:4] 0.54 0.35 0.54 0.35
#> $ reads_paired : num [1:4] 2.56e+09 9.16e+08 2.56e+09 9.16e+08
#> $ reads_paired_pct : num [1:4] 97.1 97.5 97.1 97.5
#> $ reads_paired_proper : num [1:4] 2.55e+09 9.12e+08 2.55e+09 9.12e+08
#> $ reads_paired_proper_pct : num [1:4] 96.7 97.1 96.7 97.1
#> $ reads_discordant : num [1:4] 11520728 3939608 11520728 3939608
#> $ reads_discordant_pct : num [1:4] 0.44 0.42 0.44 0.42
#> $ reads_paired_mapped_diff_chrom : num [1:4] 3992370 1220540 3992370 1220540
#> $ reads_paired_mapped_diff_chrom_pct : num [1:4] 0.16 0.13 0.16 0.13
#> $ reads_paired_mapped_diff_chrom_mapq10 : num [1:4] 2933157 846318 2933157 846318
#> $ reads_paired_mapped_diff_chrom_mapq10_pct: num [1:4] 0.11 0.09 0.11 0.09
#> $ reads_map_multiloc : num [1:4] 1.10e+08 3.84e+07 1.10e+08 3.84e+07
#> $ reads_map_multiloc_pct : num [1:4] 4.16 4.09 4.16 4.09
#> $ reads_mapq_40_inf : num [1:4] 2.40e+09 8.57e+08 2.40e+09 8.57e+08
#> $ reads_mapq_40_inf_pct : num [1:4] 90.9 91.3 90.9 91.3
#> $ reads_mapq_30_40 : num [1:4] 7124864 2469532 7124864 2469532
#> $ reads_mapq_30_40_pct : num [1:4] 0.27 0.26 0.27 0.26
#> $ reads_mapq_20_30 : num [1:4] 17744796 6147077 17744796 6147077
#> $ reads_mapq_20_30_pct : num [1:4] 0.67 0.65 0.67 0.65
#> $ reads_mapq_10_20 : num [1:4] 28433795 10003292 28433795 10003292
#> $ reads_mapq_10_20_pct : num [1:4] 1.08 1.07 1.08 1.07
#> $ reads_mapq_0_10 : num [1:4] 1.24e+08 4.32e+07 1.24e+08 4.32e+07
#> $ reads_mapq_0_10_pct : num [1:4] 4.7 4.6 4.7 4.6
#> $ reads_mapq_na_unmapped : num [1:4] 62134158 19917093 62134158 19917093
#> $ reads_mapq_na_unmapped_pct : num [1:4] 2.36 2.12 2.36 2.12
#> $ reads_indel_r1 : num [1:4] 40110680 14217155 40110680 14217155
#> $ reads_indel_r1_pct : num [1:4] 3.1 3.08 3.1 3.08
#> $ reads_indel_r2 : num [1:4] 37398760 13618249 37398760 13618249
#> $ reads_indel_r2_pct : num [1:4] 2.92 2.97 2.92 2.97
#> $ reads_splicejunc : num [1:4] NA NA NA NA
#> $ bases_tot : num [1:4] 3.98e+11 1.42e+11 3.98e+11 1.42e+11
#> $ bases_tot_r1 : num [1:4] 1.99e+11 7.09e+10 1.99e+11 7.09e+10
#> $ bases_tot_r2 : num [1:4] 1.99e+11 7.09e+10 1.99e+11 7.09e+10
#> $ bases_mapped : num [1:4] 3.89e+11 1.39e+11 3.89e+11 1.39e+11
#> $ bases_mapped_r1 : num [1:4] 1.95e+11 6.96e+10 1.95e+11 6.96e+10
#> $ bases_mapped_r2 : num [1:4] 1.93e+11 6.92e+10 1.93e+11 6.92e+10
#> $ bases_softclip : num [1:4] 4.57e+09 1.28e+09 4.57e+09 1.28e+09
#> $ bases_softclip_pct : num [1:4] 1.18 0.93 1.18 0.93
#> $ bases_softclip_r1 : num [1:4] 1.49e+09 4.82e+08 1.49e+09 4.82e+08
#> $ bases_softclip_r1_pct : num [1:4] 0.76 0.69 0.76 0.69
#> $ bases_softclip_r2 : num [1:4] 3.08e+09 8.03e+08 3.08e+09 8.03e+08
#> $ bases_softclip_r2_pct : num [1:4] 1.59 1.16 1.59 1.16
#> $ bases_hardclip : num [1:4] 0 0 0 0
#> $ bases_hardclip_pct : num [1:4] 0 0 0 0
#> $ bases_hardclip_r1 : num [1:4] 0 0 0 0
#> $ bases_hardclip_r1_pct : num [1:4] 0 0 0 0
#> $ bases_hardclip_r2 : num [1:4] 0 0 0 0
#> $ bases_hardclip_r2_pct : num [1:4] 0 0 0 0
#> $ bases_mismatched_r1 : num [1:4] 8.57e+08 3.16e+08 8.57e+08 3.16e+08
#> $ bases_mismatched_r1_pct : num [1:4] 0.44 0.45 0.44 0.45
#> $ bases_mismatched_r2 : num [1:4] 1.83e+09 4.77e+08 1.83e+09 4.77e+08
#> $ bases_mismatched_r2_pct : num [1:4] 0.94 0.69 0.94 0.69
#> $ bases_mismatched_r1_noindels : num [1:4] 7.35e+08 2.72e+08 7.35e+08 2.72e+08
#> $ bases_mismatched_r1_noindels_pct : num [1:4] 0.38 0.39 0.38 0.39
#> [list output truncated]
Every run() writes a metadata.parquet alongside the data files. It records the input directory, output directory, IDs, and the versions of R packages used:
read_parquet(file.path(outdir_d, "metadata.parquet")) |> str()
#> tibble [1 × 6] (S3: tbl_df/tbl/data.frame)
#> $ input_id : chr NA
#> $ output_id : chr NA
#> $ input_dirs : list<character> [1:1]
#> ..$ : chr "/home/runner/miniconda3/envs/pkgdown_env/lib/R/library/tidydragen/extdata"
#> ..@ ptype: chr(0)
#> $ output_dir : chr "/tmp/RtmpZVxS90/qs_dragen"
#> $ pkg_versions: list<
#> tbl_df<
#> name : character
#> version: character
#> >
#> > [1:1]
#> ..$ : tibble [2 × 2] (S3: tbl_df/tbl/data.frame)
#> .. ..$ name : chr [1:2] "nemo" "tidydragen"
#> .. ..$ version: chr [1:2] "0.1.0.9003" "0.0.0.9001"
#> ..@ ptype: tibble [0 × 2] (S3: tbl_df/tbl/data.frame)
#> .. ..$ name : chr(0)
#> .. ..$ version: chr(0)
#> $ files : list<
#> tbl_df<
#> tbl : character
#> prefix: character
#> fout : character
#> fin : character
#> >
#> > [1:1]
#> ..$ : tibble [63 × 4] (S3: tbl_df/tbl/data.frame)
#> .. ..$ tbl : chr [1:63] "dragenmap_metrics" "dragenmap_time" "dragenmap_fraglenhist" "dragenmap_trimmer" ...
#> .. ..$ prefix: chr [1:63] "sampleA" "sampleA" "sampleA" "sampleA" ...
#> .. ..$ fout : chr [1:63] "sampleA_dragenmap_metrics.parquet" "sampleA_dragenmap_time.parquet" "sampleA_dragenmap_fraglenhist.parquet" "sampleA_dragenmap_trimmer.parquet" ...
#> .. ..$ fin : chr [1:63] "/home/runner/miniconda3/envs/pkgdown_env/lib/R/library/tidydragen/extdata/dragenmap/sampleA.mapping_metrics.csv" "/home/runner/miniconda3/envs/pkgdown_env/lib/R/library/tidydragen/extdata/dragenmap/sampleA.time_metrics.csv" "/home/runner/miniconda3/envs/pkgdown_env/lib/R/library/tidydragen/extdata/dragenmap/sampleA.fragment_length_hist.csv" "/home/runner/miniconda3/envs/pkgdown_env/lib/R/library/tidydragen/extdata/dragenmap/sampleA.trimmer_metrics.csv" ...
#> ..@ ptype: tibble [0 × 4] (S3: tbl_df/tbl/data.frame)
#> .. ..$ tbl : chr(0)
#> .. ..$ prefix: chr(0)
#> .. ..$ fout : chr(0)
#> .. ..$ fin : chr(0)
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Schema table: browse every table and column for all supported DRAGEN tools
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Structure: how schemas, versioning, and the Tool/Workflow class hierarchy work
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Output naming: how region/phenotype variants are folded into the output prefix
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PostgreSQL: writing results to a database
