WGS Upscaling - NSC Storage Capacity

1 Background

---

GDx at OUSAMG is planning to upscale the WGS production to:

• 192 samples (4 proj x 48 or 2 proj x 48 + 1 proj x 96) per week (9,216 per year), with 2 NovaSeq 6000.
• 352 samples (2 fc x 64 + 3.5 fc x 64) per week, (16,896 per year), with 2 NovaSeq X Plus
• 416 samples (2 fc x 64 + 4.5 fc x 64) per week, (19,968 per year), with 2 NovaSeq X Plus and weekend work.

Do we have enough capacity in IT and bioinformatics pipelines for this upscaling?

The capacity of IT & bioinformatics pipelines can be accessed in three key areas:

1. Data transfer speed
2. Data storage (this report)
3. Pipeline capacity

2 Boston capacity breakdown

Total capacity of boston is 552.6 TiB.

As of Dec 29, 2024, 12:00 PM

The capability breakdown as of Dec 29, 2024 12:00 PM is as follows:

3 Boston usable capacity

As of Dec 29, 2024, 12:00 PM

The usable capacity as of Dec 29, 2024, 12:00 PM is 231.2 TiB.

4 Boston storage volumes

Name	Purpose
/boston	General data area
/boston/runScrach	Sequencing runs (Illumina, ONT)
/boston/projects	Research projects
/boston/common	Software, and repositories
/boston/diag	Diagnostics production
/boston/diag/transfer	Transfer area (for TSD)
/boston/runScratch/demultiplexed/delivery/tsd_sleipnir	Transfer area NSC
vm-datastore	VMware datastore (virtual hard disks)

5 Boston used storage breakdown

As of Dec 29, 2024, 12:00 PM:

The used storage as of Dec 29, 2024, 12:00 PM is 228 TiB. Details are as follows:

Figure 1: sunburst chart of used boston storage as of Dec 29, 2024 12:00 PM

Tip

The size of boston/diag/production/data/samples folder is very small (924 GiB) for 255 sample sub-folders in it due to the big files (mostly *.fastq.ora files) are hardlinks.

6 Miami (old boston) capacity breakdown

Total capacity of miami storage is 519 TiB.

As of Nov 24, 2024, 12:06 PM

The capability breakdown as of Nov 24, 2024 12:06 PM is as follows:

7 Miami (old boston) used storage breakdown

As of Nov 24, 2024, 12:06 PM:

The used storage as of Nov 24, 204, 12:06 PM is 385 TiB. Details are as follows:

Figure 2: sunburst chart of used miami storage as of Nov 24, 2024 12:06 PM

Warning diag

Even after the routine diagnostics production cleanup, diag still uses a large volume storage.

The following folders take ~80 TiB in total, which can be considered for cleanup:

Folder	Size
/miami/diag/diagInternal/ying_temp	24.0 TiB
/miami/diag/runs/veriseq	13.2 TiB
/miami/diag/staging/data	12.5 TiB
/miami/diag/transfer/dev/p22-yingsh	11.3 TiB
/miami/diag/transfer/lost-and-found	9.6 TiB
/miami/diag/diagInternal/verification	4.1 TiB
/miami/diag/staging/transfer	4.0 TiB

Warning NSC

The /miami/projects folder takes 104 TiB.

The /miami/runScratch/analysis/projects folder takes 55 TiB.

8 Boston used storage table

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8.1 /boston (228 TiB)

Directory	Logical	%use of Parent Directory	Physical
/boston/diag	96.9 TiB	51.8%	113 TiB
/boston/runScrach	83.1 TiB	44.4%	107 TiB
/boston/projects	2.87 TiB	1.5%	3.74 TiB
/boston/home	2.77 TiB	1.5%	2.82 TiB
/boston/common	1.42 TiB	0.8%	1.54 TiB

8.1.1 /boston/diag

Directory	Logical	%use of Parent Directory	Physical
/boston/diag/runs	35.8 TiB	37.0%	47.4 TiB
/boston/diag/production	33.8 TiB	34.9%	34.3 TiB
/boston/diag/transfer	15.6 TiB	16.1%	16.3 TiB
/boston/diag/nscDelivery	7.82 TiB	8.1%	10.4 TiB
/boston/diag/staging	3.84 TiB	4.0%	4.88 TiB
/boston/diag/diagInternal	1.48 GiB	0.0%	1.88 GiB

8.1.1.1 /boston/diag/production

Directory	Logical	%use of Parent Directory	Physical
/boston/diag/production/data	33.5 TiB	99.1%	33.9 TiB
/boston/diag/production/sw	238 GiB	0.7%	297 GiB
/boston/diag/production/reference	74.3 GiB	0.2%	63.9 GiB
/boston/diag/production/logs	3.32 GiB	0.0%	557 MiB
/boston/diag/production/.thirdparty	110 MiB	0.0%	148 GiB

8.1.1.2 /boston/diag/transfer

Directory	Logical	%use of Parent Directory	Physical
/boston/diag/transfer/production	14.8 TiB	97.7%	15.4 TiB

8.1.1.3 /boston/diag/staging

Directory	Logical	%use of Parent Directory	Physical
/boston/diag/staging/data	3.53 TiB	92.0%	4.54 TiB
/boston/diag/staging/sw	237 GiB	6.0%	285 GiB
/boston/diag/staging/reference	77.5 GiB	2.0%	67.5 GiB

8.1.2 /boston/runScratch

Directory	Logical	%use of Parent Directory	Physical
/boston/runScratch/NovaSeqX	34.5 TiB	41.5%	45.7 TiB
/boston/runScratch/analysis	38.8 TiB	37.0%	37.1 TiB
/boston/runScratch/demultiplexed	15.6 TiB	18.8%	20.7 TiB
/boston/runScratch/processed	1.03 TiB	1.2%	1.58 TiB
/boston/runScratch/ONT	738 GiB	0.9%	1.02 TiB
/boston/runScratch/UserData	244 GiB	0.3%	254 GiB
/boston/runScratch/test	64.7 GiB	0.1%	86.1 GiB
/boston/runScratch/PGT	16.7 GiB	0.0%	20.5 GiB
/boston/runScratch/Upgrade_software	16.7 GiB	0.0%	22.2 GiB
/boston/runScratch/mik_data	12.5 GiB	0.0%	12.5 GiB
/boston/runScratch/imm_data	4.5 GiB	0.0%	4.53 GiB

9 Expected data

The data generated by NovaSeqX depend on the settings of secondary analysis and the sequencing depth (current setting is 64 samples per flowcell).

When use Onboard DRAGEN only for demultiplexing, inhouse pipelines must be run on external DRAGEN.

9.1 Per sample estimation

9.1.1 BCL Convert only

9.1.1.1 NovaSeqX generated sequencing data

The {R1,R2}.fastq.ora files per sample is about 14 GB logical data (18 GB on disk).

Other files such as BCL files, images, logs, reports, etc. is about 47 GB logical data per sample (63 GB on disk).

Subtotal

In total, 61 GB logical data per sample (81 GB on disk).

9.1.1.2 Inhouse pipelines on external DRAGEN

With BCL Convert only, we need to run inhouse pipelines on external DRAGEN which requires input data and also generates output data.

Given the design of the inhouse pipelines, some files are duplicated in different locations.

1. Only 1 copy of any fastq.ora file is physically stored on boston, i.e., not duplicated. A fastq.ora file appears in 4 different locations:

   1. /boston/diag/nscDelivery
   2. /boston/diag/transfer/production/{normal,high,urgent}/samples or /boston/daig/transfer/production/transferred/{normal,high,urgent}/samples
   3. /boston/diag/production/data/samples
   4. /boston/diag/production/data/analyses-work/\*/result/\*/work

   1, 2 and 3 are hardlinks; 4 is symlink of 3 (within /boston/diag/produciton/data/analyses-work, files are symlinked from the Nextflow work folder.)

2. Files in /boston/diag/produciton/data/analyses-results/{singles,trios} are copies of that in /boston/diag/produciton/data/analyses-work

3. Files in /boston/diag/transfer/production/{normal,high,urgent}/analyses-results/{singles,trios} are copies of that in /boston/diag/produciton/data/analyses-work

9.1.1.2.1 analyses-work folder size

Subtotal

Average analyses-work basepipe folder size is 71 GiB (70 GiB on disk)

Average analyses-work triopipe folder size is 11 GiB (7 GiB on disk)

Average analyses-work annopipe folder size is 9 GiB (9 GiB on disk)

Adding NovaSeqX generated sequencing data, the total data per sample is 140 GiB (158 GiB on disk).

9.1.1.2.2 analyses-results folder size

Subtotal

Average analyses-results singles folder size is 55 GiB (55 GiB on disk).

Average analyses-results trios folder size is 3 GiB (3 GiB on disk).

Adding NovaSeqX generated sequencing data and analyses-work data and counting in the 2 duplicates of analyses-results, the total data per sample becomes 251 GiB (269 GiB on disk).

9.1.1.2.3 ella-incoming folder size

Total

Average ella-incoming folder size is 126 MB (127 MB on disk).

Adding NovaSeqX generated sequencing data, analyses-work data and analyses-results data, the total data per sample becomes 251 GiB (269 GiB on disk).

9.1.1.3 Number of samples (BCL Convert only, NSC all pipelines)

md`<div style="margin-bottom: 3px; line-height: 1">Assuming diagnostics usable storage for **production data** (<code>TiB</code>):</div>`

Assuming diagnostics usable storage for production data (TiB):

viewof diag_storage = Inputs.range([0, 500], {value: 155, step: 1})
md`<div style="margin-top: 18px; line-height: 1; margin-bottom: 3px;">Assuming samples per project:</div>`

diag_storage = 155

Assuming samples per project:

viewof samples_per_project = Inputs.range([0, 500], {value: 96, step: 1})
md`<div style="margin-top: 18px; line-height: 1; margin-bottom: 3px;">Assuming samples per week:</div>`

samples_per_project = 96

Assuming samples per week:

viewof samples_per_week = Inputs.range([0, 1000], {value: 192, step: 1})
md`<div style="margin-top: 18px; line-height: 1; margin-bottom: 3px;">Assuming samples per 25B flowcell:</div>`

samples_per_week = 192

Assuming samples per 25B flowcell:

viewof samples_per_25b_flowcell = Inputs.range([0, 500], {value: 64, step: 1})

html`<br><br><em><span style="font-size: 0.75em; color: #999;"> <i class="bi bi-chat-dots"></i> see <a href="#sec-miami-storage-used">miami usage breakdown</a> for possible non-production data accumulated over time.</span></em>`

samples_per_25b_flowcell = 64

see miami usage breakdown for possible non-production data accumulated over time.

import {textcolor} from "@observablehq/text-color-annotations-in-markdown"

num_samples = diag_storage*1000/269
f_num_samples = num_samples.toFixed(0)
num_projects = num_samples/samples_per_project
f_num_projects = num_projects.toFixed(1)
num_weeks = num_samples/samples_per_week
f_num_weeks = num_weeks.toFixed(1)
num_25b_flowcells = num_samples/samples_per_25b_flowcell
f_num_25b_flowcells = num_25b_flowcells.toFixed(1)

md`It equals to the total data of:`

num_samples = 576.2081784386617

f_num_samples = "576"

num_projects = 6.002168525402726

f_num_projects = "6.0"

num_weeks = 3.001084262701363

f_num_weeks = "3.0"

num_25b_flowcells = 9.003252788104088

f_num_25b_flowcells = "9.0"

It equals to the total data of:

md`<code style="font-family: monospace;">${textcolor(f_num_samples.toString().padStart(8, '\u00A0'), 'MediumSeaGreen')}</code> samples`

RuntimeError: textcolor is not defined

OJS Runtime Error (line 598, column 44)

textcolor is not defined

md`<code style="font-family: monospace;">${textcolor(f_num_projects.toString().padStart(8, '\u00A0'), 'SlateBlue')}</code> projects _(${samples_per_project} samples per project)_`

RuntimeError: textcolor is not defined

OJS Runtime Error (line 599, column 44)

textcolor is not defined

md`<code style="font-family: monospace;">${textcolor(f_num_weeks.toString().padStart(8, '\u00A0'), 'Chocolate')}</code> weeks _(${samples_per_week} samples per week)_`

RuntimeError: textcolor is not defined

OJS Runtime Error (line 600, column 44)

textcolor is not defined

md`<code style="font-family: monospace;">${textcolor(f_num_25b_flowcells.toString().padStart(8, '\u00A0'), 'Crimson')}</code> 25B flowcells _(${samples_per_25b_flowcell} samples per flowcell)_`

RuntimeError: textcolor is not defined

OJS Runtime Error (line 601, column 44)

textcolor is not defined

9.1.1.4 Number of samples (BCL Convert only, NSC basepipe only)

md`<div style="margin-bottom: 3px; line-height: 1">Assuming diagnostics usable storage for **production data** (<code>TiB</code>):</div>`

Assuming diagnostics usable storage for production data (TiB):

viewof diag_storage_b = Inputs.range([0, 500], {value: 155, step: 1})
md`<div style="margin-top: 18px; line-height: 1; margin-bottom: 3px;">Assuming samples per project:</div>`

diag_storage_b = 155

Assuming samples per project:

viewof samples_per_project_b = Inputs.range([0, 500], {value: 96, step: 1})
md`<div style="margin-top: 18px; line-height: 1; margin-bottom: 3px;">Assuming samples per week:</div>`

samples_per_project_b = 96

Assuming samples per week:

viewof samples_per_week_b = Inputs.range([0, 1000], {value: 192, step: 1})
md`<div style="margin-top: 18px; line-height: 1; margin-bottom: 3px;">Assuming samples per 25B flowcell:</div>`

samples_per_week_b = 192

Assuming samples per 25B flowcell:

viewof samples_per_25b_flowcell_b = Inputs.range([0, 500], {value: 64, step: 1})

html`<br><br><em><span style="font-size: 0.75em; color: #999;"> <i class="bi bi-chat-dots"></i> see <a href="#sec-miami-storage-used">miami usage breakdown</a> for possible non-production data accumulated over time.</span></em>`

samples_per_25b_flowcell_b = 64

see miami usage breakdown for possible non-production data accumulated over time.

num_samples_b = diag_storage_b*1000/158
f_num_samples_b = num_samples_b.toFixed(0)
num_projects_b = num_samples_b/samples_per_project_b
f_num_projects_b = num_projects_b.toFixed(1)
num_weeks_b = num_samples_b/samples_per_week_b
f_num_weeks_b = num_weeks_b.toFixed(1)
num_25b_flowcells_b = num_samples_b/samples_per_25b_flowcell_b
f_num_25b_flowcells_b = num_25b_flowcells_b.toFixed(1)

md`It equals to the total data of:`

num_samples_b = 981.0126582278481

f_num_samples_b = "981"

num_projects_b = 10.218881856540085

f_num_projects_b = "10.2"

num_weeks_b = 5.109440928270042

f_num_weeks_b = "5.1"

num_25b_flowcells_b = 15.328322784810126

f_num_25b_flowcells_b = "15.3"

It equals to the total data of:

md`<code style="font-family: monospace;">${textcolor(f_num_samples_b.toString().padStart(8, '\u00A0'), 'MediumSeaGreen')}</code> samples`

RuntimeError: textcolor is not defined

OJS Runtime Error (line 635, column 44)

textcolor is not defined

md`<code style="font-family: monospace;">${textcolor(f_num_projects_b.toString().padStart(8, '\u00A0'), 'SlateBlue')}</code> projects _(${samples_per_project_b} samples per project)_`

RuntimeError: textcolor is not defined

OJS Runtime Error (line 636, column 44)

textcolor is not defined

md`<code style="font-family: monospace;">${textcolor(f_num_weeks_b.toString().padStart(8, '\u00A0'), 'Chocolate')}</code> weeks _(${samples_per_week_b} samples per week)_`

RuntimeError: textcolor is not defined

OJS Runtime Error (line 637, column 44)

textcolor is not defined

md`<code style="font-family: monospace;">${textcolor(f_num_25b_flowcells_b.toString().padStart(8, '\u00A0'), 'Crimson')}</code> 25B flowcells _(${samples_per_25b_flowcell_b} samples per flowcell)_`

RuntimeError: textcolor is not defined

OJS Runtime Error (line 638, column 44)

textcolor is not defined

9.1.2 DRAGEN Germline (AllVariantCallers)

When secondary analysis is DRAGEN Germline (all variant callers enabled), i.e. doing both demultiplexing and variant calling with Onboard DRAGEN, in addition to NovaSeqX generated sequencing data, each sample has about 20 GB pipeline output logical data (27 GB on disk). See Section 9.1.2.1 for pipeline output files and their sizes.

Total

In total, 81 GB logical data per sample (108 GB on disk).

The inhouse pipeline and nsc-exporter changes to accommodate the DRAGEN Germline pipeline is not yet implemented. Some duplication of NovaSeqX generated data is expected.

9.1.2.1 Pipeline output files per sample

analysis/wgs435_HB12345678_b08a0667-b221-48c6-8d44-abb516a61a2b/germline_seq
├── [ 20G] germline_seq
│   ├── [ 2.6M] report.html
│   ├── [ 13M] sv
│   │   ├── [ 6.1M] results
│   │   │   ├── [ 44K] stats
│   │   │   │   ├── [ 736] alignmentStatsSummary.txt
│   │   │   │   ├── [ 20K] candidate_metrics.csv
│   │   │   │   ├── [ 535] diploidSV.sv_metrics.csv
│   │   │   │   ├── [ 4.4K] graph_metrics.csv
│   │   │   │   ├── [ 9.0K] svCandidateGenerationStats.tsv
│   │   │   │   ├── [ 6.7K] svCandidateGenerationStats.xml
│   │   │   │   └── [ 1.7K] svLocusGraphStats.tsv
│   │   │   └── [ 6.1M] variants
│   │   │   ├── [ 4.2M] candidateSV.vcf.gz
│   │   │   ├── [ 671K] candidateSV.vcf.gz.tbi
│   │   │   ├── [ 1.1M] diploidSV.vcf.gz
│   │   │   └── [ 121K] diploidSV.vcf.gz.tbi
│   │   └── [ 6.5M] workspace
│   │   ├── [ 56K] alignmentStats.xml
│   │   ├── [ 505] chromDepth.txt
│   │   ├── [ 59K] edgeRuntimeLog.txt
│   │   ├── [ 17K] genomeSegmentScanDebugInfo.txt
│   │   ├── [ 2.3K] logs
│   │   │   └── [ 2.2K] config_log.txt
│   │   └── [ 6.4M] svLocusGraph.bin
│   ├── [ 182K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.cnv.excluded_intervals.bed.gz
│   ├── [ 457K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.cnv.gff3
│   ├── [ 2.8K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.cnv.igv_session.xml
│   ├── [ 793] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.cnv_metrics.csv
│   ├── [ 70K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.cnv.vcf.gz
│   ├── [ 32] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.cnv.vcf.gz.md5sum
│   ├── [ 18K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.cnv.vcf.gz.tbi
│   ├── [ 16G] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.cram
│   ├── [ 1.3M] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.cram.crai
│   ├── [ 32] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.cram.md5sum
│   ├── [ 302] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.cyp2b6.tsv
│   ├── [ 283] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.cyp2d6.tsv
│   ├── [ 420K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.fastqc_metrics.csv
│   ├── [ 272K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.fragment_length_hist.csv
│   ├── [ 185] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.gba.tsv
│   ├── [ 2.4K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.gvcf_hethom_ratio_metrics.csv
│   ├── [ 2.4K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.gvcf_metrics.csv
│   ├── [ 46M] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.hard-filtered.baf.bw
│   ├── [ 3.8G] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.hard-filtered.gvcf.gz
│   ├── [ 32] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.hard-filtered.gvcf.gz.md5sum
│   ├── [ 1.2M] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.hard-filtered.gvcf.gz.tbi
│   ├── [ 365M] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.hard-filtered.vcf.gz
│   ├── [ 32] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.hard-filtered.vcf.gz.md5sum
│   ├── [ 1.6M] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.hard-filtered.vcf.gz.tbi
│   ├── [ 16M] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.improper.pairs.bw
│   ├── [ 429] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.insert-stats.tab
│   ├── [ 8.9K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.mapping_metrics.csv
│   ├── [ 9.2K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.metrics.json
│   ├── [ 39K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.pcr-model-0.log
│   ├── [ 115] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.pcr-model.log
│   ├── [ 1.4K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.ploidy_estimation_metrics.csv
│   ├── [ 1.8K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.ploidy.vcf.gz
│   ├── [ 32] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.ploidy.vcf.gz.md5sum
│   ├── [ 4.0K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.ploidy.vcf.gz.tbi
│   ├── [ 1.9M] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.repeats.bam
│   ├── [ 4.3K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.repeats.vcf.gz
│   ├── [ 3.9K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.repeats.vcf.gz.tbi
│   ├── [ 48K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.roh.bed
│   ├── [ 114] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.roh_metrics.csv
│   ├── [ 242K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.seg
│   ├── [ 69K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.seg.bw
│   ├── [ 247K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.seg.called
│   ├── [ 259K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.seg.called.merged
│   ├── [ 223] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.smn.tsv
│   ├── [ 1.7K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.snperror-sampler.log
│   ├── [ 535] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.sv_metrics.csv
│   ├── [ 1.1M] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.sv.vcf.gz
│   ├── [ 121K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.sv.vcf.gz.tbi
│   ├── [ 19M] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.target.counts.bw
│   ├── [ 22M] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.target.counts.diploid.bw
│   ├── [ 31M] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.target.counts.gc-corrected.gz
│   ├── [ 25M] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.target.counts.gz
│   ├── [ 1.4K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.targeted.json
│   ├── [ 22M] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.tn.bw
│   ├── [ 37M] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.tn.tsv.gz
│   ├── [ 1.4K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.trimmer_metrics.csv
│   ├── [ 7.2K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.vc_hethom_ratio_metrics.csv
│   ├── [ 2.4K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.vc_metrics.csv
│   ├── [ 2.8K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.wgs_contig_mean_cov.csv
│   ├── [ 2.1K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.wgs_coverage_metrics.csv
│   ├── [ 16K] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.wgs_fine_hist.csv
│   ├── [ 558] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.wgs_hist.csv
│   └── [ 43] wgs435_HB12345678_3c52f4e7-826d-40dd-bd98-f4356deeb098.wgs_overall_mean_cov.csv
└── [ 11K] logs
    ├── [ 258] bcl2fastq.dragen_events.csv
    ├── [ 1.1K] cmdline_1198902.txt
    ├── [ 608] cmdline_1608030.txt
    ├── [ 1.1K] cmdline_3358454.txt
    ├── [ 219] DCKR_RG-stderr_1608030.txt
    ├── [ 461] DCKR_RG-stdout_1608030.txt
    ├── [ 250] ora.dragen_events.csv
    ├── [ 1] ORA-stderr_1198902.txt
    ├── [ 3.9K] ORA-stdout_1198902.txt
    ├── [ 1] P2FSW-stderr_3358454.txt
    └── [ 2.4K] P2FSW-stdout_3358454.txt

Tip

👆 Generated by tree --du which shows the logical file size instead of disk space used.

9.1.2.2 Number of samples (DRGEN Germline)

md`<div style="margin-bottom: 3px; line-height: 1">Assuming diagnostics usable storage for **production data** (<code>TiB</code>):</div>`

Assuming diagnostics usable storage for production data (TiB):

viewof germ_diag_storage = Inputs.range([0, 500], {value: 155, step: 1})
md`<div style="margin-top: 18px; line-height: 1; margin-bottom: 3px;">Assuming samples per project:</div>`

germ_diag_storage = 155

Assuming samples per project:

viewof germ_samples_per_project = Inputs.range([0, 500], {value: 96, step: 1})
md`<div style="margin-top: 18px; line-height: 1; margin-bottom: 3px;">Assuming samples per week:</div>`

germ_samples_per_project = 96

Assuming samples per week:

viewof germ_samples_per_week = Inputs.range([0, 1000], {value: 192, step: 1})
md`<div style="margin-top: 18px; line-height: 1; margin-bottom: 3px;">Assuming samples per 25B flowcell:</div>`

germ_samples_per_week = 192

Assuming samples per 25B flowcell:

viewof germ_samples_per_25b_flowcell = Inputs.range([0, 500], {value: 64, step: 1})


html`<br><br><em><span style="font-size: 0.75em; color: #999;"> <i class="bi bi-chat-dots"></i> see <a href="#sec-miami-storage-used">miami usage breakdown</a> for possible non-production data accumulated over time.</span></em>`

germ_samples_per_25b_flowcell = 64

see miami usage breakdown for possible non-production data accumulated over time.

germ_num_samples = germ_diag_storage*1000/108
f_germ_num_samples = germ_num_samples.toFixed(0)
germ_num_projects = germ_num_samples/germ_samples_per_project
f_germ_num_projects = germ_num_projects.toFixed(1)
germ_num_weeks = germ_num_samples/germ_samples_per_week
f_germ_num_weeks = germ_num_weeks.toFixed(1)
germ_num_25b_flowcells = germ_num_samples/germ_samples_per_25b_flowcell
f_germ_num_25b_flowcells = germ_num_25b_flowcells.toFixed(1)

md`It equals to the total data of:`

germ_num_samples = 1435.1851851851852

f_germ_num_samples = "1435"

germ_num_projects = 14.949845679012347

f_germ_num_projects = "14.9"

germ_num_weeks = 7.474922839506173

f_germ_num_weeks = "7.5"

germ_num_25b_flowcells = 22.42476851851852

f_germ_num_25b_flowcells = "22.4"

It equals to the total data of:

md`<code style="font-family: monospace;">${textcolor(f_germ_num_samples.toString().padStart(8, '\u00A0'), 'MediumSeaGreen')}</code> samples`

RuntimeError: textcolor is not defined

OJS Runtime Error (line 798, column 44)

textcolor is not defined

md`<code style="font-family: monospace;">${textcolor(f_germ_num_projects.toString().padStart(8, '\u00A0'), 'SlateBlue')}</code> projects _(${germ_samples_per_project} samples per project)_`

RuntimeError: textcolor is not defined

OJS Runtime Error (line 799, column 44)

textcolor is not defined

md`<code style="font-family: monospace;">${textcolor(f_germ_num_weeks.toString().padStart(8, '\u00A0'), 'Chocolate')}</code> weeks _(${germ_samples_per_week} samples per week)_`

RuntimeError: textcolor is not defined

OJS Runtime Error (line 800, column 44)

textcolor is not defined

md`<code style="font-family: monospace;">${textcolor(f_germ_num_25b_flowcells.toString().padStart(8, '\u00A0'), 'Crimson')}</code> 25B flowcells _(${germ_samples_per_25b_flowcell} samples per flowcell)_`

RuntimeError: textcolor is not defined

OJS Runtime Error (line 801, column 44)

textcolor is not defined

9.2 Per sequencing run

Each sequencing run can have different set up.

• Flowcell side:
  • single side (single flowcell)
  • both sides (dual flowcell)
• Flowcell type:
  • 1.5B
  • 10B
  • 25B
• Secondary analysis:
  • BCL Convert
  • DRAGEN Germline
    • variant calling mode = None
    • variant calling mode = SmallVariantCaller
    • variant calling mode = AllVariantCallers (Small, Structural, CNV, Repeat Expansions, ROH, CYP2D6 etc.)

We normally run a single or dual 25B flowcell; the secondary analysis can be BCL Convert only or DRAGEN Germline with all variant callers enabled.

9.2.1 Single 25B flowcell; BCL Convert only

Single 25B flowcell (64 samples), secondary analysis is BCL Convert, i.e. only demultiplexing.

Total

3.9 TB logical data (5.1 TB on disk).

9.2.2 Dual 25B flowcell; BCL Convert only

Dual 25B flowcell (128 samples), secondary analysis is BCL Convert, i.e. only demultiplexing.

Total

7.8 TB logical data (10.2 TB on disk).

9.2.3 Dual 25B flowcell; DRAGEN Germline (AllVariantCallers)

Dual 25B flowcell (128 samples), secondary analysis is DRAGEN Germline with all variant callers enabled, i.e. doing both demultiplexing and mapping, variant calling with Onboard DRAGEN.

Total

10.5 TB logical data (14.0 TB on disk).