Germline input in O'Connell et al. 2023.
These checks assess whether the evidence supports a reproducible investigation. A source-checked score alone does not meet these requirements.
Release 2026-10-09-8cc1db47c7f9 · Evidence verified: Not verified
Exact outcomes, predictions, identifiers and evaluator are connected.
Missing or unresolved evidence
Verified: Not verified
Replay evidence includes annotations and an assessment of dependence. Unknown independence permits descriptive analysis only.
Missing or unresolved evidence
Verified: Not verified
A pinned recipe describes the inputs, environment and resource requirements.
Missing or unresolved evidence
Verified: Not verified
Separate data and exposure records support an independent test.
Missing or unresolved evidence
Verified: Not verified
Readiness describes the evidence in this release. Availability on your computer is checked separately when an investigation runs. Existing data exposure can prevent independent validation even when files are available.
No verified artifact manifest is connected to this record yet. The gaps above identify what is needed before analysis can begin.
22 evaluations · 84 results. Different protocols are not a single leaderboard.
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: CPU tools on AWS c6i.8xlarge (O'Connell et al. 2023) | Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 29.9 compute-cost us-dollar · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepVariant on CPU tools on AWS c6i.8xlarge (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 1 (AWS, C6i.8xlarge, DeepVariant), column 'Cost ($)' |
| Configuration: CPU tools on AWS c6i.8xlarge (O'Connell et al. 2023) | Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 1320 runtime minute · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepVariant on CPU tools on AWS c6i.8xlarge (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 1 (AWS, C6i.8xlarge, DeepVariant), column 'Time (min)' |
| Configuration: CPU tools on AWS c6i.8xlarge (O'Connell et al. 2023) | Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 22 runtime hour · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepVariant on CPU tools on AWS c6i.8xlarge (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 1 (AWS, C6i.8xlarge, DeepVariant), column 'Time (h)' |
| Configuration: CPU tools on AWS c6i.8xlarge (O'Connell et al. 2023) | Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 49.3 compute-cost us-dollar · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceHaplotypeCaller on CPU tools on AWS c6i.8xlarge (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 12 (AWS, C6i.8xlarge, HaplotypeCaller), column 'Cost ($)' |
| Configuration: CPU tools on AWS c6i.8xlarge (O'Connell et al. 2023) | Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 2180 runtime minute · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceHaplotypeCaller on CPU tools on AWS c6i.8xlarge (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 12 (AWS, C6i.8xlarge, HaplotypeCaller), column 'Time (min)' |
| Configuration: CPU tools on AWS c6i.8xlarge (O'Connell et al. 2023) | Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 36.3 runtime hour · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceHaplotypeCaller on CPU tools on AWS c6i.8xlarge (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 12 (AWS, C6i.8xlarge, HaplotypeCaller), column 'Time (h)' |
| Configuration: Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) | Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 29.6 compute-cost us-dollar · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepVariant on Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 2 (AWS, GPU2, DeepVariant), column 'Cost ($)' |
| Configuration: Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) | Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 0.83% cost-saving percent · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepVariant on Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 2 (AWS, GPU2, DeepVariant), column '% cost-savings' |
| Configuration: Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) | Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 145 runtime minute · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepVariant on Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 2 (AWS, GPU2, DeepVariant), column 'Time (min)' |
| Configuration: Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) | Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 2.42 runtime hour · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepVariant on Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 2 (AWS, GPU2, DeepVariant), column 'Time (h)' |
| Configuration: Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) | Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 9.07 speedup unitless · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepVariant on Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 2 (AWS, GPU2, DeepVariant), column 'Fold acceleration' |
| Configuration: Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) | Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 26.9 compute-cost us-dollar · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceHaplotypeCaller on Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 13 (AWS, GPU2, HaplotypeCaller), column 'Cost ($)' |
| Configuration: Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) | Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 45.4% cost-saving percent · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceHaplotypeCaller on Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 13 (AWS, GPU2, HaplotypeCaller), column '% cost-savings' |
| Configuration: Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) | Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 132 runtime minute · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceHaplotypeCaller on Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 13 (AWS, GPU2, HaplotypeCaller), column 'Time (min)' |
| Configuration: Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) | Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 2.2 runtime hour · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceHaplotypeCaller on Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 13 (AWS, GPU2, HaplotypeCaller), column 'Time (h)' |
| Configuration: Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) | Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 16.5 speedup unitless · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceHaplotypeCaller on Parabricks 3.7.0-1, 2 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 13 (AWS, GPU2, HaplotypeCaller), column 'Fold acceleration' |
| Configuration: Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) | Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 19.8 compute-cost us-dollar · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepVariant on Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 3 (AWS, GPU4, DeepVariant), column 'Cost ($)' |
| Configuration: Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) | Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 33.7% cost-saving percent · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepVariant on Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 3 (AWS, GPU4, DeepVariant), column '% cost-savings' |
| Configuration: Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) | Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 97.1 runtime minute · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepVariant on Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 3 (AWS, GPU4, DeepVariant), column 'Time (min)' |
| Configuration: Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) | Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 1.62 runtime hour · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepVariant on Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 3 (AWS, GPU4, DeepVariant), column 'Time (h)' |
| Configuration: Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) | Protocol: DeepVariant execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 13.6 speedup unitless · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceDeepVariant on Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-deepvariant Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 3 (AWS, GPU4, DeepVariant), column 'Fold acceleration' |
| Configuration: Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) | Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 18 compute-cost us-dollar · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceHaplotypeCaller on Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 14 (AWS, GPU4, HaplotypeCaller), column 'Cost ($)' |
| Configuration: Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) | Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 63.5% cost-saving percent · higher Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceHaplotypeCaller on Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 14 (AWS, GPU4, HaplotypeCaller), column '% cost-savings' |
| Configuration: Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) | Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 88.3 runtime minute · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceHaplotypeCaller on Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 14 (AWS, GPU4, HaplotypeCaller), column 'Time (min)' |
| Configuration: Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) | Protocol: HaplotypeCaller execution on CPU and GPU cloud and DGX machines (O'Connell et al. 2023 Table 1) Dataset: HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 | 1.47 runtime hour · lower Uncertainty: Not reported by the source: Single recorded run per cell; no repeats or intervals printed Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceHaplotypeCaller on Parabricks 3.7.0-1, 4 GPUs on AWS (O'Connell et al. 2023) model-execution-20261009-protocol-oconnell2023-haplotypecaller Aggregation: Not reported Accelerating genomic workflows using NVIDIA Parabricks · Table 1, row 14 (AWS, GPU4, HaplotypeCaller), column 'Time (h)' |
Source checking is not independent reproduction. Release 2026-10-09-8cc1db47c7f9.
A dataset supplies biological observations. The evaluation protocol defines how those observations are split, used and scored.
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
5 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.population One sample, HG002, down-sampled to 30x with Samtools v1.9; GRCh38 from the GATK reference bundle Context-only references | Accelerating genomic workflows using NVIDIA Parabricks Methods 'Sampling and algorithms' paragraph 1 Version: BMC Bioinformatics 24:221, published 2023-05-31; PMC10230726 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.source_locator Methods 'Sampling and algorithms' paragraph 1 Context-only references | Accelerating genomic workflows using NVIDIA Parabricks Methods 'Sampling and algorithms' paragraph 1 Version: BMC Bioinformatics 24:221, published 2023-05-31; PMC10230726 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.split Single sample, one recorded run per configuration Context-only references | Accelerating genomic workflows using NVIDIA Parabricks Methods 'Sampling and algorithms' paragraph 1 Version: BMC Bioinformatics 24:221, published 2023-05-31; PMC10230726 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| description Germline input in O'Connell et al. 2023. Context-only references | Accelerating genomic workflows using NVIDIA Parabricks Methods 'Sampling and algorithms' paragraph 1 Version: BMC Bioinformatics 24:221, published 2023-05-31; PMC10230726 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| name HG002 (GIAB) WGS FASTQ down-sampled to 30x, precisionFDA Truth Challenge V2 Context-only references | Accelerating genomic workflows using NVIDIA Parabricks Methods 'Sampling and algorithms' paragraph 1 Version: BMC Bioinformatics 24:221, published 2023-05-31; PMC10230726 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Release 2026-10-09-8cc1db47c7f9 · Record review: source checked
Stable ID: model-execution-20261009-data-oconnell2023-hg002-30x