Evidence claim

Evidence summary: Select a copy-number variant detection and characterisation workflow

Summary of Select a copy-number variant detection and characterisation workflow

On HG002 deletions, DRAGEN 4.2 with SV-informed CNV calling found 87 to 100% of deletions in every size bin, with precision 0.99 to 1.00; this is the developers' own benchmark. CNVnator matched it above 10 kb but found only 26% of deletions of 1 to 5 kb and 51% of 5 to 10 kb. On NA12878, LUMPY, DELLY and Manta recovered 88 to 94% of known CNVs, but only 20 to 33% of their calls matched; Manta and CNVnator were used to build this truth set, which can favour them. The other callers recovered 1 to 43%. Hwang et al. (2026) compared four exome callers with chromosomal microarray CNVs in 39 clinical exomes, scoring deletion and duplication exons together: sensitivity and PPV were 0.758 and 0.025 for ExomeDepth, 0.662 and 0.096 for CNVkit, 0.768 and 0.239 for CoNIFER and 0.170 and 0.041 for cn.MOPS. The truth set keeps only array CNVs with at least ten probes that overlap exome baits, so any other exon call counts as a false positive. After the authors removed regions where each tool called CNVs in more than 10% of the same 180-exome cohort, ExomeDepth reached 0.903 and 0.102 on a smaller reference set of 2,124 exons (Table 4); this filter is the authors' own method. For characterisation, Zhang et al. (2026) ran five automated ACMG classifiers on 3,031 expert-classified CNVs: five-category accuracy, averaged across classes in a way the paper does not state, ranged from 48.76% (X-CNV) to 69.81% (MarCNV-ISV) (Table 3). With likely and definite classes merged, every tool recognised more pathogenic deletions than pathogenic duplications (AnnotSV 95.16% versus 86.39%) (Table 4). Two of the three authors developed dbCNV and the reference includes their own laboratory's cases, so the dbCNV results are author-reported. None of the comparisons shown reports duplication detection separately, none covers tumour samples or long reads, and none evaluates a foundation model.

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Claims, original sources and review scope · Release 2026-11-02-a7f03c9e55a5
Property and statement	Original source and location	Review and provenance
Claimed field summary Context-only references	DRAGEN supplementary tables 1–17 Original source ↗ amp-source-dragen-supplementary-tables: sheet 'S4 CNV benchmarking' rows 6-10; cnv-20261009-source-gabrielaite2021-table-s2: rows with sample GB-WGS-NA12878; cnv-20261011-hwang2026-source-article: Table 4; Methods 2.2 and 2.5; cnv-20261011-zhang2026-source-article: Tables 3 and 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: MOESM3 XLSX retrieved 2026-10-07 Retrieved: 2026-10-07T12:25:22.006309+00:00	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `edf36c4d94f2f68ce26fe2d7edfb0dfdc8e4d1774367ff10551dc32cc280b74e` Hash scope: Hash scope not separately documented; inspect source record Format: curator_factual_receipt Inspected artifact
Claimed field summary Context-only references	Gabrielaite et al. 2021, Table S2 (precision and recall of CNV calling tools) Original source ↗ amp-source-dragen-supplementary-tables: sheet 'S4 CNV benchmarking' rows 6-10; cnv-20261009-source-gabrielaite2021-table-s2: rows with sample GB-WGS-NA12878; cnv-20261011-hwang2026-source-article: Table 4; Methods 2.2 and 2.5; cnv-20261011-zhang2026-source-article: Tables 3 and 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Supplementary Materials/Table S2.xlsx inside cancers-13-06283-s001.zip Retrieved: 2026-10-09T15:21:28Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `eb4bb389b248508531ca371ba80e004a573f4e85029583cff336f217307fde85` Hash scope: artifact_sha256 is the inner supplementary file. Containers: europepmc_supplementary_zip 3ecf626259edf3ee82a10b76a20be078382d4e7ecb4b4fa5145143c4d5ccbf51; cancers-13-06283-s001.zip 0fd70ccaf67605cfa4a05a6f65666bfa16812c75ac53f865860b6e8e7eb9deac. The Europe PMC zip is rebuilt per request, so its hash describes one retrieval only. Inspected artifact
Claimed field summary Context-only references	Improving CNV Detection Performance Except for Software-Specific Problematic Regions (Hwang et al., Genes 2026) Original source ↗ amp-source-dragen-supplementary-tables: sheet 'S4 CNV benchmarking' rows 6-10; cnv-20261009-source-gabrielaite2021-table-s2: rows with sample GB-WGS-NA12878; cnv-20261011-hwang2026-source-article: Table 4; Methods 2.2 and 2.5; cnv-20261011-zhang2026-source-article: Tables 3 and 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Genes 17(1):105, published 2026-01-19; PMC12841491 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `ff82a186d0adcf319b603a1100427ec0f6ed95becf040b512d0aef5e515bdc5d` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Claimed field summary Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ amp-source-dragen-supplementary-tables: sheet 'S4 CNV benchmarking' rows 6-10; cnv-20261009-source-gabrielaite2021-table-s2: rows with sample GB-WGS-NA12878; cnv-20261011-hwang2026-source-article: Table 4; Methods 2.2 and 2.5; cnv-20261011-zhang2026-source-article: Tables 3 and 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
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attributes.setting attributes.clinical_scope attributes.intended_users sha256 e86232bbdd2e9f2de3ba90f86e72b53bdd8368f9c0ddc74b7ee5870155f0a0d0 Context-only references	DRAGEN supplementary tables 1–17 Original source ↗ amp-source-dragen-supplementary-tables: sheet 'S4 CNV benchmarking' rows 6-10; cnv-20261009-source-gabrielaite2021-table-s2: rows with sample GB-WGS-NA12878; cnv-20261011-hwang2026-source-article: Table 4; Methods 2.2 and 2.5; cnv-20261011-zhang2026-source-article: Tables 3 and 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: MOESM3 XLSX retrieved 2026-10-07 Retrieved: 2026-10-07T12:25:22.006309+00:00	not individually reviewed No individual claim review recorded Audit details Field: `attributes.pins` Source artifact SHA-256: `edf36c4d94f2f68ce26fe2d7edfb0dfdc8e4d1774367ff10551dc32cc280b74e` Hash scope: Hash scope not separately documented; inspect source record Format: curator_factual_receipt Inspected artifact
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attributes.setting attributes.clinical_scope attributes.intended_users sha256 e86232bbdd2e9f2de3ba90f86e72b53bdd8368f9c0ddc74b7ee5870155f0a0d0 Context-only references	Gabrielaite et al. 2021, Table S2 (precision and recall of CNV calling tools) Original source ↗ amp-source-dragen-supplementary-tables: sheet 'S4 CNV benchmarking' rows 6-10; cnv-20261009-source-gabrielaite2021-table-s2: rows with sample GB-WGS-NA12878; cnv-20261011-hwang2026-source-article: Table 4; Methods 2.2 and 2.5; cnv-20261011-zhang2026-source-article: Tables 3 and 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. 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attributes.setting attributes.clinical_scope attributes.intended_users sha256 e86232bbdd2e9f2de3ba90f86e72b53bdd8368f9c0ddc74b7ee5870155f0a0d0 Context-only references	Improving CNV Detection Performance Except for Software-Specific Problematic Regions (Hwang et al., Genes 2026) Original source ↗ amp-source-dragen-supplementary-tables: sheet 'S4 CNV benchmarking' rows 6-10; cnv-20261009-source-gabrielaite2021-table-s2: rows with sample GB-WGS-NA12878; cnv-20261011-hwang2026-source-article: Table 4; Methods 2.2 and 2.5; cnv-20261011-zhang2026-source-article: Tables 3 and 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Genes 17(1):105, published 2026-01-19; PMC12841491 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.pins` Source artifact SHA-256: `ff82a186d0adcf319b603a1100427ec0f6ed95becf040b512d0aef5e515bdc5d` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
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attributes.setting attributes.clinical_scope attributes.intended_users sha256 e86232bbdd2e9f2de3ba90f86e72b53bdd8368f9c0ddc74b7ee5870155f0a0d0 Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ amp-source-dragen-supplementary-tables: sheet 'S4 CNV benchmarking' rows 6-10; cnv-20261009-source-gabrielaite2021-table-s2: rows with sample GB-WGS-NA12878; cnv-20261011-hwang2026-source-article: Table 4; Methods 2.2 and 2.5; cnv-20261011-zhang2026-source-article: Tables 3 and 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.pins` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Source location amp-source-dragen-supplementary-tables: sheet 'S4 CNV benchmarking' rows 6-10; cnv-20261009-source-gabrielaite2021-table-s2: rows with sample GB-WGS-NA12878; cnv-20261011-hwang2026-source-article: Table 4; Methods 2.2 and 2.5; cnv-20261011-zhang2026-source-article: Tables 3 and 4 Context-only references	DRAGEN supplementary tables 1–17 Original source ↗ amp-source-dragen-supplementary-tables: sheet 'S4 CNV benchmarking' rows 6-10; cnv-20261009-source-gabrielaite2021-table-s2: rows with sample GB-WGS-NA12878; cnv-20261011-hwang2026-source-article: Table 4; Methods 2.2 and 2.5; cnv-20261011-zhang2026-source-article: Tables 3 and 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: MOESM3 XLSX retrieved 2026-10-07 Retrieved: 2026-10-07T12:25:22.006309+00:00	not individually reviewed No individual claim review recorded Audit details Field: `attributes.source_locator` Source artifact SHA-256: `edf36c4d94f2f68ce26fe2d7edfb0dfdc8e4d1774367ff10551dc32cc280b74e` Hash scope: Hash scope not separately documented; inspect source record Format: curator_factual_receipt Inspected artifact
Source location amp-source-dragen-supplementary-tables: sheet 'S4 CNV benchmarking' rows 6-10; cnv-20261009-source-gabrielaite2021-table-s2: rows with sample GB-WGS-NA12878; cnv-20261011-hwang2026-source-article: Table 4; Methods 2.2 and 2.5; cnv-20261011-zhang2026-source-article: Tables 3 and 4 Context-only references	Gabrielaite et al. 2021, Table S2 (precision and recall of CNV calling tools) Original source ↗ amp-source-dragen-supplementary-tables: sheet 'S4 CNV benchmarking' rows 6-10; cnv-20261009-source-gabrielaite2021-table-s2: rows with sample GB-WGS-NA12878; cnv-20261011-hwang2026-source-article: Table 4; Methods 2.2 and 2.5; cnv-20261011-zhang2026-source-article: Tables 3 and 4 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Supplementary Materials/Table S2.xlsx inside cancers-13-06283-s001.zip Retrieved: 2026-10-09T15:21:28Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.source_locator` Source artifact SHA-256: `eb4bb389b248508531ca371ba80e004a573f4e85029583cff336f217307fde85` Hash scope: artifact_sha256 is the inner supplementary file. Containers: europepmc_supplementary_zip 3ecf626259edf3ee82a10b76a20be078382d4e7ecb4b4fa5145143c4d5ccbf51; cancers-13-06283-s001.zip 0fd70ccaf67605cfa4a05a6f65666bfa16812c75ac53f865860b6e8e7eb9deac. The Europe PMC zip is rebuilt per request, so its hash describes one retrieval only. Inspected artifact

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Release 2026-11-02-a7f03c9e55a5 · Record review: source checked

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DRAGEN supplementary tables 1–17 · Original source · MOESM3 XLSX retrieved 2026-10-07
Gabrielaite et al. 2021, Table S2 (precision and recall of CNV calling tools) · Original source · Supplementary Materials/Table S2.xlsx inside cancers-13-06283-s001.zip
Improving CNV Detection Performance Except for Software-Specific Problematic Regions (Hwang et al., Genes 2026) · Original source · Genes 17(1):105, published 2026-01-19; PMC12841491 full-text XML
Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Original source · Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML

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Stable ID: use-case-summary-cnv-detection-characterisation

field: summary
value: On HG002 deletions, DRAGEN 4.2 with SV-informed CNV calling found 87 to 100% of deletions in every size bin, with precision 0.99 to 1.00; this is the developers' own benchmark. CNVnator matched it above 10 kb but found only 26% of deletions of 1 to 5 kb and 51% of 5 to 10 kb. On NA12878, LUMPY, DELLY and Manta recovered 88 to 94% of known CNVs, but only 20 to 33% of their calls matched; Manta and CNVnator were used to build this truth set, which can favour them. The other callers recovered 1 to 43%. Hwang et al. (2026) compared four exome callers with chromosomal microarray CNVs in 39 clinical exomes, scoring deletion and duplication exons together: sensitivity and PPV were 0.758 and 0.025 for ExomeDepth, 0.662 and 0.096 for CNVkit, 0.768 and 0.239 for CoNIFER and 0.170 and 0.041 for cn.MOPS. The truth set keeps only array CNVs with at least ten probes that overlap exome baits, so any other exon call counts as a false positive. After the authors removed regions where each tool called CNVs in more than 10% of the same 180-exome cohort, ExomeDepth reached 0.903 and 0.102 on a smaller reference set of 2,124 exons (Table 4); this filter is the authors' own method. For characterisation, Zhang et al. (2026) ran five automated ACMG classifiers on 3,031 expert-classified CNVs: five-category accuracy, averaged across classes in a way the paper does not state, ranged from 48.76% (X-CNV) to 69.81% (MarCNV-ISV) (Table 3). With likely and definite classes merged, every tool recognised more pathogenic deletions than pathogenic duplications (AnnotSV 95.16% versus 86.39%) (Table 4). Two of the three authors developed dbCNV and the reference includes their own laboratory's cases, so the dbCNV results are author-reported. None of the comparisons shown reports duplication detection separately, none covers tumour samples or long reads, and none evaluates a foundation model.
source locator: amp-source-dragen-supplementary-tables: sheet 'S4 CNV benchmarking' rows 6-10; cnv-20261009-source-gabrielaite2021-table-s2: rows with sample GB-WGS-NA12878; cnv-20261011-hwang2026-source-article: Table 4; Methods 2.2 and 2.5; cnv-20261011-zhang2026-source-article: Tables 3 and 4
review: method: source-hash-verification; independent-cell-check; automated-source-review; reviewer: claude; reviewer note: Separate Claude review agent (reviewer-scnv), independent of the collector who drafted the summary proposal; the reviewer rewrote the text; no human review claimed; reviewed at: 2026-10-12T07:47:41Z; note: Drafted by the collector (data/omics/lit-search-20261011-uc-splicing-cnv/report.md) and rewritten by the reviewer; every number traced to stored or batch results. Descriptive summary only; no execution, human scientific review or clinical validation. See docs/reviews/use-cases/splicing-cnv-use-cases-2026-10-12.md.
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