From 8fdbe6a147fc52d5b54a2e9c047bea6b73583d9b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Marcos=20Moreno-Cabrera?= Date: Tue, 24 Dec 2024 18:08:03 +0100 Subject: [PATCH] Update README.md Added recent work benchmarking the state of the art in germline CNV calling from gene panel data. --- README.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/README.md b/README.md index 2b8b1bf..508be1e 100644 --- a/README.md +++ b/README.md @@ -598,6 +598,22 @@ In general, [BWA-mem](https://github.com/lh3/bwa) was the most consistent aligne ### CNV Callers +**Title:** [Detection of germline CNVs from gene panel data: benchmarking the state of the art](https://academic.oup.com/bib/article/26/1/bbae645/7922578) + +**Authors:** Elisabet Munté, Carla Roca, et al. + +**Journal Info:** Briefings in Bioinformatics, December 2024. + +**Description:** This work evaluated 12 germline copy number variation callers against four real-validated datasets using their default parameters, assessed the impact of modifying 107 tool parameters, and analyzed 66 tool pair combinations to produce better meta-callers. Sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and various correlation coefficients were used as benchmarking metrics. + +**Tools/methods compared:** `Atlas-CNV`, `ClearCNV`, `ClinCNV`, `CNVkit`, `Cobalt`, `CODEX2`, `CoNVaDING`, `DECoN`, `ExomeDepth`, `GATK-gCNV`, `panelcn.MOPS`, `VisCap` + +**Recommendation(s):** Results indicated that in terms of F1 score, ClinCNV and GATK-gCNV were the best CNV callers. Regarding sensitivity, GATK-gCNV also exhibited particularly high performance. + +**Additional links:** The authors published ([CNVbenchmarkeR2](https://github.com/jpuntomarcos/CNVbenchmarkeR2)), so other users can benchmark their tools on their own data. + +--- + **Title:** [Benchmark of tools for CNV detection from NGS panel data in a genetic diagnostics context](https://www.biorxiv.org/content/10.1101/850958v1) **Authors:** José Marcos Moreno-Cabrera, et al.