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Therefore, meta-caller approaches combining the results of multiple standalone tools in a consensus set of reported SV calls, are widely used. Here, SV-MeCa (Structural Variant Meta-Caller) is presented, the first SV meta-caller incorporating variant-specific quality metrics from individual VCF outputs, rather than relying solely on number and combination of tools supporting consensus SV calls. In addition, SV-MeCa offers a suitable score to rank obtained consensus SV calls according to evidence of representing true positive calls, i.e., real-world variants.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>SV-MeCa applies seven standalone SV callers and merges resulting deletion and insertion calls into a union VCF file using SURVIVOR. For each entry in the SURVIVOR-generated consensus, caller-specific quality measures are extracted from corresponding standalone VCF files, and serve as input for an either deletion- or insertion-specific XGBoost decision tree classifier, which was previously trained on the HG002 SV benchmark data provided by the Genome in a Bottle consortium. The SV-MeCa XGBoost models assign a probability to (consensus) SV calls to represent true positive calls, which can be used for ranking the final output according to evidence. Performance of SV-MeCa and four previously published meta-caller approaches were evaluated based on autosomal SV calls in samples curated by the Human Genome Structural Variation Consortium, Phase 2. With regard to F<jats:inline-formula>\n                <jats:tex-math>$$_1$$<\/jats:tex-math>\n              <\/jats:inline-formula> scores, which were 0.58 on average for deletions and 0.42 on average for insertions, SV-MeCa outperformed the other meta-callers. With regard to precision, only ConsensuSV achieved higher values (0.97 versus 0.64 on average for deletions, 0.75 versus 0.53 on average for insertions), and with regard to recall, SV-MeCa was outperformed exclusively by Meta-SV for deletions (0.55 versus 0.53).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>SV-MeCa, publicly available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/ccfboc-bioinformatics\/SV-MeCa\" ext-link-type=\"uri\">https:\/\/github.com\/ccfboc-bioinformatics\/SV-MeCa<\/jats:ext-link>, outperforms existing SV meta-caller approaches by taking variant-specific quality measures into account. Moreover, due to the XGBoost prediction probabilities serving as scores, the output of SV-MeCa can be continuously adjusted to user needs in terms of sensitivity and precision.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12859-025-06246-6","type":"journal-article","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T10:41:44Z","timestamp":1755686504000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SV-MeCa: an XGBoost-based meta-caller approach for structural variant calling from short-read data"],"prefix":"10.1186","volume":"26","author":[{"given":"Rudel Christian","family":"Nkouamedjo Fankep","sequence":"first","affiliation":[]},{"given":"Arda","family":"S\u00f6ylev","sequence":"additional","affiliation":[]},{"given":"Anna-Lena","family":"Kobiela","sequence":"additional","affiliation":[]},{"given":"Jochen","family":"Blom","sequence":"additional","affiliation":[]},{"given":"Corinna","family":"Ernst","sequence":"additional","affiliation":[]},{"given":"Susanne","family":"Motameny","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"issue":"5","key":"6246_CR1","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1038\/nrg2958","volume":"12","author":"C Alkan","year":"2011","unstructured":"Alkan C, Coe BP, Eichler EE. 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