Background Calling structural variants (SVs), i.e., genomic alterations of <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12859_2025_6246_Article_IEq1.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\ge \)</EquationSource> </InlineEquation>50bp, from whole genome short-read data remains challenging, as existing callers are known to lack accuracy and robustness. 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. Results 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<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12859_2025_6246_Article_IEq2.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(_1\)</EquationSource> </InlineEquation> 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). Conclusions SV-MeCa, publicly available at <a href="https://github.com/ccfboc-bioinformatics/SV-MeCa">https://github.com/ccfboc-bioinformatics/SV-MeCa</a>, 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.

SV-MeCa: an XGBoost-based meta-caller approach for structural variant calling from short-read data

期刊论文
出版模式：

开放获取
发表日期：

2025年8月20日

查看全文

Rudel Christian Nkouamedjo Fankep,
Arda Söylev,
Anna-Lena Kobiela,
Jochen Blom,
Corinna Ernst,
Susanne Motameny

摘要

Background

Calling structural variants (SVs), i.e., genomic alterations of \(\ge \) 50bp, from whole genome short-read data remains challenging, as existing callers are known to lack accuracy and robustness. 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.

Results

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 \(_1\) 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).

Conclusions

SV-MeCa, publicly available at https://github.com/ccfboc-bioinformatics/SV-MeCa, 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.