RAPTR-SV: a hybrid method for the detection of structural variants

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Existing software for structural variant (SV) identification in sequence data generates numerous false positives.
• This high rate of false positives complicates and increases the burden of subsequent experimental validation.

Purpose of the Study:

• To develop and evaluate RAPTR-SV, a novel algorithm for enhanced structural variant detection.
• To improve the accuracy and efficiency of identifying structural variants, particularly tandem duplications.

Main Methods:

• RAPTR-SV is implemented in Java, utilizing features from Java 8.
• The software's performance was evaluated through simulations and comparison with existing tools (Delly, LUMPY) on 1000 Genomes Project data.
• Source code and documentation are available on GitHub.

Main Results:

• RAPTR-SV demonstrated superior sensitivity and precision in simulations, recovering 66.4% of tandem duplications with 99.2% precision.
• Compared to Delly and LUMPY on real-world data, RAPTR-SV identified twice as many tandem duplications.
• RAPTR-SV significantly reduced false positive predictions, making approximately 85% fewer duplication calls than Delly.

Conclusions:

• RAPTR-SV offers a substantial improvement over existing methods for structural variant detection.
• The algorithm effectively minimizes false positives, streamlining the validation process in genomic studies.
• RAPTR-SV is a valuable tool for accurate and efficient identification of tandem duplications.