sv-channels: filtering genomic deletions using one-dimensional convolutional neural networks
Santuari, L.; Georgievska, S.; Kuzniar, A.; Pedersen, B. S.; Shneider, C.; Mehrem, S.; Ridder, L.; Kloosterman, W. P.; de Ridder, J.
Show abstract
Structural variant (SV) detection in human genomes using short-read sequencing data is hindered by false positives, arising from sequencing and mapping artifacts that mimic genuine SV signals. Despite advances, state-of-the-art SV callers like GRIDSS and Manta exhibit trade-offs between precision and recall, with GRIDSS offering the highest precision and Manta excelling in recall. To address these limitations, we introduce sv-channels, a novel deep learning model designed to improve the precision of SV detection by leveraging read information at call sites. Our method effectively reduces false positives in Mantas deletion callsets, achieving precision that surpasses GRIDSS while maintaining a recall rate comparable to Manta. This represents a significant improvement in SV detection, leveraging Mantas high recall through deep learning and paving the way for more accurate genomic analyses. The sv-channels codebase is openly accessible on GitHub at https://github.com/GooglingTheCancerGenome/sv-channels enabling further research and application in the field.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- A Complete Pedigree-Based Graph Workflow for Rare Candidate Variant Analysis 97%
- Whole-genome long-read sequencing downsampling and its effect on variant calling precision and recall 96%
- HiCanu: accurate assembly of segmental duplications, satellites, and allelic variants from high-fidelity long reads 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.