DeepVariant Files

DeepVariant:

• Continuous phasing: Long-read variant calls (PacBio and ONT) are now natively phased and phased output is generated for both vcf and gvcf formats.
• Fuzzy channels: Added “fuzzy channel” logic to ONT model for better homopolymer resolution. This results in ~20-25% error reduction compared to existing methods.
• RNA-seq support: RNA-seq model and now supported as a model type. A case-study has been added for RNA-seq data.
• Postprocessing improvement: Implemented a new multiallelic variant post-processing method called “product” which is enabled for all modes except for WES.
• Steamlining input parameters: run_deepvariant and run_deepsomatic now reads parameters from model.example_info.json files which must be present with the models to run.

DeepSomatic:

• Small model in DeepSomatic: Introduced small models for tumor-normal modes in DeepSomatic improving the runtime between 12% to 40%.

Pangenome-aware DeepVariant:

• Local reassembly improvements: Improvements in local reassembly process with de-bruijn graph that reduces total errors by ~18% in HG002 T2T truth set.

Contributions:

• Ehud Amitai (@ehudamitai) from Ultima genomics for the algorithm development of multiallelic variant post-processing method that is available as “product” option.
• Vasiliy Strelnikov (@vaxyzek) for streamlining the run_deepvariant script by enabling automatic flag loading using model.example_info.json files.
• Sowmiya Nagarajan (@sonagarajan) - for helping to update the RNA-seq model.
• Shezan Rohinton Mirzan (@shezanmirzan) for migrating small model to Keras 3 and modernizing core infrastructure.
• Francisco Unda (@fcounda) for enhancing read sampling stability, fixing non-determinism, and creating robust read sampling approach at high coverages.
• Alec Zhang (@az-e) for providing essential internal updates and maintenance to the codebase.