#### [DeepSomatic](https://github.com/google/deepsomatic): * **Steamlining input parameters**: `run_deepsomatic` now reads parameters from `model.example_info.json` files which must be present with the models to run. * **Small model in DeepSomatic**: Introduced small models for tumor-normal modes in DeepSomatic improving the runtime between 12% to 40%. 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.

#### [DeepSomatic](https://github.com/google/deepsomatic): * In this release, we are introducing [`FFPE_WGS_TUMOR_ONLY`](https://github.com/google/deepsomatic/blob/r1.9/docs/deepsomatic-case-study-ffpe-wgs-tumor-only.md) and [`FFPE_WES_TUMOR_ONLY`](https://github.com/google/deepsomatic/blob/r1.9/docs/deepsomatic-case-study-ffpe-wes-tumor-only.md) models. * The [`WGS`](https://github.com/google/deepsomatic/blob/r1.9/docs/deepsomatic-case-study-wgs.md) and [`WGS_TUMOR_ONLY`](https://github.com/google/deepsomatic/blob/r1.9/docs/deepsomatic-case-study-wgs-tumor-only.md) models have been retrained with all datasets described in the manuscript, tumor-in-normal and normal contamination datasets. * Overall, we see improved generalization because of training dataset updates. We highly recommend updating to **1.9.0** for DeepSomatic analysis. Detailed release note for DeepVariant can be found in: https://github.com/google/deepvariant/releases/tag/v1.9.0

Updates included in this release: * DeepSomatic now has tumor-normal models to support WES and ONT. * DeepSomatic now has FFPE-trained models for FFPE_WGS and FFPE_WES. * DeepSomatic now supports tumor-only variant calling with Illumina WGS, ONT and PacBio. * Improved accuracy for tumor-normal variant calling with Illumina WGS and PacBio. * Models are trained with varying purities that improve somatic calling accuracy in cases of tumor-normal cross contamination. * Improved runtime in make_examples and postprocess variants through code optimization. * Models are trained with extensive training data described in this [manuscript](https://doi.org/10.1101/2024.08.16.608331). We are sincerely grateful to: * Juan Carlos Mier (@Jmier2) for his code and PacBio model training contributions. * Jimin Park (@jimin001) (UCSC), Benedict Paten (@benedictpaten) (UCSC) and Mikhail Kolmogorov (@fenderglass) (NIH, NCI) for testing and providing analysis results. * Jorge Gonzalez (@jgonzalezmendez) for helping to improve runtime. * PacBio: Khi Pin Chua (@proteinosome), William Rowell (@williamrowell) for feedback on PacBio somatic calling.

In this release: * We fixed a bug in `call_variants` that caused the step to freeze in cases where there were no examples. This bug was observed and reported in https://github.com/google/deepvariant/issues/764, https://github.com/google/deepvariant/issues/769, https://github.com/google/deepsomatic/issues/8. * Updated `libssw` library from 1.2.4 to 1.2.5. * The same model files are used for v1.6.0 and v1.6.1 for all technologies.

* Initial Release of DeepSomatic (v1.6) * DeepSomatic versioning will attempt to keep cadence with DeepVariant. * Pre-trained models for Illumina and Pacific Biosciences data available. * We are grateful to Khi Pin Chua and William Rowell (@williamrowell), Aaron Wenger for helping us develop the PacBio model. * We are also grateful to Jimin Park (@jimin001) (UCSC), Benedict Paten (@benedictpaten) (UCSC) and Mikhail Kolmogorov (@fenderglass) (NIH, NCI) for testing and providing analysis results.