DeepVariant 1.9.0

DeepVariant:

• In this version we have updated our training scheme for the HG002 sample with the newly released HG002-T2T truth set which improves accuracy against that truth set.
• Our labeling method has been updated to accommodate the complex representation of variants which are more common in the new HG002 T2T truth set.
• Faster inference (~20% runtime reduction) achieved by improving call_variants by improving numpy array and tensor handling

DeepSomatic:

• In this release, we are introducing FFPE_WGS_TUMOR_ONLY and FFPE_WES_TUMOR_ONLY models.
• The WGS and WGS_TUMOR_ONLY 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.

DeepTrio:

• Very large speed improvement - reduced runtime by 80%. This is achieved by introducing the small model scheme to DeepTrio. We observe similar or better accuracy compared to previous versions.
• We observe the inclusion of Small model improves de novo variant accuracy for DeepTrio.

Pangenome-aware DeepVariant:

• All models have been trained with the HG002 T2T truth set which shows improved accuracy in the new T2T truth set.

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We are thankful for the contributions from:

• Ben Soudry (@ben-soudry) -- For helping to refactor the channels interface and simplifying the process of adding new channels.
• Mike Kruskal (@mkruskal-google) -- For helping to upgrade tensorflow and protobuf versions.
• Sowmiya Nagarajan (@strangest-quark) -- Working on phasing candidate variants.
• Suchismita Tripathy (@sushi15) -- Improving the SNP and INDEL metrics reporting during training.
• Francisco Unda (@fcoUnda) -- Improving the downsampling approach in make_examples to improve representations for low allele frequency variants.
• Vasiliy Strelnikov (@vaxyzek) - adding deepsomatic capabilities into nf-core: nf-core/modules#6622
• Sam Yadav (@yadavs33-roche) and Seraj Ahmad (@ahmads9-roche) for their contribution to improve the examples shuffle code.

Student researchers:

• Mobin Asri (@mobinasri) -- Further improving the implementation of pangenome-aware DeepVariant.
• Farica Zhuang (@faricazjj) -- For contributing to the phasing method within DeepVariant.