DeepVariant 1.5.0

• New model datatype: --model_type ONT_R104 is a new option. Starting from v1.5, DeepVariant natively supports ONT R10.4 simplex and duplex data.
  • For older ONT chemistry, please continue to use PEPPER-Margin-DeepVariant.
• Incorporated PacBio Revio training data in DeepVariant PacBio model. In our evaluations this single model performs well on both Sequel II and Revio datatypes. Please use DeepVariant v1.5 and later for Revio data.
• Incorporated Element Biosciences data in WGS models. We found that we could jointly train a short-read WGS model with both Illumina and Element data. Inclusion of Element data improves accuracy on Element without negative effect on Illumina. Please use the WGS model for best results on either Illumina or Element data.
• Added vg/Giraffe-mapped BAMs to DeepVariant WGS training data (alongside existing BWA). We observed that a single model can be trained for strong results with both BWA and vg/Giraffe.
• Improved DeepVariant WES model for 100bps exome sequencing thanks to user-reported issues (including #586 and #592).
• Thanks to Tong Zhu from Nvidia for his suggestion to improve the logic for shuffling reads.
• Thanks to Doron Shem-Tov (@doron-st) and Ilya Soifer (@ilyasoifer) from Ultima Genomics for adding new functionalities enabled by flags --enable_joint_realignment and --p_error.
• Thanks to Dennis Yelizarov for improving Google-internal infrastructure for running make_examples.
• Updated TensorFlow version to 2.11.0. Updated htslib version to 1.13.