Andrew Carroll

Also thanks to 20% contributors: Ben Soudry, Mike Kruskal, Sowmiya Nagarajan, Suchismita Tripathy, Francisco Unda, Vasiliy Strelnikov

And community contributions from Sam Yadav and Seraj Ahmad at Roche improving the code for custom model training

Release DeepSomatic 1.9.0 · google/deepsomatic 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 manusc...

Release led by Kishwar Shafin, contributions by Daniel Cook, Alexey Kolesnikov, Lucas Brambrink, and Pi-Chuan Chang as engineering manager.

Thanks to student researcher contributions from Farica Zhuang and Mobin Asri.

DeepSomatic release page:
github.com/google/deeps...

Release DeepVariant 1.9.0 · google/deepvariant 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 metho...

Release of DeepVariant and DeepSomatic v1.9

DV: Now train on HG002 T2T-Q100. Error reduction of 12% for Illumina and 30% for PacBio on this truth set. 25% faster. DeepTrio is 5x faster (20h -> 4h).

DS: New models FFPE_TUMOR_ONLY for {WGS, WES}. Much improved WGS models.

github.com/google/deepv...

Incredibly moving Justin Trudeau remarks:

"We have fought and died alongside you....During your darkest hours...we were always there. Standing with you, grieving with you, the American people....Canadians are a little perplexed as to why our closest friends and neighbors are choosing to target us."

You have some additional control on memory use by the number of threads you run with.

For running on GPU, I am not sure if you've seen this - github.com/google/deepv...

Which requires a little more configuration, but can let you better manage CPU-GPU tradeoffs. Definitely expert use.

Hi Eric, sorry to not notice till now. From the DV FAQ, we see the Keras model takes 16GB of memory (github.com/google/deepv...).

It's possible that pangenome-aware models will take more memory, and we do observe more memory per thread used for that. Definitely not lower than 16GB.

Great question. We were talking recently about L40S benchmarks. We don't have that data immediately on hand, but are planning to generate runtime stats for it.

They're very close - to the point that small changes of coverage or the inclusion of PCR in preparation would tip between one and the other.

Release led by DeepVariant tech lead Kishwar Shafin. Team Engineering manager Pi-Chuan Chang. Small model work led by Lucas Brambrink. Pangenome-aware led by Mobin Asri and Juan Carlos Mier. Fast pipeline by Alexey Kolesnikov. Kinnex/MAS-Seq model by Daniel Cook and Shiyi Yin from Verily. 3/3

Plots of SNP and Indel error numbers for DeepVariant models. Shows a Indel error reduction of 26% for PacBio and a ~50% SNP error reduction for ONT.

Added SPRQ to PacBio training, reducing Indel error on SPRQ by 26%. Added Platinum Pedigree training data for PacBio model, reducing errors by 34% on more extensive Platinum truth. New model and case study for Kinnex/Mas-Seq/Iso-Seq. Additional speed options for GPU pipelines 2/3

Runtime figure for new version of DeepVariant with and without small model. Showing reduction in runtime of 155 minutes to 101 minutes with Illumina, 174 minutes to 71 minutes with PacBio, and 295 minutes to 114 minutes with Oxford Nanopore.

Release of DeepVariant 1.8. Large speed improvement (~67% faster) via small model for easy sites. New Pangenome-aware option. Reduces error by ~30% for vg-mapped WGS, ~10% for BWA WGS, ~5% BWA exome. New config for custom model users, see release notes.

(github.com/google/deepv...)

Personalized Pangenome References bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution

How do we make a pangenome maximally relevant for the study of a new sample? www.biorxiv.org/content/10.1...

Release by Kishwar Shafin

Major contributions from Pi-Chuan Chang, Daniel Cook, Alexey Kolesnikov

Google 20%ers: Will Kwan, Pauline Sho, Lucas Brambrink, Mo Samman, Atilla Kiraly

UCSC for vg: @benedictpaten.bsky.social, Shloka Negi, Jimin Park, Mobin Asri

Pacbio: Billy Rowell, Nathaniel Echols

There are now custom models and case studies for CompleteGenomics instruments.

T7: github.com/google/deepv...

G400: github.com/google/deepv...

For now, these are stand alone models. We'll likely consider whether we can jointly include these in the broad WGS model later.

The changes to DeepTrio for de novo detection are substantial. We now in two steps - first for overall accuracy and then a weighted fine tuning for de novos. Our benchmarks show large improvements in de novo calling relative to the prior DeepTrio.

github.com/google/deepv...

Want to benefit from pangenomes and want a recipe?

github.com/google/deepv...

Shows a step by step process, with Docker images for how to map to a Pangenome reference w/ vg and calls w/ DeepVariant. Final calls are more accurate and in GRCh38 coordinates. Thanks to the UCSC team for co-development

Release DeepVariant 1.6.0 · google/deepvariant Improved support for haploid regions, chrX and chY. Users can specify haploid regions with a flag. Updated case studies show usage and metrics. Added pangenome workflow (FASTQ-to-VCF mapping with V...

Release of DeepVariant v1.6.

Support for haploid regions, chrX/Y.
Workflow for Pangenome FASTQ-to-VCF.
Major DeepTrio improvements for de novo variants.
Models for CompleteGenomics T7, G400
Add NovaSeqX to training data

Release by Kishwar Shafin

github.com/google/deepv...

Proud of Ayse Keskus and Asher Bryant in my group for making this happen! This work is a collaboration with Children's Mercy, UCSC and Google Health - who are also releasing the first version of DeepSomatic today: github.com/google/deeps...

GitHub - KolmogorovLab/Severus: A tool for somatic structural variant calling using long reads A tool for somatic structural variant calling using long reads - GitHub - KolmogorovLab/Severus: A tool for somatic structural variant calling using long reads

TThanks to Jimin Park and Benedict Paten from UCSC, Mikhail Kolmogorov from NCI for analysis and testing. This group has also developed Severus for somatic SV calling, and we've worked closely with them

(github.com/KolmogorovLa...)

Thanks to Khi Pin Chua and Billy Rowell from PacBio

GitHub - google/deepsomatic Contribute to google/deepsomatic development by creating an account on GitHub.

Initial release of DeepSomatic, which identifies subclonal variants when given tumor and normal BAM files. Pre-trained models and case studies available for Illumina and PacBio. Development led by Kishwar Shafin which built off a framework by Pi-Chuan Chang.

(github.com/google/deeps...)

Best resource for getting extracellular domain localization from Ensembl gene/protein IDs?

I tried the subcellular locations in HPA but the membrane annotation is mostly fully intracellular proteins.

🧪🧬🖥️🔬