Adaptive Somatic Mutations Calls with Deep Learning and Semi-Simulated Data

Remi Torracinta; Laurent Mesnard; Susan Levine; Rita Shaknovich; Maureen Hanson; Susan Levine

doi:10.1101/079087

CARVIEW

MOTORHOMES

Select Language

HTTP/2 301 date: Sun, 18 Jan 2026 09:57:47 GMT content-length: 0 location: https://doi.org/10.1101/079087 server: cloudflare vary: Origin expires: Mon, 19 Jan 2026 09:57:46 GMT permissions-policy: interest-cohort=(),browsing-topics=() cf-cache-status: DYNAMIC nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} strict-transport-security: max-age=31536000; includeSubDomains; preload report-to: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=F2qVc2ZROSj4tyBw8oU%2FLu34Bj0QTVAf%2B1k8sx82V6%2FsIIRWp3BYhChNiorkkir8xyBCWx1FC5PRKD3ZlePO16LAsVJiAw%3D%3D"}]} cf-ray: 9bfd3ae76d6df473-BLR alt-svc: h3=":443"; ma=86400 HTTP/2 302 date: Sun, 18 Jan 2026 09:57:47 GMT content-type: text/html;charset=utf-8 location: https://biorxiv.org/lookup/doi/10.1101/079087 server: cloudflare vary: Origin vary: Accept expires: Sun, 18 Jan 2026 10:11:00 GMT permissions-policy: interest-cohort=(),browsing-topics=() cf-cache-status: DYNAMIC nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} strict-transport-security: max-age=31536000; includeSubDomains; preload report-to: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=8f1R0zn0yyOy0zOf3fd5weA75qasrDH1Iv51AMU%2BS1mESKTHMwFKCYjFun1G7LLacQLh5xDdTKuG5MMEtOMRdFWcggQeKg%3D%3D"}]} cf-ray: 9bfd3ae8deecf473-BLR alt-svc: h3=":443"; ma=86400 HTTP/1.1 302 Found Date: Sun, 18 Jan 2026 09:57:47 GMT Content-Type: text/html; charset=iso-8859-1 Transfer-Encoding: chunked Connection: keep-alive server: cloudflare location: https://www.biorxiv.org/lookup/doi/10.1101/079087 cf-cache-status: DYNAMIC Nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} Report-To: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=Qt9BKjZ0wxioySVFSmO97PBWq51AFgmEj6MWJZTqcMtsu6z0xAtFXBTma%2Fvi5Ve2VdTGzy4dj%2BQg7F5UmGpTXLB2pPNEAfvVwTqM"}]} CF-RAY: 9bfd3ae96e99aed9-BOM alt-svc: h3=":443"; ma=86400 HTTP/2 301 date: Sun, 18 Jan 2026 09:57:48 GMT content-type: text/html; charset=UTF-8 location: https://www.biorxiv.org/content/10.1101/079087v1 cf-ray: 9bfd3aec8fd61ec2-BLR x-content-type-options: nosniff x-content-type-options: nosniff x-drupal-cache: MISS expires: Sun, 18 Jan 2026 10:27:48 GMT cache-control: public, max-age=1800 x-varnish-ttl: pragma: no-cache vary: Accept-Encoding x-highwire-sitecode: biorxiv x-highwire-smart-code: biorxiv_production x-varnish: 1892475598 x-varnish-cache: via: 1.1 varnish cf-cache-status: MISS set-cookie: __cf_bm=Z73NjXC5S_gRwOszYUujDDVVZUHFZ0STIDwVjIfg3hU-1768730268-1.0.1.1-jliG6oYp8JSZO_rzP0.khPTiqL9lTN3fYhb44UDQCBx7OGcAEeg0RLpjw75drXsGRx8gFbOSjjOf8hOv9XNJRXVDpOsZIoC7.jKt0zEPNeM; path=/; expires=Sun, 18-Jan-26 10:27:48 GMT; domain=.www.biorxiv.org; HttpOnly; Secure; SameSite=None server: cloudflare HTTP/2 200 date: Sun, 18 Jan 2026 09:57:49 GMT content-type: text/html; charset=utf-8 content-encoding: gzip x-content-type-options: nosniff x-content-type-options: nosniff x-drupal-cache: MISS expires: Sun, 19 Nov 1978 05:00:00 GMT cache-control: no-cache, must-revalidate set-cookie: SSESS1dd6867f1a1b90340f573dcdef3076bc=XqoHMUaKbPbqlvGEZjZBx68gobbip3OLPsy7BA54zSA; expires=Tue, 10-Feb-2026 13:31:08 GMT; path=/; domain=.biorxiv.org; secure; HttpOnly content-language: en x-frame-options: SAMEORIGIN x-generator: Drupal 7 (https://drupal.org) link: ; rel="canonical",; rel="shortlink" vary: Accept-Encoding x-highwire-sitecode: biorxiv x-highwire-smart-code: biorxiv_production x-varnish: 694603979 age: 0 via: 1.1 varnish x-varnish-ttl: x-varnish-cache: cf-cache-status: DYNAMIC server: cloudflare cf-ray: 9bfd3af07d221ec2-BLR Adaptive Somatic Mutations Calls with Deep Learning and Semi-Simulated Data | bioRxiv

ABSTRACT

A number of approaches have been developed to call somatic variation in high-throughput sequencing data. Here, we present an adaptive approach to calling somatic variations. Our approach trains a deep feed-forward neural network with semi-simulated data. Semi-simulated datasets are constructed by planting somatic mutations in real datasets where no mutations are expected. Using semi-simulated data makes it possible to train the models with millions of training examples, a usual requirement for successfully training deep learning models. We initially focus on calling variations in RNA-Seq data. We derive semi-simulated datasets from real RNA-Seq data, which offer a good representation of the data the models will be applied to. We test the models on independent semi-simulated data as well as pure simulations. On independent semi-simulated data, models achieve an AUC of 0.973. When tested on semi-simulated exome DNA datasets, we find that the models trained on RNA-Seq data remain predictive (sens 0.4 & spec 0.9 at cutoff of P > = 0.9), albeit with lower overall performance (AUC=0.737). Interestingly, while the models generalize across assay, training on RNA-Seq data lowers the confidence for a group of mutations. Haloplex exome specific training was also performed, demonstrating that the approach can produce probabilistic models tuned for specific assays and protocols. We found that the method adapts to the characteristics of experimental protocol. We further illustrate these points by training a model for a trio somatic experimental design when germline DNA of both parents is available in addition to data about the individual. These models are distributed with Goby (https://goby.campagnelab.org).

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.