Ander Diaz-Navarro
@dn-ander.bsky.social
I’m a computational biologist and biochemist working on synthetic tumor genome generation 💻🧬 Postdoctoral researcher at University of Toronto (UofT) and Ontario Institute for Cancer Research (OICR)
Grateful to my colleagues and to my supervisors, Lincoln Stein & Bo Wang, for their guidance and support. Stay tuned —more to come!
August 21, 2025 at 7:53 PM
Grateful to my colleagues and to my supervisors, Lincoln Stein & Bo Wang, for their guidance and support. Stay tuned —more to come!
8/8 More info:
Alongside the OncoGAN models and pipeline, we’ve released 800 synthetic genomes spanning 8 tumor types!
A huge thank you to all the authors for their contributions to this work!!!
📄 Preprint: tinyurl.com/yepheye3
📂 Datasets: tinyurl.com/28bpd5hs
💻 Code & Docs: tinyurl.com/mr3ku653
Alongside the OncoGAN models and pipeline, we’ve released 800 synthetic genomes spanning 8 tumor types!
A huge thank you to all the authors for their contributions to this work!!!
📄 Preprint: tinyurl.com/yepheye3
📂 Datasets: tinyurl.com/28bpd5hs
💻 Code & Docs: tinyurl.com/mr3ku653
GitHub - LincolnSteinLab/oncoGAN: A pipeline that accurately simulates high quality publicly cancer genomes (VCFs, CNAs and SVs).
A pipeline that accurately simulates high quality publicly cancer genomes (VCFs, CNAs and SVs). - LincolnSteinLab/oncoGAN
github.com
February 24, 2025 at 7:50 PM
8/8 More info:
Alongside the OncoGAN models and pipeline, we’ve released 800 synthetic genomes spanning 8 tumor types!
A huge thank you to all the authors for their contributions to this work!!!
📄 Preprint: tinyurl.com/yepheye3
📂 Datasets: tinyurl.com/28bpd5hs
💻 Code & Docs: tinyurl.com/mr3ku653
Alongside the OncoGAN models and pipeline, we’ve released 800 synthetic genomes spanning 8 tumor types!
A huge thank you to all the authors for their contributions to this work!!!
📄 Preprint: tinyurl.com/yepheye3
📂 Datasets: tinyurl.com/28bpd5hs
💻 Code & Docs: tinyurl.com/mr3ku653
7/8 Is OncoGAN useful? Absolutely!
- We tested ActiveDriverWGS on synthetic genomes to see if it could detect the same driver genes as in real patient data, proving its value in refining algorithms and defining detection limits.
- We tested ActiveDriverWGS on synthetic genomes to see if it could detect the same driver genes as in real patient data, proving its value in refining algorithms and defining detection limits.
February 24, 2025 at 7:50 PM
7/8 Is OncoGAN useful? Absolutely!
- We tested ActiveDriverWGS on synthetic genomes to see if it could detect the same driver genes as in real patient data, proving its value in refining algorithms and defining detection limits.
- We tested ActiveDriverWGS on synthetic genomes to see if it could detect the same driver genes as in real patient data, proving its value in refining algorithms and defining detection limits.
6/8 Is OncoGAN useful? Absolutely!
- We used OncoGAN simulations to augment DeepTumour’s training dataset (a tool for identifying tumor type based on somatic mutation patterns), showing performance improvements.
- We used OncoGAN simulations to augment DeepTumour’s training dataset (a tool for identifying tumor type based on somatic mutation patterns), showing performance improvements.
February 24, 2025 at 7:50 PM
6/8 Is OncoGAN useful? Absolutely!
- We used OncoGAN simulations to augment DeepTumour’s training dataset (a tool for identifying tumor type based on somatic mutation patterns), showing performance improvements.
- We used OncoGAN simulations to augment DeepTumour’s training dataset (a tool for identifying tumor type based on somatic mutation patterns), showing performance improvements.
5/8 What does OncoGAN simulate?
- Copy number alterations (CNA) and structural variants (SV): This updated version successfully simulates CNAs and SVs.
- Copy number alterations (CNA) and structural variants (SV): This updated version successfully simulates CNAs and SVs.
February 24, 2025 at 7:50 PM
5/8 What does OncoGAN simulate?
- Copy number alterations (CNA) and structural variants (SV): This updated version successfully simulates CNAs and SVs.
- Copy number alterations (CNA) and structural variants (SV): This updated version successfully simulates CNAs and SVs.
4/8 What does OncoGAN simulate?
- Tumor heterogeneity (A): Simulating donors with varying mutational burdens and characteristics.
- Tissue-specific mutational patterns (B): Accurately modeling the genomic distribution of mutations and mutational signatures unique to different tumor types.
- Tumor heterogeneity (A): Simulating donors with varying mutational burdens and characteristics.
- Tissue-specific mutational patterns (B): Accurately modeling the genomic distribution of mutations and mutational signatures unique to different tumor types.
February 24, 2025 at 7:50 PM
4/8 What does OncoGAN simulate?
- Tumor heterogeneity (A): Simulating donors with varying mutational burdens and characteristics.
- Tissue-specific mutational patterns (B): Accurately modeling the genomic distribution of mutations and mutational signatures unique to different tumor types.
- Tumor heterogeneity (A): Simulating donors with varying mutational burdens and characteristics.
- Tissue-specific mutational patterns (B): Accurately modeling the genomic distribution of mutations and mutational signatures unique to different tumor types.
3/8 Why is OncoGAN necessary?
- Benchmarking: Since the ground truth of real cancer genomes is often unknown, evaluations typically compare methods, introducing potential bias. By generating open-access synthetic genomes with a known ground truth, OncoGAN helps improve and benchmark these tools.
- Benchmarking: Since the ground truth of real cancer genomes is often unknown, evaluations typically compare methods, introducing potential bias. By generating open-access synthetic genomes with a known ground truth, OncoGAN helps improve and benchmark these tools.
February 24, 2025 at 7:50 PM
3/8 Why is OncoGAN necessary?
- Benchmarking: Since the ground truth of real cancer genomes is often unknown, evaluations typically compare methods, introducing potential bias. By generating open-access synthetic genomes with a known ground truth, OncoGAN helps improve and benchmark these tools.
- Benchmarking: Since the ground truth of real cancer genomes is often unknown, evaluations typically compare methods, introducing potential bias. By generating open-access synthetic genomes with a known ground truth, OncoGAN helps improve and benchmark these tools.
2/8 Why is OncoGAN necessary?
- Improving data sharing: We have demonstrated that OncoGAN does not leak any private patient data from its training set, a crucial factor given the sensitivity of genetic information as protected health data.
- Improving data sharing: We have demonstrated that OncoGAN does not leak any private patient data from its training set, a crucial factor given the sensitivity of genetic information as protected health data.
February 24, 2025 at 7:50 PM
2/8 Why is OncoGAN necessary?
- Improving data sharing: We have demonstrated that OncoGAN does not leak any private patient data from its training set, a crucial factor given the sensitivity of genetic information as protected health data.
- Improving data sharing: We have demonstrated that OncoGAN does not leak any private patient data from its training set, a crucial factor given the sensitivity of genetic information as protected health data.