[email protected] luke harries neural networks …...mutations (germline variant calling) • uses...
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Neural Networks for Genomic Variant CallingLuke [email protected]
* Circos Plot visualization of tumour WGS.
Agenda
• Why detect cancer mutations?• GermlineNet - Detecting inherited mutations• Spliced kernel• SomaticNet - Detecting cancer mutations
Why detecting cancer mutations? (Somatic Variant Calling)
• Cancer is caused by the progressive accumulation of DNA mutations
• Knowing which mutations have occurred allows doctors to:• Target the cancer with
chemotherapy• Detect resistance to treatment
up to seven months earlier than scans
https://www2.le.ac.uk/projects/vgec/highereducation/topics/cellcycle-mitosis-meiosis
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Next-Generation Sequencing
Sequencing DNA has a high error rate: 0.1-10%Fox et al. 2014https://www.researchgate.net/figure/Basic-principle-of-next-generation-sequencing-technologies_fig4_291171327
Output of the sequencers
Detecting cancer mutations (Somatic Variant Calling)
Existing Variant Callers
• Low concordance rate (based on different heuristics)• False assumption that read errors are independent
Cai et al 2016
DeepVariant
• Highest performance at a related task - detecting inherited mutations (Germline Variant Calling)
• Uses a deep convolutional neural network on encoded pileup images
Detecting Inherited Mutations (Germline Variant Calling)
Results for 4-fold cross-validation
Detecting cancer mutations - (Somatic Variant Calling)
Siamese Deep Convolutional Network
Omniglot Verification
Facial Verification
SomaticNet - Deep learning based somatic variant caller
SomaticNet
Results for 4-fold cross-validation
Summary
• Developed GermlineNet - a deep learning based germline variant caller, inspired by Google’s DeepVariant
• Improved GermlineNet by introducing a novel kernel design - the spliced kernel
• Developed SomaticNet - a novel approach to somatic variant calling which uses a Siamese deep convolution neural network
References
• Ryan Poplin, Pi-Chuan Chang, David Alexander, Scott Schwartz, Thomas Colthurst, Alexander Ku, Dan Newburger, Jojo Dijamco, Nam Nguyen, Pegah T. Afshar, Sam S. Gross, Lizzie Dorfman, Cory Y. McLean, and Mark A. DePristo. Creating a universal SNP and small indel variant caller with deep neural networks. bioRxiv, page 092890, 3 2018. doi: 10.1101/092890. URL https://www.biorxiv.org/content/early/2018/03/20/ 092890.
• Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov. Siamese Neural Networks for One-shot Image Recognition. Proceedings of the 32nd International Conference on Machine Learning, 2015. URL https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf.
• Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. Rethinking the Inception Architecture for Computer Vision. 12 2015. URL http://arxiv.org/abs/1512.00567.
• Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep Residual Learning for Image Recognition. 12 2015. URL http://arxiv.org/abs/1512.03385.
• Lei Cai, Wei Yuan, Zhou Zhang, Lin He, and Kuo-Chen Chou. In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data. Scientific Reports, 6(1):36540, 12 2016. ISSN 2045-2322. doi: 10.1038/ srep36540. URL http://www.nature.com/articles/srep36540.
