DeepSNP: An End-to-End Deep Neural Network with Attention-Based Localization for Breakpoint Detection in Single-Nucleotide Polymorphism Array Genomic Data

Abstract

Clinical decision-making in cancer and other diseases relies on timely and cost-effective genome-wide testing. Classical bioinformatic algorithms, such as Rawcopy, can support genomic analysis by calling genomic breakpoints and copy-number variations (CNVs), but often require manual data curation, which is error prone, time-consuming, and thus substantially increasing costs of genomic testing and hampering timely delivery of test results to the treating physician. We aimed to investigate whether deep learning algorithms can be used to learn from genome-wide single-nucleotide polymorphism array (SNPa) data and improve state-of-the-art algorithms. We developed, applied, and validated a novel deep neural network (DNN), DeepSNP. A manually curated data set of 50 SNPa analyses was used as truthset. We show that DeepSNP can learn from SNPa data and classify the presence or absence of genomic breakpoints within large genomic windows with high precision and recall. DeepSNP was compared with well-known neural network models as well as with Rawcopy. Moreover, the use of a localization unit indicates the ability to pinpoint genomic breakpoints despite their exact location not being provided while training. DeepSNP results demonstrate the potential of DNN architectures to learn from genomic SNPa data and encourage further adaptation for CNV detection in SNPa and other genomic data types.

Applying AI in Practice: Key Challenges and Lessons Learned

“…Similar to biomedical imaging, classical bioinformatic algorithms, often require manual data curation, which is error prone, extremely time-consuming, and thus has negative effects on time and cost efficiency. To overcome this problem, we developed the DeepSNP 14 network to learn from genome-wide single-nucleotide polymorphism array (SNPa) data and to classify the presence or absence of genomic breakpoints within large genomic windows with high precision and recall [16].…”

“…The implementations of the various components are managed using a container orchestration platform. The standard ONNX 16 (Open Neural Network Exchange) is used to exchange deep learning models between the different components of the tool flow.…”

“…In Step 1 a Design Space comprising admissible model architectures for hyperparamerter tuning is defined. This Design Space is configured via satellite tools that evaluate the fitness in terms of the predefined optimization criteria such as accuracy (by the Training Engine), robustness against adversarial attacks 16 https://onnx.ai/ (by the Security evaluation tool) and power (by the Power evaluation tool). The optimization is based on a) hyperparameter tuning based on a non-stochastic infinite-armed bandit approach [55], and b) a parsimonious inference strategy that aims to reduce the bit depth of the activation values from initially 8bit to 4bit by a iterative quantization and retraining steps [47].…”