MOTIVATION: Nanopore sequencing represents a significant advancement in genomics, enabling direct long-read DNA sequencing at the single-molecule level. Accurate simulation of nanopore sequencing signals from nucleotide sequences is crucial for method development and for complementing experimental data. Most existing approaches rely on predefined statistical models, which may not adequately capture the properties of experimental signal data. Furthermore, these simulators were developed for earlier versions of nanopore chemistry, which limits their applicability and adaptability to the latest flow cell data. RESULTS: To enhance the quality of artificial signals, we introduce seq2squiggle, a novel transformer-based, non-autoregressive model designed to generate nanopore sequencing signals from nucleotide sequences. Unlike existing simulators that rely on static k-mer models, our approach learns sequential contextual information from segmented signal data. We benchmark seq2squiggle against state-of-the-art simulators on real experimental R9.4.1 and R10.4.1 data, evaluating signal similarity, basecalling accuracy, and variant detection rates. Seq2squiggle consistently outperforms existing tools across multiple datasets, demonstrating superior similarity to real data and offering a robust solution for simulating nanopore sequencing signals with the latest flow cell generation. AVAILABILITY AND IMPLEMENTATION: seq2squiggle is freely available on GitHub at: github.com/ZKI-PH-ImageAnalysis/seq2squiggle.

Abstract Generative adversarial networks (GANs) are machine learning models that are used to estimate the underlying statistical structure of a given dataset and, as a result, can be used for a variety of tasks, such as image generation or anomaly detection. Despite their initial simplicity, designing an effective loss function for training GANs remains challenging, and various loss functions have been proposed, aiming to improve the performance and stability of generative models. In this study, loss function design for GANs is presented as an optimization problem solved using the Genetic Programming approach. Initial experiments were carried out using a small Deep Convolutional GAN model and the MNIST dataset in order to search experimentally for an improved loss function. The functions found were evaluated on CIFAR-10, with the best function, named GANetic loss, showing better performance and stability compared to the losses commonly used for GAN training. To further evaluate its general applicability to more challenging problems, GANetic loss was applied to two medical applications: image generation and anomaly detection. Experiments were performed with histopathological, gastrointestinal, and glaucoma images to evaluate the GANetic loss in medical image generation, resulting in improved image quality compared to the baseline models. The GANetic loss used for polyp and glaucoma images showed a strong improvement in the detection of anomalies. In summary, the GANetic loss function was evaluated on multiple datasets and applications, where it consistently outperformed alternative losses. Moreover, GANetic loss leads to stable training and reproducible results, a known weak spot of GANs. Code: https://github.com/ZKI-PH-ImageAnalysis/GANetic-Loss .