StarSignDNA: Signature tracing for accurate representation of mutational processes

All cell lineages accumulate mutations over time, increasing the probability that some lineages eventually become malignant. Many of the processes responsible for generating mutations are leaving a characteristic footprint in the genome that allows their presence to be detected. However, the mutational pattern in a tumour is usually the combined result of multiple mutational processes being at work simultaneously, and the problem of disentangling the different footprints and their relative impact then becomes a deconvolution problem. Several algorithms have been developed for this purpose, most of them involving a factorization of the mutation count matrix into two non-negative matrices, representing respectively the underlying mutational signatures and the relative weighting of (or exposure to) these signatures. Here, we introduce the StarSignDNA algorithm for mutational signature analysis, which offers efficient re-fitting and de novo mutational signature extraction. StarSignDNA is capable of deciphering well-differentiated signatures linked to known mutagenic mechanisms and suggesting clinically relevant predictions for a single patient. The package offers a command line-based interface and data visualization routines. Author summaryStarSignDNA is a novel algorithm for identifying mutational signatures from cancer sequencing data. It excels in low mutation count scenarios, balancing signature detection and true signature discovery. StarSignDNA improves prediction accuracy and biological validity by addressing challenges such as overfitting and underfitting, achieving optimal variable selection and shrinkage, and ensuring interpretability. In re-fitting, it reduces prediction variance and handles sparsity, resulting in more precise predictions. In de novo extraction, it improves the detection of challenging signatures and achieves better alignment between detected and known signatures through unsupervised cross-validation. Its unique features, including prediction confidence and customizable reference signatures, offer valuable insights for clinical single-sample analysis.