SUMMARY: Mixtum is a Python-based code that estimates ancestry contributions in a process of two-way admixture based on bi-allelic genotype data. The outcomes of Mixtum come from the geometric interpretation of the f-statistics formalism. Designed with user-friendliness as a priority, Mixtum allows to interactively handle a menu of user-supplied populations to build different mixture models in conjunction with the set of auxiliary populations required by the framework. The results are presented graphically and numerically. Importantly, Mixtum provides a novel index (an angle) that assesses the quality of the ancestral reconstruction of the model under scrutiny. The use and interpretation of the outcomes of Mixtum are explained and illustrated with case studies. AVAILABILITY AND IMPLEMENTATION: The open source code is available on GitHub at https://github.com/jmcastelo/mixtum and on Zenodo at https://doi.org/10.5281/zenodo.17789375. Mixtum is implemented in Python and runs on Linux, Windows and macOS.

MOTIVATION: Defective viral genomes (DVGs) are variants of the wild-type (wt) virus that lack the ability to complete autonomously an infectious cycle. However, in the presence of their parental (helper) wt virus, DVGs can interfere with the replication, encapsidation, and spread of functional genomes, acting as a significant selective force in viral evolution. DVGs also affect the host's immune responses and are linked to chronic infections and milder symptoms. Thus, identifying and characterizing DVGs is crucial for understanding infection prognosis. Quantifying DVGs is challenging due to their inability to sustain themselves, which makes it difficult to distinguish them from the helper virus, especially using high-throughput RNA sequencing. An accurate quantification is essential for understanding their very dynamical interactions with the helper virus. RESULTS: We present a method to simultaneously estimate the abundances of DVGs and wt genomes within a sample by identifying genomic regions with significant deviations from the expected sequencing depth. Our approach involves reconstructing the depth profile through a linear system of equations, which provides an estimate of the number of wt and DVG genomes of each type. Until now, in silico methods have only estimated the DVG-to-wt ratio for localized genomic regions. This is the first method that simultaneously estimates the proportions of wt and DVGs genome wide from short-reads RNA sequencing. AVAILABILITY AND IMPLEMENTATION: The Matlab code and the synthetic datasets are freely available at https://github.com/jmusan/wtDVGquantific.