Acid whey is a by-product generated in large quantities during dairy processing, and is characterized by its low pH and high chemical oxygen demand. Due to a lack of reliable disposal pathways, acid whey currently presents a major sustainability challenge to the dairy industry. The study presented in this paper proposes a solution to this issue by transforming yogurt acid whey (YAW) into potentially palatable and marketable beverages through yeast fermentation. In this study, five prototypes were developed and fermented by Kluyveromyces marxianus, Brettanomyces bruxellensis, Brettanomyces claussenii, Saccharomyces cerevisiae (strain: Hornindal kveik), and IOC Be Fruits (IOCBF) S. cerevisiae, respectively. Their fermentation profiles were characterized by changes in density, pH, cell count, and concentrations of ethanol and organic acids. The prototypes were also evaluated on 26 sensory attributes, which were generated through a training session with 14 participants. While S. cerevisiae (IOCBF) underwent the fastest fermentation (8 days) and B. claussenii the slowest (21 days), K. marxianus and S. cerevisiae (Hornindal kveik) showed similar fermentation rates, finishing on day 20. The change in pH of the fermentate was similar for all five strains (from around 4.45 to between 4.25 and 4.31). Cell counts remained stable throughout the fermentation for all five strains (at around 6 log colony-forming units (CFU)/mL) except in the case of S. cerevisiae (Hornindal kveik), which ultimately decreased by 1.63 log CFU/mL. B. bruxellensis was the only strain unable to utilize all of the sugars in the substrate, with residual galactose remaining after fermentation. While both S. cerevisiae (IOCBF)- and B. claussenii-fermented samples were characterized by a fruity apple aroma, the former also had an aroma characteristic of lactic acid, dairy products, bakeries and yeast. A chemical odor characteristic of petroleum, gasoline or solvents, was perceived in samples fermented by B. bruxellensis and K. marxianus. An aroma of poorly aged or rancid cheese or milk also resulted from B. bruxellensis fermentation. In terms of appearance and mouthfeel, the S. cerevisiae (IOCBF)-fermented sample was rated the cloudiest, with the heaviest body. This study provides a toolkit for product development in a potential dairy-based category of fermented alcoholic beverages, which can increase revenue for the dairy industry by upcycling the common waste product YAW.

Motivation: Strain-level microbiome profiling has revealed key insights into microbial community composition and strain dynamics. However, accurate strain-level analysis remains challenging due to limited linkage information, ambiguous read mapping, and complicating factors such as genome similarity, sequencing depth, and community complexity. These challenges are especially pronounced for short-read metagenomic data when estimating the relative abundances of multiple strains, a task critical for genotype-phenotype association studies. Results: To address this gap, we present Strainify, which enables accurate strain-level abundance estimation from short-read metagenomes with as little as 1% genome coverage. Specifically, Strainify combines (1) identification of informative variants via core genome alignment, (2) filtering of confounding variants via a window-based test, and (3) maximum likelihood estimation of strain abundances. A Shannon entropy-weighted version of the model further improves robustness in noisy, low-coverage settings by downweighting sites with low information content. Across simulated communities of varying complexity, Strainify consistently outperformed existing approaches. On mock community sequencing data, Strainify’s estimates aligned more closely with reference abundances. When applied to a longitudinal gut microbiome dataset, Strainify successfully recapitulated the reported temporal dynamics of Bacteroides ovatus strain groups, demonstrating its ability to recover biologically meaningful patterns from real-world metagenomes. Together, these results establish Strainify as a robust and versatile solution for accurate strain-level abundance estimation in short-read, low-coverage microbiome studies. Availability: The Strainify code and results are available at: https://github.com/treangenlab/Strainify