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Background/Objectives:

Quantifying bacteria's growth rates is essential for understanding their ecological roles and for building predictive models in environmental and clinical settings. Peak-to-trough ratios (PTRs) derived from shotgun metagenomes offer a culture-independent proxy for in situ growth rates of bacterial species, yet their reliable computation remains challenging.

Current spatial proteomics data analysis workflows are limited in efficiency and scalability when applied to gigapixel sized datasets. Moreover, they often lack extensive quality control tools and exhibit limited interoperability with existing spatial omics analysis ecosystems.

Intratumor heterogeneity arises from ongoing somatic evolution and complicates cancer diagnosis, prognosis, and treatment. Reconstructing evolutionary dynamics typically requires spatiotemporal samples, which are often unavailable in clinical settings. Computational approaches that can infer tumor evolutionary history from single-timepoint bulk sequencing data remain limited.

Low-complexity (LC) DNA sequences are compositionally repetitive sequences that are often associated with spurious homologous matches and variant calling artifacts. While algorithms for identifying LC sequences exist, they either lack concise mathematical definition of complexity or are inefficient with long or variable context windows.