Long-read sequencing is a powerful technique capturing multiple variants within single continuous reads. This length allows individual reads to bridge small and structural variants while carrying crucial phasing information. However, current computational tools treat small variant calling, structural variant (SV) detection and phasing as largely disconnected problems, failing to unleash the full potential of long reads. Here, we present longcallD, a unified framework utilizing local multiple-sequence alignment to simultaneously call and phase small and structural variants. By integrating germline phasing and retrotransposition hallmarks, longcallD also identifies low-fraction mosaic variants and detects mobile element insertions supported by a single read. Compared to existing methods, our unified approach substantially improves SV discovery and mosaic variants accuracy while maintaining competitive small variant calling. We anticipate that longcallD will provide a robust foundation for resolving complex genetic architectures in clinical and evolutionary applications.
Depression subtype classification from social media posts: few-shot prompting vs. fine-tuning of large language models
BackgroundSocial media provides timely proxy signals of mental health, but reliable tweet-level classification of depression subtypes remains challenging due to short, noisy text, overlapping symptomatology,