arXiv:2603.25417v1 Announce Type: new
Abstract: We present efficient approaches for extracting spaced k-mers from nucleotide sequences. They are based on bit manipulation instructions at CPU level, making them both simpler to implement and up to an order of magnitude faster than existing methods. We further evaluate common pitfalls in k-mer processing, which can cause major inefficiencies. Combined, our approaches allow the utilization of spaced k-mers in high-performance bioinformatics applications without major performance degradation, offering a throughput of up to 750MB of sequence data per second per core.
Availability: The implementation in C++20 is published under the MIT license, and freely available at https://github.com/lczech/fisk
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,