What happened
Accurate prediction of single-nucleotide variant (SNV) tolerability across the entire human genome remains a fundamental challenge in computational genomics, particularly for non-coding regions where the regulatory landscape is vast and poorly understood. Machine learning classifiers suffer from data circularity and demographic bias, while genomic language models demand massive computational resources and offer little biological interpretability. Here, we present PHACTn (Phylogeny-Aware Computing of Tolerance for nucleotide variants), a training-free, parameter-minimal method that infers nucleotide variant tolerability by traversing the mammalian phylogenetic tree and explicitly modeling the evolutionary independence of observed substitutions and their distance from the query species. With only 4 interpretable parameters, no training and no GPU requirement, PHACTn outperforms all evaluated tools on non-coding variants curated from both the ClinVar, and on non-coding variants potentially responsible for selected Mendelian diseases curated from OMIM. Additionally, it achieves state-of-the-art performance on variants within the informative range of alignment-based inference. These results establish that principled probabilistic phylogenetic modeling captures evolutionary constraint signals that large-scale sequence models fail to recover, offering a powerful, accessible, and mechanistically transparent alternative for genome-wide variant effect prediction. Update (2026-09-17): Cophylogeny simulators are not interchangeable: similarities, differences and structural biases in synthetic host-symbiont . Synthetic data are becoming increasingly important for computational studies of cophylogeny, including machine learning inference, benchmarking, and method testing. Several generators have been proposed to produce such data, but each relies on different assumptions about host-symbiont coevolution. These assumptions are often implicit and rarely examined, even though results can depend strongly on the synthetic model being used. In this article, we present a systematic structural analysis of representative cophylogeny generators under controlled scenarios. The goal is to make their assumptions explicit and to understand how these choices shape the synthetic data they produce as well as the conclusions that may be drawn from them. Update (2026-09-21): ARCHER-LD: Rapid Long-Range Linkage Disequilibrium Calculations at Biobank Scale using GPU Acceleration . Linkage disequilibrium (LD) information from one's own dataset is considered optimal for downstream analyses such as statistical fine-mapping. However, the computational complexity ({approx}N2 / 2 computations for N variants) lead most studies to use external reference panels, such as from 1000 Genomes. To capture LD across all variant pairs in biobank-scale whole-genome sequencing (WGS) datasets with hundreds of millions of variants, new computational strategies are essential. We present a novel approach that uses multi-GPU distributed computing to compute R2 for every variant pair in a dataset. On chromosome 22 (1.8 million variants) of the 30x WGS 1000 Genomes dataset, our method using eight consumer-level 12GB GPUs (NVIDIA RTX 2080TIs) takes 80 minutes, corresponding to a {approx}8x speedup. We further demonstrate true biobank-scale performance in the Penn Medicine Biobank (PMBB; 57,170 samples), computing chromosome 1 LD ({approx}1.38 million variants) in 49 minutes versus 22.7 hours for PLINK, a {approx}28x speedup. With this method, we successf Update (2026-09-23): Improving Metagenomics Classification with Kmask: Entropy-Based Masking of Low-Complexity Regions . Abstract Accurate taxonomic classification in metagenomics is often compromised by low-complexity sequences, which lead to chance matches that in turn cause sequences to be misclassified. Here we present Kmask, an entropy-based masking tool implemented for use either standalone or as part of Kraken [1,2] database construction, which replaces low-entropy regions with Ns. Using a sliding window size aligned with Kraken's default k-mer length and parameters optimized across 12 control bacterial genomes spanning a broad range of GC content, Kmask efficiently removes low-complexity sequences while retaining high-complexity regions. To benchmark performance, we applied Kmask to a newly constructed database, Microbial2025, that contains over 71,000 bacterial, archaeal, viral, and fungal genomes, and we then classified human reads against both masked and unmasked versions of the database using KrakenUniq [2]. We found that Kmask substantially reduced misclassifications, driving down the false positive rate to 5.78% from 7.52%. Notably, Kmask performed comparably to