Tootfinder

Good for Mhairi Black!
“To be honest, I’m looking around thinking, ‘There are better organisations that I could be giving a membership to than this one that I don’t feel has been making the right decisions for quite some time.’”
Her reasons for leaving the #SNP now (Trans rights, Palestine) are at least as good as mine were (the Monarchy) in 2008. She's still a person who I'd vote…

MicroTrace: A Lightweight R Tool for SNP-Based Pathogen Clustering in Outbreak Detection
Kaitao Lai
https://arxiv.org/abs/2507.08060 https://

MicroTrace: A Lightweight R Tool for SNP-Based Pathogen Clustering in Outbreak Detection
MicroTrace is an open-source R tool that performs SNP-based hierarchical clustering to detect potential transmission clusters from pathogen whole-genome sequencing (WGS) data. Designed for epidemiologists, microbiologists, and genomic surveillance teams, it processes SNP distance matrices and outputs dendrograms and cluster tables with optional metadata integration. MicroTrace enables reproducible outbreak detection workflows with minimal setup.

OpenCCA: An Open Framework to Enable Arm CCA Research
Andrin Bertschi, Shweta Shinde
https://arxiv.org/abs/2506.05129 https://arxiv.o…

OpenCCA: An Open Framework to Enable Arm CCA Research
Confidential computing has gained traction across major architectures with Intel TDX, AMD SEV-SNP, and Arm CCA. Unlike TDX and SEV-SNP, a key challenge in researching Arm CCA is the absence of hardware support, forcing researchers to develop ad-hoc performance prototypes on non-CCA Arm boards. This approach leads to duplicated efforts, inconsistent performance comparisons, and high barriers to entry. To address this, we present OpenCCA, an open research platform that enables the execution of CC…

Some very quirky facts in the background to the recent Westminster decision to label the protest organisation Palestinian Action as "terrorists", thereby making their existence illegal. [This is the archived version so no paywall.]

BMFM-DNA: A SNP-aware DNA foundation model to capture variant effects
Hongyang Li, Sanjoy Dey, Bum Chul Kwon, Michael Danziger, Michal Rosen-Tzvi, Jianying Hu, James Kozloski, Ching-Huei Tsou, Bharath Dandala, Pablo Meyer
https://arxiv.org/abs/2507.05265

BMFM-DNA: A SNP-aware DNA foundation model to capture variant effects
Large language models (LLMs) trained on text demonstrated remarkable results on natural language processing (NLP) tasks. These models have been adapted to decipher the language of DNA, where sequences of nucleotides act as "words" that encode genomic functions. However, the genome differs fundamentally from natural language, as it lacks clearly defined words or a consistent grammar. Although DNA language models (DNALMs) such as DNABERT, GENA-LM have achieved high level of performance on genome-…

DuAL-Net: A Hybrid Framework for Alzheimer's Disease Prediction from Whole-Genome Sequencing via Local SNP Windows and Global Annotations
Eun Hye Lee, Taeho Jo
https://arxiv.org/abs/2506.00673

DuAL-Net: A Hybrid Framework for Alzheimer's Disease Prediction from Whole-Genome Sequencing via Local SNP Windows and Global Annotations
Alzheimer's disease (AD) dementia is the most common form of dementia. With the emergence of disease-modifying therapies, predicting disease risk before symptom onset has become critical. We introduce DuAL-Net, a hybrid deep learning framework for AD dementia prediction using whole genome sequencing (WGS) data. DuAL-Net integrates two components: local probability modeling, which segments the genome into non-overlapping windows, and global annotation-based modeling, which annotates SNPs and reo…

Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout
Taeho Jo, Eun Hye Lee, Alzheimer's Disease Sequencing Project
https://arxiv.org/abs/2506.00662

Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout
INTRODUCTION: Alzheimer's disease (AD) is genetically complex, complicating robust classification from genomic data. METHODS: We developed a transformer-based ensemble model (TrUE-Net) using Monte Carlo Dropout for uncertainty estimation in AD classification from whole-genome sequencing (WGS). We combined a transformer that preserves single-nucleotide polymorphism (SNP) sequence structure with a concurrent random forest using flattened genotypes. An uncertainty threshold separated samples into …