Viruses exert a profound influence on both human health and the global ecosystem, yet they remain largely unexplored. Precise taxonomic classification of viral sequences is essential for discovering novel viruses, elucidating their functions, and assessing their implications for public health and environmental monitoring. Traditional taxonomy methods based on genome references are limited by the vast number of unexplored viruses, rapid mutation rates, and high genetic diversity. Additionally, highly imbalanced species distribution and significant variances in inter-species genomic distances across taxonomic units pose challenges to classifier training. Conceptualizing genomic sequences as sentences in a natural language, large language models provide novel approaches for extracting intrinsic viral genome characteristics. In this study, we introduce ViTax, a virus taxonomy classification tool powered by HyenaDNA, a large language foundation model for long-range genomic sequences at single nucleotide resolution. ViTax integrates supervised prototypical contrastive learning to address the highly imbalanced distributions across various taxonomic clades and demonstrates superior performance to current leading methods in virus taxonomy, particularly significant for long sequences. Moreover, ViTax designs a belief mapping tree using the Lowest Common Ancestor algorithm to adaptively assign a sequence to the lowest taxonomy clade with confidence. For the open-set problem, where sequences belong to novel and unexplored genera, ViTax can adaptively assign them to a higher level of known taxonomy with outstanding performance. These capabilities make ViTax a robust tool for advancing the accuracy and reliability of viral taxonomy classification. The code is available at https://github.com/Ying-Lab/ViTax.

Prokaryotic viruses, also known as phages, play a crucial role in regulating bacterial populations and influencing microbial ecology. Predicting virus-prokaryote interactions is critical for advancing phage therapy, and enhancing our understanding of microbial ecosystem dynamics. The advent of metagenomic sequencing technologies has spurred the development of numerous computational methods to infer the hosts of novel viruses. However, these methods face serious obstacles, such as limited and imbalanced data, and difficulty capturing complex sequence patterns with long sequences. In this study, we introduce ViPER, a framework integrating a large language foundation model with byte pair encoding and hierarchical contrastive learning to enhance the accuracy and robustness of virus-host association predictions. In order to extract the variable-granularity feature and meaningful subsequence patterns, ViPER leverages byte pair encoding to tokenize viral genome sequences. Then Hyena, a large language model, is applied to extract the intrinsic characteristics of viral genomes. Meanwhile, for host genomes of considerable length, multi-scale k-mer frequencies are embedded into a host feature space by a shared-weight convolutional gated linear unit, which selectively emphasizes important features while suppressing less relevant ones. Aiming to map viral and prokaryotic genomes into a unified feature space, ViPER utilizes hierarchical contrastive learning to draw related pairs closer and push unrelated pairs apart, leveraging hierarchical taxonomic information to ensure robustness across both genus and species levels. Evaluation of benchmark datasets demonstrates that ViPER significantly outperforms existing methods. In real metagenomic applications, ViPER showcases its potential to reveal a comprehensive interaction map.

Mobile Taobao Application delivers search services on multiple scenarios that take textual, visual, or product queries. This paper aims to propose a unified graph neural network for these search scenarios to leverage data from multiple scenarios and jointly optimize search performances with less training and maintenance costs. Towards this end, this paper proposes BOMGraph, BOosting Multi-scenario E-commerce Search with a unified Graph neural network. BOMGraph is embodied with several components to address challenges in multi-scenario search. It captures heterogeneous information flow across scenarios by inter-scenario and intra-scenario metapaths. It learns robust item representations by disentangling specific characteristics for different scenarios and encoding common knowledge across scenarios. It alleviates label scarcity and long-tail problems in scenarios with low traffic by contrastive learning with cross-scenario augmentation. BOMGraph has been deployed in production by Alibaba's E-commerce search advertising platform. Both offline evaluations and online A/B tests demonstrate the effectiveness of BOMGraph.

MOTIVATION: Rapid developments in sequencing technologies have boosted generating high volumes of sequence data. To archive and analyze those data, one primary step is sequence comparison. Alignment-free sequence comparison based on k-mer frequencies offers a computationally efficient solution, yet in practice, the k-mer frequency vectors for large k of practical interest lead to excessive memory and storage consumption. RESULTS: We report CRAFT, a general genomic/metagenomic search engine to learn compact representations of sequences and perform fast comparison between DNA sequences. Specifically, given genome or high throughput sequencing data as input, CRAFT maps the data into a much smaller embedding space and locates the best matching genome in the archived massive sequence repositories. With 102-104-fold reduction of storage space, CRAFT performs fast query for gigabytes of data within seconds or minutes, achieving comparable performance as six state-of-the-art alignment-free measures. AVAILABILITY AND IMPLEMENTATION: CRAFT offers a user-friendly graphical user interface with one-click installation on Windows and Linux operating systems, freely available at https://github.com/jiaxingbai/CRAFT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.