Bacteriophages are the viruses that infect bacterial cells. They are the most diverse biological entities on earth and play important roles in microbiome. According to the phage lifestyle, phages can be divided into the virulent phages and the temperate phages. Classifying virulent and temperate phages is crucial for further understanding of the phage–host interactions. Although there are several methods designed for phage lifestyle classification, they merely either consider sequence features or gene features, leading to low accuracy. A new computational method, DeePhafier, is proposed to improve classification performance on phage lifestyle. Built by several multilayer self-attention neural networks, a global self-attention neural network, and being combined by protein features of the Position Specific Scoring Matrix matrix, DeePhafier improves the classification accuracy and outperforms two benchmark methods. The accuracy of DeePhafier on five-fold cross-validation is as high as 87.54% for sequences with length >2000bp.

Viruses are the most abundant biological entities on earth and are important components of microbial communities. A metagenome contains all microorganisms from an environmental sample. Correctly identifying viruses from these mixed sequences is critical in viral analyses. It is common to identify long viral sequences, which has already been passed thought pipelines of assembly and binning. Existing deep learning-based methods divide these long sequences into short subsequences and identify them separately. This makes the relationships between them be omitted, leading to poor performance on identifying long viral sequences. In this paper, VirGrapher is proposed to improve the identification performance of long viral sequences by constructing relationships among short subsequences from long ones. VirGrapher see a long sequence as a graph and uses a Graph Convolutional Network (GCN) model to learn multilayer connections between nodes from sequences after a GCN-based node embedding model. VirGrapher achieves a better AUC value and accuracy on validation set, which is better than three benchmark methods.

Motivation: Virulence factors (VFs) mediate host adhesion, invasion, immune evasion and toxin-mediated damage, making accurate VF prediction important for understanding bacterial pathogenesis and antimicrobial intervention. Existing predictors mainly use one-dimensional (1D) protein sequences, overlooking complementary coding DNA sequence (CDS)-level information and three-dimensional (3D) structural topology. Results: We propose DeepGVS, a bimodal deep learning framework integrating CDS-derived features with protein sequence and structural representations for VF prediction. DeepGVS extracts multi-scale sequence-composition features from CDS. Concurrently, it employs a parallel graph attention network (GAT) and bidirectional Mamba (Bi-Mamba) architecture to process ESMFold-predicted structures and residue representations, capturing spatial and long-range dependencies. A neural additive model (NAM) functions as a meta-learner to integrate base-classifier predictions. DeepGVS was evaluated on the unchanged accession-level independent test set of Dataset_B and achieved an accuracy of 87.50%, corresponding to absolute improvements of 6.30, 2.60 and 1.40 percentage points over the published benchmark values of DeepVF, GTAE-VF and PLMVF, respectively. The incremental benefit of bimodal integration was dataset- and metric-dependent, and additional taxonomic analyses identified taxonomy as a potential confounding factor that does not fully reproduce the performance of the complete model. Availability and implementation: Source code, datasets and pretrained models are available at https://github.com/guoguo26/DeepGVS. The archived code version used for the reported experiments is available at https://doi.org/10.5281/zenodo.22764397.

Haplotype phasing refers to determining the haplotype sequences inherited from each parent in a diploid organism. It is a critical process for various downstream analyses, and numerous haplotype phasing methods for genomic single nucleotide polymorphisms (SNPs) have been developed. Allele-specific (AS) expression and alternative splicing play key roles in diverse biological processes. AS studies usually focus more on exonic SNPs, and multiple phased SNPs need to be combined to obtain better inferences. In this paper, we introduce an alignment-free algorithm HPTAS for haplotype phasing in AS studies. Instead of using sequence alignment to count the number of reads covering SNPs, HPTAS constructs a mapping structure from transcriptome annotations and SNPs and employs a k-mer-based approach to derive phasing counts from RNA-seq data. Using both next-generation sequencing (NGS) and the third-generation sequencing (TGS) NA12878 RNA-seq data and comparing with the most advanced algorithm in the field, we have demonstrated that HPTAS achieves high phasing accuracy and performance and that transcriptome data indeed facilitates the phasing of exonic SNPs. With the continued advancement of sequencing technology and the improvement in transcriptome annotations, HPTAS may serve as a foundation for future haplotype phasing methods.