DNA contigs binning is necessary to reconstruct metagenome-assembled genomes. Current metagenomic DNA contigs binning methods often leverage coverage profiles across multiple related metagenomes and have demonstrated strong performance on co-assembled contigs. However, in single-sample scenarios where coverage information is rare, their performance drops significantly, limiting the in-depth development of metagenomics at the individual sample level. To address this issue, we propose DCVBin, a novel single-sample metagenomic contigs binning method that incorporates semantic features extracted from a DNA language model. Specifically, our approach continues pretraining on a DNA language model to capture more domain-specific semantic representations, which are then integrated with 4-mer frequencies using a variational autoencoder. Clustering is subsequently performed using the k-means algorithm, in which the number of clusters is determined by single copy genes. Experimental results on six publicly available datasets demonstrate that DCVBin achieves high-accuracy single-sample metagenomic binning and outperforms other state-of-the-art methods. Furthermore, DCVBin is included into a disease diagnostic framework that is evaluated on a cohort of gut metagenomes from people with colorectal cancer and healthy people. The framework is shown to be accurate in predicting colorectal cancer using gut metagenomes and has identified a list of potential microbial biomarkers.

Antibiotic resistance poses a significant threat to global health, making the development of alternative strategies to combat bacterial pathogens increasingly urgent. One such promising approach is the strategic use of bacteriophages (or phages) to specifically target and eradicate antibiotic-resistant bacteria. Phages, being among the most prevalent life forms on Earth, play a critical role in maintaining ecological balance by regulating bacterial communities and driving genetic diversity. Accurate prediction of phage hosts is essential for successfully applying phage therapy. However, existing prediction models may not fully encapsulate the complex dynamics of phage-host interactions in diverse microbial environments, indicating a need for improved accuracy through more sophisticated modeling techniques. In response to this challenge, this study introduces a novel phage-host prediction model, PHPGAT, which leverages a multimodal heterogeneous knowledge graph with the advanced GATv2 (Graph Attention Network v2) framework. The model first constructs a multimodal heterogeneous knowledge graph by integrating phage-phage, host-host, and phage-host interactions to capture the intricate connections between biological entities. GATv2 is then employed to extract deep node features and learn dynamic interdependencies, generating context-aware embeddings. Finally, an inner product decoder is designed to compute the likelihood of interaction between a phage and host pair based on the embedding vectors produced by GATv2. Evaluation results using two datasets demonstrate that PHPGAT achieves precise phage host predictions and outperforms other models. PHPGAT is available at https://github.com/ZhaoZMer/PHPGAT.

Metagenome sequencing provides an unprecedented opportunity for the discovery of unknown microbes and viruses. A large number of phages and prokaryotes are mixed together in metagenomes. To study the influence of phages on human bodies and environments, it is of great significance to isolate phages from metagenomes. However, it is difficult to identify novel phages because of the diversity of their sequences and the frequent presence of short contigs in metagenomes. Here, virSearcher is developed to identify phages from metagenomes by combining the convolutional neural network (CNN) and the gene information of input sequences. Firstly, an input sequence is encoded in accordance with the different functions of its coding and the non-coding regions and then is converted into word embedding code through a word embedding layer before a convolutional layer. Meanwhile, the hit ratio of the virus genes is combined with the output of the CNN to further improve the performance of the network. The genes used by virSearcher consist of complete and incomplete genes. Experiments on several metagenomes have showed that, compared with others, virSearcher can significantly improve the performance for the identification of short sequences, while maintaining the performance for long ones. The source code of virSearcher is freely available from http://github.com/DrJackson18/virSearcher.

MOTIVATION: Viruses, the most abundant biological entities on earth, are important components of microbial communities, and as major human pathogens, they are responsible for human mortality and morbidity. The identification of viral sequences from metagenomes is critical for viral analysis. As massive quantities of short sequences are generated by next-generation sequencing, most methods utilize discrete and sparse one-hot vectors to encode nucleotide sequences, which are usually ineffective in viral identification. RESULTS: In this article, Virtifier, a deep learning-based viral identifier for sequences from metagenomic data is proposed. It includes a meaningful nucleotide sequence encoding method named Seq2Vec and a variant viral sequence predictor with an attention-based long short-term memory (LSTM) network. By utilizing a fully trained embedding matrix to encode codons, Seq2Vec can efficiently extract the relationships among those codons in a nucleotide sequence. Combined with an attention layer, the LSTM neural network can further analyze the codon relationships and sift the parts that contribute to the final features. Experimental results of three datasets have shown that Virtifier can accurately identify short viral sequences (<500 bp) from metagenomes, surpassing three widely used methods, VirFinder, DeepVirFinder and PPR-Meta. Meanwhile, a comparable performance was achieved by Virtifier at longer lengths (>5000 bp). AVAILABILITY AND IMPLEMENTATION: A Python implementation of Virtifier and the Python code developed for this study have been provided on Github https://github.com/crazyinter/Seq2Vec. The RefSeq genomes in this article are available in VirFinder at https://dx.doi.org/10.1186/s40168-017-0283-5. The CAMI Challenge Dataset 3 CAMI_high dataset in this article is available in CAMI at https://data.cami-challenge.org/participate. The real human gut metagenomes in this article are available at https://dx.doi.org/10.1101/gr.142315.112. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Viruses are the most abundant biological entities on earth, and play vital roles in many aspects of microbial communities. As major human pathogens, viruses have caused huge mortality and morbidity to human society in history. Metagenomic sequencing methods could capture all microorganisms from microbiota, with sequences of viruses mixed with these of other species. Therefore, it is necessary to identify viral sequences from metagenomes. However, existing methods perform poorly on identifying short viral sequences. To solve this problem, a deep learning based method, RNN-VirSeeker, is proposed in this paper. RNN-VirSeeker was trained by sequences of 500bp sampled from known Virus and Host RefSeq genomes. Experimental results on the testing set have shown that RNN-VirSeeker exhibited AUROC of 0.9175, recall of 0.8640 and precision of 0.9211 for sequences of 500bp, and outperformed three widely used methods, VirSorter, VirFinder, and DeepVirFinder, on identifying short viral sequences. RNN-VirSeeker was also used to identify viral sequences from a CAMI dataset and a human gut metagenome. Compared with DeepVirFinder, RNN-VirSeeker identified more viral sequences from these metagenomes and achieved greater values of AUPRC and AUROC. RNN-VirSeeker is freely available at https://github.com/crazyinter/RNN-VirSeeker.

Metagenomic contigs binning is a necessary step of metagenome analysis. After assembly, the number of contigs belonging to different genomes is usually unequal. So a metagenomic contigs dataset is a kind of imbalanced dataset and traditional fuzzy c-means method (FCM) fails to handle it very well. In this paper, we will introduce an improved version of fuzzy c-means method (IFCM) into metagenomic contigs binning. First, tetranucleotide frequencies are calculated for every contig. Second, the number of bins is roughly estimated by the distribution of genome lengths of a complete set of non-draft sequenced microbial genomes from NCBI. Then, IFCM is used to cluster DNA contigs with the estimated result. Finally, a clustering validity function is utilized to determine the binning result. We tested this method on a synthetic and two real datasets and experimental results have showed the effectiveness of this method compared with other tools.

In order to get better results and faster speed on medical image fusion, a method based on non-sub sampled contour let transform in compressed sensing was proposed. Because of the large sparsity and sharp contrast between the black and the white of medical images, the energy and average gradient were utilized to design the fusion rules to fuse the low-frequency components and the high-frequency components respectively. The image entropy, relative quality, average gradient, standard deviation and spatial frequency were used to evaluate the fusion results objectively. Experiments show that under the premise of maintaining a certain reconstruction quality the sample rates and calculation amounts are lower, the convergence can be sped up and the fusion results can be improved.