Most metagenomic studies focus on microbes at the species level, thus ignoring the different effects of different strains of the same species on the host. In this study, we explored the different microbes at the strain level in the intestines of patients with liver cirrhosis and of healthy people.
Rapidly developing single-cell multi-omics sequencing technologies generate increasingly large bodies of multimodal data. Integrating multimodal data from different sequencing technologies, i.e. mosaic data, permits larger-scale investigation with more modalities and can help to better reveal cellular heterogeneity. However, mosaic integration involves major challenges, particularly regarding modality alignment and batch effect removal. Here we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation, and batch correction of mosaic data by employing self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to other methods and reliability by evaluating its performance in full trimodal integration and various mosaic tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells (PBMCs), and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data.
At present, much attention has been paid to the ecology, economics, and social benefits of erosion control projects: however, the evaluation of an erosion control technology itself has been neglected. This study selected six soil conservation measures applied to the Loess Plateau, and a comprehensive evaluation model was developed considering the maturity of the technology, application difficulty of the technology, technology efficiency, and the potential of technology promotion. The relation between a condition attribute and a decision attribute is evaluated using rough set theory, and the decision attribute is completely dependent on the condition attribute, which indicates that the index system can better evaluate the soil conservation measures applied to the Loess Plateau. Rough set theory was used to determine the weights of evaluation indexes, which overcomes the limitation of relying only on expert opinions or index data to determine the weights. According to the comprehensive scores, the six soil conservation measures can be grouped into three levels: the first level includes economic forests, check dams, and terraces; the second level includes afforestation and conversion to grassland, and the third level includes enclosures. The results can provide a scientific basis for the promotion and application of the high-ranking soil conservation measures in the Loess Plateau. However, the comprehensive evaluation of the soil conservation measures applied to the Loess Plateau is a very complex problem. To maximize the eco-environmental benefits, land use patterns should be rationally adjusted, and corresponding soil conservation measures could be suitable for meeting the regional development goals.
Intestinal bacteria strains play crucial roles in maintaining host health. Researchers have increasingly recognized the importance of strain-level analysis in metagenomic studies. Many analysis tools and several cutting-edge sequencing techniques like single cell sequencing have been proposed to decipher strains in metagenomes. However, strain-level complexity is far from being well characterized up to date. As the indicator of strain-level complexity, metagenomic single-nucleotide polymorphisms (SNPs) have been utilized to disentangle conspecific strains. Lots of SNP-based tools have been developed to identify strains in metagenomes. However, the sufficient sequencing depth for SNP and strain-level analysis remains unclear. We conducted ultra-deep sequencing of the human gut microbiome and constructed an unbiased framework to perform reliable SNP analysis. SNP profiles of the human gut metagenome by ultra-deep sequencing were obtained. SNPs identified from conventional and ultra-deep sequencing data were thoroughly compared and the relationship between SNP identification and sequencing depth were investigated. The results show that the commonly used shallow-depth sequencing is incapable to support a systematic metagenomic SNP discovery. In contrast, ultra-deep sequencing could detect more functionally important SNPs, which leads to reliable downstream analyses and novel discoveries. We also constructed a machine learning model to provide guidance for researchers to determine the optimal sequencing depth for their projects (SNPsnp, https://github.com/labomics/SNPsnp). To conclude, the SNP profiles based on ultra-deep sequencing data extend current knowledge on metagenomics and highlights the importance of evaluating sequencing depth before starting SNP analysis. This study provides new ideas and references for future strain-level investigations.
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