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Zilong Bai

Publications and source records attributed to Zilong Bai.

2 recordsLinked to original sources

Development and evaluation of a multiplex PCR-based dual-platform targeted sequencing framework for precise differentiation of lumpy skin disease virus.

BACKGROUND: Lumpy skin disease virus (LSDV) shares over 96% genomic identity with goatpox and sheeppox viruses, presenting severe diagnostic challenges due to cross-reactivity. METHODS: To address this bottleneck, we established a targeted sequencing framework integrating multiplex PCR with short-read and long-read platforms. By sequentially screening target pathogens, identifying low-homology genes, and designing short and gradient long-fragment primer pools, we evaluated these dual-platform panels using highly homologous poxvirus samples. RESULTS: The short-read panel stably detected target viruses at inputs as low as 5.26 ×101 copies/μL. Under strict alignment criteria, LSDV mapping rates reached 42.91%, suppressing non-target signals to 3.05%. The Nanopore-Targeted Sequencing (NTS) long-amplicon strategy successfully eliminated homologous interference. By applying length-dependent diagnostic thresholds (≥ 100 reads for short amplicons; ≥ 50 reads for long amplicons), precise species-level identification was achieved, maintaining near-zero cross-reads (0-5) in ultra-long regions. Crucially, the field-deployable NTS workflow enabled complete detection in approximately 4 h. CONCLUSION: This complementary strategy seamlessly meets both laboratory demands for high-sensitivity enrichment and frontline requirements for rapid typing, providing a reliable tool for LSDV surveillance, mutation tracking, and outbreak control.

Capripoxvirus differentiation

Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique.

The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells. Here, we present a protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique called moETM. We describe steps for data preprocessing, multi-omics integration, inclusion of prior pathway knowledge, and cross-omics imputation. As a demonstration, we used the single-cell multi-omics data collected from bone marrow mononuclear cells (GSE194122) as in our original study. For complete details on the use and execution of this protocol, please refer to Zhou et al.1.

Deep Learning