Sheng Ye

Structural variants (SVs) are major sources of human genetic diversity. Despite advances in SV discovery, most single-nucleotide polymorphism (SNP)-centric frameworks overlook sequence-level information and encode overlapping variants as independent features, potentially reducing power in association studies, biasing heritability estimation, and limiting prediction accuracy. Here, we present LAVrefiner, a scalable, sequence-aware method that decomposes overlapping sequence-resolved SVs and indels into nonoverlapping refined sequence components and generates genotype matrices compatible with standard SNP-based analyses. In tandem repeat (TR) regions, LAVrefiner further supports motif-aware allele comparison and repeat copy-number estimation. When applied to variants from the 1000 Genomes Project, LAVrefiner identifies 224,708 refined variants. Simulations show that LAVrefiner is fast and memory-efficient while maintaining robust genotype representations under TR-associated sequencing noise. In gene expression analyses, models combining LAVrefiner-derived copy-number estimates with refined variants yield the highest heritability estimates and prediction accuracy in short tandem repeat (STR) and variable number tandem repeat (VNTR) regions, outperforming models based on the original SV/indel representation and existing TR copy-number tools. Cis -expression quantitative trait locus ( cis -eQTL) mapping further shows that refined variants can localize regulatory signals to specific sequence components, including an independent VNTR-derived signal at the TFRC locus. Together, LAVrefiner provides an efficient, context-aware strategy for integrating sequence-resolved SVs and indels into existing genetic analysis frameworks.

Synthetic genomics is advancing from microbial toward multicellular organisms. However, current manual methods for DNA and genome assembly remain inadequate for the efficient, large-scale production of long DNA constructs. Here, we present Programmed DNA Assembly via Cas9 and Conjugative Transfer (PACT), a method that integrates a linear vector system, bacterial conjugation, and programmable Cas9-mediated cleavage to achieve highly efficient, iterative assembly of large DNA fragments. PACT enhances assembly efficiency by ~30-fold compared to conventional circular vector strategies, enabling one-step assembly of DNA up to 80 kb. We engineered four single guide-RNA-Marker donor cassettes to support iterative assembly workflows. PACT can utilize low-recombination Escherichia coli strains as hosts to efficiently assemble Arabidopsis thaliana mitochondrial genome with high repeat units. Integrated with an automated robotic platform, we developed an unattended, high-throughput pipeline (aPACT) toward large-scale parallel DNA assembly. Using aPACT, we successfully assembled three large DNA constructs: a 210 kb digital DNA, the designed chloroplast (120 kb) and mitochondrial (350 kb) genome of A. thaliana. This automated system offers a powerful tool for scalable assembly of large DNA molecules, accelerating synthetic genomics research toward complex multicellular organisms.

Peptide therapeutics are rapidly advancing in the biopharmaceutical field, but the high cost and low throughput of experimental screening call for more efficient discovery pipelines. We present an AI-driven framework for exhaustive hexapeptide screening that integrates physical interaction data with high-dimensional sequence features. The two-stage pipeline first encodes peptide-protein complexes using a fragmentation-based representation to capture local pairing patterns. Then, docking-derived binding energies are combined with a transformer-based model to learn sequence-energy relationships for each target. Applied to NRP-1, STING, and cGAS, the workflow achieved high hit rates and identified novel hexapeptides with micromolar binding affinities. By bridging physical modeling with enriched feature representations, this framework offers a robust, generalizable solution that accelerates peptide drug discovery.

Synthetic and natural materials with favorable structural support and biocompatibility are widely employed for tissue repair. To enhance their biological performance, surface modification with bioactive factors is commonly applied. However, most current methods lack modularity and versatility, limiting their broader application in regenerative medicine. In this work, we developed a bioactive material, termed SpyME, by integrating the mussel adhesive protein (MAP) and epidermal growth factor (EGF) with the Spycatcher-Spytag system. This construct exhibits strong and versatile adhesion to a variety of substrates via the MAP module, while maintaining the bioactivity of EGF. The combined use of SpyME and Hydrosorb hydrogel significantly accelerated full-thickness wound healing in a rat model compared to Hydrosorb alone. These results indicate that SpyME can effectively functionalize biomaterials, conferring enhanced bioactivity and improved therapeutic outcomes in biomedical applications. Furthermore, the modular design of SpyME offers broad potential for diverse applications, including growth factor immobilization and surface engineering of clinical implantable materials.