PubMed HealthSearch

Biomedical subjects

Yiqing Wang

Publications and source records attributed to Yiqing Wang.

2 recordsLinked to original sources

Genome-Wide Identification of the LdARF Gene Family in Lilium davidii var. unicolor and Transient Functional Analysis of LdARF17 in Bulblet Regeneration.

Auxin response factors (ARFs) are key transcriptional regulators of the auxin signaling pathway and play important roles in plant organogenesis and regeneration. However, the functions of ARF family genes in lily scale-derived bulblet regeneration remain largely unclear. In this study, 24 LdARF genes were identified from the genome of Lilium davidii var. unicolor. Phylogenetic analysis revealed that LdARF proteins showed evolutionary conservation with ARF homologs from other monocot species. Genome-wide identification, phylogenetic analysis, and expression profiling revealed functional divergence among LdARF genes during scale-derived bulblet regeneration. Among them, LdARF17 exhibited a distinct regeneration-associated expression pattern, characterized by rapid induction after scale excision and sustained high expression during subsequent bulblet initiation and formation. Subcellular localization analysis demonstrated that LdARF17 is localized in the nucleus. Transient overexpression of LdARF17 significantly promoted bulblet regeneration and was associated with increased expression of auxin-responsive and regeneration-related genes, including IAA14, LBD16, and LBD29. These findings suggest that LdARF17 acts as a positive regulator of lily scale regeneration and may influence auxin-responsive transcriptional processes associated with early cell proliferation, providing new insights into the molecular mechanisms underlying vegetative regeneration in lilies.

Auxin response factor

scBSP: a fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data.

MOTIVATION: Emerging spatial omics technologies empower comprehensive exploration of biological systems from multi-omics perspectives in their native tissue location in 2D and 3D space. However, the limited sequencing depth, increasing spatial resolution, and growing spatial spots in spatial omics technologies present significant computational challenges in identifying biologically meaningful molecules with variable spatial distributions across various omics modalities. RESULTS: We introduce scBSP, an open-source, versatile, and user-friendly package for identifying spatially variable features in large-scale spatial omics data. scBSP demonstrates significantly enhanced computational efficiency, processing high-resolution spatial omics data within seconds, and exhibits robust cross-platform performance by consistently identifying spatially variable features with high reproducibility across various sequencing platforms. AVAILABILITY AND IMPLEMENTATION: scBSP is available for download from R CRAN at https://cran.r-project.org/web/packages/scBSP/index.html and PyPI at https://pypi.org/project/scbsp/.

Software