Biology subjects

Sun, Y. H.

Publications and source records attributed to Sun, Y. H..

6 recordsLinked to original sources

bioRxiv2025

SpaceSequest: A unified pipeline for spatial transcriptomics data analysis

BackgroundSpatial transcriptomics has emerged as one of the most powerful tools for gaining biological insights, enabling researchers to uncover intricate relationships between gene expression patterns and tissue architecture. Recent advances in the field have resulted in a variety of new platforms, including Visium, Visium HD, and Xenium from 10x Genomics, as well as GeoMx and CosMx from NanoString Technologies, which has now been acquired by the Bruker Corporation. However, the existence of diverse spatial transcriptomics platforms and various data formats poses challenges in standardizing data analysis. Thus, there remains a critical gap in the availability of a comprehensive pipeline capable of conducting end-to-end analysis that is necessary to extract biological insights from multiple spatial transcriptomics platforms. ResultsHere, we present SpaceSequest, a tailored pipeline that utilizes cutting-edge computational methodologies to conduct a thorough analysis, enabling the extraction of crucial biological insights from five major spatial transcriptomics technologies. SpaceSequest performs (1) standardized quality control and general data processing, (2) key analyses customized for each spatial platform, (3) automated cell type annotation and deconvolution, and (4) high-quality figure and analysis result generation. In addition, SpaceSequest allows for smooth integration with cellxgene VIP and Quickomics for user-friendly data access and interactive visualization. ConclusionsSpaceSequest is a unified and comprehensive pipeline designed for the analysis, visualization, and publication of spatial transcriptomics data from various platforms. The source code is available at https://github.com/interactivereport/SpaceSequest. To facilitate seamless installation and usage, we have also created a detailed Bookdown tutorial that can be accessed through https://interactivereport.github.io/SpaceSequest/tutorial/docs/index.html.

bioinformatics↗

bioRxiv2025

CompBioAgent: an LLM-powered agent for single-cell RNA-seq data exploration

AO_SCPLOWBSTRACTC_SCPLOWAdvancements in high-throughput biological technologies, particularly single-cell RNA sequencing (scRNA-seq), have generated vast amounts of complex data that offer valuable insights into gene expression, cellular behavior, and disease mechanisms. However, the analysis of such data often requires specialized computational skills and expertise, which can pose a barrier to many researchers. To address this challenge, we developed CompBioAgent, a user-friendly web application designed to democratize access to bioinformatics resources, powered by Large Language Models (LLMs). By integrating with the CellDepot, CompBioAgent allows users to easily query and explore gene expression data related to various diseases, cell types, and experimental conditions. Moreover, the tool employs Cellxgene Visualization In Plugin (Cellxgene VIP) platform to generate a range of intuitive visualizations, including violin plots, UMAP embeddings, heatmaps, etc. With its natural language interface, CompBioAgent automatically converts scientific queries into structured data requests and returns the plot of interest, enabling seamless exploration of biological data without the need for programming expertise. Such integration enhances the ability of researchers to visualize complex biological insights and identify key patterns, making high-level data analysis more accessible and efficient. A demo website and a list of examples are available at https://celldepot.bxgenomics.com/compbioagent, and the source code is released at https://github.com/interactivereport/compbioagent.

bioinformatics↗

bioRxiv2025

xOmicsShiny: an R shiny application for cross-omics data analysis and pathway mapping

SummaryWe developed xOmicsShiny, a feature-rich R Shiny-powered application that enables biologists to fully explore omics datasets across experiments and data types, with an emphasis on uncovering biological insights at the pathway level. The data merging feature ensures flexible exploration of cross-omics data, such as transcriptomics, proteomics, metabolomics, and lipidomics. The pathway mapping function covers a broad range of databases, including WikiPathways, Reactome, and KEGG pathways. In addition, xOmicsShiny offers several visualization options and analytical tasks for everyday omics data analysis, namely, PCA, Volcano plot, Venn Diagram, Heatmap, WGCNA, and advanced clustering analyses. The application employs customizable modules to perform various tasks, generating both interactive plots and publication-ready figures. This dynamic, modular design overcomes the issue of slow loading in R Shiny tools and allows it to be readily expanded by the research and developer community. Availability and implementationThe R Shiny application is publicly available at: http://xOmicsShiny.bxgenomics.com. Researchers can upload their own data to the server or use the pre-loaded demo dataset. The source code, under MIT license is provided at https://github.com/interactivereport/xOmicsShiny for local installation. A full tutorial of the application is available at https://interactivereport.github.io/xOmicsShiny/tutorial/docs/index.html. Contactyuhenry.sun@biogen.com or baohong.zhang@biogen.com Supplementary dataSupplementary data are available at bioRxiv online.

bioinformatics↗

bioRxiv2022

Revisiting chromatin packaging in mouse sperm

Mammalian sperm exhibit an unusual and heavily-compacted genomic packaging state. In addition to its role in organizing the compact and hydrodynamic sperm head, it has been proposed that sperm chromatin architecture helps to program gene expression in the early embryo. Scores of genome-wide surveys in sperm have reported patterns of chromatin accessibility, histone localization, histone modification, and chromosome folding. Here, we revisit these studies in light of recent reports that sperm obtained from the mouse epididymis are contaminated with low levels of cell-free chromatin. In the absence of proper sperm lysis we readily recapitulate multiple prominent genome-wide surveys of sperm chromatin, suggesting that these profiles primarily reflect contaminating cell-free chromatin. Removal of cell-free DNA, along with appropriate lysis conditions, are required to reveal a sperm chromatin state distinct from most previous reports. Using ATAC-Seq to explore relatively accessible genomic loci, we identify a landscape of open loci associated with early development and transcriptional control. Histone modification and chromosome folding studies also strongly support the hypothesis that prior studies suffer from contamination, but technical challenges associated with reliably preserving the architecture of the compacted sperm head prevent us from confidently assaying true localization patterns for these epigenetic marks. Together, our studies strongly argue that our knowledge of mammalian chromosome packaging remains largely incomplete, and motivate future efforts to more accurately characterize genome organization in mature sperm.

molecular biology↗

bioRxiv2021

Coupled protein synthesis and ribosome-guided piRNA processing on mRNAs

PIWI-interacting small RNAs (piRNAs) protect the germline genome and are essential for fertility. Previously, we showed that ribosomes guide the biogenesis of piRNAs from long non-coding RNAs (lncRNAs) after translating the short open reading frames (ORFs) near their 5 cap. It remained unclear, however, how ribosomes proceed downstream of ORFs and how piRNA precursors distinguish from other RNAs. It is thus important to test whether a short ORF length is required for substrate recognition for ribosome guided-piRNA biogenesis. Here, we characterized a poorly understood class of piRNAs that originate from the 3 untranslated regions (3UTRs) of protein coding genes in mice and chickens. We demonstrate that their precursors are full-length mRNAs and that post-termination 80S ribosomes guide piRNA production on 3UTRs after translation of upstream long ORFs. Similar to non-sense mediated decay (NMD), piRNA biogenesis degrades mRNA right after pioneer rounds of translation and fine-tunes protein production from mRNAs. Interestingly, however, we found that NMD, along with other surveillance pathways for ribosome recycling are temporally sequestered during the pachytene stage to allow for robust piRNA production. Although 3UTR piRNA precursor mRNAs code for distinct proteins in mice and chickens, they all harbor embedded transposable elements (TEs) and produce piRNAs that cleave TEs, suggesting that TE suppression, rather than the function of proteins, is the primary evolutionary force maintaining a subset of mRNAs as piRNA precursors. Altogether, we discover a function of the piRNA pathway in fine-tuning protein production and reveal a conserved, general piRNA biogenesis mechanism that recognizes translating RNAs regardless of their ORF length in amniotes.

molecular biology↗

bioRxiv2020

Single-molecule long-read sequencing reveals a conserved selection mechanism determining intact long RNA and miRNA profiles in sperm

Sperm contributes diverse RNAs to the zygote. While sperm small RNAs have been shown to be shaped by paternal environments and impact offspring phenotypes, we know little about long RNAs in sperm, including mRNAs and long non-coding RNAs. Here, by integrating PacBio single-molecule long reads with Illumina short reads, we found 2,778 sperm intact long transcript (SpILT) species in mouse. The SpILTs profile is evolutionarily conserved between rodents and primates. mRNAs encoding ribosomal proteins are enriched in SpILTs, and in mice they are sensitive to early trauma. Mouse and human SpILT profiles are determined by a post-transcriptional selection process during spermiogenesis, and are co-retained in sperm with base pair-complementary miRNAs. In sum, we have developed a bioinformatics pipeline to define intact transcripts, added SplLTs into the "sperm RNA code" for use in future research and potential diagnosis, and uncovered selection mechanism(s) controlling sperm RNA profiles.

genomics↗