Biology subjects

Xuan, H.

Publications and source records attributed to Xuan, H..

8 recordsLinked to original sources

bioRxiv2026

Automatic bioinformatic software named entity recognition from literature

Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated biomedical knowledge extraction and streamlined data analysis. Here we present SNAIL, a hybrid named entity recognition framework designed to automatically identify bioinformatics software and database (SW/DB) names from biomedical texts. SNAIL integrates complementary lexical and semantic modeling strategies. The lexical component captures orthographic patterns and contextual cues characteristic of SW/DB names, while the semantic component leverages contextual embeddings generated by transformer-based language models such as SciBERT, combined with an explicit token-masking strategy to enhance entity-focused representations. A large training corpus was constructed automatically through a hybrid pipeline that integrates citation-hinted extraction with large language model-assisted distillation. Evaluation on two independent benchmark datasets and real-world research articles demonstrates that SNAIL substantially outperforms existing approaches, including domain-specific methods such as bioNerDS2 and general-purpose large language models such as ChatGPT, Gemini, Grok and Claude. Applying SNAIL to large-scale literature analysis further reveals distinct journal-level preferences across bioinformatics subfields. These results demonstrate that SNAIL provides an accurate and scalable solution for identifying bioinformatics resources in scientific texts and enables systematic meta-analysis of tool usage and research trends.

bioinformatics↗

bioRxiv2026

Corpusome, a cross-body-site human microbiome corpus for representation learning

Machine-learning models of the human microbiome are trained mostly on stool samples from single cohorts, limiting cross-body-site representation and cross-study generalization. Progress is constrained less by algorithms than by the absence of a harmonized multi-body-site corpus carrying the technical metadata needed to model, rather than ignore, batch structure. Here we release Corpusome, a harmonized two-tier cross-body-site human microbiome corpus for representation learning: a harmonized corpus of 187,546 human microbiome samples integrating standardized profiles from curatedMetagenomicData, the American Gut Project, and the EBI MGnify platform. Corpusome follows a two-tier design preserving both functional depth and cross-body-site breadth: a shotgun tier (22,588 samples, 93 studies) with species- and pathway-level profiles, and a 16S tier (164,958 samples, from a full pull of 708 MGnify studies) with genus-level profiles extending coverage to oral, skin, respiratory, and urogenital sites. It spans six body sites and two modalities, with harmonized metadata for batch-aware modelling. Body-site signal exceeds technical/source variance in the 16S tier by approximately 2.4-fold.

microbiology↗

bioRxiv2026

SenSASP: A Unified, Multi-Layer Database of Senescence and SASP Genes

Research on cellular senescence and the senescence-associated secretory phenotype (SASP) draws on independently curated gene resources that differ in scope, identifiers, and update cycles, making cross-resource integration error-prone. We unified four widely used resources, CellAge, GenAge, the SenMayo signature, and the Reactome Cellular Senescence pathway, onto a single canonical identifier (the Ensembl gene ID) and enriched every gene with three annotation layers absent from all four inputs: cross-species conservation, tissue and cell-type expression, and high-confidence protein-protein interactions. Unification collapsed 1,460 summed source entries into 1,250 unique genes (210 redundant entries removed, 14.4%) while preserving full source provenance: 173 genes are corroborated by two or more resources and two (IL6, JUN) by all four. The three annotation layers reach 95.8%, 97.9%, and 93.0% of genes, with 89.4% annotated across all three. A 500-gene random sample of identifier mappings was validated against HGNC and Ensembl (98.0% exact match). The result, SenSASP, is a single, machine-readable, provenance-tracked database of harmonized identifiers and net-new functional context, illustrated here with a gene-prioritization score and a tissue-expression atlas. SenSASP is freely available at https://xuan13hao.github.io/sensasp/

cell biology↗

bioRxiv2026

A general and extensible algorithmic framework to biological sequence alignment across scales and applications

Sequence alignment underpins nearly every facet of modern genomics, from genetic testing and cancer profiling to functional genome annotation. Yet, despite decades of algorithmic innovation, most existing aligners remain narrowly optimized for specific tasks, fragmenting analytical workflows and limiting reproducibility. Here we introduce the Versatile Alignment Toolkit (VAT), a unified algorithmic framework that generalizes existing seeding and genome-indexing strategies within a single, transparent architecture. VAT employs a novel multi-view indexing scheme that integrates multiple seeding strategies and supports run-time seed-length parameterization without reindexing. A radix clustering algorithm based on hardware-efficient in-register bitonic sort accelerates multi-view table construction, ensuring scalability across large datasets. VAT delivers consistently high performance across diverse alignment tasks, including short- and long-read mapping, homology search, and whole-genome alignment, while maintaining algorithmic simplicity and adaptability. By bridging previously isolated alignment paradigms, VAT substantially reduces workflow complexity, enhances computational efficiency, and establishes an extensible foundation for future sequencing technologies. We anticipate this unification will set a new standard for flexible and reproducible sequence alignment in biomedical research and clinical genomics.

bioinformatics↗

bioRxiv2025

CNA detection from spatial transcriptomics using SpaCNA

Spatial transcriptomics (ST) enables genome-wide profiling of gene expression while preserving spatial context, yet accurate detection of copy number alterations (CNAs) in tumor ST data remain challenging. Here we present SpaCNA, a spatial-aware computational framework that integrates multi-modal spatial information to improve CNA detection. SpaCNA constructs adjacency graphs using spatial proximity and H&E stained image similarity, refines the raw gene expressions, and implements a Hidden Markov Random Field model for robust CNA state inference. Through extensive benchmarking on simulated data and multi-cancer cohorts, SpaCNA demonstrates superior accuracy and outperforms existing methods in CNA detection and tumor region identification. In real-world applications to breast cancer and colorectal cancer, SpaCNA reveals spatially distinct subclones with context-dependent interactions within the microenvironment. Additionally, applied to a 3D ST dataset of head and neck squamous cell carcinoma, SpaCNA uncovers clone-specific CNAs associated with therapeutic resistance biomarkers across multiple slices. By facilitating precise spatial mapping of CNAs and tumor architecture, SpaCNA significantly enhances our understanding of intratumoral heterogeneity and spatial evolutionary patterns.

bioinformatics↗

bioRxiv2025

Aptamer-Enabled Discovery and Clinical Analysis of Exosomal Surface Biomarkers in Hepatocellular Carcinoma

Exosomal membrane proteins hold promise as cancer biomarkers but face detection challenges due to low abundance and conformational sensitivity. We developed AptEx-ID, a microbead-displayed aptamer platform enabling high- throughput screening of hepatocellular carcinoma (HCC)-specific exosomal surface proteins. Aptamers, selected via competitive binding to tumor-derived versus healthy exosomes, demonstrated high affinity and specificity. Clinical validation using 50 HCC and 30 control plasma samples identified a 7-aptamer panel via machine learning, achieving 93.75% accuracy and 0.95 ROC-AUC. These aptamers further revealed HCC-associated biomarkers (e.g., IMPDH1, ASCC3, GTSE1), validated by siRNA knockdown and biophysical assays. Post-treatment monitoring showed aptamer signal reduction in responsive patients, highlighting their prognostic utility. This platform integrates biomarker discovery, diagnostics, and therapeutic monitoring, offering a robust approach for HCC management through exosomal surface proteome profiling.

molecular biology↗

bioRxiv2022

Phase-separated TRB-PRC2 aggregates contribute to Polycomb silencing in plants

Local accumulation of Polycomb Repressive Complex 2 (PRC2) is essential to gene silencing. Liquid-liquid phase separation (LLPS) mechanism is emerging as a paradigm to concentrate transcriptional machinery for effective gene regulation. Here, we elucidate that a rice single Myb transcription factor TRBF2 forms phase-separated droplets, which aggregate with PRC2 through direct protein interaction. Furthermore, TRB1, the closest homolog of TRBF2 in Arabidopsis, also forms phase-separated aggregates with PRC2. Mutants of TRBF2 and PRC2 component CLF display similar developmental defects, share common differentially expressed genes, and reduced H3K27me3 chromatin regions. Chromatin immunoprecipitation analysis supports that TRBF2 concentrates PRC2 at target loci to promote H3K27me3 deposition. Therefore, we propose that the aggregation of the plant-specific TRBs with PRC2 by the LLPS mechanism contributes to Polycomb silencing. One-Sentence SummaryThe phase-separated plant-specific single Myb transcription factor aggregates with PRC2 to facilitate Polycomb silencing.

plant biology↗

bioRxiv2019

High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat

BackgroundPrecise measurement of plant traits with precision and speed on large populations has emerged as a critical bottleneck in connecting genotype to phenotype in genetics and breeding. This bottleneck limits advancements in understanding plant genomes and the development of improved, high-yielding crop varieties. ResultsHere we demonstrate the application of deep learning on proximal imaging from a mobile field vehicle to directly score plant morphology and developmental stages in wheat under field conditions. We developed and trained a convolutional neural network with image datasets labeled from expert visual scores and used this breeder-trained network to directly score wheat morphology and developmental stages. For both morphological (awned) and phenological (flowering time) traits, we demonstrate high heritability and extremely high accuracy against the ground-truth values from visual scoring. Using the traits scored by the network, we tested genotype-to-phenotype association using the deep learning phenotypes and uncovered novel epistatic interactions for flowering time. Enabled by the time-series high-throughput phenotyping, we describe a new phenotype as the rate of flowering and show heritable genetic control. ConclusionsWe demonstrated a field-based high-throughput phenotyping approach using deep learning that can directly score morphological and developmental phenotypes in genetic populations. Most powerfully, the deep learning approach presented here gives a conceptual advancement in high-throughput plant phenotyping as it can potentially score any trait in any plant species through leveraging expert knowledge from breeders, geneticist, pathologists and physiologists.

genetics↗