Biology subjects

Zhang, B.

Publications and source records attributed to Zhang, B..

At least 145 records · Page 8Linked to original sources

bioRxiv2025

Central Amygdala Neuronal Ensembles Coordinate Visceral Pain and Its Affective Behaviors

Visceral pain and its affective component associated with cystitis remain poorly understood. Here, we delineate the role of central amygdala (CeA) neuronal ensembles in encoding and modulating cystitis-induced bladder pain and its affective components. Utilizing a multidisciplinary approach combining behavioral assays, optogenetic manipulations, whole-cell electrophysiology, and activity-dependent genetic labeling, we identified functionally discrete CeA subpopulations that are selectively recruited during bladder inflammation. Bidirectional optogenetic modulation of these ensembles produced opposing effects on nocifensive and anxiety-like behaviors, indicating their causal involvement. Single-nucleus RNA sequencing of FosTRAP-labeled neurons revealed distinct transcriptional signatures associated with inflammatory activation. Integrating electrophysiological and transcriptomic data, we demonstrate that cystitis drives cell type-specific adaptations in CeA circuits. These findings provide mechanistic insight and uncover a molecularly and functionally defined CeA ensemble that orchestrates the sensory and affective dimensions of visceral pain.

neuroscience↗

bioRxiv2025

XL-Ranker: A Computational Workflow for Prioritizing Protein-Protein Interactions from Cross-Linking Mass Spectrometry Data

Protein-protein interactions (PPIs) are central to virtually all biological processes, and their disruption can lead to a wide spectrum of human diseases. Cross-linking mass spectrometry (XL-MS) enables proteome-scale detection of interacting peptide pairs, from which PPIs can be inferred. However, interpretating XL-MS data to assign homologous cross-linked peptide pairs to specific protein interactions can be challenging. A major hurdle arises when a single peptide can map to multiple proteins, and when two such peptides are paired, the number of possible protein-protein interactions increases combinatorially. This mapping ambiguity can lead to inflated interaction networks that could compromise downstream analysis and biological interpretation. However, this problem has often been overlooked or addressed using heuristic approaches in previous XL-MS studies. To tackle this challenge, we developed XL-Ranker, a computational framework that combines a set cover graph algorithm and machine learning to systematically resolve peptide-mapping ambiguity and infer high-confidence PPIs from XL-MS data. Applied to an XL-MS dataset from HEK293 cells, XL-Ranker identified a high-confidence network with 880 PPIs involving 964 unique genes. Among all possible PPIs, the ones selected by XL-Ranker for inclusion in the final network had significantly higher interaction scores in the STRING database than the excluded ones. Network analysis further demonstrated that these interactions form biologically meaningful clusters, supporting the accuracy of our approach. In summary, XL-Ranker provides a practical solution to a key analytical challenge in XL-MS data interpretation, enhancing the reliability of PPI discovery.

bioinformatics↗

bioRxiv2025

TabSyM: A Generative Pipeline for Small Multi-Cohort Omics Tabular Data

Machine learning applications in biomedicine such as omics data analysis are frequently hindered by datasets that are small, high-dimensional, and affected by batch effects across different patient cohorts. To address these challenges, we introduce TabSyM, a modular generative pipeline that synthesizes high-quality, task-relevant data to improve predictive modeling. TabSyM integrates three key stages: it extends a diffusion-based model (TabDDPM) to generate new omics data, employs a novel task-aware sampling mechanism guided by Bayesian optimization to select the most informative synthetic samples, and uses a Multi-Domain Adversarial Network (MDAN) to align data distributions for cross-cohort generalization. We validated our pipeline on a challenging, real-world task of predicting 3-year survival in gastric cancer patients from high-dimensional scRNA-seq data across five cohorts. The full TabSyM pipeline achieved a 30.2% AUROC improvement over the best tree-based models and an 11.5% AUROC gain over leading automated machine learning frameworks. Furthermore, the generative and sampling components are model-agnostic and can substantially boost the performance of classical models like XGBoost independently. These results establish that combining generative modeling with task-aware sampling and domain adaptation provides a robust and effective strategy for overcoming critical data limitations in biomedical tabular data analysis.

bioinformatics↗

bioRxiv2025

MetaSage: Machine Learning-Based Prioritization of Metabolic Regulators from Multi-Omics Data

Dysregulation of metabolites is a hallmark of cancer, yet the underlying regulatory mechanisms remain poorly understood. To systematically explore metabolic regulation across cancers, we developed an XGBoost-based machine learning pipeline, MetaSage, that integrates context-agnostic knowledge graph with multi-omics datasets. Using harmonized data from 15 cohorts spanning 11 cancer types, we identified 442 variable metabolites and found that both genes and upstream metabolites showed comparable regulatory influence. Predictable metabolites, defined by a significant correlation between predicted and measured levels, were identified using our pipeline and varied widely across cohorts-partially due to the batch effect. For each predictable metabolite, key regulatory features were determined using Shapley values. This yielded 1,146 gene features and 363 precursor metabolites as important regulators. Network analysis of 22 recurrent metabolites revealed a mix of conserved and cancer type-specific regulatory patterns. Our framework enables robust discovery of metabolite regulation and therapeutic insights in cancer.

bioinformatics↗

bioRxiv2025

Expanding the Global Map of Protein Post Translational Modifications with Immunoaffinity Enrichment and nDIA Analysis on the Orbitrap Astral Mass Spectrometer

Post-translational modifications (PTMs) contribute greatly to the diversity of the human proteome by affecting protein structure, function, interactions, stability, localization, and more. The study of PTMs is essential to understand various cellular functions, disease mechanisms, and aid in the development of biomarkers and design of therapeutic targets. Owing to their diversity, dynamic nature, and low stoichiometry compared to unmodified proteome counterparts, the analysis of PTMs remains challenging. In this study, immunoaffinity enrichment of PTM peptides was combined with analysis using Data Dependent Acquisition (DDA) and narrow window Data Independent Acquisition (nDIA) on the Orbitrap Astral mass spectrometer (MS) as well as comparative analysis using the Orbitrap Fusion Lumos MS for Ubiquitination, Phosphorylation, Acetylation, Succinylation, and Methylation. Human cell line and mouse tissue samples at various input peptide amounts were immuno-enriched and mass spectrometry data was acquired on both instruments to assess depth of coverage and number of novel sites identified. The study identified a total of 88,731 unique ubiquitin sites, 64,397 phosphorylation sites (43,721 phosphoserine, 8,414 phosphothreonine and 12,262 phosphotyrosine), 11,629 acetylation, 5,272 succinylation and 1,461 mono-methylation sites. In half the acquisition time, nDIA analysis of immuno-enriched samples on Orbitrap Astral MS provided much greater depth of coverage for all PTMs compared to DDA analysis on Orbitrap Fusion Lumos MS, with up to 33-fold more PTM peptides identified and quantified. Overall, the data presented in this study demonstrates the need for enrichment for PTM detection and the utility of combining antibody-based peptide capture and nDIA on the Orbitrap Astral MS as powerful tools for discovery and profiling of protein post-translational modifications in cells and tissues.

biochemistry↗

bioRxiv2025

Molecular signatures of longevity identify compounds that extendmouse lifespan and healthspan

Longevity interventions in mammals are typically discovered on a case-by-case basis, hindering systematic geroprotector development. We developed a platform for the identification of longevity interventions integrating longevity gene expression biomarkers within and across species, in silico chemical screening, analyses of selected compounds in cell culture, short-term dietary interventions coupled with omics profiling, and ultimately lifespan studies in mice. This approach identified compounds (selumetinib, vorinostat, celastrol, AZD-8055, LY-294002) that extended lifespan and/or healthspan in aged C57BL/6JN male mice, with limited effects in females. In addition, selumetinib and vorinostat increased lifespan when administered to young, genetically heterogeneous UM-HET3 mice. Our biomarker-driven platform accelerates geroprotector discovery, offering a scalable approach to target conserved longevity pathways.

systems biology↗

bioRxiv2025

Differences in external loads of different pitch types in Chinese male college baseball players

BackgroundIn baseball, monitoring player load solely through pitch count and innings overlooks the variability introduced by different pitch types. This study analyzed differences in external load, measured as Player Load, across various pitch types using a GPS-based wearable device. Additionally, the relationship between Player Load and ball velocity was examined. MethodsFour male collegiate Division I baseball players from China (age: 18.5 {+/-} 0.58 years, height: 181.00 {+/-} 6.83 cm, weight: 78.00 {+/-} 5.48 kg) participated in this study. External loads were collected for four pitch types, fastball, changeup, curveball, and slider, using a wearable sensor device that recorded six variables: Maximum Player Load (Max PL), Maximum Rotation (Max Rot), Pitching Hourly Velocity, and Percentage of Acceleration Change in the Three Axes (Up Load %, Side Load %, and Fwd Load %). A Kruskal-Wallis non-parametric test was used to assess differences across pitch types, while stepwise multiple regression analyzed the impact of Player Load on pitching speed. ResultsSignificant differences were observed among pitch types for Max PL, Max Rot, Hourly Speed, Up Load %, and Fwd Load % (p<0.05), while Side Load % did not show a significant difference (p>0.05). Stepwise multiple regression indicated that pitching speed was influenced by Max PL, with the equation: mph = 78.816 + 7.001 Max PL, R2 = 0.192, suggesting that Max PL accounted for 19.2% of the variability in pitching speed. DiscussionExternal training loads in pitching vary by pitch type, with variable-speed pitches generating higher peak external loads, whereas fastballs are associated with greater ball speeds.

scientific communication and education↗

bioRxiv2025

DCX Enhances Glioblastoma Metabolism Through Synergistic Regulation of Glutamine Synthesis and Metabolism-Related Genes for Cellular Homeostasis

Gliomas are the most common primary intracranial tumors, comprising 81% of malignant brain tumors, and currently lack effective therapies. Recent advances in molecular biology have shown that cancer cells exploit microtubule-associated proteins (MAPs) under stress to activate various signaling pathways. This study investigates the role of Doublecortin (DCX) in glioma metabolism and its impact on tumor proliferation. In this study, CRISPR-engineered glioma models with DCX overexpression or knockdown were analyzed using integrated genomic, transcriptomic, and metabolomic approaches. Metabolic activity was assessed via RNA sequencing, Seahorse assays, and targeted mass spectrometry. Pharmacological inhibition of key pathways validated functional dependencies. We demonstrate that gliomas enriched with DCX exhibit elevated glycolytic activity while also relying on cellular respiration and oxidative phosphorylation (OXPHOS) for energy to support the abnormal proliferation of glioma cells. Upon integrative analysis of enriched genes and proteins, we observed genetic and metabolome-level signatures associated with differences in central carbon and energy metabolism in CRISPR-modified glioma cells expressing high DCX. Whole-genome transcriptome analysis revealed enriched metabolic entities promoting hydrolysis of glutamine and glutaminolysis in glioma cells and inhibition of selected differentially enriched genes with small molecule inhibitors abrogated metabolic enrichment and resulted in reduced energy levels and protein translation required for aberrant growth. Finally, we establish that DCX stimulates glutaminolysis to regulate homeostasis for energy supplements in glioma cells. Targeting DCX-mediated metabolic pathways may provide a novel therapeutic approach for glioblastoma, highlighting the potential for innovative treatments in this challenging disease.

cancer biology↗

bioRxiv2025

MimicNeoAI: An integrated pipeline for identifying microbial mimicry antigens and tumor neoantigen partners

Tumor-associated microbial antigens represent promising immunotherapy targets, yet systematic identification methods remain underdeveloped. We developed MimicNeoAI, a computational pipeline integrating BiLSTM networks to identify microbial epitopes, mutation-derived neoepitopes, and their microbial mimics from sequencing data. Training on validated epitope datasets yielded 0.90 AUC with 91% accuracy on experimental validation sets. Application to colorectal cancer revealed that microbial epitopes, despite originating from a nine-fold smaller peptide pool, generated twice the immunogenic candidates (153 vs 75) compared to mutation-derived neoepitopes. These microbial epitopes exhibited exclusive tumor-specificity with no overlap in normal tissues. Single-cell TCR sequencing confirmed clonal expansion against 75% of predicted highly immunogenic epitopes, with molecular dynamics simulations demonstrating positive correlation between predicted immunogenicity and HLA-epitope-TCR binding stability. Collectively, our pipeline systematically unveils abundant, tumor-specific, and highly immunogenic microbial epitopes, providing a computational framework for developing broadly applicable cancer immunotherapies that leverage the tumor microbiome as an untapped source of therapeutic targets.

bioinformatics↗

bioRxiv2025

Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories

Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine 1-7. However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide. We establish the lifespan normative trajectories for 296 structural brain phenotypes, encompassing global, subcortical, and cortical measures. Cross-population comparisons with Western brain charts (based on data from 56,339 participants aged 0-100 years) reveal distinct neurodevelopmental patterns in the Chinese population, including prolonged cortical and subcortical maturation, accelerated cerebellar growth, and earlier development of sensorimotor regions relative to paralimbic regions. Crucially, these Chinese-specific charts outperform Western-derived models in predicting healthy brain phenotypes and detecting pathological deviations in Chinese clinical cohorts. These findings highlight the urgent need for diverse, population-representative brain charts to advance equitable precision neuroscience and improve clinical validity across populations.

neuroscience↗

bioRxiv2025

AAV gene therapy for GBA-PD and Gaucher Disease

Mutations in GBA1, the gene encoding glucocerebrosidase (GCase), are the most common risk factor for Parkinsons Disease (PD). GBA-PD patients are a genetic subpopulation of PD carrying heterozygous mutations in GBA1. Additionally, bi-allelic mutations in GBA1 cause Gaucher Disease (GD), a lysosomal storage disorder. Loss of GCase activity, a lysosomal enzyme leads to the accumulation of lipid substrates, disrupting lipid homeostasis and promoting cellular toxicity. Here, we report an AAV-mediated GBA1 replacement strategy to treat GD and GBA-PD by a one-time infusion via intravenous (GD Type 1) or intra-CSF (GBA-PD) route of administration. We engineered human GCase to be readily secretable to facilitate broad cross-correction. We developed CBE (conduritol {beta}-epoxide) induced lipid accumulation models to assess efficacy in mice and non-human primates (NHPs) to assess efficacy of our engineered constructs. Based on data across species, across different routes of administration, we nominated AAV.GMU01 SS3-GBA1 as our lead candidate. SS3-GBA1 is robustly secreted, cross-corrected across tissues and promotes lipid clearance. By comparing human GCase levels in AAV-treated NHP brains to healthy human donor brains, we demonstrate that AAV.GMU01 SS3-GBA1 replenishes the GCase deficit seen in GBA-PD patients, thus, restoring GCase to near-physiological levels Importantly, AAV.GMU01 SS3-GBA1 is well-tolerated with no adverse findings. Collectively, we establish a therapeutic strategy for the treatment of Gaucher Disease and GBA-PD with a single gene therapy product. One Sentence SummaryA novel gene therapy strategy for GBA1-PD and Gaucher disease with an engineered payload that robustly cross-corrects enhancing therapeutic footprint

neuroscience↗

bioRxiv2025

OriGene: A Self-Evolving Virtual Disease Biologist Automating Therapeutic Target Discovery

Here, we present OriGene, a self-evolving multi-agent system that functions as a virtual disease biologist, systematically identifying original and mechanistically grounded therapeutic targets at scale. OriGenes architecture integrates over 600 specialized tools through a Model Context Protocol (MCP), enabling it to reason across diverse data modalities including genomics, protein networks, pharmacology, clinical records and literature evidence, to generate and prioritize target discovery hypotheses. We implemented a strategy combining a knowledge graph-based Tool RAG with an advanced agent selection mechanism to enable dynamic, context-aware tool deployment. Through a self-evolving framework, OriGene continuously integrates human and experimental feedback to iteratively refine its core thinking templates, tool composition, and analytical protocols, thereby enhancing both accuracy and adaptability over time. To comprehensively evaluate its performance, we established TRQA, an original benchmark comprising over 1,900 expert-level question-answer pairs spanning a wide range of diseases and target classes. OriGene consistently outperforms human experts, leading research agents, and state-of-the-art large language models in accuracy, recall, and robustness, particularly under conditions of data sparsity or noise. Critically, OriGene nominated previously underexplored therapeutic targets for liver (GPR160) and colorectal cancer (ARG2), which demonstrated significant anti-tumor activity in patient-derived organoid and tumor fragment models mirroring human clinical exposures. These findings demonstrate OriGenes potential as a scalable and adaptive platform for AI-driven discovery of mechanistically grounded therapeutic targets, offering a new paradigm to accelerate drug development.

bioinformatics↗

bioRxiv2025

Proliferation of Early and Mature Osteoblasts Is Required for Bone Fracture Healing in Mice

Fractures heal by rapid formation of mineralized callus. Essential to this process is proliferation of periosteal cells to supply bone-forming osteoblasts. To better understand the role of cell proliferation in fracture healing, we asked: How is callus composition altered when osteolineage cells proliferation is blocked? Do mature osteoblasts proliferate to contribute to callus formation? First, mice expressing herpes simplex virus-thymidine kinase (HSV-TK) driven by the 3.6Col1a1 promoter were treated with ganciclovir (GCV) to ablate proliferating osteolineage cells for 5 or 10 days. Analysis of callus cells using single-cell RNA-seq revealed that GCV-treated Col1-TK mice had fewer osteoblasts and chondrocytes than control mice, with more myofibroblasts and immune cells, consistent with fibrous nonunion. In controls, 15-30% of callus cells that expressed the early osteoblast markers osterix (OSX) and the late marker osteocalcin (OCN) were in the cell cycle. Next, we targeted proliferating osteoblasts at different stages of differentiation by crossing Osx-CreERT2, Ocn-Cre and Dmp1-CreERT2 mice with novel ROSA-TK mice. Following fracture, each Cre;ROSA-TK mouse line exhibited poorer radiographic healing, decreased callus bone volume and a shift from callus bone to fibrous tissue. We conclude that osteoblasts, often considered post-mitotic, proliferate after fracture to contribute to formation of mineralized callus essential to healing.

physiology↗

bioRxiv2025

Analysis of isobaric quantitative proteomic data using TMT-Integrator and FragPipe computational platform

Isobaric mass tags, such as iTRAQ and TMT, are widely utilized for peptide and protein quantification in multiplex quantitative proteomics. We present TMT-Integrator, a bioinformatics tool for processing quantitation results from TMT and iTRAQ experiments, offering integrative reports at the gene, protein, peptide, and post-translational modification site levels. We demonstrate the versatility of TMT-Integrator using five publicly available TMT datasets: an E. coli dataset with 13 spike-in proteins, the clear cell renal cell carcinoma (ccRCC) whole proteome and phosphopeptide-enriched datasets from the Clinical Proteomic Tumor Analysis Consortium, and two human cell lysate datasets showcasing the latest advances with the Astral instrument and TMT 35-plex reagents. Integrated into the widely used FragPipe computational platform (https://fragpipe.nesvilab.org/), TMT-Integrator is a core component of TMT and iTRAQ data analysis workflows. We evaluate the FragPipe/TMT-Integrator analysis pipelines performance against MaxQuant and Proteome Discoverer with multiple benchmarks, facilitated by the bioinformatics tool OmicsEV. Our results show that FragPipe/TMT-Integrator quantifies more proteins in the E. coli and ccRCC whole proteome datasets, identifies more phosphorylated sites in the ccRCC phosphoproteome dataset, and delivers overall more robust quantification performance compared to other tools.

bioinformatics↗

bioRxiv2025

Contrastive Representation Learning for Single Cell Phenotyping in Whole Slide Imaging of Enrichment-free Liquid Biopsy

Tumor-associated cells derived from a liquid biopsy are promising biomarkers for cancer detection, diagnosis, prognosis, and monitoring. However, their rarity, heterogeneity and plasticity make precise identification and biological characterization challenging for clinical utility. Enrichment-free approaches using whole slide imaging of all circulating cells offer a comprehensive and unbiased strategy for capturing the full spectrum of tumor-associated cell phenotypes. However, current analysis methods often depend on engineered features and manual expert review, making them sensitive to technical variations and subjective biases. These limitations highlight the need for a better feature representation to improve performance and reproducibility of applications in large-scale patient cohort analyses. In this study, we present a deep contrastive learning framework for learning features of all circulating cells, enabling robust identification and stratification of single cells in whole slide immunofluorescence microscopy images. We demonstrate performance of learned features in classification of diverse cell phenotypes in the liquid biopsy, achieving an accuracy of 92.64%. We further demonstrate that learned features improve performance in downstream applications such as outlier detection and clustering. Lastly, our feature representation enables automated identification and enumeration of distinct rare cell phenotypes, achieving average F1-score of 0.93 across cell lines mimicking circulating tumor cells and endothelial cells in contrived samples and average F1-score of 0.858 across CTC phenotypes in clinical samples. This workflow has significant implications for scalable analysis of tumor-associated cellular biomarkers in clinical prognosis and personalized treatment strategies.

cancer biology↗

bioRxiv2025

Overcoming treatment resistance mediated by the bone marrow vascular niche in acute myeloid leukemia

Acute myeloid leukemia (AML) is a hematologic malignancy originating in the bone marrow, frequently progressing to extramedullary sites. Despite advances in molecularly targeted therapies and hematopoietic stem cell transplantation, clinical outcomes remain suboptimal. Tyrosine kinase inhibitors (TKIs) confer benefit in a subset of AML patients harboring FLT3-ITD mutations, yet relapse and resistance are common. These failures are driven by both intrinsic properties of leukemic stem cells (LSCs)--a quiescent, self-renewing population--and extrinsic cues from the tumor microenvironment. We previously demonstrated that arteriolar endothelial cells (ECs) produce miR-126, which is transferred to LSCs, promoting quiescence, treatment resistance, and niche retention. During disease progression, TNF- secreted by expanding blasts suppresses EC miR-126 production, enabling LSCs and their progeny to proliferate. Following TKI administration, blast reduction lowers TNF- levels, restoring EC miR-126 production and enabling LSCs to re-enter quiescence--thereby escaping therapy and facilitating relapse. To investigate this dynamic, we developed an agent-based computational model of the AML bone marrow microenvironment, parameterized with in vitro and in vivo data. The model captures vascular niche remodeling and the feedback between leukemic populations and endothelial signaling. Simulations reveal that LSC protection mediated by miR-126 can be overcome by combining TKIs with miRisten, a miR-126 inhibitor. When administered on a defined schedule, this combination disrupts the protective niche and enhances LSC eradication. These findings underscore the therapeutic potential of targeting microenvironmental feedback to overcome resistance and prevent AML relapse.

cancer biology↗

bioRxiv2025

Tspecies, Rapid Optimization for Estimating Species Divergence Time Using Ks Distribution

Ks distribution, the distribution of the synonymous substitutions, has been widely used to estimate the species divergence using orthologous genes. However, conventional approaches often ignore the underlying bias that species divergence is delayed to average gene divergence by 2Ne generations, where Ne represents the ancestral effective population size, due to the lack of scalable methods for Ne inference. Here, we demonstrate through simulations that Ks distribution variance correlates with Ne, enabling direct estimation of ancestral population parameters from standard Ks data. Leveraging this relationship, we present Tspecies, an R package that corrects divergence time estimates using only substitution rates and Ks distributions, without requiring additional genomic data. Our practical application of Tspecies in Liriodendron has inferred a divergence time between North American and East Asian lineages (3.49 Ma) that align with late Pliocene cooling, and a large ancestral Ne ([~]5 x 105) consistent with fossil evidence. By incorporating a readily estimated Ne, our tool resolves a long-standing bias in Ks-based dating while maintaining computational efficiency and broad applicability. Tspecies is freely available under an MIT license at https://github.com/limj0987/Tspecies.git.

evolutionary biology↗

bioRxiv2025

RBPSignal: A deep learning approach for predicting RNA-Protein binding signals

RNA-binding proteins play critical roles in post-transcriptional regulation by interacting with RNA to regulate various cellular processes. Inferring the binding signals and sequence patterns of these interactions is essential for elucidating the mechanisms underlying gene expression regulation. In this study, we present RBPSignal, a deep learning-based computational tool designed to predict RBP binding signals on RNAs and identify potential sequence motifs associated with these interactions. RBPSignal leverages the deep learning framework and trains on comprehensive eCLIP datasets, demonstrating an enhanced predictive accuracy. Furthermore, the integration of model interpretability through Integrated Gradients enables the detailed analysis of binding motif syntax. We validate the efficacy of RBPSignal on chromosome data and compare the discovered motifs with existing motif database, showcasing its ability not only to predict binding signals but also to uncover sequence patterns correlated with binding signals. Our findings provide insights into the sequence specificity of RBPs and explore the protein-protein interaction networks. RBPSignal serves as a valuable tool for exploring RBP-RNA interaction landscapes, facilitating further investigations into the regulatory networks underlying gene expression. The web server is freely available at http://www.csbio.sjtu.edu.cn/bioinf/RBPSignal/.

bioinformatics↗