Biology subjects

Xiang, G.

Publications and source records attributed to Xiang, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Transcriptional profiling of extraocular motor neurons reveals sim1a as a candidate strabismus-related gene

Strabismus, or misalignment of the eyes, is a heritable disorder frequently associated with vision loss and decreased quality of life. Incomitant strabismus, where the degree of misalignment differs based on gaze angle, can arise from mutations in genes that regulate the development of extraocular motor neurons. To date, few such genes have been identified. The extraocular motor system is highly conserved across vertebrates, suggesting a comparative transcriptomic discovery approach would be fruitful. Using bulk and single-cell sequencing in a small accessible vertebrate, the larval zebrafish, we identified genes expressed in subpopulations of extraocular motor neurons in cranial nuclei nIII/nIV. We next assessed extraocular motor neuron number and vestibulo-ocular reflex performance after CRISPR/Cas9-mediated mutagenesis of three genes with suggestive expression patterns: sim1a, nav2a, one-cut1, and one known to disrupt nIII/nIV motor neuron specification: phox2a. Loss of sim1a impaired the vestibulo-ocular reflex without change to nIII/nIV motor neuron number. Our data suggest that constitutive disruptions to sim1a can impair nIII/nIV-dependent eye movements. More broadly, our work illuminates considerable transcriptomic diversity among extraocular motor neuron subpopulations, and establishes a pipeline to identify genes relevant to ocular motor disease etiology.

neuroscience↗

bioRxiv2025

AlphaGEM Enables Precise Genome-Scale Metabolic Modelling by Integrating Protein Structure Alignment with deep-learning-based Dark Metabolism Mining

Constructing high-quality genome-scale metabolic models (GEMs) for less-studied species remains challenging. To address this, we developed AlphaGEM, a versatile toolbox leveraging proteome-scale structural alignment and deep-learning-based predictions for efficient genomic mining to generate GEMs ready for applications. Our findings show that the structural alignment or protein-language-model-based prediction (i.e., PLMSearch), could identify more homologous protein relationships than sequence-blast-based alignment, contributing to the accurate profiling of metabolism from target organisms. Additionally, AlphaGEM encompasses an ensemble procedure empowered by multiple deep learning toolboxes to effectively mine the dark metabolic functions encoded by nonhomologous proteins, significantly expanding species-specific metabolic networks. We demonstrate AlphaGEMs accuracy by building GEMs for eukaryotes (e.g., S. pombe, C. albicans) and prokaryotes (e.g., K. pneumoniae, B. subtilis), achieving predictions comparable to manually curated models while outperforming existing tools. AlphaGEM also successfully reconstructs GEMs for M. musculus and C. griseus, showcasing its great potential for uncovering dark metabolism in complex mammals. Lastly, we demonstrate that AlphaGEM could facilitate the automatic GEMs reconstruction for 332 distinct yeast species with high prediction fidelity. In conclusion, AlphaGEM provides unprecedented opportunities for the precise, rapid construction of GEMs across diverse domains, which sets a solid foundation for universal functional analysis of non-model organisms having genome sequences available.

bioinformatics↗

bioRxiv2025

Efficient Cell Factory Design by Combining Meta-Heuristic Algorithm with Enzyme Constrained Metabolic Models

The rational design of high-performance microbial cell factories remains a central challenge in sustainable biomanufacturing due to the complexity of metabolic networks and the difficulty of predicting synergistic genetic interventions. Despite recent advances in strain design algorithms, predicting combinatorial targets remains computationally prohibitive due to the combinatorial explosion. Here, we present MetaStrain, a unified computational framework that integrates enzyme-constrained models (ecModels) with meta-heuristic algorithms to identify non-intuitive combinatorial gene targets for improving product yields. MetaStrain first performs pre-screening through a modified enforced objective flux scanning algorithm to reduce the dimensionality of candidate genes and annotate editing strategies. The subsequent search module employs flexible individual encodings compatible with diverse meta-heuristic algorithms and adopts a bottom-up phenotype evaluation strategy enabling efficient exploration of the combinatorial design space. Integrated redundancy analysis tools further identify single and fixed-size combinatorial strategies, facilitating direct experimental implementation. Computational simulation in Saccharomyces cerevisiae reveals significant enhancements in 2-phenylethanol and spermidine biosynthesis, while controlling target count and covering experimentally validated targets. Experimental validation in Escherichia coli further confirmed the algorithms predictive power, achieving up to a 61.25% increase in L-tryptophan titer of the five-target combination strain. Overall, MetaStrain achieves high computational efficiency, stable convergence, and broad adaptability across diverse metabolic targets. This study provides a powerful tool for metabolic engineering, bridging computational prediction and experimental realization, and highlighting the potential of meta-heuristic optimization in synthetic biology.

bioinformatics↗

bioRxiv2025

Decoding yeast transcriptional regulation via a data- and mechanism-driven distributed large-scale network model

The complex transcriptional regulatory relationships among genes influence gene expression levels and play crucial roles in determining cellular phenotypes. Here, we present a novel distributed large-scale transcriptional regulatory neural network model (DLTRNM) by integrating prior knowledge into the reconstruction of pre-trained machine learning models followed with fine-tuning. Taken S. cerevisiae as the example, the transcriptional regulatory relationships, carefully compiled and documented, are utilized to define interactions between TFs and their TGs within DLTRNM. Subsequently, DLTRNM is pre-trained on pan-transcriptomic data and fine-tuned with time-series data, enabling it to accurately predict dynamic regulatory correlations between TFs and TGs, such as simulating TG responses by tuning the expression of TFs. In addition, DLTRNM can assist in identifying potential key TFs for a subset of genes, thus simplifying the complex and interrelated transcriptional regulatory networks (TRN). Based on the key TFs, it can also refine previously reported transcriptional regulatory subnetworks and highlight the core regulatory networks. DLTRNM offers a powerful tool for studying transcriptional regulation with reduced computational demands and enhanced interpretability. Thus, this study represents a significant step forward in systems biology for understanding the complicated transcriptional regulation within cells.

bioinformatics↗

bioRxiv2025

Mechanism Underlying Carvacrol-induced Alveolar Bone Repair via the Phosphatidylinositol 3-kinase Pathway in Periodontal Disease

BackgroundConventional antibiotic treatments do not restore the structure of periodontal tissues and can cause antibiotic resistance. This study describes the mechanism by which carvacrol regulates alveolar bone osteoblasts in periodontal disease. MethodsA rat model of periodontal disease (P group) was created by ligating the first molar and injecting lipopolysaccharide (LPS). Additionally, a control model (C group) was created. The treatment models received low (L group), medium (M group), and high (H group) doses of carvacrol hydrogel. In vitro, rat osteoblast cells were divided into the C, P, L, M, H, and carvacrol + LY294002 (Car+LY group) groups. Immunohistochemical staining analyzed collagen type I and runt-related transcription factor 2 expressions. Western blot analysis detected the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/glycogen synthase kinase 3 beta (GSK-3{beta}) pathway-related and osteoblastic proteins. Quantitative reverse transcription polymerase chain reaction measured the expression of inflammatory factors and osteoblastic proteins. The alkaline phosphatase (ALP) colorimetric kit and Alizarin Red S staining kit assessed the osteogenic ability. Detect the expression of COL1 in osteoblasts by immunofluorescence (IF). Transmission electron microscopy detected cell apoptosis. ResultsCarvacrol hydrogel alleviated periodontal symptoms, upregulated PI3K/AKT/GSK-3{beta} pathway-related and osteoblastic proteins, and increased ALP expression and calcified nodules. However, it decreased cell apoptosis and inflammatory factors. LY294002 inhibited the PI3K/AKT pathway and decreased osteoblastic protein expression, ALP coloration, and calcified nodules. ConclusionCarvacrol hydrogel promotes the proliferation and differentiation of alveolar bone osteoblasts by activating the PI3K pathway and inhibiting inflammation-induced bone resorption. This study emphasizes the potential of carvacrol for treating periodontal diseases.

molecular biology↗

bioRxiv2024

JOnTADS: a unified caller for TADs and stripes in Hi-C data

Topologically associating domains (TADs) and stripes are important architectural structures on Hi-C data that are important for gene regulation. We present Joint Optimized nested TADs and Stripes (JOn-TADS), a unified caller for TADs and stripes in Hi-C data. JOnTADS effectively identifies hierarchical TADs and stripes in population Hi-C and micro-C datasets, and hierarchical TADs in single-cell Hi-C data. It provides robust identifications aligned with known biology and effectively captures interaction frequency variations in contact maps across diverse contexts. When multiple samples are available, JOn-TADS leverages shared information across samples in TAD identification, reducing unwanted variation in TAD boundary identification while preserving biological differences. This approach enables robust identifications in single-cell Hi-C data, effectively addressing challenges posed by data sparsity. JOnTADS is computationally efficient and requires minimal user tuning.

bioinformatics↗

bioRxiv2024

Single-cell multiomics reveals a gene regulatory circuit promoting leukemia cell differentiation

Cancer differentiation therapy aims to induce maturation of neoplastic cells, yet how cell fate is determined in oncogenic cellular contexts is poorly understood. Here we integrate chromatin accessibility and transcriptome analysis at single-cell levels to investigate the mechanism driving the differentiation trajectory of the PML/RAR+ acute promyeloid leukemia (APL) cell line model, NB4, under the induction of all-trans-retinoid acid (ATRA). We show that differentiating NB4 cells navigate through an intermediate cell fate decision point that leads to either terminal granulopoiesis or a transient immature-lymphocyte-like state. Functional perturbation studies indicate that ATRA signaling activates discrete PML/RAR-target enhancers to induce a positive feedforward gene regulatory circuit involving two myeloid-lineage transcription factors, SPI1 and CEBPE, which is both necessary and sufficient to activate a myelocytic gene expression program in NB4. Further, ectopic expression of SPI1 and CEBPE also promotes myelocytic differentiation in non-APL leukemia cell lines HL60 and K562. Together, these results shed light on the differentiation trajectory of therapy-induced cell decisions of APL and suggest a gene regulatory circuit that may be broadly exploited to promote terminal differentiation of leukemia.

cancer biology↗

bioRxiv2023

DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D Structures

Turnover numbers (kcat), which indicate an enzymes catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes kcat is always time-consuming. Recently, the prediction of kcat using deep learning models has mitigated this problem. However, the accuracy and robustness in kcat prediction still needs to be improved significantly, particularly when dealing with enzymes with low sequence similarity compared to those within the training dataset. Herein, we present DeepEnzyme, a cutting-edge deep learning model that combines the most recent Transformer and Graph Convolutional Network (GCN) architectures. To improve the prediction accuracy, DeepEnzyme was trained by leveraging the integrated features from both sequences and 3D structures. Consequently, our model exhibits remarkable robustness when processing enzymes with low sequence similarity compared to those in the training dataset by utilizing additional features from high-quality protein 3D structures. DeepEnzyme also makes it possible to evaluate how point mutations affect the catalytic activity of the enzyme, which helps identify residue sites that are crucial for the catalytic function. In summary, DeepEnzyme represents a pioneering effort in predicting enzymes kcat values with superior accuracy and robustness compared to previous algorithms. This advancement will significantly contribute to our comprehension of enzyme function and its evolutionary patterns across species.

bioinformatics↗

bioRxiv2023

A novel antagonist of the CCL5/CCR5 axis suppresses the tumor growth and metastasis of triple-negative breast cancer by CCR5-YAP1 regulation.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC) with a high mortality rate, and few effective therapeutic strategies are available. CCL5/CCR5 is an appealing immunotherapeutic target for TNBC. However, its signaling mechanism is poorly understood and its direct antagonists have not been reported. Here, we developed a high-throughput screening (HTS) assay for discovering its antagonists. Verteporfin was identified as a more selective and potent antagonist than the known CCR5 antagonist maraviroc. Without photodynamic therapy, verteporfin demonstrated significant inhibition on TNBC tumor growth through immune regulation, remarkable suppression of lung metastasis by cell-intrinsic mechanism, and a significant extension of overall survival in vivo. Mechanistically, CCR5 was found to be essential for expression of the key hippo effector YAP1. It promoted YAP1 transcription via HIF-1 and exerted further control over the migration of CD8+ T, NK, and MDSC immune cells through chemokines CXCL16 and CXCL8 which were identified from RNA-seq. Moreover, the CCR5-YAP1 axis played a vital role in promoting metastasis by modulating {beta}-catenin and core epithelial-mesenchymal transition transcription factors ZEB1 and ZEB2. It is noteworthy that the regulatory relationship between CCR5 and YAP1 was observed across various BC subtypes, TNBC patients, and showed potential relevance in fifteen additional cancer types. Overall, this study introduced an easy-to-use HTS assay that streamlines the discovery of CCL5/CCR5 axis antagonists. Verteporfin was identified as a specific molecular probe of this axis with great potentials as a therapeutic agent for treating sixteen malignant diseases characterized by heightened CCR5 and YAP1 levels.

cancer biology↗

bioRxiv2023

Synaptic plasticity via receptor tyrosine kinase / G protein-coupled receptor crosstalk

Cellular signaling involves a large repertoire of membrane receptors operating in overlapping spatiotemporal regimes and targeting many common intracellular effectors. However, both the molecular mechanisms and physiological roles of crosstalk between receptors, especially those from different superfamilies, are poorly understood. We find that the receptor tyrosine kinase (RTK), TrkB, and the G protein-coupled receptor (GPCR), metabotropic glutamate receptor 5 (mGluR5), together mediate a novel form of hippocampal synaptic plasticity in response to brain-derived neurotrophic factor (BDNF). Activated TrkB enhances constitutive mGluR5 activity to initiate a mode-switch that drives BDNF-dependent sustained, oscillatory Ca2+ signaling and enhanced MAP kinase activation. This crosstalk is mediated, in part, by synergy between G{beta}{gamma}, released by TrkB, and Gq-GTP, released by mGluR5, to enable a previously unidentified form of physiologically relevant RTK/GPCR crosstalk.

cell biology↗

bioRxiv2023

JMnorm: a novel Joint Multi-feature normalization method for integrative and comparative epigenomics

Combinatorial patterns of epigenetic features reflect transcriptional states and functions of genomic regions. While many epigenetic features have correlated relationships, most existing data normalization approaches analyze each feature independently. Such strategies may distort relationships between functionally correlated epigenetic features and hinder biological interpretation. We present a novel approach named JMnorm that simultaneously normalizes multiple epigenetic features across cell types, species, and experimental conditions by leveraging information from partially correlated epigenetic features. We demonstrate that JMnorm-normalized data can better preserve cross-epigenetic-feature correlations across different cell types and enhance consistency between biological replicates than data normalized by other methods. Additionally, we show that JMnorm-normalized data can consistently improve the performance of various downstream analyses, which include candidate cis-regulatory element clustering, cross-cell-type gene expression prediction, detection of transcription factor binding and changes upon perturbations. These findings suggest that JMnorm effectively minimizes technical noise while preserving true biologically significant relationships between epigenetic datasets. We anticipate that JMnorm will enhance integrative and comparative epigenomics. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/545004v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1f07f2borg.highwire.dtl.DTLVardef@2cfbb7org.highwire.dtl.DTLVardef@141cc4borg.highwire.dtl.DTLVardef@cb757f_HPS_FORMAT_FIGEXP M_FIG JMnorm can jointly normalize multiple epigenetic features between the target sample and the reference. C_FIG

systems biology↗

bioRxiv2023

Cross-species regulatory landscapes and elements revealed by novel joint systematic integration of human and mouse blood cell epigenomes

Knowledge of locations and activities of cis-regulatory elements (CREs) is needed to decipher basic mechanisms of gene regulation and to understand the impact of genetic variants on complex traits. Previous studies identified candidate CREs (cCREs) using epigenetic features in one species, making comparisons difficult between species. In contrast, we conducted an interspecies study defining epigenetic states and identifying cCREs in blood cell types to generate regulatory maps that are comparable between species, using integrative modeling of eight epigenetic features jointly in human and mouse in our Validated Systematic Integration (VISION) Project. The resulting catalogs of cCREs are useful resources for further studies of gene regulation in blood cells, indicated by high overlap with known functional elements and strong enrichment for human genetic variants associated with blood cell phenotypes. The contribution of each epigenetic state in cCREs to gene regulation, inferred from a multivariate regression, was used to estimate epigenetic state Regulatory Potential (esRP) scores for each cCRE in each cell type, which were used to categorize dynamic changes in cCREs. Groups of cCREs displaying similar patterns of regulatory activity in human and mouse cell types, obtained by joint clustering on esRP scores, harbored distinctive transcription factor binding motifs that were similar between species. An interspecies comparison of cCREs revealed both conserved and species-specific patterns of epigenetic evolution. Finally, we showed that comparisons of the epigenetic landscape between species can reveal elements with similar roles in regulation, even in the absence of genomic sequence alignment.

genomics↗

bioRxiv2022

MetaTiME: Meta-components of the Tumor Immune Microenvironment

Recent advances in single-cell RNA sequencing have revealed heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can reveal common cell types and states in the tumor microenvironment (TME). We developed a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

genomics↗

bioRxiv2022

The Optimal Combination of Scanning Speed and Fiber-to-Stone Distance for Effective and Efficient Dusting during Holmium: YAG Laser Lithotripsy

ObjectivesTo investigate mechanistically the effects of fiber scanning speed (vfiber) and fiber tip-to-stone standoff distance (SD) on dusting efficiency during Holmium (Ho): YAG laser lithotripsy (LL) Materials and MethodsPre-soaked BegoStone samples (23 x 23 x 4 mm, W x L x H) were treated in water using a clinical Ho:YAG laser in dusting mode (0.2 J pulse energy delivered at 20 Hz frequency) at three different SDs (0.10, 0.25 and 0.50 mm) with vfiber in the range of 0 to 10 mm/s. Stone damage was quantified by optical coherence tomography, bubble dynamics were captured by high-speed imaging, and associated pressure transients were measured using a needle hydrophone. To compare photothermal ablation vs. cavitation in stone dusting, the experiments were further repeated in air (photothermal ablation only), and in water with the fiber tip advanced at a short (0.25 mm) offset distance (OSD) from a ureteroscope to mitigate the bubble collapse toward the stone surface, thus eliminating cavitation-induced damage. ResultsCompared to the craters produced by a stationary fiber, the damage troughs produced by a scanning fiber after 100 pulses were significantly larger in volume. The optimal vfiber for maximum dusting efficiency was found to be 3.5 mm/s for SD = 0.10 [~] 0.25 mm, resulting in long (17.5 mm), shallow (0.14 - 0.15 mm) and narrow (0.3 - 0.4 mm) troughs. In contrast, the maximum stone damage was produced at an optimal vfiber of 0.5 mm/s for SD = 0.50 mm, which generates much shorter (2.5 mm), yet deeper (0.35 mm) and wider (1.4 mm) troughs. Greater stone damage was produced in water than in air, especially at vfiber = 0 - 3.5 mm/s. With the scope end placed near the fiber tip, stone damage could be significantly reduced in water by 29% - 58% for SD = 0.10 mm, by 51% - 82% for SD = 0.25 mm, and by 66% - 100 % for SD = 0.50 mm, compared to those produced without the scope. Together, these findings suggest that cavitation plays an indispensable role in stone dusting by scanning treatment. Moreover, under clinically relevant vfiber (1 [~] 3 mm/s), dusting at SD = 0.5 mm (i.e., non-contact mode) may leverage higher frequency of the laser (e.g., 40 to 120 Hz) to harvest the full potential of cavitation damage while significantly reducing the procedure time, compared to its counterpart at SD = 0.1 mm (i.e., contact mode) that promotes photothermal ablation. ConclusionDusting efficiency during Ho:YAG LL may be substantially improved by utilizing the optimal combination of vfiber and SD for a given frequency.

bioengineering↗

bioRxiv2022

Role of Paravertebral muscle myostatin upregulation in the development of idiopathic scoliosis

Paravertebral muscle (PVM) abnormalities play important roles in the pathogenesis of idiopathic scoliosis (IS), and elevated oxidative stress could result in PVM injury in IS patients, but the underlying mechanism of oxidative stress generation is still unclear. Increased apoptosis, impaired myogenesis and elevated oxidative stress were found in primary skeletal muscle mesenchymal progenitor cells (hSM-MPCs), which are essential for the myogenesis process of vertebrate skeletal muscles, of IS patients. Through RNA-sequencing and further analysis, we identified significantly upregulated myostatin (MSTN) in IS hSM-MPCs. Overexpression of MSTN in hSM-MPCs from control patients increased the expression of NADPH oxidase 4, promoted reactive oxygen species production and apoptosis, and suppressed myogenesis. However, MSTN knockdown decreased the expression of NADPH oxidase 4, inhibited reactive oxygen species production and apoptosis, and enhanced myogenesis in IS hSM-MPCs. In addition, overexpression of MSTN in the PVMs of mice induced elevated oxidative stress and scoliosis without abnormal vertebral structure. Altogether, our study suggested that abnormal PVM changes and accumulated oxidative stress in IS patients may result from upregulation of MSTN, which could contribute to the development of IS.

pathology↗

bioRxiv2021

Differences in interactions between transmembrane domains tune the activation of metabotropic glutamate receptors

The metabotropic glutamate receptors (mGluRs) form a family of neuromodulatory G protein-coupled receptors that contain both a seven-helix transmembrane domain (TMD) and a large extracellular ligand-binding domain (LBD) which enables stable dimerization. While numerous studies have revealed variability across subtypes in the initial activation steps at the level of LBD dimers, an understanding of inter-TMD interaction and rearrangement remains limited. Here we use a combination of single molecule fluorescence, molecular dynamics, functional assays, and conformational sensors to reveal that distinct TMD assembly properties drive differences between mGluR subtypes. We uncover a variable region within transmembrane helix 4 (TM4) that contributes to homo- and heterodimerization in a subtype-specific manner and tunes orthosteric, allosteric and basal activation. We also confirm a critical role for a conserved inter-TM6 interface in stabilizing the active state during orthosteric or allosteric activation. Together this study informs a working model of inter-TMD rearrangement that drives mGluR function.

neuroscience↗

bioRxiv2020

High-dimensional association detection in large scale genomic data

Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation, but they still remain computationally challenging. To address this we introduce CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology that learns patterns of condition-specificity present in genomic data. CLIMB provides a generic framework facilitating a host of analyses, such as clustering genomic features sharing similar condition-specific patterns and identifying which of these features are involved in cell fate commitment. We apply CLIMB to three sets of hematopoietic data, which examine CTCF ChIP-seq measured in 17 different cell populations, RNA-seq measured across constituent cell populations in three committed lineages, and DNase-seq in 38 cell populations. Our results show that CLIMB improves upon existing alternatives in statistical precision, while capturing interpretable and biologically relevant clusters in the data.

bioinformatics↗

bioRxiv2020

S3V2-IDEAS: a package for normalizing, denoising and integrating epigenomic datasets across different cell types

SummaryEpigenetic modifications reflect key aspects of transcriptional regulation, and many epigenomic data sets have been generated under many biological contexts to provide insights into regulatory processes. However, the technical noise in epigenomic data sets and the many dimensions (features) examined make it challenging to effectively extract biologically meaningful inferences from these data sets. We developed a package that reduces noise while normalizing the epigenomic data by a novel normalization method, followed by integrative dimensional reduction by learning and assigning epigenetic states. This package, called S3V2-IDEAS, can be used to identify epigenetic states for multiple features, or identify signal intensity states and a master peak list across different cell types for a single feature. We illustrate the outputs and performance of S3V2-IDEAS using 137 epigenomics data sets from the VISION project that provides ValIdated Systematic IntegratiON of epigenomic data in hematopoiesis. Availability and implementationS3V2-IDEAS pipeline is freely available as open source software released under an MIT license at: https://github.com/guanjue/S3V2_IDEAS_ESMP Contactrch8@psu.edu, gzx103@psu.edu Supplementary informationS3V2-IDEAS-bioinfo-supplementary-materials.pdf

bioinformatics↗