LEUTX regulates porcine embryonic genome activation in somatic cell nuclear transfer embryos.

Emerging evidence highlights the regulatory role of paired-like (PRD-like) homeobox transcription factors (TFs) in embryonic genome activation (EGA). However, the majority of PRD-like genes are lost in rodents, thus prompting an investigation into PRD-like TFs in other mammals. Here, we showed that PRD-like TFs were transiently expressed during EGA in human, monkey, and porcine fertilized embryos, yet they exhibited inadequate expression in their cloned embryos. This study, using pig as the research model, identified LEUTX as a key PRD-like activator of porcine EGA through genomic profiling and found that LEUTX overexpression restored EGA failure and improved preimplantation development and cloning efficiency in porcine cloned embryos. Mechanistically, LEUTX opened EGA-related genomic regions and established histone acetylation via recruiting acetyltransferases p300 and KAT2A. These findings reveal the regulatory mechanism of LEUTX to govern EGA in pigs, which may provide valuable insights into the study of early embryo development for other non-rodent mammals.

Advances in functional lipid nanoparticles: from drug delivery platforms to clinical applications.

Since Doxil's first clinical approval in 1995, lipid nanoparticles have garnered great interest and shown exceptional therapeutic efficacy. It is clear from the licensure of two RNA treatments and the mRNA-COVID-19 vaccination that lipid nanoparticles have immense potential for delivering nucleic acids. The review begins with a list of lipid nanoparticle types, such as liposomes and solid lipid nanoparticles. Then it moves on to the earliest lipid nanoparticle forms, outlining how lipid is used in a variety of industries and how it is used as a versatile nanocarrier platform. Lipid nanoparticles must then be functionally modified. Various approaches have been proposed for the synthesis of lipid nanoparticles, such as High-Pressure Homogenization (HPH), microemulsion methods, solvent-based emulsification techniques, solvent injection, phase reversal, and membrane contractors. High-pressure homogenization is the most commonly used method. All of the methods listed above follow four basic steps, as depicted in the flowchart below. Out of these four steps, the process of dispersing lipids in an aqueous medium to produce liposomes is the most unpredictable step. A short outline of the characterization of lipid nanoparticles follows discussions of applications for the trapping and transporting of various small molecules. It highlights the use of rapamycin-coated lipid nanoparticles in glioblastoma and how lipid nanoparticles function as a conjugator in the delivery of anticancer-targeting nucleic acids. High biocompatibility, ease of production, scalability, non-toxicity, and tailored distribution are just a meager of the enticing allowances of using lipid nanoparticles as drug delivery vehicles. Due to the present constraints in drug delivery, more research is required to utterly realize the potential of lipid nanoparticles for possible clinical and therapeutic purposes.

KLF4 facilitates chromatin accessibility remodeling in porcine early embryos.

Chromatin accessibility remodeling driven by pioneer factors is critical for the development of early embryos. Current studies have illustrated several pioneer factors as being important for agricultural animals, but what are the pioneer factors and how the pioneer factors remodel the chromatin accessibility in porcine early embryos is not clear. By employing low-input DNase-seq (liDNase-seq), we profiled the landscapes of chromatin accessibility in porcine early embryos and uncovered a unique chromatin accessibility reprogramming pattern during porcine preimplantation development. Our data revealed that KLF4 played critical roles in remodeling chromatin accessibility in porcine early embryos. Knocking down of KLF4 led to the reduction of chromatin accessibility in early embryos, whereas KLF4 overexpression promoted the chromatin openness in porcine blastocysts. Furthermore, KLF4 deficiency resulted in mitochondrial dysfunction and developmental failure of porcine embryos. In addition, we found that overexpression of KLF4 in blastocysts promoted lipid droplet accumulation, whereas knockdown of KLF4 disrupted this process. Taken together, our study revealed the chromatin accessibility dynamics and identified KLF4 as a key regulator in chromatin accessibility and cellular metabolism during porcine preimplantation embryo development.

DegronMD: Leveraging Evolutionary and Structural Features for Deciphering Protein-Targeted Degradation, Mutations, and Drug Response to Degrons.

Protein-targeted degradation is an emerging and promising therapeutic approach. The specificity of degradation and the maintenance of cellular homeostasis are determined by the interactions between E3 ubiquitin ligase and degradation signals, known as degrons. The human genome encodes over 600 E3 ligases; however, only a small number of targeted degron instances have been identified so far. In this study, we introduced DegronMD, an open knowledgebase designed for the investigation of degrons, their associated dysfunctional events, and drug responses. We revealed that degrons are evolutionarily conserved and tend to occur near the sites of protein translational modifications, particularly in the regions of disordered structure and higher solvent accessibility. Through pattern recognition and machine learning techniques, we constructed the degrome landscape across the human proteome, yielding over 18,000 new degrons for targeted protein degradation. Furthermore, dysfunction of degrons disrupts the degradation process and leads to the abnormal accumulation of proteins; this process is associated with various types of human cancers. Based on the estimated phenotypic changes induced by somatic mutations, we systematically quantified and assessed the impact of mutations on degron function in pan-cancers; these results helped to build a global mutational map on human degrome, including 89,318 actionable mutations that may induce the dysfunction of degrons and disrupt protein degradation pathways. Multiomics integrative analysis unveiled over 400 drug resistance events associated with the mutations in functional degrons. DegronMD, accessible at https://bioinfo.uth.edu/degronmd, is a useful resource to explore the biological mechanisms, infer protein degradation, and assist with drug discovery and design on degrons.

A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms.

Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing thyroid-related abnormalities. The aim of this research is to improve the diagnosis accuracy of thyroid abnormality detection models that can be utilized to alleviate undue pressure on healthcare professionals. In this research, we proposed deep learning, metaheuristics, and a MCDM algorithms-based framework to detect thyroid-related abnormalities from ultrasound and histopathological images. The proposed method uses three recently developed deep learning techniques (DeiT, Swin Transformer, and Mixer-MLP) to extract features from the thyroid image datasets. The feature extraction techniques are based on the Image Transformer and MLP models. There is a large number of redundant features that can overfit the classifiers and reduce the generalization capabilities of the classifiers. In order to avoid the overfitting problem, six feature transformation techniques (PCA, TSVD, FastICA, ISOMAP, LLE, and UMP) are analyzed to reduce the dimensionality of the data. There are five different classifiers (LR, NB, SVC, KNN, and RF) evaluated using the 5-fold stratified cross-validation technique on the transformed dataset. Both datasets exhibit large class imbalances and hence, the stratified cross-validation technique is used to evaluate the performance. The MEREC-TOPSIS MCDM technique is used for ranking the evaluated models at different analysis stages. In the first stage, the best feature extraction and classification techniques are chosen, whereas, in the second stage, the best dimensionality reduction method is evaluated in wrapper feature selection mode. Two best-ranked models are further selected for the weighted average ensemble learning and features selection using the recently proposed meta-heuristics FOX-optimization algorithm. The PCA+FOX optimization-based feature selection + random forest model achieved the highest TOPSIS score and performed exceptionally well with an accuracy of 99.13%, F2-score of 98.82%, and AUC-ROC score of 99.13% on the ultrasound dataset. Similarly, the model achieved an accuracy score of 90.65%, an F2-score of 92.01%, and an AUC-ROC score of 95.48% on the histopathological dataset. This study exploits the combination novelty of different algorithms in order to improve the thyroid cancer diagnosis capabilities. This proposed framework outperforms the current state-of-the-art diagnostic methods for thyroid-related abnormalities in ultrasound and histopathological datasets and can significantly aid medical professionals by reducing the excessive burden on the medical fraternity.

Lipidomic Characterization of Oocytes at Single-Cell Level Using Nanoflow Chromatography-Trapped Ion Mobility Spectrometry-Mass Spectrometry.

Mass spectrometry (MS)-based lipidomic has become a powerful tool for studying lipids in biological systems. However, lipidome analysis at the single-cell level remains a challenge. Here, we report a highly sensitive lipidomic workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS)-MS. This approach enables the high-coverage identification of lipidome landscape at the single-oocyte level. By using the proposed method, comprehensive lipid changes in porcine oocytes during their maturation were revealed. The results provide valuable insights into the structural changes of lipid molecules during porcine oocyte maturation, highlighting the significance of sphingolipids and glycerophospholipids. This study offers a new approach to the single-cell lipidomic.

Dynamic Control of Contractile Force in Engineered Heart Tissue.

Three-dimensional engineered heart tissues (EHTs) derived from human induced pluripotent stem cells (iPSCs) have become an important resource for both drug toxicity screening and research on heart disease. A key metric of EHT phenotype is the contractile (twitch) force with which the tissue spontaneously beats. It is well-known that cardiac muscle contractility - its ability to do mechanical work - depends on tissue prestrain (preload) and external resistance (afterload). OBJECTIVES: Here, we demonstrate a technique to control afterload while monitoring contractile force exerted by EHTs. METHODS: We developed an apparatus that can regulate EHT boundary conditions using real-time feedback control. The system is comprised of a pair of piezoelectric actuators that can strain the scaffold and a microscope that can measure EHT force and length. Closed loop control allows dynamic regulation of effective EHT boundary stiffness. RESULTS: When controlled to switch instantaneously from auxotonic to isometric boundary conditions, EHT twitch force immediately doubled. Changes in EHT twitch force as a function of effective boundary stiffness were characterized and compared to twitch force in auxotonic conditions. CONCLUSION: EHT contractility can be regulated dynamically through feedback control of effective boundary stiffness. SIGNIFICANCE: The capacity to alter the mechanical boundary conditions of an engineered tissue dynamically offers a new way to probe tissue mechanics. This could be used to mimic afterload changes that occur naturally in disease, or to improve mechanical techniques for EHT maturation.

Benchmark of embedding-based methods for accurate and transferable prediction of drug response.

Prediction of therapy response has been a major challenge in cancer precision medicine due to the extensive tumor heterogeneity. Recently, several deep learning methods have been developed to predict drug response by utilizing various omics data. Most of them train models by using the drug-response screening data generated from cell lines and then use these models to predict response in cancer patient data. In this study, we focus on and evaluate deep learning methods using transcriptome data for the long-standing question of personalized drug-response prediction. We developed an embedding-based approach for drug-response prediction and benchmarked similar methods for their performance. For all methods, we used pretreatment transcriptome data to train models and then conducted a comprehensive evaluation and comparison of the models using cross-panels, cross-datasets and target genes. We further validated the methods using three independent datasets assessing multiple compounds for their predictive capability of drug response, survival outcome and cell line status. As a result, the methods building on gene embeddings had an overall competitive performance with reduced overfitting when we applied evaluation parameters for model fitting as well as the correlation with clinical outcomes in the validation data. We further developed an ensemble model to combine the results from the three most competitive methods for an overall prediction. Finally, we developed DrVAEN (https://bioinfo.uth.edu/drvaen), a user-friendly and easy-accessible web-server that hosts all these methods for drug-response prediction and model comparison for broad use in cancer research, method evaluation and drug development.

Rewired m6A epitranscriptomic networks link mutant p53 to neoplastic transformation.

N6-methyladenosine (m6A), one of the most prevalent mRNA modifications in eukaryotes, plays a critical role in modulating both biological and pathological processes. However, it is unknown whether mutant p53 neomorphic oncogenic functions exploit dysregulation of m6A epitranscriptomic networks. Here, we investigate Li-Fraumeni syndrome (LFS)-associated neoplastic transformation driven by mutant p53 in iPSC-derived astrocytes, the cell-of-origin of gliomas. We find that mutant p53 but not wild-type (WT) p53 physically interacts with SVIL to recruit the H3K4me3 methyltransferase MLL1 to activate the expression of m6A reader YTHDF2, culminating in an oncogenic phenotype. Aberrant YTHDF2 upregulation markedly hampers expression of multiple m6A-marked tumor-suppressing transcripts, including CDKN2B and SPOCK2, and induces oncogenic reprogramming. Mutant p53 neoplastic behaviors are significantly impaired by genetic depletion of YTHDF2 or by pharmacological inhibition using MLL1 complex inhibitors. Our study reveals how mutant p53 hijacks epigenetic and epitranscriptomic machinery to initiate gliomagenesis and suggests potential treatment strategies for LFS gliomas.

scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies.

BACKGROUND: The rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes. RESULTS: scGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in 40 genome-wide association studies (GWAS) datasets (average sample size N ≈ 154,000) using 18 scRNA-seq datasets from nine major human/mouse tissues (totaling 1.08 million cells) and identified 2533 trait and cell-type associations, each with significant modules for further investigation. The module genes were validated using disease or clinically annotated references from ClinVar, OMIM, and pLI variants. CONCLUSIONS: We showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.

Coordination of zygotic genome activation entry and exit by H3K4me3 and H3K27me3 in porcine early embryos.

Histone modifications are critical epigenetic indicators of chromatin state associated with gene expression. Although the reprogramming patterns of H3K4me3 and H3K27me3 have been elucidated in mouse and human preimplantation embryos, the relationship between these marks and zygotic genome activation (ZGA) remains poorly understood. By ultra-low-input native chromatin immunoprecipitation and sequencing, we profiled global H3K4me3 and H3K27me3 in porcine oocytes and in vitro fertilized (IVF) embryos. We found that promoters of ZGA genes occupied sharp H3K4me3 peaks in oocytes, and these peaks became broader after fertilization, and reshaped into sharp again during ZGA. By simultaneous depletion of H3K4me3 demethylase KDM5B and KDM5C, we determined that broad H3K4me3 domain maintenance impaired ZGA gene expression, suggesting its function to prevent premature ZGA entry. By contrast, broad H3K27me3 domains underwent global removal upon fertilization, followed by a re-establishment for H3K4me3/H3K27me3 bivalency in morulae. We also found that bivalent marks were deposited at promoters of ZGA genes, and inhibiting this deposition was correlated with the activation of ZGA genes. It suggests that promoter bivalency contributes to ZGA exit in porcine embryos. Moreover, we demonstrated that aberrant reprogramming of H3K4me3 and H3K27me3 triggered ZGA dysregulation in somatic cell nuclear transfer (SCNT) embryos, whereas H3K27me3-mediated imprinting did not exist in porcine IVF and SCNT embryos. Our findings highlight two previously unknown epigenetic reprogramming modes coordinated with ZGA in porcine preimplantation embryos. Finally, the similarities observed between porcine and human histone modification dynamics suggest that the porcine embryo may also be a useful model for human embryo research.

Hierarchical Accumulation of Histone Variant H2A.Z Regulates Transcriptional States and Histone Modifications in Early Mammalian Embryos.

Early embryos undergo extensive epigenetic reprogramming to achieve gamete-to-embryo transition, which involves the loading and removal of histone variant H2A.Z on chromatin. However, how does H2A.Z regulate gene expression and histone modifications during preimplantation development remains unrevealed. Here, by using ultra-low-input native chromatin immunoprecipitation and sequencing, the genome-wide distribution of H2A.Z is delineated in mouse oocytes and early embryos. These landscapes indicate that paternal H2A.Z is removed upon fertilization, followed by unbiased accumulation on parental genomes during zygotic genome activation (ZGA). Remarkably, H2A.Z exhibits hierarchical accumulation as different peak types at promoters: promoters with double H2A.Z peaks are colocalized with H3K4me3 and indicate transcriptional activation; promoters with a single H2A.Z peak are more likely to occupy bivalent marks (H3K4me3+H3K27me3) and indicate development gene suppression; promoters with no H2A.Z accumulation exhibit persisting gene silencing in early embryos. Moreover, H2A.Z depletion changes the enrichment of histone modifications and RNA polymerase II binding at promoters, resulting in abnormal gene expression and developmental arrest during lineage commitment. Furthermore, similar transcription and accumulation patterns between mouse and porcine embryos indicate that a dual role of H2A.Z in regulating the epigenome required for proper gene expression is conserved during mammalian preimplantation development.

WebCSEA: web-based cell-type-specific enrichment analysis of genes.

Human complex traits and common diseases show tissue- and cell-type- specificity. Recently, single-cell RNA sequencing (scRNA-seq) technology has successfully depicted cellular heterogeneity in human tissue, providing an unprecedented opportunity to understand the context-specific expression of complex trait-associated genes in human tissue-cell types (TCs). Here, we present the first web-based application to quickly assess the cell-type-specificity of genes, named Web-based Cell-type Specific Enrichment Analysis of Genes (WebCSEA, available at https://bioinfo.uth.edu/webcsea/). Specifically, we curated a total of 111 scRNA-seq panels of human tissues and 1,355 TCs from 61 different general tissues across 11 human organ systems. We adapted our previous decoding tissue-specificity (deTS) algorithm to measure the enrichment for each tissue-cell type (TC). To overcome the potential bias from the number of signature genes between different TCs, we further developed a permutation-based method that accurately estimates the TC-specificity of a given inquiry gene list. WebCSEA also provides an interactive heatmap that displays the cell-type specificity across 1355 human TCs, and other interactive and static visualizations of cell-type specificity by human organ system, developmental stage, and top-ranked tissues and cell types. In short, WebCSEA is a one-click application that provides a comprehensive exploration of the TC-specificity of genes among human major TC map.

Hereditary retinoblastoma iPSC model reveals aberrant spliceosome function driving bone malignancies.

The RB1 gene is frequently mutated in human cancers but its role in tumorigenesis remains incompletely defined. Using an induced pluripotent stem cell (iPSC) model of hereditary retinoblastoma (RB), we report that the spliceosome is an up-regulated target responding to oncogenic stress in RB1-mutant cells. By investigating transcriptomes and genome occupancies in RB iPSC­derived osteoblasts (OBs), we discover that both E2F3a, which mediates spliceosomal gene expression, and pRB, which antagonizes E2F3a, coregulate more than one-third of spliceosomal genes by cobinding to their promoters or enhancers. Pharmacological inhibition of the spliceosome in RB1-mutant cells leads to global intron retention, decreased cell proliferation, and impaired tumorigenesis. Tumor specimen studies and genome-wide TCGA (The Cancer Genome Atlas) expression profile analyses support the clinical relevance of pRB and E2F3a in modulating spliceosomal gene expression in multiple cancer types including osteosarcoma (OS). High levels of pRB/E2F3a­regulated spliceosomal genes are associated with poor OS patient survival. Collectively, these findings reveal an undiscovered connection between pRB, E2F3a, the spliceosome, and tumorigenesis, pointing to the spliceosomal machinery as a potentially widespread therapeutic vulnerability of pRB-deficient cancers.

Evaluating water resource carrying capacity using the deep learning method: a case study of Yunnan, Southwest China.

Water resource carrying capacity (WRCC) is an important index for measuring the relations between water resource systems and socio-economic-environmental development. In view of the difficulty in describing the complex and nonlinear relationships between the WRCC and indicators using traditional methods, this study introduces deep learning theory and proposes a novel deep neural network named WRCC-Net for WRCC assessment. Unlike typical network structures, we constructed a hierarchical structure that can indicate the index system in WRCC evaluation. Furthermore, we utilized a residual learning technique to increase the network depth for fitting the complex relationship between the WRCC state and indicators. The proposed deep learning method was applied to solve the real-world WRCC problem by taking the Yunnan province (Southwest China) as the case area. The WRCC was assessed from the following five dimensions: the water resources, social, economic, ecological environment, and coordination subsystems. Performance evaluation shows the advantages of the proposed WRCC-Net over the typical deep feed-forward network and shallow methods. Therefore, the proposed method provides a new way of evaluating the WRCC state and has potential for WRCC research. Overall, the WRCC evaluation using the WRCC-Net shows that the state of the WRCC in Yunnan constantly decreased from 2008 to 2018. These central-eastern areas in the Yunnan province, such as Kunming, Qujing, and Yuxi, are under an unfavorable capacity state. Measures, such as improving water resources management and increasing water utilization efficiency, should be considered in water resource planning in Yunnan province for the sustainable development of water resources.

Angiogenic gene networks are dysregulated in opioid use disorder: evidence from multi-omics and imaging of postmortem human brain.

Opioid use disorder (OUD) is a public health crisis in the U.S. that causes over 50 thousand deaths annually due to overdose. Using next-generation RNA sequencing and proteomics techniques, we identified 394 differentially expressed (DE) coding and long noncoding (lnc) RNAs as well as 213 DE proteins in Brodmann Area 9 of OUD subjects. The RNA and protein changes converged on pro-angiogenic gene networks and cytokine signaling pathways. Four genes (LGALS3, SLC2A1, PCLD1, and VAMP1) were dysregulated in both RNA and protein. Dissecting these DE genes and networks, we found cell type-specific effects with enrichment in astrocyte, endothelial, and microglia correlated genes. Weighted-genome correlation network analysis (WGCNA) revealed cell-type correlated networks including an astrocytic/endothelial/microglia network involved in angiogenic cytokine signaling as well as a neuronal network involved in synaptic vesicle formation. In addition, using ex vivo magnetic resonance imaging, we identified increased vascularization in postmortem brains from a subset of subjects with OUD. This is the first study integrating dysregulation of angiogenic gene networks in OUD with qualitative imaging evidence of hypervascularization in postmortem brain. Understanding the neurovascular effects of OUD is critical in this time of widespread opioid use.

Genome-Wide Correlation of DNA Methylation and Gene Expression in Postmortem Brain Tissues of Opioid Use Disorder Patients.

BACKGROUND: Opioid use disorder (OUD) affects millions of people, causing nearly 50 000 deaths annually in the United States. While opioid exposure and OUD are known to cause widespread transcriptomic and epigenetic changes, few studies in human samples have been conducted. Understanding how OUD affects the brain at the molecular level could help decipher disease pathogenesis and shed light on OUD treatment. METHODS: We generated genome-wide transcriptomic and DNA methylation profiles of 22 OUD subjects and 19 non-psychiatric controls. We applied weighted gene co-expression network analysis to identify genetic markers consistently associated with OUD at both transcriptomic and methylomic levels. We then performed functional enrichment for biological interpretation. We employed cross-omics analysis to uncover OUD-specific regulatory networks. RESULTS: We found 6 OUD-associated co-expression gene modules and 6 co-methylation modules (false discovery rate <0.1). Genes in these modules are involved in astrocyte and glial cell differentiation, gliogenesis, response to organic substance, and response to cytokine (false discovery rate <0.05). Cross-omics analysis revealed immune-related transcription regulators, suggesting the role of transcription factor-targeted regulatory networks in OUD pathogenesis. CONCLUSIONS: Our integrative analysis of multi-omics data in OUD postmortem brain samples suggested complex gene regulatory mechanisms involved in OUD-associated expression patterns. Candidate genes and their upstream regulators revealed in astrocyte, and glial cells could provide new insights into OUD treatment development.

DeepFun: a deep learning sequence-based model to decipher non-coding variant effect in a tissue- and cell type-specific manner.

More than 90% of the genetic variants identified from genome-wide association studies (GWAS) are located in non-coding regions of the human genome. Here, we present a user-friendly web server, DeepFun (https://bioinfo.uth.edu/deepfun/), to assess the functional activity of non-coding genetic variants. This new server is built on a convolutional neural network (CNN) framework that has been extensively evaluated. Specifically, we collected chromatin profiles from ENCODE and Roadmap projects to construct the feature space, including 1548 DNase I accessibility, 1536 histone mark, and 4795 transcription factor binding profiles covering 225 tissues or cell types. With such comprehensive epigenomics annotations, DeepFun expands the functionality of existing non-coding variant prioritizing tools to provide a more specific functional assessment on non-coding variants in a tissue- and cell type-specific manner. By using the datasets from various GWAS studies, we conducted independent validations and demonstrated the functions of the DeepFun web server in predicting the effect of a non-coding variant in a specific tissue or cell type, as well as visualizing the potential motifs in the region around variants. We expect our server will be widely used in genetics, functional genomics, and disease studies.