YY1 knockout in pro-B cells impairs lineage commitment, enabling unusual hematopoietic lineage plasticity.

During B-cell development, cells progress through multiple developmental stages, with the pro-B-cell stage defining commitment to the B-cell lineage. YY1 is a ubiquitous transcription factor that is capable of both activation and repression functions. We found here that knockout of YY1 at the pro-B-cell stage eliminates B lineage commitment. YY1 knockout pro-B cells can generate T lineage cells in vitro using the OP9-DL4 feeder system and in vivo after injection into sublethally irradiated Rag1-/- mice. These T lineage-like cells lose their B lineage transcript profile and gain a T-cell lineage profile. Single-cell RNA-seq experiments showed that as YY1 knockout pro-B cells transition into T lineage cells in vitro, various cell clusters adopt transcript profiles representing a multiplicity of hematopoietic lineages, indicating unusual lineage plasticity. In addition, YY1 KO pro-B cells in vivo can give rise to other hematopoietic lineages in vivo. Evaluation of RNA-seq, scRNA-seq, ChIP-seq, and scATAC-seq data indicates that YY1 controls numerous chromatin-modifying proteins leading to increased accessibility of alternative lineage genes in YY1 knockout pro-B cells. Given the ubiquitous nature of YY1 and its dual activation and repression functions, YY1 may regulate commitment in multiple cell lineages.

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data.

Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer linkage prediction, and enhancer-gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relations associated with pseudotime, as well as key TF-enhancer-gene relations and TF cooperation underlying tumor cells.

Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling.

Annotation of the cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.

Kidney Organoid Modeling of WT1 Mutations Reveals Key Regulatory Paths Underlying Podocyte Development.

Wilms tumor-1(WT1) is a crucial transcription factor that regulates podocyte development. However, the epigenomic mechanism underlying the function of WT1 during podocyte development has yet to be fully elucidated. Here, single-cell chromatin accessibility and gene expression maps of foetal kidneys and kidney organoids are generated. Functional implications of WT1-targeted genes, which are crucial for the development of podocytes and the maintenance of their structure, including BMPER/PAX2/MAGI2 that regulates WNT signaling pathway, MYH9 that maintains actin filament organization and NPHS1 that modulates cell junction assembly are identified. To further illustrate the functional importance of WT1-mediated transcriptional regulation during podocyte development, cultured and implanted patient-derived kidney organoids derived from the Induced Pluripotent Stem Cell (iPSCs) of a patient with a heterozygous missense mutation in WT1 are generated. Results from single-cell RNA sequencing (scRNA-seq) and functional assays confirm that the WT1 mutation leads to delays in podocyte development and causes damage to cell structures, due to its failure to activate the targeting genes MAGI2, MYH9, and NPHS1. Notably, correcting the mutation in the patient iPSCs using CRISPR-Cas9 gene editing rescues the podocyte phenotype. Collectively, this work elucidates the WT1-related epigenomic landscape with respect to human podocyte development and identifies the disease-causing role of a WT1 mutation.

Type I interferons induce an epigenetically distinct memory B cell subset in chronic viral infection.

Memory B cells (MBCs) are key providers of long-lived immunity against infectious disease, yet in chronic viral infection, they do not produce effective protection. How chronic viral infection disrupts MBC development and whether such changes are reversible remain unknown. Through single-cell (sc)ATAC-seq and scRNA-seq during acute versus chronic lymphocytic choriomeningitis viral infection, we identified a memory subset enriched for interferon (IFN)-stimulated genes (ISGs) during chronic infection that was distinct from the T-bet+ subset normally associated with chronic infection. Blockade of IFNAR-1 early in infection transformed the chromatin landscape of chronic MBCs, decreasing accessibility at ISG-inducing transcription factor binding motifs and inducing phenotypic changes in the dominating MBC subset, with a decrease in the ISG subset and an increase in CD11c+CD80+ cells. However, timing was critical, with MBCs resistant to intervention at 4 weeks post-infection. Together, our research identifies a key mechanism to instruct MBC identity during viral infection.

Single-cell sequencing analysis within biologically relevant dimensions.

The currently predominant approach to transcriptomic and epigenomic single-cell analysis depends on a rigid perspective constrained by reduced dimensions and algorithmically derived and annotated clusters. Here, we developed Seqtometry (sequencing-to-measurement), a single-cell analytical strategy based on biologically relevant dimensions enabled by advanced scoring with multiple gene sets (signatures) for examination of gene expression and accessibility across various organ systems. By utilizing information only in the form of specific signatures, Seqtometry bypasses unsupervised clustering and individual annotations of clusters. Instead, Seqtometry combines qualitative and quantitative cell-type identification with specific characterization of diverse biological processes under experimental or disease conditions. Comprehensive analysis by Seqtometry of various immune cells as well as other cells from different organs and disease-induced states, including multiple myeloma and Alzheimer's disease, surpasses corresponding cluster-based analytical output. We propose Seqtometry as a single-cell sequencing analysis approach applicable for both basic and clinical research.

Exploring distinct properties of endometrial stem cells through advanced single-cell analysis platforms.

The endometrium is a dynamic tissue that undergoes cyclic changes in response to ovarian hormones during the menstrual cycle. These changes are crucial for pregnancy establishment and maintenance. Endometrial stem cells play a pivotal role in endometrial regeneration and repair by differentiating into various cell types within the endometrium. However, their involvement in endometrial disorders such as endometriosis, infertility, and endometrial cancer is still not fully understood yet. Traditional bulk sequencing methods have limitations in capturing heterogeneity and complexity of endometrial stem cell populations. To overcome these limitations, recent single-cell analysis techniques, including single-cell RNA sequencing (scRNA-Seq), single-cell ATAC sequencing (scATAC-Seq), and spatial transcriptomics, have emerged as valuable tools for studying endometrial stem cells. In this review, although there are still many technical limitations that require improvement, we will summarize the current state-of-the-art single-cell analysis techniques for endometrial stem cells and explore their relevance to related diseases. We will discuss studies utilizing various single-cell analysis platforms to identify and characterize distinct endometrial stem cell populations and investigate their dynamic changes in gene expression and epigenetic patterns during menstrual cycle and differentiation processes. These techniques enable the identification of rare cell populations, capture heterogeneity of cell populations within the endometrium, and provide potential targets for more effective therapies.

Genome-wide profiling of transcription factor activity in primary liver cancer using single-cell ATAC sequencing.

Primary liver cancer (PLC) consists of two main histological subtypes; hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). The role of transcription factors (TFs) in malignant hepatobiliary lineage commitment between HCC and iCCA remains underexplored. Here, we present genome-wide profiling of transcription regulatory elements of 16 PLC patients using single-cell assay for transposase accessible chromatin sequencing. Single-cell open chromatin profiles reflect the compositional diversity of liver cancer, identifying both malignant and microenvironment component cells. TF motif enrichment levels of 31 TFs strongly discriminate HCC from iCCA tumors. These TFs are members of the nuclear/retinoid receptor, POU, or ETS motif families. POU factors are associated with prognostic features in iCCA. Overall, nuclear receptors, ETS and POU TF motif families delineate transcription regulation between HCC and iCCA tumors, which may be relevant to development and selection of PLC subtype-specific therapeutics.

Characterization of human pluripotent stem cell differentiation by single-cell dual-omics analyses.

In vivo differentiation of human pluripotent stem cells (hPSCs) has unique advantages, such as multilineage differentiation, angiogenesis, and close cell-cell interactions. To systematically investigate multilineage differentiation mechanisms of hPSCs, we constructed the in vivo hPSC differentiation landscape containing 239,670 cells using teratoma models. We identified 43 cell types, inferred 18 cell differentiation trajectories, and characterized common and specific gene regulation patterns during hPSC differentiation at both transcriptional and epigenetic levels. Additionally, we developed the developmental single-cell Basic Local Alignment Search Tool (dscBLAST), an R-based cell identification tool, to simplify the identification processes of developmental cells. Using dscBLAST, we aligned cells in multiple differentiation models to normally developing cells to further understand their differentiation states. Overall, our study offers new insights into stem cell differentiation and human embryonic development; dscBLAST shows favorable cell identification performance, providing a powerful identification tool for developmental cells.

Friend leukemia integration 1 overexpression decreases endometrial receptivity and induces embryo implantation failure by promoting PART1 transcription in the endometrial epithelial cells.

Background: In vitro fertilization-embryo transfer (IVF-ET) is a crucial assisted reproductive technology for treating infertility. However, recurrent implantation failure (RIF), a significant challenge in IVF-ET success, remains unresolved. This study aimed to explore the role and mechanism of FLI1 in endometrial receptivity and RIF. Methods: Differential endometrial cell proportions between patients with RIF and control subjects were assessed using single-cell RNA sequencing (scRNA-seq) analysis. The chromatin accessibility of FLI1 in the luteal endometrial tissue of patients with RIF and control subjects was examined using the single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq). FLI1 mRNA and protein levels were gauged by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Cell viability and migration were examined via cell counting kit (CCK)-8 and scratch healing assays. Epithelial-mesenchymal transition markers were analyzed using western blotting. Mechanisms underlying FLI1's regulation of PART1 transcription and expression in endometrial epithelial cells were explored using chromatin immunoprecipitation and dual-luciferase reporter assays. Adeno-associated virus (AAV) carrying epithelial cell-specific FLI1/PART1 overexpression sequences was uterinely injected in mice to assess FLI1/PART1 effects. Results: scRNA-seq revealed diminished endometrial epithelial cell proportions in RIF patients. Meanwhile, scATAC-seq indicated enhanced chromatin accessibility of FLI1 in these cells. FLI1 exhibited specific expression in RIF patients' endometrial epithelial cells. Specific FLI1 overexpression inhibited embryo implantation, while knockdown enhanced it. Pregnant mice injected with AAV encoding FLI1 overexpression had significantly lower implantation than AAV-negative controls. FLI1 binding to PART1 promoter heightened PART1 transcription and expression in endometrial epithelial cells. Rescue experiments illustrated FLI1's role in embryo implantation by boosting PART1 expression. PART1 was notably elevated in RIF patients' luteal endometrial tissue and non-receptive endometrial epithelial cells (HEC-1-A). Specific PART1 overexpression dampened embryo implantation, whereas knockdown promoted it. Pregnant mice injected with AAV encoding PART1 had lower implantation than negative controls. PART1 knockdown mitigated FLI1's inhibitory impact on HEC-1-A cell viability and migration. Conclusions: FLI1 overexpression in the endometrial epithelial cells of patients with RIF inhibited embryo implantation by binding to the PART1 promoter region to promote PART1 expression. These findings can aid in the development of novel therapeutic targets for RIF.

Single-Cell Transcriptional and Epigenetic Profiles of Male Breast Cancer Nominate Salient Cancer-Specific Enhancers.

Male breast cancer represents about 1% of all breast cancer diagnoses and, although there are some similarities between male and female breast cancer, the paucity of data available on male breast cancer makes it difficult to establish targeted therapies. To date, most male breast cancers (MBCs) are treated according to protocols established for female breast cancer (FBC). Thus, defining the transcriptional and epigenetic landscape of MBC with improved resolution is critical for developing better avenues for therapeutic intervention. In this study, we present matched transcriptional (scRNA-seq) and epigenetic (scATAC-seq) profiles at single-cell resolution of two treatment naïve MBC tumors processed immediately after surgical resection. These data enable the detection of differentially expressed genes between male and female breast tumors across immune, stromal, and malignant cell types, to highlight several genes that may have therapeutic implications. Notably, MYC target genes and mTORC1 signaling genes were significantly upregulated in the malignant cells of MBC compared to the female counterparts. To understand how the regulatory landscape of MBC gives rise to these male-specific gene expression patterns, we leveraged the scATAC-seq data to systematically link changes in chromatin accessibility to changes in gene expression within each cell type. We observed cancer-specific rewiring of several salient enhancers and posit that these enhancers have a higher regulatory load than lineage-specific enhancers. We highlight two examples of previously unannotated cancer-cell-specific enhancers of ANXA2 and PRDX4 gene expression and show evidence for super-enhancer regulation of LAMB3 and CD47 in male breast cancer cells. Overall, this dataset annotates clinically relevant regulatory networks in male breast tumors, providing a useful resource that expands our current understanding of the gene expression programs that underlie the biology of MBC.

Single-cell analyses reveal cannabidiol rewires tumor microenvironment via inhibiting alternative activation of macrophage and synergizes with anti-PD-1 in colon cancer.

Colorectal tumors often create an immunosuppressive microenvironment that prevents them from responding to immunotherapy. Cannabidiol (CBD) is a non-psychoactive natural active ingredient from the cannabis plant that has various pharmacological effects, including neuroprotective, antiemetic, anti-inflammatory, and antineoplastic activities. This study aimed to elucidate the specific anticancer mechanism of CBD by single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) technologies. Here, we report that CBD inhibits colorectal cancer progression by modulating the suppressive tumor microenvironment (TME). Our single-cell transcriptome and ATAC sequencing results showed that CBD suppressed M2-like macrophages and promoted M1-like macrophages in tumors both in strength and quantity. Furthermore, CBD significantly enhanced the interaction between M1-like macrophages and tumor cells and restored the intrinsic anti-tumor properties of macrophages, thereby preventing tumor progression. Mechanistically, CBD altered the metabolic pattern of macrophages and related anti-tumor signaling pathways. We found that CBD inhibited the alternative activation of macrophages and shifted the metabolic process from oxidative phosphorylation and fatty acid oxidation to glycolysis by inhibiting the phosphatidylinositol 3-kinase-protein kinase B signaling pathway and related downstream target genes. Furthermore, CBD-mediated macrophage plasticity enhanced the response to anti-programmed cell death protein-1 (PD-1) immunotherapy in xenografted mice. Taken together, we provide new insights into the anti-tumor effects of CBD.

StemnesScoRe: an R package to estimate the stemness of glioma cancer cells at single-cell resolution.

Background/aim: Glioblastoma is the most heterogeneous and the most difficult-to-treat type of brain tumor and one of the deadliest among all cancers. The high plasticity of glioma cancer stem cells and the resistance they develop against multiple modalities of therapy, along with their high heterogeneity, are the main challenges faced during treatment of glioblastoma. Therefore, a better understanding of the stemness characteristics of glioblastoma cells is needed. With the development of various single-cell technologies and increasing applications of machine learning, indices based on transcriptomic and/or epigenomic data have been developed to quantitatively measure cellular states and stemness. In this study, we aimed to develop a glioma-specific stemness score model using scATAC-seq data for the first time. Materials and methods: We first applied three powerful machine-learning algorithms, i.e. random forest, gradient boosting, and extreme gradient boosting, to glioblastoma scRNA-seq data to discover the most important genes associated with cellular states. We then identified promoter and enhancer regions associated with these genes. After downloading the scATAC-seq peaks and their read counts for each patient, we identified the overlapping regions between the single-cell peaks and the peaks of genes obtained through machine-learning algorithms. Then we calculated read counts that were mapped to these overlapping regions. We finally developed a model capable of estimating the stemness score for each glioma cell using overlapping regions and the importance of genes predictive of glioblastoma cellular states. We also created an R package, accessible to all researchers regardless of their coding proficiency. Results: Our results showed that mesenchymal-like stem cells display higher stemness scores compared to neural-progenitor-, oligodendrocyte-progenitor-, and astrocyte-like cells. Conclusion: scATAC-seq can be used to assess heterogeneity in glioblastoma and identify cells with high stemness characteristics. The package is publicly available at https://github.com/Necla/StemnesScoRe and includes documentation with implementation of a real-data experiment.

Joint analysis of scATAC-seq datasets using epiConv.

BACKGROUND: Technical improvement in ATAC-seq makes it possible for high throughput profiling the chromatin states of single cells. However, data from multiple sources frequently show strong technical variations, which is referred to as batch effects. In order to perform joint analysis across multiple datasets, specialized method is required to remove technical variations between datasets while keep biological information. RESULTS: Here we present an algorithm named epiConv to perform joint analyses on scATAC-seq datasets. We first show that epiConv better corrects batch effects and is less prone to over-fitting problem than existing methods on a collection of PBMC datasets. In a collection of mouse brain data, we show that epiConv is capable of aligning low-depth scATAC-Seq from co-assay data (simultaneous profiling of transcriptome and chromatin) onto high-quality ATAC-seq reference and increasing the resolution of chromatin profiles of co-assay data. Finally, we show that epiConv can be used to integrate cells from different biological conditions (T cells in normal vs. germ-free mouse; normal vs. malignant hematopoiesis), which reveals hidden cell populations that would otherwise be undetectable. CONCLUSIONS: In this study, we introduce epiConv to integrate multiple scATAC-seq datasets and perform joint analysis on them. Through several case studies, we show that epiConv removes the batch effects and retains the biological signal. Moreover, joint analysis across multiple datasets improves the performance of clustering and differentially accessible peak calling, especially when the biological signal is weak in single dataset.

A Genetic Bottleneck of Mitochondrial DNA During Human Lymphocyte Development.

Mitochondria are essential organelles in eukaryotic cells that provide critical support for energetic and metabolic homeostasis. Although the elimination of pathogenic mitochondrial DNA (mtDNA) mutations in somatic cells has been observed, the mechanisms to maintain proper functions despite their mtDNA mutation load are poorly understood. In this study, we analyzed somatic mtDNA mutations in more than 30,000 single human peripheral and bone marrow mononuclear cells. We observed a significant overrepresentation of homoplasmic mtDNA mutations in B, T, and natural killer (NK) lymphocytes. Intriguingly, their overall mutational burden was lower than that in hematopoietic progenitors and myeloid cells. This characteristic mtDNA mutational landscape indicates a genetic bottleneck during lymphoid development, as confirmed with single-cell datasets from multiple platforms and individuals. We further demonstrated that mtDNA replication lags behind cell proliferation in both pro-B and pre-B progenitor cells, thus likely causing the genetic bottleneck by diluting mtDNA copies per cell. Through computational simulations and approximate Bayesian computation (ABC), we recapitulated this lymphocyte-specific mutational landscape and estimated the minimal mtDNA copies as <30 in T, B, and NK lineages. Our integrative analysis revealed a novel process of a lymphoid-specific mtDNA genetic bottleneck, thus illuminating a potential mechanism used by highly metabolically active immune cells to limit their mtDNA mutation load.

A multi-omic single-cell landscape of human gynecologic malignancies.

Deconvolution of regulatory mechanisms that drive transcriptional programs in cancer cells is key to understanding tumor biology. Herein, we present matched transcriptome (scRNA-seq) and chromatin accessibility (scATAC-seq) profiles at single-cell resolution from human ovarian and endometrial tumors processed immediately following surgical resection. This dataset reveals the complex cellular heterogeneity of these tumors and enabled us to quantitatively link variation in chromatin accessibility to gene expression. We show that malignant cells acquire previously unannotated regulatory elements to drive hallmark cancer pathways. Moreover, malignant cells from within the same patients show substantial variation in chromatin accessibility linked to transcriptional output, highlighting the importance of intratumoral heterogeneity. Finally, we infer the malignant cell type-specific activity of transcription factors. By defining the regulatory logic of cancer cells, this work reveals an important reliance on oncogenic regulatory elements and highlights the ability of matched scRNA-seq/scATAC-seq to uncover clinically relevant mechanisms of tumorigenesis in gynecologic cancers.

LISA2: Learning Complex Single-Cell Trajectory and Expression Trends.

Single-cell transcriptional and epigenomics profiles have been applied in a variety of tissues and diseases for discovering new cell types, differentiation trajectories, and gene regulatory networks. Many methods such as Monocle 2/3, URD, and STREAM have been developed for tree-based trajectory building. Here, we propose a fast and flexible trajectory learning method, LISA2, for single-cell data analysis. This new method has two distinctive features: (1) LISA2 utilizes specified leaves and root to reduce the complexity for building the developmental trajectory, especially for some special cases such as rare cell populations and adjacent terminal cell states; and (2) LISA2 is applicable for both transcriptomics and epigenomics data. LISA2 visualizes complex trajectories using 3D Landmark ISOmetric feature MAPping (L-ISOMAP). We apply LISA2 to simulation and real datasets in cerebellum, diencephalon, and hematopoietic stem cells including both single-cell transcriptomics data and single-cell assay for transposase-accessible chromatin data. LISA2 is efficient in estimating single-cell trajectory and expression trends for different kinds of molecular state of cells.

Single-cell analyses reveal suppressive tumor microenvironment of human colorectal cancer.

Profiling heterologous cell types within tumors is essential to decipher tumor microenvironment that shapes tumor progress and determines the outcome of therapeutic response. Here, we comprehensively characterized transcriptomes of 34,037 single cells obtained from 12 treatment-naïve patients with colorectal cancer. Our comprehensive evaluation revealed attenuated B-cell antigen presentation, distinct regulatory T-cell clusters with different origin and novel polyfunctional tumor associated macrophages associated with CRC. Moreover, we identified expanded XCL1+ T-cell clusters associated with tumor mutational burden high status. We further explored the underlying molecular mechanisms by profiling epigenetic landscape and inferring transcription factor motifs using single-cell ATAC-seq. Our dataset and analysis approaches herein provide a rich resource for further study of the impact of immune cells and translational research for human colorectal cancer.

Mammary gland development from a single cell 'omics view.

Understanding the complexity and heterogeneity of mammary cell subpopulations is vital to delineate the mechanisms behind breast cancer development, progression and prevention. Increasingly sophisticated tools for investigating these cell subtypes has led to the development of a greater understanding of these cell subtypes, complex interplay of certain subtypes and their developmental potential. Of note, increasing accessibility and affordability of single cell technologies has led to a plethora of studies being published containing data from mammary cell subtypes and their differentiation potential in both mice and human data sets. Here, we review the different types of single cell technologies and how they have been used to improve our understanding of mammary gland development.

Enhancement and Imputation of Peak Signal Enables Accurate Cell-Type Classification in scATAC-seq.

Single-cell Assay Transposase Accessible Chromatin sequencing (scATAC-seq) has been widely used in profiling genome-wide chromatin accessibility in thousands of individual cells. However, compared with single-cell RNA-seq, the peaks of scATAC-seq are much sparser due to the lower copy numbers (diploid in humans) and the inherent missing signals, which makes it more challenging to classify cell type based on specific expressed gene or other canonical markers. Here, we present svmATAC, a support vector machine (SVM)-based method for accurately identifying cell types in scATAC-seq datasets by enhancing peak signal strength and imputing signals through patterns of co-accessibility. We applied svmATAC to several scATAC-seq data from human immune cells, human hematopoietic system cells, and peripheral blood mononuclear cells. The benchmark results showed that svmATAC is free of literature-based markers and robust across datasets in different libraries and platforms. The source code of svmATAC is available at https://github.com/mrcuizhe/svmATAC under the MIT license.