Biology subjects

Li, J. J.

Publications and source records attributed to Li, J. J..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Does pre-speech auditory modulation reflect processes related to feedback monitoring or speech movement planning?

Previous studies have revealed that auditory processing is modulated during the planning phase immediately prior to speech onset. To date, the functional relevance of this pre-speech auditory modulation (PSAM) remains unknown. Here, we investigated whether PSAM reflects neuronal processes that are associated with preparing auditory cortex for optimized feedback monitoring as reflected in online speech corrections. Combining electroencephalographic PSAM data from a previous data set with new acoustic measures of the same participants speech, we asked whether individual speakers extent of PSAM is correlated with the implementation of within-vowel articulatory adjustments during /b/-vowel-/d/ word productions. Online articulatory adjustments were quantified as the extent of change in inter-trial formant variability from vowel onset to vowel midpoint (a phenomenon known as centering). This approach allowed us to also consider inter-trial variability in formant production and its possible relation to PSAM at vowel onset and midpoint separately. Results showed that inter-trial formant variability was significantly smaller at vowel midpoint than at vowel onset. PSAM was not significantly correlated with this amount of change in variability as an index of within-vowel adjustments. Surprisingly, PSAM was negatively correlated with inter-trial formant variability not only in the middle but also at the very onset of the vowels. Thus, speakers with more PSAM produced formants that were already less variable at vowel onset. Findings suggest that PSAM may reflect processes that influence speech acoustics as early as vowel onset and, thus, that are directly involved in motor command preparation (feedforward control) rather than output monitoring (feedback control).

neuroscience↗

bioRxiv2024

Targeting circadian transcriptional programs in triple negative breast cancer through a cis-regulatory mechanism

Circadian clock genes are emerging targets in many types of cancer, but their mechanistic contributions to tumor progression are still largely unknown. This makes it challenging to stratify patient populations and develop corresponding treatments. In this work, we show that in breast cancer, the disrupted expression of circadian genes has the potential to serve as biomarkers. We also show that the master circadian transcription factors (TFs) BMAL1 and CLOCK are required for the proliferation of metastatic mesenchymal stem-like (mMSL) triple-negative breast cancer (TNBC) cells. Using currently available small molecule modulators, we found that a stabilizer of cryptochrome 2 (CRY2), the direct repressor of BMAL1 and CLOCK transcriptional activity, synergizes with inhibitors of proteasome, which is required for BMAL1 and CLOCK function, to repress a transcriptional program comprising circadian cycling genes in mMSL TNBC cells. Omics analyses on drug-treated cells implied that this repression of transcription is mediated by the transcription factor binding sites (TFBSs) features in the cis-regulatory elements (CRE) of clock-controlled genes. Through a massive parallel reporter assay, we defined a set of CRE features that are potentially repressed by the specific drug combination. The identification of cis-element enrichment might serve as a new concept of defining and targeting tumor types through the modulation of cis-regulatory programs, and ultimately provide a new paradigm of therapy design for cancer types with unclear drivers like TNBC.

cancer biology↗

bioRxiv2024

Systematic Evaluation of Cell Type Deconvolution Methods for Plasma Cell-free DNA

Plasma cell-free DNA (cfDNA) is derived from cellular death in various tissues. Investigating the origin of cfDNA through tissue/cell type deconvolution allows us to detect changes in tissue homeostasis that occur during disease progression or in response to treatment. Consequently, cfDNA has emerged as a valuable noninvasive biomarker for disease detection and treatment monitoring. Although there are numerous methylation-based methods of cfDNA cell type deconvolution available, a comprehensive and systematic evaluation of these methods has yet to be conducted. In this study, we thoroughly benchmarked five previously published methods: MethAtlas, cfNOMe, CelFiE, CelFEER, and UXM. Utilizing deep whole-genome bisulfite sequencing data from 35 human cell types, we generated cfDNA mixtures with known ground truth to assess the deconvolution performance under various scenarios. Our findings indicate that different factors, including sequencing depth, reference marker selection, and reference completeness, influence cell type deconvolution performance. Notably, omitting cell types present in a mixture from the reference leads to suboptimal results. Despite each method exhibited distinct performances under various scenarios, CelFEER and UXM exhibit overall superior performance compared to the others. In summary, we comprehensively evaluated factors influencing methylation-based cfDNA cell type deconvolution and proposed general guidelines to maximize the performance.

bioinformatics↗

bioRxiv2024

Post-death Vesicles of Senescent Bone Marrow Mesenchymal Stromal Polyploids Promote Macrophage Aging and Breast Cancer

Potential systemic factors contributing to aging-associated breast cancer (BC) remain elusive. Here, we reveal that the polyploid giant cells (PGCs) that contain more than two sets of genomes prevailing in aging and cancerous tissues constitute 5-10% of healthy female bone marrow mesenchymal stromal cells (fBMSCs). The PGCs can repair DNA damage and stimulate neighboring cells for clonal expansion. However, dying PGCs in advanced-senescent fBMSCs can form "spikings" which are then separated into membraned mtDNA-containing vesicles (Senescent PGC-Spiking Bodies; SPSBs). SPSB-phagocytosed macrophages accelerate aging with diminished clearance on BC cells and protumor M2 polarization. SPSB-carried mitochondrial OXPHOS components are enriched in BC of elder patients and associated with poor prognosis. SPSB-incorporated breast epithelial cells develop aggressive characteristics and PGCs resembling the polyploid giant cancer cells (PGCCs) in clonogenic BC cells and cancer tissues. These findings highlight an aging BMSC-induced BC risk mediated by SPSB-induced macrophage dysfunction and epithelial cell precancerous transition. SIGNIFICANCEMechanisms underlying aging-associated cancer risk remain unelucidated. This work demonstrates that polyploid giant cells (PGCs) in bone marrow mesenchymal stromal cells (BMSCs) from healthy female bone marrow donors can boost neighboring cell proliferation for clonal expansion. However, the dying-senescent PGCs in the advanced-senescent fBMSCs can form "spikings" which are separated into mitochondrial DNA (mtDNA)-containing spiking bodies (senescent PGC-spiking bodies; SPSBs). The SPSBs promote macrophage aging and breast epithelial cell protumorigenic transition and form polyploid giant cancer cells. These results demonstrate a new form of ghost message from dying-senescent BMSCs, that may serve as a systemic factor contributing to aging-associated immunosuppression and breast cancer risk. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/583755v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@f07b60org.highwire.dtl.DTLVardef@33ef98org.highwire.dtl.DTLVardef@1b720c8org.highwire.dtl.DTLVardef@1c9514d_HPS_FORMAT_FIGEXP M_FIG Xie et al demonstrate that the polyploid giant cells (PGCs) in the juvenile phase expansion of female bone marrow mesenchymal stromal cells (fBMSCs) can boost neighboring cell proliferation for clonal expansion. However, when fBMSCs enter to the advanced-senescent phase, the dying-senescent PGCs form "spikings" which are then separated into membraned vesicles termed Senescent PGC spiking bodies, SPSBs). The SPSBs carrying fragmented mitochondrial elements and OXPHOS proteins can be phagocytosed by macrophage and breast epithelial cells leading to macrophage aging and breast epithelial protumorigenic transition. The SPSBs are demonstrated to be a new form of post-cell death vesicle from aging BMSCs and may serve as a systemic factor contributing to aging-associated immunosuppression and breast cancer risk. C_FIG

cancer biology↗

bioRxiv2023

Sord deficient rats develop a motor-predominant peripheral neuropathy unveiling novel pathophysiological insights

Biallelic SORD mutations cause one of the most frequent forms of recessive hereditary neuropathy, estimated to affect approximately 10,000 patients in North America and Europe alone. Pathogenic SORD loss-of-function changes in the encoded enzyme sorbitol dehydrogenase result in abnormally high sorbitol levels in cells and serum. How sorbitol accumulation leads to peripheral neuropathy remains to be elucidated. A reproducible animal model for SORD neuropathy is essential to illuminate the pathogenesis of SORD deficiency and for preclinical studies of potential therapies. Therefore, we have generated a Sord knockout (KO), Sord-/-, Sprague Dawley rat, to model the human disease and to investigate the pathophysiology underlying SORD deficiency. We have characterized the phenotype in these rats with a battery of behavioral tests as well as biochemical, physiological, and comprehensive histological examinations. Sord-/- rats had remarkably increased levels of sorbitol in serum, cerebral spinal fluid (CSF), and peripheral nerve. Moreover, serum from Sord-/- rats contained significantly increased levels of neurofilament light chain, NfL, an established biomarker for axonal degeneration. Motor performance significantly declined in Sord-/- animals starting at [~]7 months of age. Gait analysis evaluated with video motion tracking confirmed abnormal gait patterns in the hindlimbs. Motor nerve conduction velocities of the tibial nerves were slowed. Light and electron microscopy of the peripheral nervous system revealed degenerating myelinated axons, de- and remyelinated axons, and a likely pathognomonic finding - enlarged "ballooned" myelin sheaths. These findings mainly affected myelinated motor axons; myelinated sensory axons were largely spared. In summary, Sord-/- rats develop a motor-predominant neuropathy that closely resembles the human phenotype. Our studies revealed novel significant aspects of SORD deficiency, and this model will lead to an improved understanding of the pathophysiology and the therapeutic options for SORD neuropathy.

neuroscience↗

bioRxiv2023

Benchmarking computational methods to identify spatially variable genes and peaks

Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes. Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Here, we present a systematic evaluation of 14 methods using 60 simulated datasets generated by four different simulation strategies, 12 real-world transcriptomics, and three spatial ATAC-seq datasets. We find that spatialDE2 consistently outperforms the other benchmarked methods, and Morans I achieves competitive performance in different experimental settings. Moreover, our results reveal that more specialized algorithms are needed to identify spatially variable peaks.

bioinformatics↗

bioRxiv2023

Decoding Heterogenous Single-cell Perturbation Responses

Understanding diverse responses of individual cells to the same perturbation is central to many biological and biomedical problems. Current methods, however, do not precisely quantify the strength of perturbation responses and, more importantly, reveal new biological insights from heterogeneity in responses. Here we introduce the perturbation-response score (PS), based on constrained quadratic optimization, to quantify diverse perturbation responses at a single-cell level. Applied to single-cell transcriptomes of large-scale genetic perturbation datasets (e.g., Perturb-seq), PS outperforms existing methods for quantifying partial gene perturbation responses. In addition, PS presents two major advances. First, PS enables large-scale, single-cell-resolution dosage analysis of perturbation, without the need to titrate perturbation strength. By analyzing the dose-response patterns of over 2,000 essential genes in Perturb-seq, we identify two distinct patterns, depending on whether a moderate reduction in their expression induces strong downstream expression alterations. Second, PS identifies intrinsic and extrinsic biological determinants of perturbation responses. We demonstrate the application of PS in contexts such as T cell stimulation, latent HIV-1 expression, and pancreatic cell differentiation. Notably, PS unveiled a previously unrecognized, cell-type-specific role of coiled-coil domain containing 6 (CCDC6) in guiding liver and pancreatic lineage decisions, where CCDC6 knockouts drive the endoderm cell differentiation towards liver lineage, rather than pancreatic lineage. The PS approach provides an innovative method for dose-to-function analysis and will enable new biological discoveries from single-cell perturbation datasets. One sentence summaryWe present a method to quantify diverse perturbation responses and discover novel biological insights in single-cell perturbation datasets.

bioinformatics↗

bioRxiv2023

Unveiling the Intercompartmental Signaling Axis: Mitochondrial to ER Stress Response (MERSR) and its Impact on Proteostasis

Maintaining protein homeostasis is essential for cellular health. Our previous research uncovered a cross-compartmental Mitochondrial to Cytosolic Stress Response, activated by the perturbation of mitochondrial proteostasis, which ultimately results in the improvement of proteostasis in the cytosol. Here, we found that this signaling axis also influences the unfolded protein response of the endoplasmic reticulum (UPRER), suggesting the presence of a Mitochondria to ER Stress Response (MERSR). During MERSR, the IRE1 branch of UPRER is inhibited, introducing a previously unknown regulatory component of MCSR. Moreover, proteostasis is enhanced through the upregulation of the PERK-eIF2 signaling pathway, increasing phosphorylation of eIF2 and improving the ERs ability to handle proteostasis. MERSR activation in both polyglutamine and amyloid-beta peptide-expressing C. elegans disease models also led to improvement in both aggregate burden and overall disease outcome. These findings shed light on the coordination between the mitochondria and the ER in maintaining cellular proteostasis and provide further evidence for the importance of intercompartmental signaling.

cell biology↗

bioRxiv2023

ClipperQTL: ultrafast and powerful eGene identification method

A central task in expression quantitative trait locus (eQTL) analysis is to identify cis-eGenes (henceforth "eGenes"), i.e., genes whose expression levels are regulated by at least one local genetic variant. Among the existing eGene identification methods, FastQTL is considered the gold standard but is computationally expensive as it requires thousands of permutations for each gene. Alternative methods such as eigenMT and TreeQTL have lower power than FastQTL. In this work, we propose ClipperQTL, which reduces the number of permutations needed from thousands to 20 for data sets with large sample sizes (> 450) by using the contrastive strategy developed in Clipper; for data sets with smaller sample sizes, it uses the same permutation-based approach as FastQTL. We show that ClipperQTL performs as well as FastQTL and runs about 500 times faster if the contrastive strategy is used and 50 times faster if the conventional permutation-based approach is used. The R package ClipperQTL is available at https://github.com/heatherjzhou/ClipperQTL.

genetics↗

bioRxiv2023

ClusterDE: a post-clustering differential expression (DE) method robust to false-positive inflation caused by double dipping

Post-clustering differential analysis is widely used across omics, including single-cell and spatial transcriptomics, multi-omics, population-scale bulk transcriptomics, and microbiome data. After a clustering algorithm finds clusters based on omics features as putative cell types, spatial domains, or subpopulations, statistical tests are typically applied to the same data to identify differential features as potential markers. Because the features that drive clustering are inherently more likely to appear differential, this clustering-induced bias can yield false-positive markers and mislead the interpretation of clusters as meaningful biological entities, especially when clusters are spurious, resulting in ambiguously defined cell types, spatial domains, or subpopulations. This dual challenge---determining whether clusters themselves are reliable and, if so, identifying their true markers---requires a unified statistical solution. To address this challenge, we propose ClusterDE, a statistical method designed to identify post-clustering differential features (with differentially expressed (DE) genes as a primary example) as reliable markers of cell types, spatial domains, or subpopulations while controlling the false discovery rate (FDR), regardless of clustering quality. The core of ClusterDE involves generating synthetic null data as an in silico negative control representing a single homogeneous group (such as a cell type, spatial domain, or population), allowing for the detection and removal of spurious markers caused by clustering-induced bias. In single-cell and spatial transcriptomics analyses, ClusterDE controls the FDR, prioritizes canonical cell-type and spatial-domain markers among the top discoveries, and de-prioritizes housekeeping genes, and it can refine underlying cell-type hierarchies by merging spurious or over-clustered groups. ClusterDE also mitigates spurious bifurcating trajectories in single-cell analyses that arise from over-clustering and retrospectively evaluates contested cell-type claims in high-profile studies, including a retracted human fetal cerebellum atlas and a challenged COVID-19 developing-neutrophil trajectory. At atlas scale, applying ClusterDE to the Allen Human Middle Temporal Gyrus taxonomy merges fine-grained clusters that are not statistically supported by single-cell transcriptomics, while preserving transcriptomically distinct and spatially supported cell types. Moreover, built-in synthetic null quality checks allow users to experiment with multiple state-of-the-art simulators and retain those that pass the diagnostics for their specific datasets. ClusterDE is compatible with widely used analysis pipelines such as Seurat and Scanpy and supports flexible, data-specific synthetic null generation, enhancing post-clustering inference across diverse omics modalities, with demonstrated efficacy in population-scale bulk transcriptomics and microbiome analyses.

bioinformatics↗

bioRxiv2023

NSD2 maintains lineage plasticity and castration-resistance in neuroendocrine prostate cancer

The clinical use of potent androgen receptor (AR) inhibitors has promoted the emergence of novel subtypes of metastatic castration-resistant prostate cancer (mCRPC), including neuroendocrine prostate cancer (CRPC-NE), which is highly aggressive and lethal1. These mCRPC subtypes display increased lineage plasticity and often lack AR expression2-5. Here we show that neuroendocrine differentiation and castration-resistance in CRPC-NE are maintained by the activity of Nuclear Receptor Binding SET Domain Protein 2 (NSD2)6, which catalyzes histone H3 lysine 36 dimethylation (H3K36me2). We find that organoid lines established from genetically-engineered mice7 recapitulate key features of human CRPC-NE, and can display transdifferentiation to neuroendocrine states in culture. CRPC-NE organoids express elevated levels of NSD2 and H3K36me2 marks, but relatively low levels of H3K27me3, consistent with antagonism of EZH2 activity by H3K36me2. Human CRPC-NE but not primary NEPC tumors expresses high levels of NSD2, consistent with a key role for NSD2 in lineage plasticity, and high NSD2 expression in mCRPC correlates with poor survival outcomes. Notably, CRISPR/Cas9 targeting of NSD2 or expression of a dominant-negative oncohistone H3.3K36M mutant results in loss of neuroendocrine phenotypes and restores responsiveness to the AR inhibitor enzalutamide in mouse and human CRPC-NE organoids and grafts. Our findings indicate that NSD2 inhibition can reverse lineage plasticity and castration-resistance, and provide a potential new therapeutic target for CRPC-NE.

cancer biology↗

bioRxiv2023

scDEED: a statistical method for detecting dubious 2D single-cell embeddings

Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-SNE and UMAP are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAPs 2D embedding might not reliably inform the similarities among cell clusters. Motivated by this challenge, we developed a statistical method, scDEED, for detecting dubious cell embeddings output by any 2D-embedding method. By calculating a reliability score for every cell embedding, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. Applied to multiple scRNA-seq datasets, scDEED demonstrates its effectiveness for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.

bioinformatics↗

bioRxiv2023

Benchmarking the Autoencoder Design for Imputing Single-Cell RNA Sequencing Data

Autoencoders are the backbones of many imputation methods that aim to relieve the sparsity issue in single-cell RNA sequencing (scRNA-seq) data. The imputation performance of an autoencoder relies on both the neural network architecture and the hyperparameter choice. So far, literature in the single-cell field lacks a formal discussion on how to design the neural network and choose the hyperparameters. Here, we conducted an empirical study to answer this question. Our study used many real and simulated scRNA-seq datasets to examine the impacts of the neural network architecture, the activation function, and the regularization strategy on imputation accuracy and downstream analyses. Our results show that (i) deeper and narrower autoencoders generally lead to better imputation performance; (ii) the sigmoid and tanh activation functions consistently outperform other commonly used functions including ReLU; (iii) regularization improves the accuracy of imputation and downstream cell clustering and DE gene analyses. Notably, our results differ from common practices in the computer vision field regarding the activation function and the regularization strategy. Overall, our study offers practical guidance on how to optimize the autoencoder design for scRNA-seq data imputation.

bioinformatics↗

bioRxiv2022

scCDC: a computational method for gene-specific contamination detection and correction in single-cell and single-nucleus RNA-seq data

In droplet-based single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) assays, systematic contamination of ambient RNA molecules biases the estimation of genuine transcriptional levels. To correct the contamination, several computational methods have been developed. However, these methods do not distinguish the contamination-causing genes and thus either under- or over-corrected the contamination in our in-house snRNA-seq data of virgin and lactating mammary glands. Hence, we developed scCDC as the first method that specifically detects the contamination-causing genes and only corrects the expression counts of these genes. Benchmarked against existing methods on synthetic and real scRNA-seq and snRNA-seq datasets, scCDC achieved the best contamination correction accuracy with minimal data alteration. Moreover, scCDC applies to processed scRNA-seq and snRNA-seq data with empty droplets removed. In conclusion, scCDC is a flexible, accurate decontamination method that detects the contamination-causing genes, corrects the contamination, and avoids the over-correction of other genes.

bioinformatics↗

bioRxiv2022

A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics

In the single-cell and spatial omics field, computational challenges include method benchmarking, data interpretation, and in silico data generation. To address these challenges, we propose an all-in-one statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real datasets. Furthermore, using a unified probabilistic model for single-cell and spatial omics data, scDesign3 can infer biologically meaningful parameters, assess the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations, and generate in silico negative and positive controls for benchmarking computational tools.

bioinformatics↗

bioRxiv2022

Understanding the effects of mesenchymal stromal cell therapy for treating osteoarthritis using an in vitro co-culture model

Osteoarthritis (OA) is a leading cause of chronic pain and disability, for which there is no cure. Mesenchymal stromal cells (MSCs) have been used in clinical trials for treating OA due to their unique functions to send paracrine anti-inflammatory and trophic signals. Interestingly, these studies have shown mainly short-term effects of MSCs in improving pain and joint function, rather than sustained and consistent benefits. This may reflect a change or loss in the therapeutic effects of MSCs after intra-articular injection. This study aimed to unravel the reasons behind the variable efficacy of MSC injections for OA using an in vitro co-culture model. Osteoarthritic human synovial fibroblasts (OA-HSFs) exposed to MSCs showed short-term downregulation of pro-inflammatory and pro-catabolic genes, but the MSCs showed upregulation of pro-inflammatory genes and impaired ability to undergo osteogenesis and chondrogenesis in the presence of OA-HSFs. Moreover, short-term exposure of OA-HSFs to MSCs was insufficient for inducing sustained changes to their diseased behaviour. These findings suggest MSCs may not provide long-term effects in correcting the OA joint environment due to adopting the diseased phenotype of the surrounding tissues, which have important implications in the future development of effective stem cell-based OA treatments with long-term therapeutic efficacy.

cell biology↗

bioRxiv2022

Wilcoxon rank-sum test still outperforms dearseq after accounting for the normalization impact in semi-synthetic RNA-seq data simulation

In this response to the correspondence by Hejblum et al. [1], we clarify the reasons why we ran the Wilcoxon rank-sum test on the semi-synthetic RNA-seq samples without normalization, and why we could only run dearseq with its built-in normalization, in our published study [2]. We also argue that no normalization should be performed on the semi-synthetic samples. Hence, for a fairer method comparison and using the updated dearseq package by Hejblum et al., we re-run the six differential expression methods (DESeq2, edgeR, limma-voom, dearseq, NOISeq, and the Wilcoxon rank-sum test) without normalizing the semi-synthetic samples, i.e., under the "No normalization" scheme in [1]. Our updated results show that the Wilcoxon rank-sum test is still the best method in terms of false discovery rate (FDR) control and power performance under all settings investigated.

bioinformatics↗

bioRxiv2022

scReadSim: a single-cell multi-omics read simulator

Benchmarking single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) computational tools demands simulators to generate realistic sequencing reads. However, none of the few read simulators aim to mimic real data. To fill this gap, we introduce scReadSim, a single-cell RNA-seq and ATAC-seq read simulator that allows user-specified ground truths and generates synthetic sequencing reads (in FASTQ and BAM formats) by mimicking real data. At both read-sequence and read-count levels, scReadSim mimics real scRNA-seq and scATAC-seq data. Moreover, scReadSim provides ground truths, including unique molecular identifier counts for scRNA-seq and open chromatin regions for scATAC-seq. In particular, scReadSim allows users to design cell-type-specific ground-truth open chromatin regions for scATAC-seq data generation. In benchmark applications of scReadSim, we show that cell-ranger is a preferred scRNA-seq UMI deduplication tool, and HMMRATAC and MACS3 achieve top performance in scATAC-seq peak calling.

bioinformatics↗