Exhaustive identification of genome-wide binding events of transcriptional regulators

Genome-wide binding assays aspire to map the complete binding pattern of gene regulators. Common practice relies on replication-duplicates or triplicates-and high stringency statistics to favor false negatives over false positives. Here we show that duplicates and triplicates of CUT&RUN are not sufficient to discover the entire activity of transcriptional regulators. We introduce ICEBERG (Increased Capture of Enrichment By Exhaustive Replicate aGgregation), a pipeline that harnesses large numbers of CUT&RUN replicates to discover the full set of binding events and chart the line between false positives and false negatives. We employed ICEBERG to map the full set of H3K4me3-marked regions, the targets of the co-factor β-catenin, and those of the transcription factor TBX3, in human colorectal cancer cells. The ICEBERG datasets allow benchmarking of individual replicates, comparing the performance of peak calling and replication approaches, and expose the arbitrary nature of strategies to identify reproducible peaks. Instead of a static view of genomic targets, ICEBERG establishes a spectrum of detection probabilities across the genome for a given factor, underlying the intrinsic dynamicity of its mechanism of action, and permitting to distinguish frequent from rare regulation events. Finally, ICEBERG discovered instances, undetectable with other approaches, that underlie novel mechanisms of colorectal cancer progression.

MMGAT: a graph attention network framework for ATAC-seq motifs finding

BACKGROUND

Motif finding in Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data is essential to reveal the intricacies of transcription factor binding sites (TFBSs) and their pivotal roles in gene regulation. Deep learning technologies including convolutional neural networks (CNNs) and graph neural networks (GNNs), have achieved success in finding ATAC-seq motifs. However, CNN-based methods are limited by the fixed width of the convolutional kernel, which makes it difficult to find multiple transcription factor binding sites with different lengths. GNN-based methods has the limitation of using the edge weight information directly, makes it difficult to aggregate the neighboring nodes' information more efficiently when representing node embedding.

RESULTS

To address this challenge, we developed a novel graph attention network framework named MMGAT, which employs an attention mechanism to adjust the attention coefficients among different nodes. And then MMGAT finds multiple ATAC-seq motifs based on the attention coefficients of sequence nodes and k-mer nodes as well as the coexisting probability of k-mers. Our approach achieved better performance on the human ATAC-seq datasets compared to existing tools, as evidenced the highest scores on the precision, recall, F1_score, ACC, AUC, and PRC metrics, as well as finding 389 higher quality motifs. To validate the performance of MMGAT in predicting TFBSs and finding motifs on more datasets, we enlarged the number of the human ATAC-seq datasets to 180 and newly integrated 80 mouse ATAC-seq datasets for multi-species experimental validation. Specifically on the mouse ATAC-seq dataset, MMGAT also achieved the highest scores on six metrics and found 356 higher-quality motifs. To facilitate researchers in utilizing MMGAT, we have also developed a user-friendly web server named MMGAT-S that hosts the MMGAT method and ATAC-seq motif finding results.

CONCLUSIONS

The advanced methodology MMGAT provides a robust tool for finding ATAC-seq motifs, and the comprehensive server MMGAT-S makes a significant contribution to genomics research. The open-source code of MMGAT can be found at https://github.com/xiaotianr/MMGAT , and MMGAT-S is freely available at https://www.mmgraphws.com/MMGAT-S/ .

Combined transcriptomic and ChIPseq analyses of the Bordetella pertussis RisA regulon

The regulation of Bordetella pertussis virulence is mediated by the two-component system BvgA/S, which activates the transcription of virulence-activated genes (vags). In the avirulent phase, the vags are not expressed, but instead, virulence-repressed genes (vrgs) are expressed, under the control of another two-component system, RisA/K. Here, we combined transcriptomic and chromatin immunoprecipitation sequencing (ChIPseq) data to examine the RisA/K regulon. We performed RNAseq analyses of RisA-deficient and RisA-phosphoablative B. pertussis mutants cultivated in virulent and avirulent conditions. We confirmed that the expression of most vrgs is regulated by phosphorylated RisA. However, the expression of some, including those involved in flagellum biosynthesis and chemotaxis, requires RisA independently of phosphorylation. Many RisA-regulated genes encode proteins with regulatory functions, suggesting multiple RisA regulation cascades. By ChIPseq analyses, we identified 430 RisA-binding sites, 208 within promoter regions, 201 within open reading frames, and 21 in non-coding regions. RisA binding was demonstrated in the promoter regions of most vrgs and, surprisingly, of some vags, as well as for other genes not identified as vags or vrgs. Unexpectedly, many genes, including some vags, like prn, brpL, bipA, and cyaA, contain a BvgA-binding site and a RisA-binding site, which increases the complexity of the RisAK/BvgAS network in B. pertussis virulence regulation.IMPORTANCEThe expression of virulence-activated genes (vags) of Bordetella pertussis, the etiological agent of whooping cough, is under the transcriptional control of the two-component system BvgA/S, which allows the bacterium to switch between virulent and avirulent phases. In addition, the more recently identified two-component system RisA/K is required for the expression of B. pertussis genes, collectively named vrgs, that are repressed during the virulent phase but activated during the avirulent phase. We have characterized the RisA/K regulon by combined transcriptomic and chromatin immunoprecipitation sequencing analyses. We identified more than 400 RisA-binding sites. Many of them are localized in promoter regions, especially vrgs, but some were found within open reading frames and in non-coding regions. Surprisingly, RisA-binding sites were also found in promoter regions of some vags, illustrating the previously underappreciated complexity of virulence regulation in B. pertussis.

Unravelling the genetic basis of Schizophrenia

Neuronal development is a highly regulated mechanism that is central to organismal function in animals. In humans, disruptions to this process can lead to a range of neurodevelopmental phenotypes, including Schizophrenia (SCZ). SCZ has a significant genetic component, whereby an individual with an SCZ affected family member is eight times more likely to develop the disease than someone with no family history of SCZ. By examining a combination of genomic, transcriptomic and epigenomic datasets, large-scale 'omics' studies aim to delineate the relationship between genetic variation and abnormal cellular activity in the SCZ brain. Herein, we provide a brief overview of some of the key omics methods currently being used in SCZ research, including RNA-seq, the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and high-throughput chromosome conformation capture (3C) approaches (e.g., Hi-C), as well as single-cell/nuclei iterations of these methods. We also discuss how these techniques are being employed to further our understanding of the genetic basis of SCZ, and to identify associated molecular pathways, biomarkers, and candidate drug targets.

scifi-ATAC-seq: massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing

Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called scifi-ATAC-seq, single-cell combinatorial fluidic indexing ATAC-sequencing, which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using the 10X Genomics platform. With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples can be indexed in a single emulsion reaction, representing an approximately 20-fold increase in throughput compared to the standard 10X Genomics workflow.

ChIP-seq analysis found IL21R, a target gene of GTF2I-the susceptibility gene for primary biliary cholangitis in Chinese Han

AIMS

Aimed to identify a new susceptibility gene associated with primary biliary cholangitis (PBC) in Chinese Han and investigate the possible mechanism of that gene in PBC.

METHODS

A total of 466 PBC and 694 healthy controls (HC) were included in our study, and genotyping GTF2I gene variants by Sequenom. CD19 + B cells were isolated for Chromatin immunoprecipitation sequencing (ChIP-seq). Additionally, MEME-ChIP was utilized to perform searches for known motifs and de novo motif discovery. The GTF2I ChIP-seq of hematopoietic cell line (K562) results were obtained from ENCODE (GSE176987, GSE177691). The Genomic HyperBrowser was used to determine overlap and hierarchal clustering between ours and ENCODE datasets.

RESULTS

The frequency of the rs117026326 variant T allele was significantly higher in PBC patients than that in HC (20.26% compared with 13.89%, Pc = 1.09E-04). Furthermore, we observed an elevated proportion of GTF2I binding site located in the upstream and 5' UTR of genes in PBC in comparison with HC. Additionally, an in-depth analysis of IL21R region revealed that GTF2I might bind to the IL21R promoter to regulate the expression of the IL21R, with four peaks of GTF2I binding sites, including three increased binding sites in upstream, one increased binding site in 5' UTR. Motif analysis by MEME-ChIP uncovered five significant motifs. A significant overlap between our ChIP and GSE176987, GSE17769 were found by the Genomic HyperBroswer.

CONCLUSIONS

Our study confirmed that GTF2I was associated with PBC in Chinese Han. Furthermore, our gene function analysis indicated that IL21R may be the target gene regulated by GTF2I.

Iterative assay for transposase-accessible chromatin by sequencing to isolate functionally relevant neuronal subtypes

The Drosophila brain contains tens of thousands of distinct cell types. Thousands of different transgenic lines reproducibly target specific neuron subsets, yet most still express in several cell types. Furthermore, most lines were developed without a priori knowledge of where the transgenes would be expressed. To aid in the development of cell type-specific tools for neuronal identification and manipulation, we developed an iterative assay for transposase-accessible chromatin (ATAC) approach. Open chromatin regions (OCRs) enriched in neurons, compared to whole bodies, drove transgene expression preferentially in subsets of neurons. A second round of ATAC-seq from these specific neuron subsets revealed additional enriched OCR2s that further restricted transgene expression within the chosen neuron subset. This approach allows for continued refinement of transgene expression, and we used it to identify neurons relevant for sleep behavior. Furthermore, this approach is widely applicable to other cell types and to other organisms.

txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility

We develop a large-scale single-cell ATAC-seq method by combining Tn5-based pre-indexing with 10× Genomics barcoding, enabling the indexing of up to 200,000 nuclei across multiple samples in a single reaction. We profile 449,953 nuclei across diverse tissues, including the human cortex, mouse brain, human lung, mouse lung, mouse liver, and lung tissue from a club cell secretory protein knockout (CC16-/-) model. Our study of CC16-/- nuclei uncovers previously underappreciated technical artifacts derived from remnant 129 mouse strain genetic material, which cause profound cell-type-specific changes in regulatory elements near many genes, thereby confounding the interpretation of this commonly referenced mouse model.

Comparative study on chromatin loop callers using Hi-C data reveals their effectiveness

BACKGROUND

Chromosome is one of the most fundamental part of cell biology where DNA holds the hierarchical information. DNA compacts its size by forming loops, and these regions house various protein particles, including CTCF, SMC3, H3 histone. Numerous sequencing methods, such as Hi-C, ChIP-seq, and Micro-C, have been developed to investigate these properties. Utilizing these data, scientists have developed a variety of loop prediction techniques that have greatly improved their methods for characterizing loop prediction and related aspects.

RESULTS

In this study, we categorized 22 loop calling methods and conducted a comprehensive study of 11 of them. Additionally, we have provided detailed insights into the methodologies underlying these algorithms for loop detection, categorizing them into five distinct groups based on their fundamental approaches. Furthermore, we have included critical information such as resolution, input and output formats, and parameters. For this analysis, we utilized the GM12878 Hi-C datasets at 5 KB, 10 KB, 100 KB and 250 KB resolutions. Our evaluation criteria encompassed various factors, including memory usages, running time, sequencing depth, and recovery of protein-specific sites such as CTCF, H3K27ac, and RNAPII.

CONCLUSION

This analysis offers insights into the loop detection processes of each method, along with the strengths and weaknesses of each, enabling readers to effectively choose suitable methods for their datasets. We evaluate the capabilities of these tools and introduce a novel Biological, Consistency, and Computational robustness score ( B C C score ) to measure their overall robustness ensuring a comprehensive evaluation of their performance.

GNNMF: a multi-view graph neural network for ATAC-seq motif finding

BACKGROUND

The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) utilizes the Transposase Tn5 to probe open chromatic, which simultaneously reveals multiple transcription factor binding sites (TFBSs) compared to traditional technologies. Deep learning (DL) technology, including convolutional neural networks (CNNs), has successfully found motifs from ATAC-seq data. Due to the limitation of the width of convolutional kernels, the existing models only find motifs with fixed lengths. A Graph neural network (GNN) can work on non-Euclidean data, which has the potential to find ATAC-seq motifs with different lengths. However, the existing GNN models ignored the relationships among ATAC-seq sequences, and their parameter settings should be improved.

RESULTS

In this study, we proposed a novel GNN model named GNNMF to find ATAC-seq motifs via GNN and background coexisting probability. Our experiment has been conducted on 200 human datasets and 80 mouse datasets, demonstrated that GNNMF has improved the area of eight metrics radar scores of 4.92% and 6.81% respectively, and found more motifs than did the existing models.

CONCLUSIONS

In this study, we developed a novel model named GNNMF for finding multiple ATAC-seq motifs. GNNMF built a multi-view heterogeneous graph by using ATAC-seq sequences, and utilized background coexisting probability and the iterloss to find different lengths of ATAC-seq motifs and optimize the parameter sets. Compared to existing models, GNNMF achieved the best performance on TFBS prediction and ATAC-seq motif finding, which demonstrates that our improvement is available for ATAC-seq motif finding.

Protocol for detecting RBM33-binding sites in HEK293T cells using PAR-CLIP-seq

RNA-binding proteins (RBPs) regulate gene expression both co-transcriptionally and post-transcriptionally. Here, we provide a protocol for photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation followed by next-generation sequencing (PAR-CLIP-seq). PAR-CLIP-seq is a transcriptome-scale technique for identifying in vivo binding sites of RBPs at the single-nucleotide level. We detail procedures for the establishment of FLAG-RBM33 stable cell line, the sequencing library preparation, and the data analysis.

Protocol for chromatin accessibility profiling of human endothelial cells cultured under fluid shear stress using ATAC-seq

Chromatin accessibility influences gene regulation and can be quantified using assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq). Recapitulating in vivo fluid shear stress (FSS) mechano-regimes in vitro allows the study of atheroprone and atheroprotective mechanisms. In this protocol, we show how to culture and harvest endothelial cells from microfluidic channels for the preparation of ATAC-seq, highlighting optional growth factor stimulation and different FSS rates. This extends the application of ATAC-seq to the analysis of in vitro mechanically stimulated cells. For complete details on the use and execution of this protocol, please refer to Jatzlau et al.1.

Chromatin Accessibility Profiling and Data Analysis Using ATAC-seq in Nothobranchius furzeri

The state of genome-wide chromatin accessibility in cells, tissues, or organisms can be investigated with a technique called assay for transposase-accessible chromatin using sequencing (ATAC-seq). ATAC-seq is a powerful approach for profiling the epigenomic landscape of cells using very low input materials. Analysis of chromatin accessibility data allows for prediction of gene expression and identification of regulatory elements such as potential enhancers and specific transcription-factor binding sites. Here, we describe an optimized ATAC-seq protocol for the preparation of isolated nuclei and subsequent next-generation sequencing from whole embryos and tissues of the African turquoise killifish (Nothobranchius furzeri). Importantly, we provide an overview of a pipeline for processing and analyzing ATAC-seq data from killifish.

Disease-associated astrocyte epigenetic memory promotes CNS pathology

Disease-associated astrocyte subsets contribute to the pathology of neurologic diseases, including multiple sclerosis and experimental autoimmune encephalomyelitis1-8 (EAE), an experimental model for multiple sclerosis. However, little is known about the stability of these astrocyte subsets and their ability to integrate past stimulation events. Here we report the identification of an epigenetically controlled memory astrocyte subset that exhibits exacerbated pro-inflammatory responses upon rechallenge. Specifically, using a combination of single-cell RNA sequencing, assay for transposase-accessible chromatin with sequencing, chromatin immunoprecipitation with sequencing, focused interrogation of cells by nucleic acid detection and sequencing, and cell-specific in vivo CRISPR-Cas9-based genetic perturbation studies we established that astrocyte memory is controlled by the metabolic enzyme ATP-citrate lyase (ACLY), which produces acetyl coenzyme A (acetyl-CoA) that is used by histone acetyltransferase p300 to control chromatin accessibility. The number of ACLY+p300+ memory astrocytes is increased in acute and chronic EAE models, and their genetic inactivation ameliorated EAE. We also detected the pro-inflammatory memory phenotype in human astrocytes in vitro; single-cell RNA sequencing and immunohistochemistry studies detected increased numbers of ACLY+p300+ astrocytes in chronic multiple sclerosis lesions. In summary, these studies define an epigenetically controlled memory astrocyte subset that promotes CNS pathology in EAE and, potentially, multiple sclerosis. These findings may guide novel therapeutic approaches for multiple sclerosis and other neurologic diseases.

Integrative ATAC-seq and RNA-seq analysis associated with diabetic nephropathy and identification of novel targets for treatment by dapagliflozin

Dapagliflozin (DAPA) are clinically effective in improving diabetic nephropathy (DN). However, whether and how chromatin accessibility changed by DN responds to DAPA treatment is unclear. Therefore, we performed ATAC-seq, RNA-seq, and weighted gene correlation network analysis to identify the chromatin accessibility, the messenger RNA (mRNA) expression, and the correlation between clinical phenotypes and mRNA expression using kidney from three mouse groups: db/m mice (Controls), db/db mice (case group), and those treated with DAPA (treatment group). RNA-Seq and ATAC-seq conjoint analysis revealed many overlapping pathways and networks suggesting that the transcriptional changes of DN and DAPA intervention largely occured dependently on chromatin remodeling. Specifically, the results showed that some key signal transduction pathways, such as immune dysfunction, glucolipid metabolism, oxidative stress and xenobiotic and endobiotic metabolism, were repeatedly enriched in the analysis of the RNA-seq data alone, as well as combined analysis with ATAC-seq data. Furthermore, we identified some candidate genes (UDP glucuronosyltransferase 1 family, Dock2, Tbc1d10c, etc.) and transcriptional regulators (KLF6 and GFI1) that might be associated with DN and DAPA restoration. These reversed genes and regulators confirmed that pathways related to immune response and metabolism pathways were critically involved in DN progression.

Integrated ATAC-seq and RNA-seq data analysis identifies transcription factors related to rice stripe virus infection in Oryza sativa

Animal studies have shown that virus infection causes changes in host chromatin accessibility, but little is known about changes in chromatin accessibility of plants infected by viruses and its potential impact. Here, rice infected by rice stripe virus (RSV) was used to investigate virus-induced changes in chromatin accessibility. Our analysis identified a total of 6462 open- and 3587 closed-differentially accessible chromatin regions (DACRs) in rice under RSV infection by ATAC-seq. Additionally, by integrating ATAC-seq and RNA-seq, 349 up-regulated genes in open-DACRs and 126 down-regulated genes in closed-DACRs were identified, of which 34 transcription factors (TFs) were further identified by search of upstream motifs. Transcription levels of eight of these TFs were validated by reverse transcription-PCR. Importantly, four of these TFs (OsWRKY77, OsWRKY28, OsZFP12 and OsERF91) interacted with RSV proteins and are therefore predicted to play important roles in RSV infection. This is the first application of ATAC-seq and RNA-seq techniques to analyse changes in rice chromatin accessibility caused by RSV infection. Integrating ATAC-seq and RNA-seq provides a new approach to select candidate TFs in response to virus infection.

Mapping catalytically engaged TOP2B in neurons reveals the principles of topoisomerase action within the genome

We trapped catalytically engaged topoisomerase IIβ (TOP2B) in covalent DNA cleavage complexes (TOP2Bccs) and mapped their positions genome-wide in cultured mouse cortical neurons. We report that TOP2Bcc distribution varies with both nucleosome and compartmental chromosome organization. While TOP2Bccs in gene bodies correlate with their level of transcription, highly expressed genes that lack the usually associated chromatin marks, such as H3K36me3, show reduced TOP2Bccs, suggesting that histone posttranslational modifications regulate TOP2B activity. Promoters with high RNA polymerase II occupancy show elevated TOP2B chromatin immunoprecipitation sequencing signals but low TOP2Bccs, indicating that TOP2B catalytic engagement is curtailed at active promoters. Surprisingly, either poisoning or inhibiting TOP2B increases nascent transcription at most genes and enhancers but reduces transcription within long genes. These effects are independent of transcript length and instead correlate with the presence of intragenic enhancers. Together, these results clarify how cells modulate the catalytic engagement of topoisomerases to affect transcription.

Integration analysis of ATAC-seq and RNA-seq provides insight into fatty acid biosynthesis in Schizochytrium limacinum under nitrogen limitation stress

BACKGROUND

Schizochytrium limacinum holds significant value utilized in the industrial-scale synthesis of natural DHA. Nitrogen-limited treatment can effectively increase the content of fatty acids and DHA, but there is currently no research on chromatin accessibility during the process of transcript regulation. The objective of this research was to delve into the workings of fatty acid production in S. limacinum by examining the accessibility of promoters and profiling gene expressions.

RESULTS

Results showed that differentially accessible chromatin regions (DARs)-associated genes were enriched in fatty acid metabolism, signal transduction mechanisms, and energy production. By identifying and annotating DARs-associated motifs, the study obtained 54 target transcription factor classes, including BPC, RAMOSA1, SPI1, MYC, and MYB families. Transcriptomics results revealed that several differentially expressed genes (DEGs), including SlFAD2, SlALDH, SlCAS1, SlNSDHL, and SlDGKI, are directly related to the biosynthesis of fatty acids, meanwhile, SlRPS6KA, SlCAMK1, SlMYB3R1, and SlMYB3R5 serve as transcription factors that could potentially influence the regulation of fatty acid production. In the integration analysis of DARs and ATAC-seq, 13 genes were identified, which were shared by both DEGs and DARs-associated genes, including SlCAKM, SlRP2, SlSHOC2, SlTN, SlSGK2, SlHMP, SlOGT, SlclpB, and SlDNAAF3.

CONCLUSIONS

SlCAKM may act as a negative regulator of fatty acid and DHA synthesis, while SlSGK2 may act as a positive regulator, which requires further study in the future. These insights enhance our comprehension of the processes underlying fatty acid and DHA production in S. limacinum. They also supply a foundational theoretical framework and practical assistance for the development of strains rich in fatty acids and DHA.

Churros: a Docker-based pipeline for large-scale epigenomic analysis

The epigenome, which reflects the modifications on chromatin or DNA sequences, provides crucial insight into gene expression regulation and cellular activity. With the continuous accumulation of epigenomic datasets such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) data, there is a great demand for a streamlined pipeline to consistently process them, especially for large-dataset comparisons involving hundreds of samples. Here, we present Churros, an end-to-end epigenomic analysis pipeline that is environmentally independent and optimized for handling large-scale data. We successfully demonstrated the effectiveness of Churros by analyzing large-scale ChIP-seq datasets with the hg38 or Telomere-to-Telomere (T2T) human reference genome. We found that applying T2T to the typical analysis workflow has important impacts on read mapping, quality checks, and peak calling. We also introduced a useful feature to study context-specific epigenomic landscapes. Churros will contribute a comprehensive and unified resource for analyzing large-scale epigenomic data.

ChIP-Seq analysis reveals PRKACB as a target gene of HOXC13 involved in rabbit hair follicle development

Dermal papilla cells (DPCs) are key regulators of hair follicle (HF) development and growth, which not only regulate HF growth and cycling but play a role in the pathogenesis of hair loss. The transcription factor Homeobox C13 (HOXC13) can modulate the growth and development of HFs. Nevertheless, the specific genes and pathways regulated by HOXC13 in DPCs have yet to be determined. Thus, to gain a better understanding of genomic binding sites involved in HOXC13-regulated HF development, chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) was performed on rabbit DPCs with pcDNA3.1-3 × Flag-HOXC13 overexpression. A complete set of 9670 enrichment peaks was acquired by applying HOXC13-Flag ChIP. Subsequently, the peak sequence was annotated to the rabbit genome, revealing that 6.1 % of the peaks were identified within in the promoter region. Thereafter, five annotated genes were verified using RT-qPCR. The peak-associated genes were mainly enriched in signaling pathways related to HF development, such as MAPK and PI3K-Akt. Furthermore, by using a dual-luciferase reporter assay, we found that HOXC13 can target the protein kinase cAMP‑dependent catalytic β (PRKACB) promoter region (-1596 ∼ -1107 bp) and inhibit its transcription, which was consistent with data obtained from ChIP-seq analysis. Overexpression of PRKACB gene significantly modulated the expression of BCL2, WNT2, LEF1, and SFRP2 genes related to HF development as determined by RT-qPCR (P < 0.01, P < 0.05). The CCK-8 and flow cytometry assays showed that PRKACB significantly inhibited the proliferation of DPCs and promoted apoptosis (P < 0.01). In conclusion, our research revealed that PRKACB has the potential to serve as a novel target gene of HOXC13, contributing to the regulation of the proliferation and apoptosis of DPCs. The process of identifying global target genes can contribute to the understanding of the intricate pathways that HOXC13 regulates in the growth of HFs.

An in silico procedure for generating protein-mediated chromatin interaction data and comparison of significant interaction calling methods

The ability to simulate high-throughput data with high fidelity to real experimental data is fundamental for benchmarking methods used to detect true long-range chromatin interactions mediated by a specific protein. Yet, such tools are not currently available. To fill this gap, we develop an in silico experimental procedure, ChIA-Sim, which imitates the experimental procedures that produce real ChIA-PET, Hi-ChIP, or PLAC-seq data. We show the fidelity of ChIA-Sim to real data by using guiding characteristics of several real datasets to generate data using the simulation procedure. We also used ChIA-Sim data to demonstrate the use of our in silico procedure in benchmarking methods for significant interactions analysis by evaluating four methods for significant interaction calling (SIC). In particular, we assessed each method's performance in terms of correct identification of long-range interactions. We further analyzed four experimental datasets from publicly available databases and shew that the trend of the results are consistent with those seen in data generated from ChIA-Sim. This serves as additional evidence that ChIA-Sim closely resembles data produced from the experimental protocols it models after.

Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types

Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and studies integrating GWAS with scRNA-seq have shown promise, but studies integrating GWAS with scATAC-seq have been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases/traits (average N = 298 K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (respectively adult) brain cell types for 22 (respectively 23) of 28 traits using scATAC-seq, and for 8 (respectively 17) of 28 traits using scRNA-seq. Significant scATAC-seq enrichments included fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases/traits and inform future analyses.

Peak Scores Significantly Depend on the Relationships between Contextual Signals in ChIP-Seq Peaks

Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) is a central genome-wide method for in vivo analyses of DNA-protein interactions in various cellular conditions. Numerous studies have demonstrated the complex contextual organization of ChIP-seq peak sequences and the presence of binding sites for transcription factors in them. We assessed the dependence of the ChIP-seq peak score on the presence of different contextual signals in the peak sequences by analyzing these sequences from several ChIP-seq experiments using our fully enumerative GPU-based de novo motif discovery method, Argo_CUDA. Analysis revealed sets of significant IUPAC motifs corresponding to the binding sites of the target and partner transcription factors. For these ChIP-seq experiments, multiple regression models were constructed, demonstrating a significant dependence of the peak scores on the presence in the peak sequences of not only highly significant target motifs but also less significant motifs corresponding to the binding sites of the partner transcription factors. A significant correlation was shown between the presence of the target motifs FOXA2 and the partner motifs HNF4G, which found experimental confirmation in the scientific literature, demonstrating the important contribution of the partner transcription factors to the binding of the target transcription factor to DNA and, consequently, their important contribution to the peak score.

Dynamic Evolution of Repetitive Elements and Chromatin States in Apis mellifera Subspecies

In this study, we elucidate the contribution of repetitive DNA sequences to the establishment of social structures in honeybees (Apis mellifera). Despite recent advancements in understanding the molecular mechanisms underlying the formation of honeybee castes, primarily associated with Notch signaling, the comprehensive identification of specific genomic cis-regulatory sequences remains elusive. Our objective is to characterize the repetitive landscape within the genomes of two honeybee subspecies, namely A. m. mellifera and A. m. ligustica. An observed recent burst of repeats in A. m. mellifera highlights a notable distinction between the two subspecies. After that, we transitioned to identifying differentially expressed DNA elements that may function as cis-regulatory elements. Nevertheless, the expression of these sequences showed minimal disparity in the transcriptome during caste differentiation, a pivotal process in honeybee eusocial organization. Despite this, chromatin segmentation, facilitated by ATAC-seq, ChIP-seq, and RNA-seq data, revealed a distinct chromatin state associated with repeats. Lastly, an analysis of sequence divergence among elements indicates successive changes in repeat states, correlating with their respective time of origin. Collectively, these findings propose a potential role of repeats in acquiring novel regulatory functions.

scATAC-Ref: a reference of scATAC-seq with known cell labels in multiple species

Chromatin accessibility profiles at single cell resolution can reveal cell type-specific regulatory programs, help dissect highly specialized cell functions and trace cell origin and evolution. Accurate cell type assignment is critical for effectively gaining biological and pathological insights, but is difficult in scATAC-seq. Hence, by extensively reviewing the literature, we designed scATAC-Ref (https://bio.liclab.net/scATAC-Ref/), a manually curated scATAC-seq database aimed at providing a comprehensive, high-quality source of chromatin accessibility profiles with known cell labels across broad cell types. Currently, scATAC-Ref comprises 1 694 372 cells with known cell labels, across various biological conditions, >400 cell/tissue types and five species. We used uniform system environment and software parameters to perform comprehensive downstream analysis on these chromatin accessibility profiles with known labels, including gene activity score, TF enrichment score, differential chromatin accessibility regions, pathway/GO term enrichment analysis and co-accessibility interactions. The scATAC-Ref also provided a user-friendly interface to query, browse and visualize cell types of interest, thereby providing a valuable resource for exploring epigenetic regulation in different tissues and cell types.

Single-Nucleus ATAC-seq for Mapping Chromatin Accessibility in Individual Cells of Murine Hearts

During the last decade a wide range of single-cell and single-nucleus next-generation sequencing techniques have been developed, which revolutionized detection of rare cell populations, enabling creation of comprehensive cell atlases of complex organs and tissues. State-of-the-art methods do not only allow classical transcriptomics of individual cells but also comprise a number of epigenetic approaches, including assessment of chromatin accessibility by single-nucleus Assay for Transposase Accessible Chromatin ATAC-seq (snATAC-seq). The snATAC-seq assay detects "open chromatin," a term for low nucleosome occupancy of genomic regions, which is a prerequisite for effective transcription factor binding. Information about open chromatin at the single-nucleus level helps to recognize epigenetic changes, sometimes before transcription of respective genes occurs. snATAC-seq detects cellular heterogeneity in otherwise still transcriptionally and/or morphologically homogeneous cell populations. Chromatin accessibility assays may be used to detect epigenetic changes in cardiac lineages during heart development, chromatin landscape changes during aging, and epigenetic alterations in heart diseases. Here, we provide an optimized protocol for snATAC-seq of murine hearts. We describe isolation of single nuclei from snap-frozen hearts, provide hints for preparation of libraries suitable for snATAC-seq next-generation sequencing (NGS) using the Chromium 10× platform, and give general recommendations for downstream analysis using conventional bioinformatic pipelines and packages. The protocol should serve as a beginner's guide to generate high-quality snATAC-seq datasets and to perform chromatin accessibility analysis of individual heart-derived cell nuclei.

Chromatin Immunoprecipitation Sequencing (ChIP-Seq) Assay in Food Allergy Research

Chromatin immunoprecipitation (ChIP) analysis followed by genome-wide sequencing represents the most efficient method for studying the complex interactions between nuclear proteins and their associated genomic DNA. This chapter aims to present a ChIP-Sequencing (ChIP-Seq) analysis protocol used for the investigation of different epigenetic modifications in diseases such as food allergy. Starting by treating cells with cross-linking reagents such as formaldehyde and ending up with profound bioinformatic analysis have made it plausible to understand how transcription factors and histone modifications localize and exert their effects on their associated genes.

Uniform quantification of single-nucleus ATAC-seq data with Paired-Insertion Counting (PIC) and a model-based insertion rate estimator

Existing approaches to scoring single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) feature matrices from sequencing reads are inconsistent, affecting downstream analyses and displaying artifacts. We show that, even with sparse single-cell data, quantitative counts are informative for estimating the regulatory state of a cell, which calls for a consistent treatment. We propose Paired-Insertion Counting as a uniform method for snATAC-seq feature characterization and provide a probability model for inferring latent insertion dynamics from snATAC-seq count matrices.

Visualization of the landscape of the read alignment shape of ATAC-seq data using Hellinger distance metric

Assay for Transposase-Accessible Chromatin using high-throughput sequencing (ATAC-seq) is the popular technique using next-generation sequencing to measure chromatin accessibility and identify open chromatin regions. While read alignment shape information of next-generation sequencing data with intensity information has been used in various bioinformatics methods, few studies have focused on pure shape information alone. In this study, we investigated what types of ATAC-seq read alignment shapes are observed for the promoter region and whether the pure shape information was related or unrelated to other gene features. We introduced a novel concept and pipeline for handling the pure shape information of NGS data as probability distributions and quantifying their dissimilarities by information theory. Based on this concept, we demonstrate that the pure shape information of ATAC-seq data is correlated with chromatin openness and some gene characteristics. On the other hand, it is suggested that the pure information of ATAC-seq read alignment shape is unlikely to contain additional information to explain differences in RNA expression. Our study suggests that viewing the read alignment shape of NGS data as probability distributions enables us to capture the characteristics of the genome-wide landscape of such data in a non-parametric manner.

Modeling fragment counts improves single-cell ATAC-seq analysis

Single-cell ATAC sequencing coverage in regulatory regions is typically binarized as an indicator of open chromatin. Here we show that binarization is an unnecessary step that neither improves goodness of fit, clustering, cell type identification nor batch integration. Fragment counts, but not read counts, should instead be modeled, which preserves quantitative regulatory information. These results have immediate implications for single-cell ATAC sequencing analysis.

Integration of ATAC-Seq and RNA-Seq Analysis to Identify Key Genes in the Longissimus Dorsi Muscle Development of the Tianzhu White Yak

During the postnatal stages, skeletal muscle development undergoes a series of meticulously regulated alterations in gene expression. However, limited studies have employed chromatin accessibility to unravel the underlying molecular mechanisms governing muscle development in yak species. Therefore, we conducted an analysis of both gene expression levels and chromatin accessibility to comprehensively characterize the dynamic genome-wide chromatin accessibility during muscle growth and development in the Tianzhu white yak, thereby elucidating the features of accessible chromatin regions throughout this process. Initially, we compared the differences in chromatin accessibility between two groups and observed that calves exhibited higher levels of chromatin accessibility compared to adult cattle, particularly within ±2 kb of the transcription start site (TSS). In order to investigate the correlation between alterations in chromatin accessible regions and variations in gene expression levels, we employed a combination of ATAC-seq and RNA-seq techniques, leading to the identification of 18 central transcriptional factors (TFs) and 110 key genes with significant effects. Through further analysis, we successfully identified several TFs, including Sp1, YY1, MyoG, MEF2A and MEF2C, as well as a number of candidate genes (ANKRD2, ANKRD1, BTG2 and LMOD3) which may be closely associated with muscle growth and development. Moreover, we constructed an interactive network program encompassing hub TFs and key genes related to muscle growth and development. This innovative approach provided valuable insights into the molecular mechanism underlying skeletal muscle development in the postnatal stages of Tianzhu white yaks while also establishing a solid theoretical foundation for future research on yak muscle development.

PAPerFly: Partial Assembly-based Peak Finder for ab initio binding site reconstruction

BACKGROUND

The specific recognition of a DNA locus by a given transcription factor is a widely studied issue. It is generally agreed that the recognition can be influenced not only by the binding motif but by the larger context of the binding site. In this work, we present a novel heuristic algorithm that can reconstruct the unique binding sites captured in a sequencing experiment without using the reference genome.

RESULTS

We present PAPerFly, the Partial Assembly-based Peak Finder, a tool for the binding site and binding context reconstruction from the sequencing data without any prior knowledge. This tool operates without the need to know the reference genome of the respective organism. We employ algorithmic approaches that are used during genome assembly. The proposed algorithm constructs a de Bruijn graph from the sequencing data. Based on this graph, sequences and their enrichment are reconstructed using a novel heuristic algorithm. The reconstructed sequences are aligned and the peaks in the sequence enrichment are identified. Our approach was tested by processing several ChIP-seq experiments available in the ENCODE database and comparing the results of Paperfly and standard methods.

CONCLUSIONS

We show that PAPerFly, an algorithm tailored for experiment analysis without the reference genome, yields better results than an aggregation of ChIP-seq agnostic tools. Our tool is freely available at https://github.com/Caeph/paperfly/ or on Zenodo ( https://doi.org/10.5281/zenodo.7116424 ).

Integration of ATAC-seq and RNA-seq identifies MX1-mediated AP-1 transcriptional regulation as a therapeutic target for Down syndrome

BACKGROUND

Growing evidence has suggested that Type I Interferon (I-IFN) plays a potential role in the pathogenesis of Down Syndrome (DS). This work investigates the underlying function of MX1, an effector gene of I-IFN, in DS-associated transcriptional regulation and phenotypic modulation.

METHODS

We performed assay for transposase-accessible chromatin with high-throughout sequencing (ATAC-seq) to explore the difference of chromatin accessibility between DS derived amniocytes (DSACs) and controls. We then combined the annotated differentially expressed genes (DEGs) and enriched transcriptional factors (TFs) targeting the promoter region from ATAC-seq results with the DEGs in RNA-seq, to identify key genes and pathways involved in alterations of biological processes and pathways in DS.

RESULTS

Binding motif analysis showed a significant increase in chromatin accessibility of genes related to neural cell function, among others, in DSACs, which is primarily regulated by members of the activator protein-1 (AP-1) transcriptional factor family. Further studies indicated that MX Dynamin Like GTPase 1 (MX1), defined as one of the key effector genes of I-IFN, is a critical upstream regulator. Its overexpression induced expression of AP-1 TFs and mediated inflammatory response, thus leading to decreased cellular viability of DS cells. Moreover, treatment with specific AP-1 inhibitor T-5224 improved DS-associated phenotypes in DSACs.

CONCLUSIONS

This study demonstrates that MX1-mediated AP-1 activation is partially responsible for cellular dysfunction of DS. T-5224 effectively ameliorated DS-associated phenotypes in DSACs, suggesting it as a potential treatment option for DS patients.

TAD evolutionary and functional characterization reveals diversity in mammalian TAD boundary properties and function

Topological associating domains (TADs) are self-interacting genomic units crucial for shaping gene regulation patterns. Despite their importance, the extent of their evolutionary conservation and its functional implications remain largely unknown. In this study, we generate Hi-C and ChIP-seq data and compare TAD organization across four primate and four rodent species and characterize the genetic and epigenetic properties of TAD boundaries in correspondence to their evolutionary conservation. We find 14% of all human TAD boundaries to be shared among all eight species (ultraconserved), while 15% are human-specific. Ultraconserved TAD boundaries have stronger insulation strength, CTCF binding, and enrichment of older retrotransposons compared to species-specific boundaries. CRISPR-Cas9 knockouts of an ultraconserved boundary in a mouse model lead to tissue-specific gene expression changes and morphological phenotypes. Deletion of a human-specific boundary near the autism-related AUTS2 gene results in the upregulation of this gene in neurons. Overall, our study provides pertinent TAD boundary evolutionary conservation annotations and showcases the functional importance of TAD evolution.

High-capacity sample multiplexing for single cell chromatin accessibility profiling

Single-cell chromatin accessibility has emerged as a powerful means of understanding the epigenetic landscape of diverse tissues and cell types, but profiling cells from many independent specimens is challenging and costly. Here we describe a novel approach, sciPlex-ATAC-seq, which uses unmodified DNA oligos as sample-specific nuclear labels, enabling the concurrent profiling of chromatin accessibility within single nuclei from virtually unlimited specimens or experimental conditions. We first demonstrate our method with a chemical epigenomics screen, in which we identify drug-altered distal regulatory sites predictive of compound- and dose-dependent effects on transcription. We then analyze cell type-specific chromatin changes in PBMCs from multiple donors responding to synthetic and allogeneic immune stimulation. We quantify stimulation-altered immune cell compositions and isolate the unique effects of allogeneic stimulation on chromatin accessibility specific to T-lymphocytes. Finally, we observe that impaired global chromatin decondensation often coincides with chemical inhibition of allogeneic T-cell activation.

Analysis of Chromatin Accessibility Changes Induced by BMMC Recognition of Foot-and-Mouth Disease Virus-like Particles through ATAC-seq

Mast cells can recognize foot-and-mouth disease virus-like particles (FMDV-VLPs) via mannose receptors (MRs) to produce differentially expressed cytokines. The regulatory role of chromatin accessibility in this process is unclear. Bone marrow-derived mast cells (BMMCs) were cultured, and an assay of transposase-accessible chromatin sequencing (ATAC-seq) was applied to demonstrate the regulation of chromatin accessibility in response to the BMMCs' recognition of FMDV-VLPs. A pathway enrichment analysis showed that peaks associated with the nuclear factor-κB (NF-κB), mitogen-activated protein kinase (MAPK), phosphatidylinositol 3 kinase-protein kinase B (PI3K-Akt), and other signaling pathways, especially the NF-κB pathway, were involved in the BMMCs' recognition of VLPs. Moreover, transcription factors including SP1, NRF1, AP1, GATA3, microphthalmia-associated transcription factor (MITF), and NF-κB-p65 may bind to the motifs with altered chromatin accessibility to regulate gene transcription. Furthermore, the expression of NF-κB, interleukin (IL)-9, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ in the BMMCs of the VLP group increased compared with that of the BMMCs in the control group, whereas the expression of IL-10 did not differ significantly between groups. After inhibiting the MRs, the expression of NF-κB, IL-9, TNF-α, and IFN-γ decreased significantly, whereas the expression of IL-10 increased. The expression of MAPK and IL-6 showed no significant change after MR inhibition. This study demonstrated that MRs expressed on BMMCs can affect the NF-κB pathway by changing chromatin accessibility to regulate the transcription of specific cytokines, ultimately leading to the differential expression of cytokines. These data provide a theoretical basis and new ideas for the development of a novel vaccine for FMD.

Integrated analysis of ATAC-seq and RNA-seq unveils the role of ferroptosis in PM2.5-induced asthma exacerbation

BACKGROUND

PM2.5 exposure increases asthma exacerbation risk and worsens airway inflammation and mucus secretion, but the underlying mechanisms, especially the epigenetic modification changes, are not fully understood.

METHODS

ATAC-seq was conducted in Beas-2B cells to explore the differential chromatin accessibilities before and after exposure to PM2.5. RNA-seq was applied to screen the differentially expressed genes (DEGs) as well. The integrated analysis of ATAC-seq and RNA-seq was performed. The key up-regulated genes in the ferroptosis signaling pathway were identified by combined analysis with the FerrDb database and then verified. Meanwhile, to access the role of PM2.5-induced ferroptosis in asthma mice, house dust mites (HDM) were employed to conduct an allergic asthma mice model, and the ferroptosis-specific inhibitor (Ferrostatin-1, Fer-1) was used. The H&E staining, PAS staining, airway hyperresponsiveness, and bronchoalveolar lavage fluid (BALF) cell counting were used to investigate the impact of PM2.5-induced ferroptosis in asthma mice.

RESULTS

A total of 4,921 regions with differential accessibility were identified, encompassing 4,031 unique genes. Among these, 250 regions exhibited increased accessibility while 4,671 regions displayed reduced accessibility. Through the integrated analysis of ATAC-seq and RNA-seq, ferroptosis was determined as the key enriched pathway based on up-regulated DEGs and increased chromatin accessibilities. Furthermore, the decreased cell viability, accelerated lipid peroxide and morphological changes in mitochondria observed upon PM2.5 exposure were rescued by Fer-1, which are indicative of ferroptosis. By overlapping with ferroptosis-related genes from the FerrDb database, FTH1 and FTL were identified as the prominent up-regulated genes with increased chromatin accessibility in ferroptosis pathway. In addition, ChIP-qPCR analysis indicated that histone modification like H3K4me3 and H3K27ac positively regulated FTH1 and FTL expression. Subsequently, in PM2.5-exposed asthmatic mice, inhibition of ferroptosis effectively attenuated airway inflammation and mucus secretion.

CONCLUSION

These findings shed light on the molecular mechanisms underlying PM2.5-induced asthma exacerbation, with epigenetic modifications playing a pivotal role. Furthermore, it suggests the therapeutic potential of targeting ferroptosis as an intervention strategy.

ROCCO: a robust method for detection of open chromatin via convex optimization

MOTIVATION

Analysis of open chromatin regions across multiple samples from two or more distinct conditions can determine altered gene regulatory patterns associated with biological phenotypes and complex traits. The ATAC-seq assay allows for tractable genome-wide open chromatin profiling of large numbers of samples. Stable, broadly applicable genomic annotations of open chromatin regions are not available. Thus, most studies first identify open regions using peak calling methods for each sample independently. These are then heuristically combined to obtain a consensus peak set. Reconciling sample-specific peak results post hoc from larger cohorts is particularly challenging, and informative spatial features specific to open chromatin signals are not leveraged effectively.

RESULTS

We propose a novel method, ROCCO, that determines consensus open chromatin regions across multiple samples simultaneously. ROCCO employs robust summary statistics and solves a constrained optimization problem formulated to account for both enrichment and spatial dependence of open chromatin signal data. We show this formulation admits attractive theoretical and conceptual properties as well as superior empirical performance compared to current methodology.

AVAILABILITY AND IMPLEMENTATION

Source code, documentation, and usage demos for ROCCO are available on GitHub at: https://github.com/nolan-h-hamilton/ROCCO. ROCCO can also be installed as a stand-alone binary utility using pip/PyPI.

Cell-free chromatin immunoprecipitation to detect molecular pathways in heart transplantation

Existing monitoring approaches in heart transplantation lack the sensitivity to provide deep molecular assessments to guide management, or require endomyocardial biopsy, an invasive and blind procedure that lacks the precision to reliably obtain biopsy samples from diseased sites. This study examined plasma cell-free DNA chromatin immunoprecipitation sequencing (cfChIP-seq) as a noninvasive proxy to define molecular gene sets and sources of tissue injury in heart transplant patients. In healthy controls and in heart transplant patients, cfChIP-seq reliably detected housekeeping genes. cfChIP-seq identified differential gene signals of relevant immune and nonimmune molecular pathways that were predominantly down-regulated in immunosuppressed heart transplant patients compared with healthy controls. cfChIP-seq also identified cell-free DNA tissue sources. Compared with healthy controls, heart transplant patients demonstrated greater cell-free DNA from tissue types associated with heart transplant complications, including the heart, hematopoietic cells, lungs, liver, and vascular endothelium. cfChIP-seq may therefore be a reliable approach to profile dynamic assessments of molecular pathways and sources of tissue injury in heart transplant patients.

MESIA: multi-epigenome sample integration approach for precise peak calling

The assay for transposase-accessible chromatin with sequencing (ATAC-seq) is the most widely used method for measuring chromatin accessibility. Researchers have included multi-sample replication in ATAC-seq experimental designs. In epigenomic analysis, researchers should measure subtle changes in the peak by considering the read depth of individual samples. It is important to determine whether the peaks of each replication have an integrative meaning for the region of interest observed during multi-sample integration. We developed multi-epigenome sample integration approach for precise peak calling (MESIA), which integrates replication with high representativeness and reproducibility in multi-sample replication and determines the optimal peak. After identifying the reproducibility between all replications, our method integrated multiple samples determined as representative replicates. MESIA detected 6.06 times more peaks, and the value of the peaks was 1.32 times higher than the previously used method. MESIA is a shell-script-based open-source code that provides researchers involved in the epigenome with comprehensive insights.

CEMIG: prediction of the cis-regulatory motif using the de Bruijn graph from ATAC-seq

Sequence motif discovery algorithms enhance the identification of novel deoxyribonucleic acid sequences with pivotal biological significance, especially transcription factor (TF)-binding motifs. The advent of assay for transposase-accessible chromatin using sequencing (ATAC-seq) has broadened the toolkit for motif characterization. Nonetheless, prevailing computational approaches have focused on delineating TF-binding footprints, with motif discovery receiving less attention. Herein, we present Cis rEgulatory Motif Influence using de Bruijn Graph (CEMIG), an algorithm leveraging de Bruijn and Hamming distance graph paradigms to predict and map motif sites. Assessment on 129 ATAC-seq datasets from the Cistrome Data Browser demonstrates CEMIG's exceptional performance, surpassing three established methodologies on four evaluative metrics. CEMIG accurately identifies both cell-type-specific and common TF motifs within GM12878 and K562 cell lines, demonstrating its comparative genomic capabilities in the identification of evolutionary conservation and cell-type specificity. In-depth transcriptional and functional genomic studies have validated the functional relevance of CEMIG-identified motifs across various cell types. CEMIG is available at https://github.com/OSU-BMBL/CEMIG, developed in C++ to ensure cross-platform compatibility with Linux, macOS and Windows operating systems.

Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information

BACKGROUND

Correlation metrics are widely utilized in genomics analysis and often implemented with little regard to assumptions of normality, homoscedasticity, and independence of values. This is especially true when comparing values between replicated sequencing experiments that probe chromatin accessibility, such as assays for transposase-accessible chromatin via sequencing (ATAC-seq). Such data can possess several regions across the human genome with little to no sequencing depth and are thus non-normal with a large portion of zero values. Despite distributed use in the epigenomics field, few studies have evaluated and benchmarked how correlation and association statistics behave across ATAC-seq experiments with known differences or the effects of removing specific outliers from the data. Here, we developed a computational simulation of ATAC-seq data to elucidate the behavior of correlation statistics and to compare their accuracy under set conditions of reproducibility.

RESULTS

Using these simulations, we monitored the behavior of several correlation statistics, including the Pearson's R and Spearman's [Formula: see text] coefficients as well as Kendall's [Formula: see text] and Top-Down correlation. We also test the behavior of association measures, including the coefficient of determination R[Formula: see text], Kendall's W, and normalized mutual information. Our experiments reveal an insensitivity of most statistics, including Spearman's [Formula: see text], Kendall's [Formula: see text], and Kendall's W, to increasing differences between simulated ATAC-seq replicates. The removal of co-zeros (regions lacking mapped sequenced reads) between simulated experiments greatly improves the estimates of correlation and association. After removing co-zeros, the R[Formula: see text] coefficient and normalized mutual information display the best performance, having a closer one-to-one relationship with the known portion of shared, enhanced loci between simulated replicates. When comparing values between experimental ATAC-seq data using a random forest model, mutual information best predicts ATAC-seq replicate relationships.

CONCLUSIONS

Collectively, this study demonstrates how measures of correlation and association can behave in epigenomics experiments. We provide improved strategies for quantifying relationships in these increasingly prevalent and important chromatin accessibility assays.

scReadSim: a single-cell RNA-seq and ATAC-seq read simulator

Benchmarking single-cell RNA-seq (scRNA-seq) and single-cell Assay for Transposase-Accessible Chromatin using sequencing (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 a FASTQ or BAM file) 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 (UMI) 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 UMI-tools achieves the top accuracy in scRNA-seq UMI deduplication, and HMMRATAC and MACS3 achieve the top performance in scATAC-seq peak calling.

Seq2science: an end-to-end workflow for functional genomics analysis

Sequencing databases contain enormous amounts of functional genomics data, making them an extensive resource for genome-scale analysis. Reanalyzing publicly available data, and integrating it with new, project-specific data sets, can be invaluable. With current technologies, genomic experiments have become feasible for virtually any species of interest. However, using and integrating this data comes with its challenges, such as standardized and reproducible analysis. Seq2science is a multi-purpose workflow that covers preprocessing, quality control, visualization, and analysis of functional genomics sequencing data. It facilitates the downloading of sequencing data from all major databases, including NCBI SRA, EBI ENA, DDBJ, GSA, and ENCODE. Furthermore, it automates the retrieval of any genome assembly available from Ensembl, NCBI, and UCSC. It has been tested on a variety of species, and includes diverse workflows such as ATAC-, RNA-, and ChIP-seq. It consists of both generic as well as advanced steps, such as differential gene expression or peak accessibility analysis and differential motif analysis. Seq2science is built on the Snakemake workflow language and thus can be run on a range of computing infrastructures. It is available at https://github.com/vanheeringen-lab/seq2science.

CLIP-Seq analysis enables the design of protective ribosomal RNA bait oligonucleotides against C9ORF72 ALS/FTD poly-GR pathophysiology

Amyotrophic lateral sclerosis and frontotemporal dementia patients with a hexanucleotide repeat expansion in C9ORF72 (C9-HRE) accumulate poly-GR and poly-PR aggregates. The pathogenicity of these arginine-rich dipeptide repeats (R-DPRs) is thought to be driven by their propensity to bind low-complexity domains of multivalent proteins. However, the ability of R-DPRs to bind native RNA and the significance of this interaction remain unclear. Here, we used computational and experimental approaches to characterize the physicochemical properties of R-DPRs and their interaction with RNA. We find that poly-GR predominantly binds ribosomal RNA (rRNA) in cells and exhibits an interaction that is predicted to be energetically stronger than that for associated ribosomal proteins. Critically, modified rRNA "bait" oligonucleotides restore poly-GR-associated ribosomal deficits and ameliorate poly-GR toxicity in patient neurons and Drosophila models. Our work strengthens the hypothesis that ribosomal function is impaired by R-DPRs, highlights a role for direct rRNA binding in mediating ribosomal dysfunction, and presents a strategy for protecting against C9-HRE pathophysiological mechanisms.

ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data

BACKGROUND

High oncogene expression in cancer cells is a major cause of rapid tumor progression and drug resistance. Recent cancer genome research has shown that oncogenes as well as regulatory elements can be amplified in the form of extrachromosomal DNA (ecDNA) or subsequently integrated into chromosomes as homogeneously staining regions (HSRs). These genome-level variants lead to the overexpression of the corresponding oncogenes, resulting in poor prognosis. Most existing detection methods identify ecDNA using whole genome sequencing (WGS) data. However, these techniques usually detect many false positive regions owing to chromosomal DNA interference.

RESULTS

In the present study, an algorithm called "ATACAmp" that can identify ecDNA/HSRs in tumor genomes using ATAC-seq data has been described. High chromatin accessibility, one of the characteristics of ecDNA, makes ATAC-seq naturally enriched in ecDNA and reduces chromosomal DNA interference. The algorithm was validated using ATAC-seq data from cell lines that have been experimentally determined to contain ecDNA regions. ATACAmp accurately identified the majority of validated ecDNA regions. AmpliconArchitect, the widely used ecDNA detecting tool, was used to detect ecDNA regions based on the WGS data of the same cell lines. Additionally, the Circle-finder software, another tool that utilizes ATAC-seq data, was assessed. The results showed that ATACAmp exhibited higher accuracy than AmpliconArchitect and Circle-finder. Moreover, ATACAmp supported the analysis of single-cell ATAC-seq data, which linked ecDNA to specific cells.

CONCLUSIONS

ATACAmp, written in Python, is freely available on GitHub under the MIT license: https://github.com/chsmiss/ATAC-amp . Using ATAC-seq data, ATACAmp offers a novel analytical approach that is distinct from the conventional use of WGS data. Thus, this method has the potential to reduce the cost and technical complexity associated ecDNA analysis.

Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet

RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the cross-prediction of RNA-protein binding events across diverse cell lines and tissue contexts. Here, we develop HDRNet, an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events under diverse cellular conditions. Our results demonstrate that HDRNet can accurately and efficiently identify binding sites, particularly for dynamic prediction, outperforming other state-of-the-art models on 261 linear RNA datasets from both eCLIP and CLIP-seq, supplemented with additional tissue data. Moreover, we conduct motif and interpretation analyses to provide fresh insights into the pathological mechanisms underlying RNA-RBP interactions from various perspectives. Our functional genomic analysis further explores the gene-human disease associations, uncovering previously uncharacterized observations for a broad range of genetic disorders.

Chromatin accessibility profiling of targeted cell populations with laser capture microdissection coupled to ATAC-seq

Spatially resolved omics technologies reveal context-dependent cellular regulatory networks in tissues of interest. Beyond transcriptome analysis, information on epigenetic traits and chromatin accessibility can provide further insights on gene regulation in health and disease. Nevertheless, compared to the enormous advancements in spatial transcriptomics technologies, the field of spatial epigenomics is much younger and still underexplored. In this study, we report laser capture microdissection coupled to ATAC-seq (LCM-ATAC-seq) applied to fresh frozen samples for the spatial characterization of chromatin accessibility. We first demonstrate the efficient use of LCM coupled to in situ tagmentation and evaluate its performance as a function of cell number, microdissected areas, and tissue type. Further, we demonstrate its use for the targeted chromatin accessibility analysis of discrete contiguous or scattered cell populations in tissues via single-nuclei capture based on immunostaining for specific cellular markers.

Validating a re-implementation of an algorithm to integrate transcriptome and ChIP-seq data

Transcription factor binding to a gene regulatory region induces or represses its expression. Binding and expression target analysis (BETA) integrates the binding and gene expression data to predict this function. First, the regulatory potential of the factor is modeled based on the distance of its binding sites from the transcription start sites in a decay function. Then the differential expression statistics from an experiment where this factor was perturbed represent the binding effect. The rank product of the two values is employed to order in importance. This algorithm was originally implemented in Python. We reimplemented the algorithm in R to take advantage of existing data structures and other tools for downstream analyses. Here, we attempted to replicate the findings in the original BETA paper. We applied the new implementation to the same datasets using default and varying inputs and cutoffs. We successfully replicated the original results. Moreover, we showed that the method was appropriately influenced by varying the input and was robust to choices of cutoffs in statistical testing.

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.