Biology subjects

Davis, E. S.

Publications and source records attributed to Davis, E. S..

12 recordsLinked to original sources

bioRxiv2023

The tidyomics ecosystem: Enhancing omic data analyses

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

bioinformatics↗

bioRxiv2023

Guiding the design of well-powered Hi-C experiments to detect differential loops

3D chromatin structure plays an important role in gene regulation by connecting regulatory regions and gene promoters. The ability to detect the formation and loss of these loops in various cell types and conditions provides valuable information on the mechanisms driving these cell states and is critical for understanding how long-range gene regulation works. Hi-C is a powerful technique used to characterize three-dimensional chromatin structure; however, Hi-C can quickly become a costly and labor-intensive endeavor, and proper planning is required to determine how to best use time and resources while maintaining experimental rigor and well-powered results. To facilitate better planning and interpretation of Hi-C experiments, we have conducted a detailed evaluation of statistical power using publicly available Hi-C datasets paying particular attention to the impact of loop size on Hi-C contacts and fold change compression. In addition, we have developed Hi-C Poweraid, a publicly-hosted web application to investigate these findings (http://phanstiel-lab.med.unc.edu/poweraid/). For experiments involving well-replicated cell lines, we recommend a total sequencing depth of at least 6 billion contacts per condition, split between at least 2 replicates in order to achieve the power to detect the majority of differential loops. For experiments with higher variation, more replicates and deeper sequencing depths are required. Exact values and recommendations for specific cases can be determined through the use of Hi-C Poweraid. This tool simplifies the complexities behind calculating power for Hi-C data and will provide useful information on the amount of well-powered loops an experiment will be able to detect given a specific set of experimental parameters, such as sequencing depth, replicates, and the sizes of the loops of interest. This will allow for more efficient use of time and resources and more accurate interpretation of experimental results. HIGHLIGHTSO_LIDistance-dependent Hi-C contacts and fold change compression influence power C_LIO_LIIntegrative modeling of Hi-C power provides guidance for experimental design C_LIO_LIAn interactive web app aids the design of differential Hi-C experiments C_LI

bioinformatics↗

bioRxiv2022

Proximity-dependent recruitment of Polycomb Repressive Complexes by the lncRNA Airn

During mouse embryogenesis, expression of the lncRNA Airn induces gene silencing and recruits Polycomb Repressive Complexes (PRCs) to varying extents over a 15 megabase domain. The mechanisms remain unclear. Using high-resolution approaches, we show in mouse trophoblast stem cells that Airn expression induces long-range changes to chromatin architecture that coincide with PRC-directed modifications and center around CpG island promoters that contact the Airn locus even in the absence of Airn expression. Intensity of contact between Airn lncRNA and target chromatin correlated with underlying intensity of PRC-directed chromatin modifications. Deletion of CpG islands that form contacts with Airn altered long-distance silencing and PRC activity in a manner that correlated with changes in chromatin architecture. We conclude that Airn is a potent cis-acting lncRNA whose primary functions of transcriptional repression and PRC recruitment are controlled by an equilibratory network of DNA regulatory elements that modulate its frequency of contact with target chromatin.

genomics↗

bioRxiv2022

excluderanges: exclusion sets for T2T-CHM13, GRCm39, and other genome assemblies

SummaryExclusion regions are sections of reference genomes with abnormal pileups of short sequencing reads. Removing reads overlapping them improves biological signal, and these benefits are most pronounced in differential analysis settings. Several labs created exclusion region sets, available primarily through ENCODE and Github. However, the variety of exclusion sets creates uncertainty which sets to use. Furthermore, gap regions (e.g., centromeres, telomeres, short arms) create additional considerations in generating exclusion sets. We generated exclusion sets for the latest human T2T-CHM13 and mouse GRCm39 genomes and systematically assembled and annotated these and other sets in the excluderanges R/Bioconductor data package, also accessible via the BEDbase.org API. The package provides unified access to 82 GenomicRanges objects covering six organisms, multiple genome assemblies and types of exclusion regions. For human hg38 genome assembly, we recommend hg38.Kundaje.GRCh38_unified_blacklist as the most well-curated and annotated, and sets generated by the Blacklist tool for other organisms. Availability and implementationhttps://bioconductor.org/packages/excluderanges/ ContactMikhail G. Dozmorov (mdozmorov@vcu.edu) Supplementary informationPackage website: https://dozmorovlab.github.io/excluderanges/

bioinformatics↗

bioRxiv2022

Chromatin loop dynamics during cellular differentiation are associated with changes to both anchor and internal regulatory features

3D chromatin structure has been shown to play a role in regulating gene transcription during biological transitions. While our understanding of loop formation and maintenance is rapidly improving, much less is known about the mechanisms driving changes in looping and the impact of differential looping on gene transcription. One limitation has been a lack of well powered differential looping data sets. To address this, we conducted a deeply sequenced Hi-C time course of megakaryocyte development comprising 4 biological replicates and 6 billion reads per time point. Statistical analysis revealed 1,503 differential loops. Gained loops were enriched for AP-1 occupancy and correlated with increased expression of genes at their anchors. Lost loops were characterized by increases in expression of genes within the loop boundaries. Linear modeling revealed that changes in histone H3 K27 acetylation, chromatin accessibility, and JUN binding in between the loop anchors were as predictive of changes in loop strength as changes to CTCF and/or cohesin occupancy at loop anchors. Finally, we built linear models and found that incorporating the dynamics of enhancer acetylation and loop strength increased accuracy of gene expression predictions.

genomics↗

bioRxiv2022

bootRanges: Flexible generation of null sets of genomic ranges for hypothesis testing

bootRanges provides fast functions for generation of bootstrapped genomic ranges representing the null sets in enrichment analysis. We show that shuffling or permutation schemes may result in overly narrow test statistics null distributions, while creating new ranges sets with a block bootstrap preserves local genomic correlation structure and generates more reliable null distributions. It can also be used in more complex analyses, such as accessing correlations between cis-regulatory elements (CREs) and genes across cell types or providing optimized thresholds, e.g. log fold change (logFC) from differential analysis. The bootRanges functions are available in the R/Bioconductor package nullranges at https://bioconductor.org/packages/nullranges.

bioinformatics↗

bioRxiv2022

matchRanges: Generating null hypothesis genomic ranges via covariate-matched sampling

Deriving biological insights from genomic data commonly requires comparing attributes of selected genomic loci to a null set of loci. The selection of this null set is non trivial, as it requires careful consideration of potential covariates, a problem that is exacerbated by the non-uniform distribution of genomic features including genes, enhancers, and transcription factor binding sites. Propensity score-based covariate matching methods allow selection of null sets from a pool of possible items while controlling for multiple covariates; however, existing packages do not operate on genomic data classes and can be slow for large data sets making them difficult to integrate into genomic workflows. To address this, we developed matchRanges, a propensity score-based covariate matching method for the efficient and convenient generation of matched null ranges from a set of background ranges within the Bioconductor framework.

bioinformatics↗

bioRxiv2022

3D Chromatin Structure in Chondrocytes Identifies Putative Osteoarthritis Risk Genes

Genome-wide association studies (GWAS) have identified over 100 loci associated with osteoarthrtis (OA) risk, but the majority of OA risk variants are non-coding, making it difficult to identify the impacted genes for further study and therapeutic development. To address this need, we used a multi-omic approach and genome editing to identify and functionally characterize potential OA risk genes. Computational analysis of GWAS and ChIP-seq data revealed that chondrocyte regulatory loci are enriched for OA risk variants. We constructed a chondrocyte specific regulatory network by mapping 3D chromatin structure and active enhancers in human chondrocytes. We then intersected these data with our previously collected RNA-seq dataset of chondrocytes responding to fibronectin fragment (FN-f), a known OA trigger. Integration of the three genomic datasets with recently reported OA GWAS variants revealed a refined set of putative causal OA variants and their potential target genes. One of the novel putative target genes identified was SOCS2, which was connected to a putative causal variant by a 170 Kb loop and is differentially regulated in response to FN-f. CRISPR-Cas9-mediated deletion of SOCS2 in primary human chondrocytes from three independent donors led to heightened expression of inflammatory markers after FN-f treatment. These data suggest that SOCS2 plays a role in resolving inflammation in response to cartilage matrix damage and provides a possible mechanistic explanation for its influence on OA risk. In total, we identified 56 unique putative OA risk genes for further research and potential therapeutic development.

genomics↗

bioRxiv2022

Temporal analysis suggests a reciprocal relationship between 3D chromatin structure and transcription

To infer potential causal relationships between 3D chromatin structure, enhancers, and gene transcription, we mapped each feature in a genome-wide fashion across eight narrowly-spaced timepoints of macrophage activation. Enhancers and genes connected by loops exhibited stronger correlations between histone H3K27 acetylation and expression than can be explained by genomic distance or physical proximity alone. Changes in acetylation at looped distal enhancers preceded changes in gene expression. Changes in gene expression exhibit a directional bias at differential loop anchors; gained loops are associated with increased expression of genes oriented away from the center of the loop, while lost loops were often accompanied by high levels of transcription with the loop boundaries themselves. Taken together, these results are consistent with a reciprocal relationship in which loops can facilitate increased transcription by connecting promoters to distal enhancers while high levels of transcription can impede loop formation. HIGHLIGHTSO_LILPS + IFN{gamma} triggers genome-wide changes in chromatin looping, enhancer acetylation, and gene expression C_LIO_LILooped enhancer-promoter pairs exhibit ordered and correlated changes in acetylation and expression C_LIO_LIChanges in gene expression exhibit a directional bias at differential loop anchors C_LIO_LILost loops are associated with high levels of transcription within loop boundaries C_LI

genomics↗

bioRxiv2022

A multi-omic dissection of super-enhancer driven oncogenic gene expression programs in ovarian cancer

The human genome contains regulatory elements, such as enhancers, that are often rewired by cancer cells for the activation of genes that promote tumorigenesis and resistance to therapy. This is especially true for cancers that have little or no known driver mutations within protein coding genes, such as ovarian cancer. Herein, we have utilized an integrated set of genomic and epigenomic datasets to identify clinically relevant super-enhancers that are preferentially amplified in ovarian cancer patients. We have systematically probed the top 86 super-enhancers, using CRISPR-interference and CRISPR-deletion assays coupled to RNA-sequencing, to nominate two salient super-enhancers that drive proliferation and migration of cancer cells. Utilizing Hi-C, we constructed chromatin interaction maps that enabled the annotation of direct target genes for these super-enhancers and later confirmed their activity specifically within the cancer cell compartment of human tumors using single-cell genomics data. Together, our multi-omic approach has examined a number of fundamental questions about how regulatory information encoded into super-enhancers drives gene expression networks that underlie the biology of ovarian cancer.

cancer biology↗

bioRxiv2021

Fine-mapping of nuclear compartments using ultra-deep Hi-C shows that active promoter and enhancer elements localize in the active A compartment even when adjacent sequences do not

Megabase-scale intervals of active, gene-rich and inactive, gene-poor chromatin are known to segregate, forming the A and B compartments. Fine mapping of the contents of these A and B compartments has been hitherto impossible, owing to the extraordinary sequencing depths required to distinguish between the long-range contact patterns of individual loci, and to the computational complexity of the associated calculations. Here, we generate the largest published in situ Hi-C map to date, spanning 33 billion contacts. We also develop a computational method, dubbed PCA of Sparse, SUper Massive Matrices (POSSUMM), that is capable of efficiently calculating eigenvectors for sparse matrices with millions of rows and columns. Applying POSSUMM to our Hi-C dataset makes it possible to assign loci to the A and B compartment at 500 bp resolution. We find that loci frequently alternate between compartments as one moves along the contour of the genome, such that the median compartment interval is only 12.5 kb long. Contrary to the findings in coarse-resolution compartment profiles, we find that individual genes are not uniformly positioned in either the A compartment or the B compartment. Instead, essentially all (95%) active gene promoters localize in the A compartment, but the likelihood of localizing in the A compartment declines along the body of active genes, such that the transcriptional termini of long genes (>60 kb) tend to localize in the B compartment. Similarly, nearly all active enhancers elements (95%) localize in the A compartment, even when the flanking sequences are comprised entirely of inactive chromatin and localize in the B compartment. These results are consistent with a model in which DNA-bound regulatory complexes give rise to phase separation at the scale of individual DNA elements.

genomics↗

bioRxiv2021

Plotgardener: Cultivating precise multi-panel figures in R

The R programming language is one of the most widely used programming languages for transforming raw genomic data sets into meaningful biological conclusions through analysis and visualization, which has been largely facilitated by infrastructure and tools developed by the Bioconductor project. However, existing plotting packages rely on relative positioning and sizing of plots, which is often sufficient for exploratory analysis but is poorly suited for the creation of publication-quality multi-panel images inherent to scientific manuscript preparation. We present plotgardener, a coordinate-based genomic data visualization package that offers a new paradigm for multi-plot figure generation in R. Plotgardener allows precise, programmatic control over the placement, aesthetics, and arrangements of plots while maximizing user experience through fast and memory-efficient data access, support for a wide variety of data and file types, and tight integration with the Bioconductor environment. Plotgardener also allows precise placement and sizing of ggplot2 plots, making it an invaluable tool for R users and data scientists from virtually any discipline. AvailabilityPackage: https://bioconductor.org/packages/plotgardener Code: https://github.com/PhanstielLab/plotgardener Documentation: https://phanstiellab.github.io/plotgardener/

bioinformatics↗