Biology subjects

Davis, E. S.

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

7 recordsLinked to original sources

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↗