Biology subjects

Boyle, A. P.

Publications and source records attributed to Boyle, A. P..

30 records · Page 2Linked to original sources

bioRxiv2021

Cas9 targeted enrichment of mobile elements using nanopore sequencing

Mobile element insertions (MEIs) are highly repetitive genomic sequences that contribute to inter- and intra-individual genetic variation and can lead to genetic disorders. Targeted and whole-genome approaches using short-read sequencing have been developed to identify reference and non-reference MEIs; however, the read length hampers detection of these elements in complex genomic regions. Here, we pair Cas9 targeted nanopore sequencing with computational methodologies to capture active MEIs in human genomes. We demonstrate parallel enrichment for distinct classes of MEIs, averaging 44% of reads on targeted signals. We show an individual flow cell can recover a remarkable fraction of MEIs (97% L1Hs, 93% AluYb, 51% AluYa, 99% SVA_F, and 65% SVA_E). We identify twenty-one non-reference MEIs in GM12878 overlooked by modern, long-read analysis pipelines, primarily in repetitive genomic regions. This work introduces the utility of nanopore sequencing for MEI enrichment and lays the foundation for rapid discovery of elusive, repetitive genetic elements.

genetics↗

bioRxiv2020

DNA methylation directs nucleosome positioning in RNA-mediated transcriptional silencing

Repressive chromatin modifications are instrumental in regulation of gene expression and transposon silencing. In Arabidopsis thaliana, transcriptional silencing is performed by the RNA-directed DNA methylation (RdDM) pathway. In this process, two specialized RNA polymerases, Pol IV and Pol V, produce non-coding RNAs, which recruit several RNA-binding proteins and lead to the establishment of repressive chromatin marks. An important feature of chromatin is nucleosome positioning, which has also been implicated in RdDM. We show that RdDM affects nucleosomes via the SWI/SNF chromatin remodeling complex. This leads to the establishment of nucleosomes on methylated regions, which counteracts the general depletion of DNA methylation on nucleosomal regions. Nucleosome placement by RdDM has no detectable effects on the pattern of DNA methylation. Instead, DNA methylation by RdDM and other pathways affects nucleosome positioning. We propose a model where DNA methylation serves as one of the determinants of nucleosome positioning.

plant biology↗

bioRxiv2020

Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data

Revealing the gene targets of distal regulatory elements is challenging yet critical for interpreting regulome data. Experiment-derived enhancer-gene links are restricted to a small set of enhancers and/or cell types, while the accuracy of genome-wide approaches remains elusive due to the lack of a systematic evaluation. We combined multiple spatial and in silico approaches for defining enhancer locations and linking them to their target genes aggregated across >500 cell types, generating 1,860 human genome-wide distal Enhancer to Target gene Definitions (EnTDefs). To evaluate performance, we used gene set enrichment testing on 87 independent ENCODE ChIP-seq datasets of 34 transcription factors (TFs) and assessed concordance of results with known TF Gene Ontology (GO) annotations., assuming that greater concordance with TF-GO annotation signifies better enrichment results and thus more accurate enhancer-to-gene assignments. Notably, the top ranked 741 (40%) EnTDefs significantly outperformed the common, naive approach of linking distal regions to the nearest genes (FDR < 0.05), and the top 10 ranked EnTDefs performed well when applied to ChIP-seq data of other cell types. These general EnTDefs also showed comparable performance to EnTDefs generated using cell-type-specific data. Our findings illustrate the power of our approach to provide genome-wide interpretation regardless of cell type.

bioinformatics↗

bioRxiv2020

F-Seq2: improving the feature density based peak caller with dynamic statistics

Genomic and epigenomic features are captured at a genome-wide level by using high-throughput sequencing technologies. Peak calling is one of the first essential steps in analyzing these features by delineating regions such as open chromatin regions and transcription factor binding sites. Our original peak calling software, F-Seq, has been widely used and shown to be the most sensitive and accurate peak caller for DNase I hypersensitive sites sequencing (DNase-seq) data. However, F-Seq lacks support for user-input control dataset nor reporting test statistics, limiting its ability to capture systematic and experimental biases and accurately estimate background distributions. Here we present an improved version, F-Seq2, which combined the power of kernel density estimation and a dynamic "continuous" Poisson distribution to robustly account for local biases and solve ties when ranking candidate peaks. In F-score and motif distance analysis, we demonstrated the superior performance of F-Seq2 than other competing peak callers used by the ENCODE Consortium on simulated and real ATAC-seq and ChIP-seq datasets. The output of F-Seq2 is suitable for irreproducible discovery rate (IDR) analysis as the test statistics calculated for individual candidate summit and ties are robustly solved.

bioinformatics↗

bioRxiv2020

SEMplMe: A tool for integrating DNA methylation effects in transcription factor binding affinity predictions

MotivationAberrant DNA methylation in transcription factor binding sites has been shown to lead to anomalous gene regulation that is strongly associated with human disease. However, the majority of methylation-sensitive positions within transcription factor binding sites remain unknown. Here we introduce SEMplMe, a computational tool to generate predictions of the effect of methylation on transcription factor binding strength in every position within a transcription factors motif. ResultsSEMplMe uses ChIP-seq and whole genome bisulfite sequencing to predict effects of methylation within binding sites. SEMplMe validates known methylation sensitive and insensitive positions within a binding motif, identifies cell type specific transcription factor binding driven by methylation, and outperforms SELEX-based predictions for CTCF. These predictions can be used to identify aberrant sites of DNA methylation contributing to human disease. Availability and ImplementationSEMplMe is available from https://github.com/Boyle-Lab/SEMplMe. Contactapboyle@umich.edu Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

The inducible lac operator-repressor system is functional in zebrafish cells

BackgroundZebrafish are a foundational model organism for studying the spatio-temporal activity of genes and their regulatory sequences. A variety of approaches are currently available for editing genes and modifying gene expression in zebrafish, including RNAi, Cre/lox, and CRISPR-Cas9. However, the lac operator-repressor system, a component of the E. coli lac operon which has been adapted for use in many other species and is a valuable, flexible tool for studying the inducible modulation of gene expression, has not previously been tested in zebrafish. ResultsHere we demonstrate that the lac operator-repressor system robustly decreases expression of firefly luciferase in cultured zebrafish fibroblast cells. Our work establishes the lac operator-repressor system as a promising tool for the manipulation of gene expression in whole zebrafish. ConclusionsOur results lay the groundwork for the development of lac-based reporter assays in zebrafish, and adds to the tools available for investigating dynamic gene expression in embryogenesis. We believe that this work will catalyze the development of new reporter assay systems to investigate uncharacterized regulatory elements and their cell-type specific activities.

molecular biology↗

bioRxiv2019

MapGL: Inferring evolutionary gain and loss of short genomic sequence features by phylogenetic maximum parsimony.

Comparative genomics studies are growing in number partly because of their unique ability to provide insight into shared and divergent biology between species. Of particular interest is the use of phylogenetic methods to infer the evolutionary history of cis-regulatory sequence features, which contribute strongly to phenotypic divergence and are frequently gained and lost in eutherian genomes. Understanding the mechanisms by which cis-regulatory element turnover generate emergent phenotypes is crucial to our understanding of adaptive evolution. Ancestral reconstruction methods can place species-specific cis-regulatory features in their evolutionary context, thus increasing our understanding of the process of regulatory sequence turnover. However, applying these methods to gain and loss of cis-regulatory features currently requires complex workflows which represent a potential barrier to widespread adoption by a broad scientific community. MapGL simplifies phylogenetic inference of the evolutionary history of short genomic sequence features by combining the necessary steps into a single piece of software with a simple set of inputs and outputs.

genomics↗

bioRxiv2019

TRACE: transcription factor footprinting using DNase I hypersensitivity data and DNA sequence

Transcription is tightly regulated by cis-regulatory DNA elements where transcription factors can bind. Thus, identification of transcription factor binding sites is key to understanding gene expression and whole regulatory networks within a cell. The standard approaches for transcription factor binding sites (TFBSs) prediction such as position weight matrices (PWMs) and chromatin immunoprecipitation followed by sequencing (ChIP-seq) are widely used but have their drawbacks such as high false positive rates and limited antibody availability, respectively. Several computational footprinting algorithms have been developed to detect TFBSs by investigating chromatin accessibility patterns, but also have their limitations. To improve on these methods, we have developed a footprinting method to predict Transcription factor footpRints in Active Chromatin Elements (TRACE). Trace incorporates DNase-seq data and PWMs within a multivariate Hidden Markov Model (HMM) to detect footprint-like regions with matching motifs. Trace is an unsupervised method that accurately annotates binding sites for specific TFs automatically with no requirement on pre-generated candidate binding sites or ChIP-seq training data. Compared to published footprinting algorithms, TRACE has the best overall performance with the distinct advantage of targeting multiple motifs in a single model.

bioinformatics↗

bioRxiv2019

Transposable elements strongly contribute to cell-specific and species-specific looping diversity in mammalian genomes.

BackgroundChromatin looping is exceedingly important to gene regulation and a host of other nuclear processes. Many recent insights into 3D chromatin structure across species and cell types have contributed to our understanding of the principles governing chromatin looping. However, 3D genome evolution and how it relates to Mendelian selection remain largely unexplored. CTCF, an insulator protein found at most loop anchors, has been described as the \"master weaver\" of mammalian genomes, and variations in CTCF occupancy are known to influence looping divergence. A large fraction of mammalian CTCF binding sites fall within transposable elements (TEs) but their contributions to looping variation are unknown. Here we investigated the effect of TE-driven CTCF binding site expansions on chromatin looping in human and mouse.\n\nResultsTEs have broadly contributed to CTCF binding and loop boundary specification, primarily forming variable loops across species and cell types and contributing nearly 1/3 of species-specific and cell-specific loops.\n\nConclusionsOur results demonstrate that TE activity is a major source of looping variability across species and cell types. Thus, TE-mediated CTCF expansions explain a large fraction of population-level looping variation and may play a role in adaptive evolution.

genomics↗

bioRxiv2019

Predicting the effects of SNPs on transcription factor binding affinity

GWAS have revealed that 88% of disease associated SNPs reside in noncoding regions. However, noncoding SNPs remain understudied, partly because they are challenging to prioritize for experimental validation. To address this deficiency, we developed the SNP effect matrix pipeline (SEMpl). SEMpl estimates transcription factor binding affinity by observing differences in ChIP-seq signal intensity for SNPs within functional transcription factor binding sites genome-wide. By cataloging the effects of every possible mutation within the transcription factor binding site motif, SEMpl can predict the consequences of SNPs to transcription factor binding. This knowledge can be used to identify potential disease-causing regulatory loci.

bioinformatics↗

bioRxiv2018

Poly-Enrich: Count-based Methods for Gene Set Enrichment Testing with Genomic Regions and Updates to ChIP-Enrich

Gene set enrichment (GSE) testing enhances the biological interpretation of ChIP-seq data and other large sets of genomic regions. Our group has previously introduced two GSE methods for genomic regions: ChIP-Enrich for narrow regions and Broad-Enrich for broad genomic regions, such as histone modifications. Here, we introduce new methods and extensions that more appropriately analyze sets of genomic regions with vastly different properties. First, we introduce Poly-Enrich, which models the number of peaks assigned to a gene using a generalized additive model with a negative binomial family to determine gene set enrichment, while adjusting for gene locus length (#bps associated with each gene). This is the first method that controls for locus length while accounting for the number of peaks per gene and variability among genes. We also introduce a flexible weighting approach to incorporate region scores, a hybrid enrichment approach, and support for new gene set databases and reference genomes/species. As opposed to ChIP-Enrich, Poly-Enrich works well even when nearly all genes have a peak. To illustrate this, we used Poly-Enrich to characterize the pathways and types of genic regions (introns, promoters, etc) enriched with different families of repetitive elements. By comparing ChIP-Enrich and Poly-Enrich results from ENCODE ChIP-seq data, we found that the optimal test depends more on the pathway being regulated than on the transcription factor or other properties of the dataset. Using known transcription factor functions, we discovered clusters of related biological processes consistently better modeled with either the binary score method (ChIP-Enrich) or count based method (Poly-Enrich). This suggests that the regulation of certain processes is more often modified by multiple binding events (count-based), while others tend to require only one (binary). Our new hybrid method handles this by automatically choosing the optimal method, with correct FDR-adjustment. Author SummaryAlthough every cell in our body contains the same DNA, our cells perform vastly different functions due to differences in how our genes are regulated. Certain regions of the genome are bound by DNA binding proteins (transcription factors), which regulate the expression of nearby genes. After an experiment to identify a large set of these regions, we can then model the association of these regions with various cellular pathways and biological processes. This analysis helps understand the overall biological effect that the binding events have on the cells. For example, if genes relating to apoptosis tend to have the transcription factor, Bcl-2, bind more often nearby, then Bcl-2 is likely to have a vital role in regulating apoptosis. The specifics of how to best perform this analysis is still being researched and depends on properties of the set of genomic regions. Here, we introduce a new, more flexible method that counts the number of occurrences per gene and models that in a sophisticated statistical test, and compare it to a previous method. We show that the optimal method depends on multiple factors, and the new method, Poly-Enrich, allows interesting findings in scenarios where the previous method failed.

bioinformatics↗

bioRxiv2017

Transcription factor co-binding patterns drive conserved regulatory outcomes

The mouse has been widely used as a model system in which to study human genetic mechanisms. However, part of the difficulty in translating findings from mouse is that, despite high levels of gene conservation, regulatory control networks between human and mouse have been extensively rewired. To understand common themes of regulatory control we look beyond physical sharing of regulatory sequence, where extensive turnover of individual transcription factor binding sites complicates cross-species prediction of specific functions, and instead look at conserved properties of the regulatory code itself. We define regulatory conservation in terms of a grammar with shared, species-specific, and tissue-specific segments, and show that this grammar is more predictive of shared chromatin states and gene expression profiles than shared occupancy alone. Furthermore, we demonstrate a marked enrichment of disease associated variation in conserved grammatical patterns. These findings offer new understanding of transcriptional regulatory mechanisms shared between human and mouse.

genomics↗