Biology subjects

Farrell, N.

Publications and source records attributed to Farrell, N..

5 recordsLinked to original sources

bioRxiv2022

The ENCODE Imputation Challenge: A critical assessment of methods for cross-cell type imputation of epigenomic profiles

Functional genomics experiments are invaluable for understanding mechanisms of gene regulation. However, comprehensively performing all such experiments, even across a fixed set of sample and assay types, is often infeasible in practice. A promising alternative to performing experiments exhaustively is to, instead, perform a core set of experiments and subsequently use machine learning methods to impute the remaining experiments. However, questions remain as to the quality of the imputations, the best approaches for performing imputations, and even what performance measures meaningfully evaluate performance of such models. In this work, we address these questions by comprehensively analyzing imputations from 23 imputation models submitted to the ENCODE Imputation Challenge. We find that measuring the quality of imputations is significantly more challenging than reported in the literature, and is confounded by three factors: major distributional shifts that arise because of differences in data collection and processing over time, the amount of available data per cell type, and redundancy among performance measures. Our systematic analyses suggest several steps that are necessary, but also simple, for fairly evaluating the performance of such models, as well as promising directions for more robust research in this area.

bioinformatics↗

bioRxiv2021

Multi-tissue integrative analysis of personal epigenomes

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of personal epigenomes, for [~]25 tissues and >10 assays in four donors (>1500 open-access functional genomic and proteomic datasets, in total). Each dataset is mapped to a matched, diploid personal genome, which has long-read phasing and structural variants. The mappings enable us to identify >1 million loci with allele-specific behavior. These loci exhibit coordinated epigenetic activity along haplotypes and less conservation than matched, non-allele-specific loci, in a fashion broadly paralleling tissue-specificity. Surprisingly, they can be accurately modelled just based on local nucleotide-sequence context. Combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci and enables models for transferring known eQTLs to difficult-to-profile tissues. Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

genomics↗

bioRxiv2020

Controlling wireframe DNA origami nuclease degradation with minor groove binders

Virus-like DNA nanoparticles have emerged as promising vaccine and gene delivery platforms due to their programmable nature that offers independent control over size, shape, and functionalization. However, as biodegradable materials, their utility for specific therapeutic indications depends on their structural integrity during biodistribution to efficiently target cells, tissues, or organs. Here, we explore reversible minor groove binders to control the degradation half-lives of wireframe DNA origami. Bare, two-helix DNA nanoparticles were found to be stable under typical cell culture conditions in presence of bovine serum, yet they remain susceptible to endonucleases, specifically DNAse I. Moreover, they degrade rapidly in mouse serum, suggesting species-specific degradation. Blocking minor groove accessibility with diamidines resulted in substantial protection against endonucleases, specifically DNAse-I. This strategy was found to be compatible with both varying wireframe DNA origami architectures and functionalization with protein antigens. Our stabilization strategy offers distinct physicochemical properties compared with established cationic polymer-based methods, with synergistic therapeutic potential for minor groove binder delivery for infectious diseases and cancer.

bioengineering↗

bioRxiv2019

Alignment and quantification of ChIP-exo crosslinking patterns reveal the spatial organization of protein-DNA complexes

The ChIP-exo assay precisely delineates protein-DNA crosslinking patterns by combining chromatin immunoprecipitation with 5' to 3' exonuclease digestion. Within a regulatory complex, the physical distance of a regulatory protein to DNA affects crosslinking efficiencies. Therefore, the spatial organization of a protein-DNA complex could potentially be inferred by analyzing how crosslinking signatures vary between the subunits of a regulatory complex. Here, we present a computational framework that aligns ChIP-exo crosslinking patterns from multiple proteins across a set of coordinately bound regulatory regions, and which detects and quantifies protein-DNA crosslinking events within the aligned profiles. By producing consistent measurements of protein-DNA crosslinking strengths across multiple proteins, our approach enables characterization of relative spatial organization within a regulatory complex. We demonstrate that our approach can recover aspects of regulatory complex spatial organization when applied to collections of ChIP-exo data that profile regulatory machinery at yeast ribosomal protein genes and yeast tRNA genes. We also demonstrate the ability to quantify changes in protein-DNA complex organization across conditions by applying our approach to data profiling Drosophila Pol II transcriptional components. Our results suggest that principled analyses of ChIP-exo crosslinking patterns enable inference of spatial organization within protein-DNA complexes.

bioinformatics↗

bioRxiv2018

Characterizing protein-DNA binding event subtypes in ChIP-exo data

MotivationRegulatory proteins associate with the genome either by directly binding cognate DNA motifs or via protein-protein interactions with other regulators. Each recruitment mechanism may be associated with distinct motifs and may also result in distinct characteristic patterns in high-resolution protein-DNA binding assays. For example, the ChIP-exo protocol precisely characterizes protein-DNA crosslinking patterns by combining chromatin immunoprecipitation (ChIP) with 5 [->] 3 exonuclease digestion. Since different regulatory complexes will result in different protein-DNA crosslinking signatures, analysis of ChIP-exo tag enrichment patterns should enable detection of multiple protein-DNA binding modes for a given regulatory protein. However, current ChIP-exo analysis methods either treat all binding events as being of a uniform type or rely on motifs to cluster binding events into subtypes.\n\nResultsTo systematically detect multiple protein-DNA interaction modes in a single ChIP-exo experiment, we introduce the ChIP-exo mixture model (ChExMix). ChExMix probabilistically models the genomic locations and subtype memberships of binding events using both ChIP-exo tag distribution patterns and DNA motifs. We demonstrate that ChExMix achieves accurate detection and classification of binding event subtypes using in silico mixed ChIP-exo data. We further demonstrate the unique analysis abilities of ChExMix using a collection of ChIP-exo experiments that profile the binding of key transcription factors in MCF-7 cells. In these data, ChExMix identifies possible recruitment mechanisms of FoxA1 and ER, thus demonstrating that ChExMix can effectively stratify ChIP-exo binding events into biologically meaningful subtypes.\n\nAvailabilityChExMix is available from https://github.com/seqcode/chexmix\n\nContactmahony@psu.edu

bioinformatics↗