SCEPTRE improves calibration and sensitivity in single-cell CRISPR screen analysis

AbstractSingle-cell CRISPR screens are a promising biotechnology for mapping regulatory elements to target genes at genome-wide scale. However, technical factors like sequencing depth impact not only expression measurement but also perturbation detection, creating a confounding effect. We demonstrate on two single-cell CRISPR screens how these challenges cause calibration issues. We propose SCEPTRE: analysis of single-cell perturbation screens via conditional resampling, which infers associations between perturbations and expression by resampling the former according to a working model for perturbation detection probability in each cell. SCEPTRE demonstrates very good calibration and sensitivity on CRISPR screen data, yielding hundreds of new regulatory relationships supported by orthogonal biological evidence.

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Conditional resampling improves calibration and sensitivity in single-cell CRISPR screen analysis

10.1101/2020.08.13.250092 ◽

2020 ◽

Cited By ~ 1

Author(s):

Eugene Katsevich ◽

Timothy Barry ◽

Kathryn Roeder

Keyword(s):

Single Cell ◽

Functional Data ◽

Large Scale ◽

Model Misspecification ◽

Screen Analysis ◽

Confounding Effect ◽

Crispr Screen ◽

Expression Model ◽

Technical Factors ◽

Expression Measurement

Single-cell CRISPR screens are an emerging biotechnology promising unprecedented insights into gene regulation. However, the analysis of these screens presents significant statistical challenges. For example, technical factors like sequencing depth impact not only expression measurement but also perturbation detection, creating a confounding effect. We demonstrate on two recent large-scale single-cell CRISPR screens how these challenges cause calibration issues among existing analysis methods. To address these challenges, we propose SCEPTRE: analysis of single-cell perturbation screens via conditional resampling. This methodology, designed to avoid calibration issues due to technical confounders and expression model misspecification, infers associations between perturbations and expression by resampling the former according to a working model for perturbation detection probability in each cell. SCETPRE demonstrates excellent calibration and sensitivity on the CRISPR screen data and yields 200 new regulatory relationships, many of which are supported by existing functional data.

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Chromatin accessibility dynamics of myogenesis at single cell resolution

10.1101/155473 ◽

2017 ◽

Cited By ~ 11

Author(s):

Hannah A. Pliner ◽

Jonathan Packer ◽

José L. McFaline-Figueroa ◽

Darren A. Cusanovich ◽

Riza Daza ◽

...

Keyword(s):

Single Cell ◽

Chromatin Remodeling ◽

Target Genes ◽

Regulatory Elements ◽

Chromatin Accessibility ◽

New Approach ◽

Genome Wide ◽

A Genome ◽

Histone Modifying Enzymes ◽

Dna Regulatory Elements

AbstractOver a million DNA regulatory elements have been cataloged in the human genome, but linking these elements to the genes that they regulate remains challenging. We introduce Cicero, a statistical method that connects regulatory elements to target genes using single cell chromatin accessibility data. We apply Cicero to investigate how thousands of dynamically accessible elements orchestrate gene regulation in differentiating myoblasts. Groups of co-accessible regulatory elements linked by Cicero meet criteria of “chromatin hubs”, in that they are physically proximal, interact with a common set of transcription factors, and undergo coordinated changes in histone marks that are predictive of gene expression. Pseudotemporal analysis revealed a subset of elements bound by MYOD in myoblasts that exhibit early opening, potentially serving as the initial sites of recruitment of chromatin remodeling and histone-modifying enzymes. The methodological framework described here constitutes a powerful new approach for elucidating the architecture, grammar and mechanisms of cis-regulation on a genome-wide basis.

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HCR-FlowFISH: A flexible CRISPR screening method to identify cis-regulatory elements and their target genes

10.1101/2020.05.11.078675 ◽

2020 ◽

Author(s):

SK Reilly ◽

SJ Gosai ◽

A Gutierrez ◽

JC Ulirsch ◽

M Kanai ◽

...

Keyword(s):

Gene Expression ◽

Target Genes ◽

Screening Method ◽

Cell Types ◽

Regulatory Elements ◽

Hybridization Chain Reaction ◽

Genome Wide ◽

Wide Range ◽

Causal Variants ◽

Endogenous Loci

AbstractCRISPR screens for cis-regulatory elements (CREs) have shown unprecedented power to endogenously characterize the non-coding genome. To characterize CREs we developed HCR-FlowFISH (Hybridization Chain Reaction Fluorescent In-Situ Hybridization coupled with Flow Cytometry), which directly quantifies native transcripts within their endogenous loci following CRISPR perturbations of regulatory elements, eliminating the need for restrictive phenotypic assays such as growth or transcript-tagging. HCR-FlowFISH accurately quantifies gene expression across a wide range of transcript levels and cell types. We also developed CASA (CRISPR Activity Screen Analysis), a hierarchical Bayesian model to identify and quantify CRE activity. Using >270,000 perturbations, we identified CREs for GATA1, HDAC6, ERP29, LMO2, MEF2C, CD164, NMU, FEN1 and the FADS gene cluster. Our methods detect subtle gene expression changes and identify CREs regulating multiple genes, sometimes at different magnitudes and directions. We demonstrate the power of HCR-FlowFISH to parse genome-wide association signals by nominating causal variants and target genes.

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Addressing the Missing Heritability Problem With the Help of Regulatory Features

Evolutionary Bioinformatics ◽

10.1177/1176934319860861 ◽

2019 ◽

Vol 15 ◽

pp. 117693431986086

Author(s):

Shan-Shan Dong ◽

Yan Guo ◽

Tie-Lin Yang

Keyword(s):

Target Genes ◽

Association Studies ◽

Complex Diseases ◽

Regulatory Elements ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Susceptibility Loci ◽

Missing Heritability ◽

Genome Wide ◽

Missing Heritability Problem

Genome-wide association studies (GWASs) have successfully identified thousands of susceptibility loci for human complex diseases. However, missing heritability is still a challenging problem. Considering most GWAS loci are located in regulatory elements, we recently developed a pipeline named functional disease-associated single-nucleotide polymorphisms (SNPs) prediction (FDSP), to predict novel susceptibility loci for complex diseases based on the interpretation of regulatory features and published GWAS results with machine learning. When applied to type 2 diabetes and hypertension, the predicted susceptibility loci by FDSP were proved to be capable of explaining additional heritability. In addition, potential target genes of the predicted positive SNPs were significantly enriched in disease-related pathways. Our results suggested that taking regulatory features into consideration might be a useful way to address the missing heritability problem. We hope FDSP could offer help for the identification of novel susceptibility loci for complex diseases.

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Beyond the ENCODE project: using genomics and epigenomics strategies to study enhancer evolution

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2013.0022 ◽

2013 ◽

Vol 368 (1632) ◽

pp. 20130022 ◽

Cited By ~ 13

Author(s):

Noboru Jo Sakabe ◽

Marcelo A. Nobrega

Keyword(s):

Gene Expression ◽

Target Genes ◽

Expression Patterns ◽

Dna Interaction ◽

Regulatory Elements ◽

Specific Gene ◽

Genome Wide ◽

Identify Transcription Factor ◽

Specific Proteins ◽

Species Specific

The complex expression patterns observed for many genes are often regulated by distal transcription enhancers. Changes in the nucleotide sequences of enhancers may therefore lead to changes in gene expression, representing a central mechanism by which organisms evolve. With the development of the experimental technique of chromatin immunoprecipitation (ChIP), in which discrete regions of the genome bound by specific proteins can be identified, it is now possible to identify transcription factor binding events (putative cis -regulatory elements) in entire genomes. Comparing protein–DNA binding maps allows us, for the first time, to attempt to identify regulatory differences and infer global patterns of change in gene expression across species. Here, we review studies that used genome-wide ChIP to study the evolution of enhancers. The trend is one of high divergence of cis -regulatory elements between species, possibly compensated by extensive creation and loss of regulatory elements and rewiring of their target genes. We speculate on the meaning of the differences observed and discuss that although ChIP experiments identify the biochemical event of protein–DNA interaction, it cannot determine whether the event results in a biological function, and therefore more studies are required to establish the effect of divergence of binding events on species-specific gene expression.

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Single-cell ATAC-seq Signal Extraction and Enhancement with SCATE

10.1101/795609 ◽

2019 ◽

Cited By ~ 1

Author(s):

Zhicheng Ji ◽

Weiqiang Zhou ◽

Hongkai Ji

Keyword(s):

Single Cell ◽

State Of The Art ◽

Single Cells ◽

Regulatory Elements ◽

Single Cell Sequencing ◽

Statistical Framework ◽

Genome Wide ◽

Cell Subpopulations ◽

Accessible Chromatin ◽

Regulatory Landscape

AbstractSingle-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscape in single cells. Single-cell ATAC-seq data are sparse and noisy. Analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. We present a new statistical framework, SCATE, that adaptively integrates information from co-activated CREs, similar cells, and publicly available regulome data to substantially increase the accuracy for estimating activities of individual CREs. We show that using SCATE, one can better reconstruct the regulatory landscape of a heterogeneous sample.

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Genome-Wide cis-Regulatory Element Based Discovery of Auxin-Responsive Genes in Higher Plant

Genes ◽

10.3390/genes13010024 ◽

2021 ◽

Vol 13 (1) ◽

pp. 24

Author(s):

Jianfei Wu ◽

Fan Gao ◽

Tongtong Li ◽

Haixia Guo ◽

Li Zhang ◽

...

Keyword(s):

Target Genes ◽

Regulatory Element ◽

Regulatory Elements ◽

Auxin Response ◽

Higher Plant ◽

Response Factors ◽

Auxin Response Factors ◽

Genome Wide ◽

A Genome ◽

Almost All

Auxin has a profound impact on plant physiology and participates in almost all aspects of plant development processes. Auxin exerts profound pleiotropic effects on plant growth and differentiation by regulating the auxin response genes’ expressions. The classical auxin reaction is usually mediated by auxin response factors (ARFs), which bind to the auxin response element (AuxRE) in the promoter region of the target gene. Experiments have generated only a limited number of plant genes with well-characterized functions. It is still unknown how many genes respond to exogenous auxin treatment. An economical and effective method was proposed for the genome-wide discovery of genes responsive to auxin in a model plant, Arabidopsis thaliana (A. thaliana). Our method relies on cis-regulatory-element-based targeted gene finding across different promoters in a genome. We first exploit and analyze auxin-specific cis-regulatory elements for the transcription of the target genes, and then identify putative auxin responsive genes whose promoters contain the elements in the collection of over 25,800 promoters in the A. thaliana genome. Evaluating our result by comparing with a published database and the literature, we found that this method has an accuracy rate of 65.2% (309/474) for predicting candidate genes responsive to auxin. Chromosome distribution and annotation of the putative auxin-responsive genes predicted here were also mined. The results can markedly decrease the number of identified but merely potential auxin target genes and also provide useful clues for improving the annotation of gene that lack functional information.

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Cis-regulatory elements and human evolution

10.1101/005652 ◽

2014 ◽

Author(s):

Adam Siepel ◽

Leonardo Arbiza

Keyword(s):

Transcriptional Regulation ◽

Human Evolution ◽

Large Scale ◽

Regulatory Elements ◽

Data Sets ◽

Regulatory Evolution ◽

Genome Wide ◽

A Genome ◽

Wide Scale ◽

Human Polymorphism

Modification of gene regulation has long been considered an important force in human evolution, particularly through changes to cis-regulatory elements (CREs) that function in transcriptional regulation. For decades, however, the study of cis-regulatory evolution was severely limited by the available data. New data sets describing the locations of CREs and genetic variation within and between species have now made it possible to study CRE evolution much more directly on a genome-wide scale. Here, we review recent research on the evolution of CREs in humans based on large-scale genomic data sets. We consider inferences based on primate divergence,human polymorphism, and combinations of divergence and polymorphism. We then consider "new frontiers" in this field stemming from recent research on transcriptional regulation.

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EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue/cell types across nine species

Nucleic Acids Research ◽

10.1093/nar/gkz980 ◽

2019 ◽

Cited By ~ 8

Author(s):

Tianshun Gao ◽

Jiang Qian

Keyword(s):

Large Scale ◽

Target Gene ◽

Target Genes ◽

Cell Types ◽

Regulatory Elements ◽

Tissue Cell ◽

Normal Tissues ◽

Genome Wide ◽

Wide Range ◽

User Friendly

Abstract Enhancers are distal cis-regulatory elements that activate the transcription of their target genes. They regulate a wide range of important biological functions and processes, including embryogenesis, development, and homeostasis. As more and more large-scale technologies were developed for enhancer identification, a comprehensive database is highly desirable for enhancer annotation based on various genome-wide profiling datasets across different species. Here, we present an updated database EnhancerAtlas 2.0 (http://www.enhanceratlas.org/indexv2.php), covering 586 tissue/cell types that include a large number of normal tissues, cancer cell lines, and cells at different development stages across nine species. Overall, the database contains 13 494 603 enhancers, which were obtained from 16 055 datasets using 12 high-throughput experiment methods (e.g. H3K4me1/H3K27ac, DNase-seq/ATAC-seq, P300, POLR2A, CAGE, ChIA-PET, GRO-seq, STARR-seq and MPRA). The updated version is a huge expansion of the first version, which only contains the enhancers in human cells. In addition, we predicted enhancer–target gene relationships in human, mouse and fly. Finally, the users can search enhancers and enhancer–target gene relationships through five user-friendly, interactive modules. We believe the new annotation of enhancers in EnhancerAtlas 2.0 will facilitate users to perform useful functional analysis of enhancers in various genomes.

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Computational prediction of CRISPR-impaired non-coding regulatory regions

10.1101/2020.12.22.423923 ◽

2020 ◽

Author(s):

Nina Baumgarten ◽

Florian Schmidt ◽

Martin Wegner ◽

Marie Hebel ◽

Manuel Kaulich ◽

...

Keyword(s):

Computational Prediction ◽

Regulatory Elements ◽

Cell Type ◽

Coding Regions ◽

Gene Coding ◽

Genome Wide ◽

A Genome ◽

Crispr Screen ◽

Genomic Regions ◽

Made In

AbstractGenome-wide CRISPR screens are becoming more widespread and allow the simultaneous interrogation of thousands of genomic regions. Although recent progress has been made in the analysis of CRISPR screens, it is still an open problem how to interpret CRISPR mutations in non-coding regions of the genome. Most of the tools concentrate on the interpretation of mutations introduced in gene coding regions. We introduce a computational pipeline that uses epigenomic information about regulatory elements for the interpretation of CRISPR mutations in non-coding regions. We illustrate our approach on the analysis of a genome-wide CRISPR screen in hTERT-RPE-1 cells and reveal novel regulatory elements that mediate chemoresistance against doxorubicin in these cells. We infer links to established and to novel chemoresistance genes. Our approach is general and can be applied on any cell type and with different CRISPR enzymes.

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