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  • 1
    In: Nature Ecology & Evolution, Springer Science and Business Media LLC, Vol. 2, No. 3 ( 2018-01-29), p. 537-548
    Type of Medium: Online Resource
    ISSN: 2397-334X
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
    detail.hit.zdb_id: 2879715-2
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  • 2
    In: Cell, Elsevier BV, Vol. 184, No. 7 ( 2021-04), p. 1724-1739.e16
    Type of Medium: Online Resource
    ISSN: 0092-8674
    RVK:
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 187009-9
    detail.hit.zdb_id: 2001951-8
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    Elsevier BV ; 2020
    In:  Trends in Genetics Vol. 36, No. 4 ( 2020-04), p. 243-258
    In: Trends in Genetics, Elsevier BV, Vol. 36, No. 4 ( 2020-04), p. 243-258
    Type of Medium: Online Resource
    ISSN: 0168-9525
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 2010993-3
    SSG: 12
    SSG: 15,3
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  • 4
    In: Genome Research, Cold Spring Harbor Laboratory, Vol. 27, No. 11 ( 2017-11), p. 1816-1829
    Abstract: Most studies of responses to transcriptional stimuli measure changes in cellular mRNA concentrations. By sequencing nascent RNA instead, it is possible to detect changes in transcription in minutes rather than hours and thereby distinguish primary from secondary responses to regulatory signals. Here, we describe the use of PRO-seq to characterize the immediate transcriptional response in human cells to celastrol, a compound derived from traditional Chinese medicine that has potent anti-inflammatory, tumor-inhibitory, and obesity-controlling effects. Celastrol is known to elicit a cellular stress response resembling the response to heat shock, but the transcriptional basis of this response remains unclear. Our analysis of PRO-seq data for K562 cells reveals dramatic transcriptional effects soon after celastrol treatment at a broad collection of both coding and noncoding transcription units. This transcriptional response occurred in two major waves, one within 10 min, and a second 40–60 min after treatment. Transcriptional activity was generally repressed by celastrol, but one distinct group of genes, enriched for roles in the heat shock response, displayed strong activation. Using a regression approach, we identified key transcription factors that appear to drive these transcriptional responses, including members of the E2F and RFX families. We also found sequence-based evidence that particular transcription factors drive the activation of enhancers. We observed increased polymerase pausing at both genes and enhancers, suggesting that pause release may be widely inhibited during the celastrol response. Our study demonstrates that a careful analysis of PRO-seq time-course data can disentangle key aspects of a complex transcriptional response, and it provides new insights into the activity of a powerful pharmacological agent.
    Type of Medium: Online Resource
    ISSN: 1088-9051 , 1549-5469
    RVK:
    Language: English
    Publisher: Cold Spring Harbor Laboratory
    Publication Date: 2017
    detail.hit.zdb_id: 1483456-X
    SSG: 12
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  • 5
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2017
    In:  Nature Genetics Vol. 49, No. 1 ( 2017-1), p. 2-3
    In: Nature Genetics, Springer Science and Business Media LLC, Vol. 49, No. 1 ( 2017-1), p. 2-3
    Type of Medium: Online Resource
    ISSN: 1061-4036 , 1546-1718
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 1494946-5
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2022
    In:  Nature Communications Vol. 13, No. 1 ( 2022-07-25)
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2022-07-25)
    Abstract: Large-scale genome sequencing has enabled the measurement of strong purifying selection in protein-coding genes. Here we describe a new method, called ExtRaINSIGHT, for measuring such selection in noncoding as well as coding regions of the human genome. ExtRaINSIGHT estimates the prevalence of “ultraselection” by the fractional depletion of rare single-nucleotide variants, after controlling for variation in mutation rates. Applying ExtRaINSIGHT to 71,702 whole genome sequences from gnomAD v3, we find abundant ultraselection in evolutionarily ancient miRNAs and neuronal protein-coding genes, as well as at splice sites. By contrast, we find much less ultraselection in other noncoding RNAs and transcription factor binding sites, and only modest levels in ultraconserved elements. We estimate that ~0.4–0.7% of the human genome is ultraselected, implying ~ 0.26–0.51 strongly deleterious mutations per generation. Overall, our study sheds new light on the genome-wide distribution of fitness effects by combining deep sequencing data and classical theory from population genetics.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2553671-0
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  • 7
    In: BMC Biology, Springer Science and Business Media LLC, Vol. 19, No. 1 ( 2021-12)
    Abstract: The concentrations of distinct types of RNA in cells result from a dynamic equilibrium between RNA synthesis and decay. Despite the critical importance of RNA decay rates, current approaches for measuring them are generally labor-intensive, limited in sensitivity, and/or disruptive to normal cellular processes. Here, we introduce a simple method for estimating relative RNA half-lives that is based on two standard and widely available high-throughput assays: Precision Run-On sequencing (PRO-seq) and RNA sequencing (RNA-seq). Results Our method treats PRO-seq as a measure of transcription rate and RNA-seq as a measure of RNA concentration, and estimates the rate of RNA decay required for a steady-state equilibrium. We show that this approach can be used to assay relative RNA half-lives genome-wide, with good accuracy and sensitivity for both coding and noncoding transcription units. Using a structural equation model (SEM), we test several features of transcription units, nearby DNA sequences, and nearby epigenomic marks for associations with RNA stability after controlling for their effects on transcription. We find that RNA splicing-related features are positively correlated with RNA stability, whereas features related to miRNA binding and DNA methylation are negatively correlated with RNA stability. Furthermore, we find that a measure based on U1 binding and polyadenylation sites distinguishes between unstable noncoding and stable coding transcripts but is not predictive of relative stability within the mRNA or lincRNA classes. We also identify several histone modifications that are associated with RNA stability. Conclusion We introduce an approach for estimating the relative half-lives of individual RNAs. Together, our estimation method and systematic analysis shed light on the pervasive impacts of RNA stability on cellular RNA concentrations.
    Type of Medium: Online Resource
    ISSN: 1741-7007
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2133020-7
    SSG: 12
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  • 8
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2020
    In:  Molecular Biology and Evolution Vol. 37, No. 7 ( 2020-07-01), p. 2137-2152
    In: Molecular Biology and Evolution, Oxford University Press (OUP), Vol. 37, No. 7 ( 2020-07-01), p. 2137-2152
    Abstract: Evolutionary changes in gene expression are often driven by gains and losses of cis-regulatory elements (CREs). The dynamics of CRE evolution can be examined using multispecies epigenomic data, but so far such analyses have generally been descriptive and model-free. Here, we introduce a probabilistic modeling framework for the evolution of CREs that operates directly on raw chromatin immunoprecipitation and sequencing (ChIP-seq) data and fully considers the phylogenetic relationships among species. Our framework includes a phylogenetic hidden Markov model, called epiPhyloHMM, for identifying the locations of multiply aligned CREs, and a combined phylogenetic and generalized linear model, called phyloGLM, for accounting for the influence of a rich set of genomic features in describing their evolutionary dynamics. We apply these methods to previously published ChIP-seq data for the H3K4me3 and H3K27ac histone modifications in liver tissue from nine mammals. We find that enhancers are gained and lost during mammalian evolution at about twice the rate of promoters, and that turnover rates are negatively correlated with DNA sequence conservation, expression level, and tissue breadth, and positively correlated with distance from the transcription start site, consistent with previous findings. In addition, we find that the predicted dosage sensitivity of target genes positively correlates with DNA sequence constraint in CREs but not with turnover rates, perhaps owing to differences in the effect sizes of the relevant mutations. Altogether, our probabilistic modeling framework enables a variety of powerful new analyses.
    Type of Medium: Online Resource
    ISSN: 0737-4038 , 1537-1719
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2024221-9
    SSG: 12
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  • 9
    In: eLife, eLife Sciences Publications, Ltd, Vol. 9 ( 2020-06-23)
    Abstract: The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here, we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.
    Type of Medium: Online Resource
    ISSN: 2050-084X
    Language: English
    Publisher: eLife Sciences Publications, Ltd
    Publication Date: 2020
    detail.hit.zdb_id: 2687154-3
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  • 10
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2021
    In:  Bioinformatics Vol. 37, No. 24 ( 2021-12-11), p. 4727-4736
    In: Bioinformatics, Oxford University Press (OUP), Vol. 37, No. 24 ( 2021-12-11), p. 4727-4736
    Abstract: Quantification of isoform abundance has been extensively studied at the mature RNA level using RNA-seq but not at the level of precursor RNAs using nascent RNA sequencing. Results We address this problem with a new computational method called Deconvolution of Expression for Nascent RNA-sequencing data (DENR), which models nascent RNA-sequencing read-counts as a mixture of user-provided isoforms. The baseline algorithm is enhanced by machine-learning predictions of active transcription start sites and an adjustment for the typical ‘shape profile’ of read-counts along a transcription unit. We show that DENR outperforms simple read-count-based methods for estimating gene and isoform abundances, and that transcription of multiple pre-RNA isoforms per gene is widespread, with frequent differences between cell types. In addition, we provide evidence that a majority of human isoform diversity derives from primary transcription rather than from post-transcriptional processes. Availability and implementation DENR and nascentRNASim are freely available at https://github.com/CshlSiepelLab/DENR (version v1.0.0) and https://github.com/CshlSiepelLab/nascentRNASim (version v0.3.0). Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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