Systematic evaluation of factors influencing ChIP-seq fidelity

Chen, Yiwen; Nègre, Nicolas; Li, Qunhua; Mieczkowska, Joanna O.; Slattery, Matthew; Liu, Tao; Zhang, Yong; Kim, Tae Kyung; He, Housheng Hansen; Zieba, Jennifer; Ruan, Yijun; Bickel, Peter J.; Myers, R; Wold, B; White, Kevin P.; Lieb, Jason D.; Liu, X. Shirley

doi:10.1038/nmeth.1985

Cited by 155 publications

(201 citation statements)

References 36 publications

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“…We hypothesize that this is due to global biases in ChIP-seq read distributions; for example, it was suggested that ChIP is biased toward open regions of chromatin (Chen et al 2012). Therefore, the high ChIP enrichment baseline may reflect the fact that only a subset of the genome is ''ChIP-able,'' whereas the whole genome is represented in the input library.…”

Section: Meta-analysis Of Chip-seq Signal In 25-kb Flanks Of Te Insermentioning

confidence: 99%

piRNA pathway targets active LINE1 elements to establish the repressive H3K9me3 mark in germ cells

et al. 2014

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Transposable elements (TEs) occupy a large fraction of metazoan genomes and pose a constant threat to genomic integrity. This threat is particularly critical in germ cells, as changes in the genome that are induced by TEs will be transmitted to the next generation. Small noncoding piwi-interacting RNAs (piRNAs) recognize and silence a diverse set of TEs in germ cells. In mice, piRNA-guided transposon repression correlates with establishment of CpG DNA methylation on their sequences, yet the mechanism and the spectrum of genomic targets of piRNA silencing are unknown. Here we show that in addition to DNA methylation, the piRNA pathway is required to maintain a high level of the repressive H3K9me3 histone modification on long interspersed nuclear elements (LINEs) in germ cells. piRNA-dependent chromatin repression targets exclusively full-length elements of actively transposing LINE families, demonstrating the remarkable ability of the piRNA pathway to recognize active elements among the large number of genomic transposon fragments.

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Section: Meta-analysis Of Chip-seq Signal In 25-kb Flanks Of Te Insermentioning

confidence: 99%

piRNA pathway targets active LINE1 elements to establish the repressive H3K9me3 mark in germ cells

et al. 2014

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“…GRγ has been similarly examined by others [6]. Importantly, we did paired-end sequencing of the ChIP-seq library, which improves the dynamic range of occupancy by allowing use of both ends of the double stranded molecule to determine whether the originating fragment was unique [8]. We identified GBRs (Additional file 2) using MACS2 a , a software package for the analysis of ChIP-seq data, and assigned the GBRs to genes (Additional file 3) based on proximity (see Materials and Methods, 'GBR to gene assignment') [9].…”

Section: A477t and Grγ Selectively Occupy Gbrs Near Genes That Gain Amentioning

confidence: 99%

Glucocorticoid receptor binds half sites as a monomer and regulates specific target genes

et al. 2014

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Background: Glucocorticoid receptor (GR) is a hormone-activated, DNA-binding transcriptional regulatory factor that controls inflammation, metabolism, stress responses, and other physiological processes. In vitro, GR binds as an inverted dimer to a motif consisting of two imperfectly palindromic 6 bp half sites separated by 3 bp spacers. In vivo, GR employs different patterns of functional surfaces of GR to regulate different target genes. The relationships between GR genomic binding and functional surface utilization have not been defined.

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“…More specifically, ChIP-seq is a multi-step experiment where biases may be introduced at each step [19,20], leading to a generally limited data reproducibility [21,22]. Among others, the amount of input material, efficiency of antibody, sequencing quality and depth may vary considerably from an experiment to another [19,20].…”

Section: Introductionmentioning

confidence: 99%

An introduction to computational tools for differential binding analysis with ChIP‐seq data

Tu¹,

Shao²

2017

Quant. Biol.

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Background: Gene transcription in eukaryotic cells is collectively controlled by a large panel of chromatin associated proteins and ChIP-seq is now widely used to locate their binding sites along the whole genome. Inferring the differential binding sites of these proteins between biological conditions by comparing the corresponding ChIP-seq samples is of general interest, yet it is still a computationally challenging task. Results: Here, we briefly review the computational tools developed in recent years for differential binding analysis with ChIP-seq data. The methods are extensively classified by their strategy of statistical modeling and scope of application. Finally, a decision tree is presented for choosing proper tools based on the specific dataset. Conclusions: Computational tools for differential binding analysis with ChIP-seq data vary significantly with respect to their applicability and performance. This review can serve as a practical guide for readers to select appropriate tools for their own datasets.

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Systematic evaluation of factors influencing ChIP-seq fidelity

Cited by 155 publications

References 36 publications

piRNA pathway targets active LINE1 elements to establish the repressive H3K9me3 mark in germ cells

piRNA pathway targets active LINE1 elements to establish the repressive H3K9me3 mark in germ cells

Glucocorticoid receptor binds half sites as a monomer and regulates specific target genes

An introduction to computational tools for differential binding analysis with ChIP‐seq data

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