Transcriptional Regulatory Networks for CD4 T Cell Differentiation

Christie, Darah; Zhu, Jinfang

doi:10.1007/82_2014_372

Cited by 48 publications

(54 citation statements)

References 253 publications

(371 reference statements)

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“…We show that our software maintains high consistency while Hichipper's sensitivity goes down rapidly when read count goes down. Our software is set at a FDR of 0.01. of T cell activation (Christie and Zhu, 2014) and confirming the accuracy of our peak calling method.…”

Section: Fig 2 True Positive Rate Vs Number Of Peaks Recalled From supporting

confidence: 78%

HiChIP-Peaks: A HiChIP peak calling algorithm

Shi

Rattray

Orozco

2019

Preprint

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Motivation:HiChIP is a powerful tool to interrogate 3D chromatin organization. Current tools to analyse chromatin looping mechanisms using HiChIP data require the identification of loop anchors to work properly. However, current approaches to discover these anchors from HiChIP data are not satisfactory, having either a very high false discovery rate or strong dependence on sequencing depth. Moreover, these tools do not allow quantitative comparison of peaks across different samples, failing to fully exploit the information available from HiChIP datasets. Results: We develop a new tool based on a representation of HiChIP data centred on the re-ligation sites to identify peaks from HiChIP datasets, which can subsequently be used in other tools for loop discovery. This increases the reliability of these tools and improves recall rate as sequencing depth is reduced. We also provide a method to count reads mapping to peaks across samples, which can be used for differential peak analysis using HiChIP data.

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Section: Fig 2 True Positive Rate Vs Number Of Peaks Recalled From supporting

confidence: 78%

HiChIP-Peaks: A HiChIP peak calling algorithm

Shi

Rattray

Orozco

2019

Preprint

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“…7,10,11 In a previous study, serum interleukin-9 (IL-9) level was reported to be significantly higher in AD patients. 14 It has already been shown that Th9 cells are involved in the development of allergic asthma and allergic rhinitis. Transcription factor PU.1 has been shown to be essentially required for development and maintenance of these Th9 cells.…”

Section: Introductionmentioning

confidence: 99%

“…Transcription factor PU.1 has been shown to be essentially required for development and maintenance of these Th9 cells. 14 It has already been shown that Th9 cells are involved in the development of allergic asthma and allergic rhinitis. 15,16 However, the possible role of a Th9 cells-derived increased IL-9 production in AD patients remains insufficiently investigated.…”

Section: Introductionmentioning

confidence: 99%

Expression levels of transcription factor PU.1 and interleukin‐9 in atopic dermatitis and their relation to disease severity and eruption types

et al. 2017

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“…4). Individual interactions between TFs and genes in this "core" have been the focus of many studies (Christie and Zhu 2014;Li et al 2014), which we leverage to evaluate the ATAC-based Th17 core TRNs: moderately reinforced TF-mRNA-and prior-based TFA TRNs individually and combined into a final ATAC-only TRN (as described in Methods). To visually compare these networks, we developed jp_gene_viz, a Jupyter-notebook TRN visualization tool.…”

Section: "Core" Th17 Trns Contain Literature-supported Tf-gene Interamentioning

confidence: 99%

Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells

Miraldi

Pokrovskii

Watters

et al. 2018

Preprint

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Transcriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The Assay for Transposase Accessible Chromatin (ATAC)-seq, coupled with transcription-factor motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to influence gene expression modeling. We rigorously test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources (plentiful gene expression data, TF knock-outs and ChIP-seq experiments). In this resource-rich mammalian setting, our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF KO, ChIP-seq). We highlight newly discovered roles for individual TFs and groups of TFs (“TF-TF modules”) in Th17 gene regulation. Given the popularity of ATAC-seq, which provides high-resolution with low sample input requirements, we anticipate that application of our methods will improve TRN inference in new mammalian systems, especially in vivo, for cells directly from humans and animal models.

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Transcriptional Regulatory Networks for CD4 T Cell Differentiation

Cited by 48 publications

References 253 publications

HiChIP-Peaks: A HiChIP peak calling algorithm

HiChIP-Peaks: A HiChIP peak calling algorithm

Expression levels of transcription factor PU.1 and interleukin‐9 in atopic dermatitis and their relation to disease severity and eruption types

Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells

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