NGseqBasic - a single-command UNIX tool for ATAC-seq, DNaseI-seq, Cut-and-Run, and ChIP-seq data mapping, high-resolution visualisation, and quality control

Telenius, Jelena; Hughes, Jim R.

doi:10.1101/393413

Cited by 30 publications

(27 citation statements)

References 33 publications

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“…All data used are listed in Supplementary Table 1. All additional data, was aligned to hg19 using the NGseqBasic pipeline 34 . Peaks were called with macs2 (default parameters, -q 0.01).…”

Section: Methodsmentioning

confidence: 99%

DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning

Gosden

Downes

Brown

et al. 2019

Preprint

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Understanding 3D genome structure requires high throughput, genome-wide approaches. However, assays for all vs. all chromatin interaction mapping are expensive and time consuming, which severely restricts their usage for large-scale mutagenesis screens or for mapping the impact of sequence variants. Computational models sophisticated enough to grasp the determinants of chromatin folding provide a unique window into the functional determinants of 3D genome structure as well as the effects of genome variation. A chromatin interaction predictor should work at the base pair level but also incorporate large-scale genomic context to simultaneously capture the large scale and intricate structures of chromatin architecture. Similarly, to be a flexible and generalisable approach it should also be applicable to data it has not been explicitly trained on. To develop a model with these properties, we designed a deep neuronal network (deepC) that utilizes transfer learning to accurately predict chromatin interactions from DNA sequence at megabase scale. The model generalizes well to unseen chromosomes and works across cell types, Hi-C data resolutions and a range of sequencing depths. DeepC integrates DNA sequence context on an unprecedented scale, bridging the different levels of resolution from base pairs to TADs. We demonstrate how this model allows us to investigate sequence determinants of chromatin folding at genome-wide scale and to predict the importance of regulatory elements and the impact of sequence variations.

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“…All data used are listed in Supplementary Table 1. All additional data, was aligned to hg19 using the NGseqBasic pipeline 34 . Peaks were called with macs2 (default parameters, -q 0.01).…”

Section: Methodsmentioning

confidence: 99%

DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning

Gosden

Downes

Brown

et al. 2019

Preprint

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“…ATAC-seq-analysis. Reads were mapped to the mouse mm9 genome and PCR duplicates removed using NGseqBasic 56 . Technical replicates were merged and peaks called using MACS2 57 .…”

Section: Tiled-c-analysismentioning

confidence: 99%

“…com/oudelaar/TiledC. ATAC-seq data were analyzed with the NGseqBasic pipeline 56 and scripts available at https://github.com/rbeagrie/alpha-tiledc. Single-cell RNA-seq data were analyzed with scripts available at https://github.com/Hoohm/CITE-seq-Count and https://github.com/rbeagrie/alpha-tiledc.…”

Section: Data Availabilitymentioning

confidence: 99%

Dynamics of the 4D genome during in vivo lineage specification and differentiation

et al. 2020

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Mammalian gene expression patterns are controlled by regulatory elements, which interact within topologically associating domains (TADs). The relationship between activation of regulatory elements, formation of structural chromatin interactions and gene expression during development is unclear. Here, we present Tiled-C, a low-input chromosome conformation capture (3C) technique. We use this approach to study chromatin architecture at high spatial and temporal resolution through in vivo mouse erythroid differentiation. Integrated analysis of chromatin accessibility and single-cell expression data shows that regulatory elements gradually become accessible within pre-existing TADs during early differentiation. This is followed by structural reorganization within the TAD and formation of specific contacts between enhancers and promoters. Our high-resolution data show that these enhancer-promoter interactions are not established prior to gene expression, but formed gradually during differentiation, concomitant with progressive upregulation of gene activity. Together, these results provide new insight into the close, interdependent relationship between chromatin architecture and gene regulation during development.

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“…Data from both methods were analysed with an in-house pipeline as described. 21,23 In each case, for visualization, alignment files from two or three biological replicates were normalized to reads per kilobase per million mapped reads (RPKM) and averaged.…”

Section: Atac-seq and Chip-seqmentioning

confidence: 99%

ScalableIn VitroProduction of Defined Mouse Erythroblasts

Francis¹,

Harold²,

Beagrie³

et al. 2020

Preprint

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Mouse embryonic stem cells (mESCs) can be manipulated in vitro to recapitulate the process of erythropoiesis, during which multipotent cells undergo lineage specification, differentiation and maturation to produce erythroid cells. Although useful for identifying specific progenitors and precursors, this system has not been fully exploited as a source of cells to analyse erythropoiesis. Here, we establish a protocol in which characterised erythroblasts can be isolated in a scalable manner from differentiated embryoid bodies (EBs). Using transcriptional and epigenetic analysis, we demonstrate that this system faithfully recapitulates normal primitive erythropoiesis and fully reproduces the effects of natural and engineered mutations seen in primary cells obtained from mouse models. We anticipate this system to be of great value in reducing the time and costs of generating and maintaining mouse lines in a number of research scenarios.Key PointsScalable purification of primitive-like erythroid cells from in vitro differentiated mESCs offers tractable tools for genetic studiesIn vitro derived erythroid cells recapitulate wild type and engineered mutation phenotypes observed in primary cells obtained from mouse models

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NGseqBasic - a single-command UNIX tool for ATAC-seq, DNaseI-seq, Cut-and-Run, and ChIP-seq data mapping, high-resolution visualisation, and quality control

Cited by 30 publications

References 33 publications

DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning

DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning

Dynamics of the 4D genome during in vivo lineage specification and differentiation

ScalableIn VitroProduction of Defined Mouse Erythroblasts

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