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Genome graphs detect human polymorphisms in active epigenomic state during influenza infection

ABSTRACT: Genome graphs detect human polymorphisms in active epigenomic state during influenza infection

PROVIDER: PRJNA937192 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR23566219_1.fastq.gz	Fastqsanger.gz
	SRR23566219_2.fastq.gz	Fastqsanger.gz
	SRR23566220_1.fastq.gz	Fastqsanger.gz
	SRR23566220_2.fastq.gz	Fastqsanger.gz
	SRR23566221_1.fastq.gz	Fastqsanger.gz

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Genome graphs detect human polymorphisms in active epigenomic state during influenza infection [ATAC-seq]

Project description:Genome graphs detect human polymorphisms in active epigenomic state during influenza infection [ATAC-seq]

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Genome graphs detect human polymorphisms in active epigenomic state during influenza infection [ChIP-seq]

Project description:Genetic variants, including mobile element insertions (MEIs), are known to impact the epigenome. We hypothesized that the use of a genome graph, which encapsulates genetic diversity, could reveal missing epigenomic signal. We tested this in a dataset obtained by sequencing the epigenome of monocyte-derived macrophages from 35 ancestrally diverse individuals before and after Influenza virus infection, which also allowed us to investigate the potential role of MEIs in immunity. After characterizing genetic variants in this cohort using linked-reads, including 5140 Alu, 316 L1, 94 SVAs and 48 ERVs, we incorporated them into a genome graph. Mapping epigenetic data to this graph revealed 2.5%, 3.0% and 2.3% novel peaks for H3K4me1 and H3K27ac ChIP-seq and ATAC-seq respectively. Notably, using a genome graph also modified quantitative trait loci estimates and we observed 375 polymorphic MEIs in active epigenomic state. For example, we found an AluYh3 polymorphism whose chromatin state changed after infection and that was associated with the expression of TRIM25, a gene that restricts influenza RNA synthesis. Our results demonstrate that graph genomes can reveal regulatory regions that would have been overlooked by other approaches.

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Genome graphs detect human polymorphisms in active epigenomic state during influenza infection.

Project description:Genetic variants, including mobile element insertions (MEIs), are known to impact the epigenome. We hypothesized that genome graphs, which encapsulate genetic diversity, could reveal missing epigenomic signals. To test this, we sequenced the epigenome of monocyte-derived macrophages from 35 ancestrally diverse individuals before and after influenza infection, allowing us to investigate the role of MEIs in immunity. We characterized genetic variants and MEIs using linked reads and built a genome graph. Mapping epigenetic data revealed 2.3%-3% novel peaks for H3K4me1, H3K27ac chromatin immunoprecipitation sequencing (ChIP-seq), and ATAC-seq. Additionally, the use of a genome graph modified some quantitative trait loci estimates and revealed 375 polymorphic MEIs in an active epigenomic state. Among these is an AluYh3 polymorphism whose chromatin state changed after infection and was associated with the expression of TRIM25, a gene that restricts influenza RNA synthesis. Our results demonstrate that graph genomes can reveal regulatory regions that would have been overlooked by other approaches.

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Genome graphs detect human polymorphisms in active epigenomic state during influenza infection

Project description:This SuperSeries is composed of the SubSeries listed below.

2023-02-21 | GSE225708 | GEO

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