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A Protocol for Epigenetic Imprinting Analysis with RNA-Seq Data

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Transcriptome Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1751))

Abstract

Genomic imprinting is an epigenetic regulatory mechanism that operates through expression of certain genes from maternal or paternal in a parent-of-origin-specific manner. Imprinted genes have been identified in diverse biological systems that are implicated in some human diseases and in embryonic and seed developmental programs in plants. The molecular underpinning programs and mechanisms involved in imprinting are yet to be explored in depth in plants. The recent advances in RNA-Seq-based methods and technologies offer an opportunity to systematically analyze epigenetic imprinting that operates at the whole genome level in the model and crop plants. We are interested using Arabidopsis model system, to investigate gene expression patterns associated with parent of origin and their implications to imprinting during embryo and seed development. Toward this, we have generated early embryo development RNA-Seq-based transcriptome datasets in F1s from a genetic cross between two diverse Arabidopsis thaliana ecotypes Col-0 and Tsu-1. With the data, we developed a protocol for evaluating the maternal and paternal contributions of genes during the early stages of embryo development after fertilization. This protocol is also designed to consider the contamination from other potential seed tissues, sequencing quality, proper processing of sequenced reads and variant calling, and appropriate inference of the parental contributions based on the parent-of-origin-specific single-nucleotide polymorphisms within the expressed genes. The approach, methods and the protocol developed in this study can be used for evaluating the effects of epigenetic imprinting in plants.

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Correspondence to Edwin Wang .

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Zou, J., Xiang, D., Datla, R., Wang, E. (2018). A Protocol for Epigenetic Imprinting Analysis with RNA-Seq Data. In: Wang, Y., Sun, Ma. (eds) Transcriptome Data Analysis. Methods in Molecular Biology, vol 1751. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7710-9_14

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  • DOI: https://doi.org/10.1007/978-1-4939-7710-9_14

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7709-3

  • Online ISBN: 978-1-4939-7710-9

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