非モデル作物におけるDAP-seq実験実施のための標準化プロトコルとバイオインフォマティクスパイプライン

The cistrome comprises genomic loci that regulate gene expression, playing a crucial role in defining cellular identity and function. Analyzing cistrome data reveals key molecular mechanisms underlying grapevine growth, development, and environmental responses. Identifying transcription factors that bind specific DNA sequences allows researchers to dissect the complex regulatory networks controlling gene expression. Moreover, this analysis can help pinpoint targets for crop improvement, as traits like fruit quality, disease resistance, and abiotic stress tolerance are often regulated by transcription factors. DNA affinity purification sequencing (DAP-seq) is a high-throughput, cost-effective method for mapping the cistrome, providing valuable insights into transcriptional regulation. This technique relies on the in vitro affinity purification of genomic DNA-protein complexes, followed by high-throughput sequencing of eluted DNA fragments. Unlike other in vitro DNA-binding assays, such as protein-binding microarrays (PBM) and systematic evolution of ligands by exponential enrichment (SELEX), DAP-seq allows transcription factors to interact directly with plant-derived genomic DNA, capturing all potential binding sites. The resulting data closely resemble those from chromatin immunoprecipitation sequencing (ChIP-seq) but are obtained much faster. Initially developed in Arabidopsis, DAP-seq has since been applied to several crops, including maize, tomato, and grapevine, generating extensive cistrome datasets and deepening our understanding of gene regulatory regions. However, despite its power in elucidating crop biology, DAP-seq faces certain limitations, particularly concerning the size and complexity of plant genomes. This chapter presents detailed protocols for DAP-seq studies aimed at the unbiased identification of transcription factor binding sites in crops. Additionally, we outline a standardized pipeline for DAP-seq data analysis, encompassing raw sequencing data processing (i.e., trimming, filtering, and read alignment), as well as peak calling and motif discovery analysis. This approach enables the efficient and scalable identification of transcription factor binding profiles in diverse crop species.