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Genome-wide analysis of replication timing by next-generation sequencing with E/L Repli-seq

生物 DNA测序 遗传学 基因组 计算生物学 深度测序 基因组学 DNA 基因
作者
Claire Marchal,Takayo Sasaki,Daniel L. Vera,Korey A. Wilson,Jiao Sima,Juan Carlos Rivera‐Mulia,Claudia Trevilla‐García,Coralin Nogues,Ebtesam Nafie,David M. Gilbert
出处
期刊:Nature Protocols [Springer Nature]
卷期号:13 (5): 819-839 被引量:200
标识
DOI:10.1038/nprot.2017.148
摘要

This protocol describes production and bioinformatics analysis pipelines for E/L Repli-seq, an extension of the earlier Repli-chip protocol, allowing rapid genome-wide replication-timing analysis by next-generation sequencing. This protocol is an extension to: , 870–895 (2014); doi:10.1038/nprot.2011.328; published online 02 June 2011 Cycling cells duplicate their DNA content during S phase, following a defined program called replication timing (RT). Early- and late-replicating regions differ in terms of mutation rates, transcriptional activity, chromatin marks and subnuclear position. Moreover, RT is regulated during development and is altered in diseases. Here, we describe E/L Repli-seq, an extension of our Repli-chip protocol. E/L Repli-seq is a rapid, robust and relatively inexpensive protocol for analyzing RT by next-generation sequencing (NGS), allowing genome-wide assessment of how cellular processes are linked to RT. Briefly, cells are pulse-labeled with BrdU, and early and late S-phase fractions are sorted by flow cytometry. Labeled nascent DNA is immunoprecipitated from both fractions and sequenced. Data processing leads to a single bedGraph file containing the ratio of nascent DNA from early versus late S-phase fractions. The results are comparable to those of Repli-chip, with the additional benefits of genome-wide sequence information and an increased dynamic range. We also provide computational pipelines for downstream analyses, for parsing phased genomes using single-nucleotide polymorphisms (SNPs) to analyze RT allelic asynchrony, and for direct comparison to Repli-chip data. This protocol can be performed in up to 3 d before sequencing, and requires basic cellular and molecular biology skills, as well as a basic understanding of Unix and R.
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