Genome-wide analysis of replication timing by next-generation sequencing with E/L Repli-seq.
Claire MarchalTakayo SasakiDaniel L VeraKorey WilsonJiao SimaJuan Carlos Rivera-MuliaClaudia Trevilla-GarcíaCoralin NoguesEbtesam H O NafieDavid M GilbertPublished in: Nature protocols (2018)
This protocol is an extension to: Nat. Protoc. 6, 870-895 (2014); doi:10.1038/nprot.2011.328; published online 02 June 2011Cycling 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.
Keyphrases
- dna damage
- genome wide
- circulating tumor
- circulating tumor cells
- copy number
- dna methylation
- induced apoptosis
- randomized controlled trial
- flow cytometry
- single molecule
- cell free
- single cell
- cell cycle arrest
- high throughput
- rna seq
- gene expression
- electronic health record
- genome wide analysis
- transcription factor
- pet imaging
- big data
- health information
- healthcare
- endoplasmic reticulum stress
- data analysis
- cell death
- systematic review
- positron emission tomography
- quality improvement
- high intensity
- machine learning
- nucleic acid
- deep learning