The protocol presented here can easily be applied to perform DRIP-seq instead
of the higher resolution, strand-specific, DRIPc-seq. DRIP-seq is technically less
demanding, requires less starting material, and is less time-consuming, but still
provides useful and robust information on R-loop distribution in a genome. We
typically recommend that users first successfully perform DRIP-seq before attempting
the DRIPc-seq protocol. The major difference between both methods pertains to the
construction of high-throughput sequencing libraries. An alternate library
construction step is provided for DRIP-seq. Other DRIP protocols have been published
recently and could be useful references for users 25 (link),26 (link).
R-loop mapping by DRIP-seq and DRIPc-seq enables users to measure the
steady-state distribution of R-loop structures in any genome of interest. More
importantly, these methods allow understanding global changes in R-loop distribution
or dynamics caused by genetic perturbations (gene knockouts or knockdowns) or by
chemical treatments (drugs, hormones). Published examples include the response of
human breast cancer cells to transcription induction by estrogen 27 or the consequences of silencing
DNA topoisomerase 1 in human HEK293 cells 28 (link). These techniques can be applied to any cell type for which
sufficient starting material can be obtained. R-loop profiles have been successfully
generated using DRIP-seq and or DRIPc-seq in murine cells 23 (link) and in Schizzosaccharomyces
pombe29 (link). DRIP methods, when followed by
quantitative PCR (qPCR) instead of high-throughput sequencing, are useful to
estimate the quantity of R-loops at specific loci in a cell population. This is best
expressed as the fraction of the input DNA that was immunoprecipitated (% input),
with no further normalization, as is commonly used in chromatin immunoprecipitation
(ChIP) assays (seeBox 1 ). In all
cases, and in particular when changes in R-loop patterns under perturbed conditions
are of interest, it is essential to profile gene expression globally in parallel to
performing DRIP analysis. Since R-loops form co-transcriptionally, true changes in
R-loop formation or resolution must be disentangled from simple changes caused by
transcriptional effects.
of the higher resolution, strand-specific, DRIPc-seq. DRIP-seq is technically less
demanding, requires less starting material, and is less time-consuming, but still
provides useful and robust information on R-loop distribution in a genome. We
typically recommend that users first successfully perform DRIP-seq before attempting
the DRIPc-seq protocol. The major difference between both methods pertains to the
construction of high-throughput sequencing libraries. An alternate library
construction step is provided for DRIP-seq. Other DRIP protocols have been published
recently and could be useful references for users 25 (link),26 (link).
R-loop mapping by DRIP-seq and DRIPc-seq enables users to measure the
steady-state distribution of R-loop structures in any genome of interest. More
importantly, these methods allow understanding global changes in R-loop distribution
or dynamics caused by genetic perturbations (gene knockouts or knockdowns) or by
chemical treatments (drugs, hormones). Published examples include the response of
human breast cancer cells to transcription induction by estrogen 27 or the consequences of silencing
DNA topoisomerase 1 in human HEK293 cells 28 (link). These techniques can be applied to any cell type for which
sufficient starting material can be obtained. R-loop profiles have been successfully
generated using DRIP-seq and or DRIPc-seq in murine cells 23 (link) and in Schizzosaccharomyces
pombe29 (link). DRIP methods, when followed by
quantitative PCR (qPCR) instead of high-throughput sequencing, are useful to
estimate the quantity of R-loops at specific loci in a cell population. This is best
expressed as the fraction of the input DNA that was immunoprecipitated (% input),
with no further normalization, as is commonly used in chromatin immunoprecipitation
(ChIP) assays (see
cases, and in particular when changes in R-loop patterns under perturbed conditions
are of interest, it is essential to profile gene expression globally in parallel to
performing DRIP analysis. Since R-loops form co-transcriptionally, true changes in
R-loop formation or resolution must be disentangled from simple changes caused by
transcriptional effects.
