Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 285

The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.

High-resolution, strand-specific R-loop mapping via S9.6-based DNA–RNA immunoprecipitation and high-throughput sequencing

Open access
Sanz, Lionel A., Chédin, Frédéric·2019·Nature Protocols

Abstract

R-loops are prevalent three-stranded non-B DNA structures composed of an RNA–DNA hybrid and a single strand of DNA. R-loops are implicated in various basic nuclear processes, such as class-switch recombination, transcription termination and chromatin patterning. Perturbations in R-loop metabolism have been linked to genomic instability and have been implicated in human disorders, including cancer. As a consequence, the accurate mapping of these structures has been of increasing interest in recent years. Here, we describe two related immunoprecipitation-based methods for mapping R-loop structures: basic DRIP-seq (DNA–RNA immunoprecipitation followed by high-throughput DNA sequencing), an easy, robust, but resolution-limited technique; and DRIPc-seq (DNA–RNA immunoprecipitation followed by cDNA conversion coupled to high-throughput sequencing), a high-resolution and strand-specific iteration of the method that permits accurate R-loop mapping genome wide. Briefly, after gentle DNA extraction and restriction digestion with a cocktail of enzymes, R-loop structures are immunoprecipitated with the anti-RNA–DNA hybrid S9.6 antibody. Compared with DRIP-seq, in which the immunoprecipitated DNA is directly sequenced, DRIPc-seq permits the recovery of the RNA moiety of R-loops, and these RNA strands are subjected to strand-specific RNA sequencing (RNA-seq) analysis. DRIPc-seq can be performed in 5 d and can be applied to any cell type, provided sufficient starting material can be collected. Accurately mapping R-loop distribution in various cell lines and under varied conditions is essential to understanding the formation, roles and dynamic resolution of these important structures. R-loops, DNA–RNA hybrid structures found throughout the genome, are relevant to both normal and disease states. DRIPc-seq, based on immunoprecipitation with the S9.6 antibody recognizing DNA–RNA hybrids, permits genome-wide mapping of R-loops.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41596-019-0159-1

Cites

49 of 49 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 49 references

Cited by

See all 285

Metadata

Authors
Sanz, L. A., & Chédin, F.
Venue
Nature Protocols
Published
2019-05-03
Volume
14
Issue
6
Pages
1734-1755
ISSN
  • 1750-2799 (ISSN-L)
  • 1754-2189
OpenAlex
W2943579502
Cited by
285
References
49
FWCI
6.95
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: High-resolution, strand-specific R-loop mapping via S9.6-based DNA–RNA immunoprecipitation and high-throughput sequencing. https://app.cochat.ai/papers/10.1038/s41596-019-0159-1
The paper
Sanz, L. A., & Chédin, F. (2019). High-resolution, strand-specific R-loop mapping via S9.6-based DNA–RNA immunoprecipitation and high-throughput sequencing. Nature Protocols. https://doi.org/10.1038/s41596-019-0159-1

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.