Evidence map›Paper›PMID 37277562›Full record

ReviewNature protocols2023

Detect-seq, a chemical labeling and biotin pull-down approach for the unbiased and genome-wide off-target evaluation of programmable cytosine base editors.

Zhixin Lei, Haowei Meng, Xichen Rao, Huanan Zhao, Chengqi Yi

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zhixin Lei *Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
Haowei Meng *State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China.ORCID 0000-0001-9695-5060
Xichen RaoState Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China.ORCID 0000-0003-2450-4521
Huanan ZhaoSchool of Life Sciences, Tsinghua University, Beijing, China.ORCID 0000-0002-4934-4731
Chengqi YiPeking-Tsinghua Center for Life Sciences, Peking University, Beijing, China. chengqi.yi@pku.edu.cn.ORCID 0000-0003-2540-9729

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Programmable cytosine base editors show promising approaches for correcting pathogenic mutations; yet, their off-target effects have been of great concern. Detect-seq (dU-detection enabled by C-to-T transition during sequencing) is an unbiased, sensitive method for the off-target evaluation of programmable cytosine base editors. It profiles the editome by tracing the editing intermediate dU, which is introduced inside living cells and edited by programmable cytosine base editors. The genomic DNA is extracted, preprocessed and labeled by successive chemical and enzymatic reactions, followed by biotin pull-down to enrich the dU-containing loci for sequencing. Here, we describe a detailed protocol for performing the Detect-seq experiment, and a customized, open-source, bioinformatic pipeline for analyzing the characteristic Detect-seq data is also provided. Unlike those previous whole-genome sequencing-based methods, Detect-seq uses an enrichment strategy and hence is endowed with great sensitivity, a higher signal-to-noise ratio and no requirement for high sequencing depth. Furthermore, Detect-seq is widely applicable for both mitotic and postmitotic biological systems. The entire protocol typically takes 5 d from the genomic DNA extraction to sequencing and ~1 week for data analysis.

Indexed as

BiotinGene EditingCRISPR-Cas SystemsCytosineDNAGenomeBiotinCytosineDNA

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.