How can we study RNA that is in the process of being synthesized? For over half a century, researchers have developed diverse approaches to isolate and sequence nascent RNA and reveal layers of transcriptional control that are invisible when capturing only mature RNA. For example, nascent RNA sequencing (RNA-seq) can provide insight into polymerase movement, transcription rates, and unstable or noncoding RNAs.
Historically, there are three major classes of nascent RNA-seq protocols. First, there are approaches that rely on nuclear run-on assays, such as global run-on-sequencing (GRO-seq). During the run-on assay, a nucleotide analog is incorporated into actively transcribed RNA to allow immunoprecipitation of nascent RNA. Second, nascent RNA can be captured by immunoprecipitating RNA polymerase II (Pol II), such as in plant native elongating transcript sequencing (pNET-seq). Finally, after chromatin isolation, Pol II can remain bound and chromatin-bound (CB) RNA can be sequenced (Weber et al. 2014). Each of these methods has strengths and weaknesses, making it difficult to choose the best approach for specific research needs.
In the latest issue of The Plant Cell, Min Liu and colleagues systematically compare the major nascent RNA-seq approaches—GRO-seq, pNET-seq, and CB RNA-seq—and provide detailed guidance for selecting a method.

Liu M, Zhu J, Huang H, Chen Y, Dong Z. (2023) Comparative analysis of nascent RNA sequencing methods and their applications in studies of cotranscriptional splicing dynamics. Plant Cell 35(12):4304-4324. [abstract]
How can we study RNA that is in the process of being synthesized? For over half a century, researchers have developed diverse approaches to isolate and sequence nascent RNA and reveal layers of transcriptional control that are invisible when capturing only mature RNA. For example, nascent RNA sequencing (RNA-seq) can provide insight into polymerase movement, transcription rates, and unstable or noncoding RNAs.
Historically, there are three major classes of nascent RNA-seq protocols. First, there are approaches that rely on nuclear run-on assays, such as global run-on-sequencing (GRO-seq). During the run-on assay, a nucleotide analog is incorporated into actively transcribed RNA to allow immunoprecipitation of nascent RNA. Second, nascent RNA can be captured by immunoprecipitating RNA polymerase II (Pol II), such as in plant native elongating transcript sequencing (pNET-seq). Finally, after chromatin isolation, Pol II can remain bound and chromatin-bound (CB) RNA can be sequenced (Weber et al. 2014). Each of these methods has strengths and weaknesses, making it difficult to choose the best approach for specific research needs.
In the latest issue of The Plant Cell, Min Liu and colleagues systematically compare the major nascent RNA-seq approaches—GRO-seq, pNET-seq, and CB RNA-seq—and provide detailed guidance for selecting a method.
Liu M, Zhu J, Huang H, Chen Y, Dong Z. (2023) Comparative analysis of nascent RNA sequencing methods and their applications in studies of cotranscriptional splicing dynamics. Plant Cell 35(12):4304-4324. [abstract]
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How can we study RNA that is in the process of being synthesized? For over half a century, researchers have developed diverse approaches to isolate and sequence nascent RNA and reveal layers of transcriptional control that are invisible when capturing only mature RNA. For example, nascent RNA sequencing (RNA-seq) can provide insight into polymerase movement, transcription rates, and unstable or noncoding RNAs.
Historically, there are three major classes of nascent RNA-seq protocols. First, there are approaches that rely on nuclear run-on assays, such as global run-on-sequencing (GRO-seq). During the run-on assay, a nucleotide analog is incorporated into actively transcribed RNA to allow immunoprecipitation of nascent RNA. Second, nascent RNA can be captured by immunoprecipitating RNA polymerase II (Pol II), such as in plant native elongating transcript sequencing (pNET-seq). Finally, after chromatin isolation, Pol II can remain bound and chromatin-bound (CB) RNA can be sequenced (Weber et al. 2014). Each of these methods has strengths and weaknesses, making it difficult to choose the best approach for specific research needs.
In the latest issue of The Plant Cell, Min Liu and colleagues systematically compare the major nascent RNA-seq approaches—GRO-seq, pNET-seq, and CB RNA-seq—and provide detailed guidance for selecting a method.
Liu M, Zhu J, Huang H, Chen Y, Dong Z. (2023) Comparative analysis of nascent RNA sequencing methods and their applications in studies of cotranscriptional splicing dynamics. Plant Cell 35(12):4304-4324. [abstract]
Related Posts
Benchmarking RNA sequencing for more accurate alternative splicing analysis
RNA Sequencing identifies new tick-borne virus that causes flu-like illness
Small RNA sequencing reveals regulatory roles for sdRNAs in acute myeloid leukemia
POND-seq enables non-destructive RNA sequencing in living cells
Worm’s radical transformation shows metamorphosis can change the functions of cells
New method allows scientists to follow gene activity over time in the same cells
Single-cell and single-embryo RNA sequencing
RNA sequencing reveals functional chimeric mRNAs in mammalian immunity
Deep learning improves microRNA target prediction from sequence
Atlas of the brain’s striatum could guide researchers to new drug treatments
scLS – a computationally efficient differentially expressed gene detection algorithm
Spatial mapping of RNA turnover kinetics in the mouse brain
Immune cells offer insights on billion-dollar virus
SPIDER improves spatial transcriptomics data using single-cell RNA sequencing
Ultrafast and reference-free sequence discovery in single-cell data
ARCADIA combines RNA sequencing and spatial proteomics to reveal how tissue location shapes cell behavior
An end-to-end computational framework for “Record-seq” transcriptional recording data
A functionally integrated cross-tissue alternative splicing program during short-term calorie restriction
ExoShorkie – predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning
Dietary oxidized plant sterol shifts macrophage state to fuel aortic inflammation
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