Quantifying RNA synthesis at rate-limiting steps of transcription using nascent RNA-sequencing data

Summary

Nascent RNA-sequencing tracks transcription at nucleotide resolution. The genomic distribution of engaged transcription complexes, in turn, uncovers functional genomic regions. Here, we provide analytical steps to (1) identify transcribed regulatory elements de novo genome-wide, (2) quantify engaged transcription complexes at enhancers, promoter-proximal regions, divergent transcripts, gene bodies, and termination windows, and (3) measure distribution of transcription machineries and regulatory proteins across functional genomic regions. This protocol tracks engaged transcription complexes across functional genomic regions demonstrated in human K562 erythroleukemia cells.

For complete details on the use and execution of this protocol, please refer to Vihervaara et al. (2021).

Graphical abstract

Highlights

• •Identification of transcribed regulatory elements de novo genome-wide
• •Quantification of engaged transcription complexes at functional genomic regions
• •Measuring distribution of transcription regulators across the functional genomic regions
• •Revealing functional genomic regions from nascent transcription data

Nascent RNA-sequencing tracks transcription at nucleotide resolution. The genomic distribution of engaged transcription complexes, in turn, uncovers functional genomic regions. Here, we provide analytical steps to (1) identify transcribed regulatory elements de novo genome-wide, (2) quantify engaged transcription complexes at enhancers, promoter-proximal regions, divergent transcripts, gene bodies, and termination windows, and (3) measure distribution of transcription machineries and regulatory proteins across functional genomic regions. This protocol tracks engaged transcription complexes across functional genomic regions demonstrated in human K562 erythroleukemia cells.

Before you begin

Nascent transcription reveals functional genomic regions

Transcription is a fundamental process in every organism, and its coordination defines RNA synthesis in the cell. A plethora of transcription factors, cofactors, chromatin remodelers and RNA-processing complexes orchestrate the process of RNA synthesis (reviewed in Wissink et al., 2019). The transcriptional regulators interact, directly or indirectly, with functional genomic regions, including promoters, enhancers, gene bodies and termination windows. Each cell executes a transcription program that reflects the cellular identity, cellular functions and the intra- and extracellular signaling pathways (reviewed in Zeitlinger and Stark, 2010; Vihervaara et al., 2018). Consequently, the repertoire of active genes and regulatory elements differs in distinct cells, and the activity of functional genomic regions can rapidly adapt to changes in intra- and extra-cellular conditions.

Enhancers are distal regulatory elements that generally produce short and unstable transcripts called enhancer RNAs (eRNAs; Kim et al., 2010). Enhancer transcription has been shown to precede activation of genes (Kim et al., 2010; (Kaikkonen et al., 2013); Arner et al., 2015), and eRNA production moderately correlates with the target genes’ activities ((Henriques et al., 2018); Mikhaylichenko et al., 2018). Intriguingly, promoters and enhancers display a unified transcriptional architecture where two core initiation regions drive transcription into opposing directions (Core et al., 2008, 2014, reviewed in Andersson and Sandelin, 2020). This pattern of divergent transcription conveys function to enhancers (Tippens et al., 2020), and it allows identification of transcribed regulatory elements de novo (Danko et al., 2015; Azofeifa and Dowell, 2017; Vihervaara et al., 2017; Chu et al., 2018; Wang et al., 2019).

Transcriptional activity from genes and enhancers is controlled at the level of local chromatin environment, recruitment and progression of RNA Polymerase II (Pol II) complexes, and the three-dimensional architecture of the linear DNA molecules (reviewed in Field and Adelman, 2020; Schoenfelder and Fraser, 2019). The regulatory signals at the chromatin culminate at rate-limiting steps of transcription (Figure 1), constituting of 1) chromatin opening, 2) assembly of the Pre-Initiation Complex, 3) initiation of transcription, 4) promoter-proximal Pol II pausing, 5) promoter-proximal Pol II pause-release, 6) productive elongation, 7) co-transcriptional RNA processing, 8) transcript cleavage, 9) transcription termination, and 10) recycling of Pol II (reviewed in Fuda et al., 2009; Wissink et al., 2019). The rate-limiting steps of transcription occur at distinct genomic regions (Figure 1). Consequently, assays that track nascent transcription in a population of cells, display characteristic ‘signatures’ of engaged Pol II molecules at transcribed enhancers, active promoters, transcription start sites (TSSs), promoter-proximal pause-regions, gene bodies, cleavage and polyadenylation sites (CPSs), and termination windows (Figures 1B–1D). Here, we provide data-analytical steps to identify transcriptionally active functional genomic regions in the investigated condition. The nucleotide-resolution power of nascent RNA sequencing is further harnessed to estimate the number of engaged Pol II complexes at each identified functional genomic region. Quantifying engaged Pol II complexes and transcriptional regulators at distinct genomic regions provides a framework for detailed analyses of nascent transcription and its regulation.

Figure 1.

Steps of transcription occur at distinct functional genomic regions

(A) Schematic presentation of rate-limiting steps of transcription. 1) Closed chromatin is opened by pioneer transcription factors (orange triangle) and chromatin remodelers. 2) Pre-initiation complex (PIC), including TBP and TFIIF, assembles at the core promoters and positions Pol II to initiation. 3) Pol II initiates transcription, 4) pauses at the promoter-proximal region, and 5) is released by P-TEFb into 6) productive elongation. 7) Co-transcriptional processes, such as 5′-capping and splicing, occur while Pol II progresses through the gene. At the end of the gene, 8) the transcript is cleaved and polyadenylated (polyA). 9) XRN2 degrades the uncapped transcript, chasing Pol II and causing transcription to terminate. 10) Pol II is released from the chromatin and recycled into new initiation.

(B) Profile of nascent transcription at the distinct regions of a typical gene in humans. Transcription profile at (C) promoter and (D) enhancer. The gene in (B) and (C) is RPSA, and the enhancer in (D) is one of the functionally verified enhancers of the beta-globin locus control element.

Obtaining datasets of nascent RNA sequencing

This protocol relies on strand-specific mapping of RNA synthesis. The strand-specificity is essential for identification of active promoters and enhancers de novo based on their pattern of divergent transcription (Core et al., 2008, 2014; Danko et al., 2015; Seila et al., 2008; Tippens et al., 2018, 2020; Vihervaara et al., 2017; Wang et al., 2019). Besides strand-specific mapping of RNA synthesis, the precise positions of active sites of transcription are required to quantify engaged Pol II complexes at individual functional genomic regions. Libraries of nascent transcripts can be prepared with different techniques (reviewed in Wissink et al., 2019). We use and recommend nascent transcription data obtained with Precision Run-On sequencing (PRO-seq; Kwak et al., 2013), which marks active sites of transcription with a biotin-coupled nucleotide and isolates nascent transcripts from other cellular RNAs with a streptavidin pulldown (Figure 2A). Consequently, PRO-seq provides a purified population of nascent RNAs and a single-nucleotide resolution of the active sites of transcription genome-wide. With certain limitations and caution (Wissink et al., 2019; Wang et al., 2019), the protocol reported here can also be applied to data derived from other strand-specific techniques, such as Global Run-On sequencing (GRO-seq; Core et al., 2008) and mammalian NET-seq (Mayer et al., 2015; Nojima et al., 2015). In mNET-seq, antibodies are utilized to pulldown Pol II, and subsequently, the accompanying RNA is sequenced. This strategy works in general well, but can bring down also non-nascent RNAs, such as spliceosomal RNA (Nojima et al., 2018) or other chromatin-associated RNA species (reviewed in Li and Fu, 2019). GRO-seq utilizes BrdU-labelled nucleotides that allow Pol II to run on longer than one base (Core et al., 2008), providing a method to pull down nascent transcripts and map the active sites of transcription in estimated 30-50nt resolution (Kwak et al., 2013). Furthermore, techniques that detect transcription initiation, such as Start-seq (Nechaev et al., 2010) and PRO-cap (Mahat et al., 2016a), can be used for mapping the functional genomic regions, but since they do not track transcription beyond the initiation sites, they do not allow quantification of engaged Pol II complexes across the distinct genomic regions.

Figure 2.

Generating nucleotide-resolution maps of nascent transcription

(A) Schematic overview of PRO-seq library preparation. A biotinylated nucleotide is incorporated into each active site of transcription. The incorporated biotin-nucleotides allow isolation and purification of nascent transcripts. Adapters are ligated and the nascent transcripts are reverse transcribed. Active sites of transcription are illustrated at two example reads encoded by the plus (red) and the minus (blue) strands.

(B) Left panel: Genomic coordinates of each mapped read (chromosome, start coordinate, end coordinate) are reported in a bed file. The coordinate representing the active site of transcription is indicated for two schematic reads, and boxed for each read in the example bed file. Right panel: The active sites of transcription are extracted from the coordinates of whole mapped reads and reported in a new bed file.

(C) Active sites of transcription are counted at each genomic coordinate. The resulting coverage files report the density of engaged transcription complexes at each nt. The left panel shows engaged transcription at MYC. The middle panel is an inset of the MYC promoter, and the right panel shows Pol II counts at individual nts of its promoter-proximal region.

Required data format of nascent RNAs

Wet-lab protocols (Mahat et al., 2016a) and computation pipelines (https://github.com/Vihervaara/PRO-seq-analyses; https://github.com/Danko-Lab/tutorials/blob/master/PRO-seq.md) for PRO-seq have been reported. Here, we begin with sequenced and mapped PRO-seq reads in bed format (Figure 2B) and strand-specific coverage files in bigWig format (Figure 2C). The bed format is a human-readable matrix that reports the genomic coordinates of each mapped read (single-end sequencing) or fragment (paired-end sequencing). Each row in the bed file lists one aligned entity (read or fragment) while the columns report its genomic coordinates (chromosome, start nucleotide, end nucleotide) and the strand that encodes the transcript (Figure 2B, left panel). For simplicity, we refer to the mapped entities as reads regardless of whether they were derived from single-end or paired-end sequencing. Coverage files include human-readable bedgraph (Figure 2C) and unreadable bigWig files where each genomic coordinate gets a value of intensity, reporting the count of transcription complexes at each nucleotide. Missing values equal to zero. We exemplify the protocol using PRO-seq data generated in human K562 cells (Vihervaara et al., 2021), remapped against the latest hg38 reference genome. The bigWig and bed files, used here as input (see steps 1 and 13, respectively), can be directly obtained from Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) using accession code GSE181161.

Downloading RefGene file with coordinates of gene transcripts

We derive functional genomic regions from the profile of active transcription and the refGene-annotated coordinates of gene transcripts. The refGene list of transcripts can be downloaded from the UCSC genome golden path: http://hgdownload.soe.ucsc.edu/goldenPath/. Here, the human hg38 file (2020-08-17) is downloaded in step 4 using wget (https://www.gnu.org/software/wget/).

De novo identification of active enhancers

Enhancers can be identified de novo using the pattern of divergent transcription (Danko et al., 2015; Azofeifa and Dowell, 2017; Vihervaara et al., 2017; Chu et al., 2018; Wang et al., 2019). In this protocol, active promoters and enhancers are identified using discriminative Regulatory-Element detection from Global run-on sequencing (dREG; Wang et al., 2019). dREG inputs unnormalized bigWig files of PRO-seq data and outputs coordinates of divergent transcription. dREG can be used either via the web interface (https://django.dreg.scigap.org/) or installed locally (https://github.com/Danko-Lab/dREG). For broad accessibility, the web-based tool is used here. Unnormalized bigWig files are generally provided with PRO-seq data. If needed, the code below converts a bed file reporting the whole read (Figure 2B, left panel) into two 3′ coverage bedgraph files (Figure 2C). One of the bedgraph files reports active sites of transcription at the plus strand, the other at the minus strand. The bedgraph files are further processed to human unreadable bigWig format with the bedgraphToBigWig tool (https://www.encodeproject.org/software/bedgraphtobigwig/). The required chrSizes.txt file is a two-column data frame that contains the chromosome names and the sizes (in nucleotides, nts), obtainable for the appropriate genome from http://hgdownload.cse.ucsc.edu/goldenpath/.

awk ‘$6 == "+"’ PROseq.bed | genomeCoverageBed -i stdin -3 -bg -g chrSizes.txt > PROseq_pl.bedgraph

awk ‘$6 == "-"’ PROseq.bed | genomeCoverageBed -i stdin -3 -bg -g chrSizes.txt > PROseq_temp.bedgraph

awk ‘{$4=$4∗-1; print}’ PROseq_temp.bedgraph > PROseq_mn.bedgraph

bedgraphToBigWig PROseq_pl.bedgraph chrSizes.txt PROseq_pl.bigWig

bedgraphToBigWig PROseq_mn.bedgraph chrSizes.txt PROseq_mn.bigWig

Analyzing genomic distribution of transcriptional regulators

Regulatory proteins coordinate every step of transcription. In this protocol, the functional genomic regions are first identified and color-coded to show enhancer regions in green, sites of divergent transcription (div) in purple, promoter-proximal regions (pp) in orange, gene body (gb) in black, region around CPS in light blue and termination window (tw) in pink (Figure 3A and 3B). After identification of the functional genomic regions, the number of engaged Pol II molecules at each region is derived from the PRO-seq data (Figures 3C and 3D). Finally, the protocol analyzes distribution of selected transcriptional regulators across the functional genomic regions (Figure 3E). Generating datasets for transcription factor binding sites is beyond the scope of this protocol. However, multiple techniques have been established to study protein and RNA localization to the genome, and raw data and peak coordinates for many chromatin-associated proteins can be obtained from databases such as ENCODE, modENCODE and GEO. We exemplify the localization of transcriptional regulators and chromatin marks using chromatin immunoprecipitation (ChIP)-seq data (Consortium EP, 2011). The factors and histone marks analyzed here are TATA Box Binding Protein (TBP) , which is a component of the Pre-Initiation Complex, cell type-specific factor GATA1 , Negative Elongation Factor (NELF) that controls Pol II pausing, Cohesin Complex Component RAD21, architectural protein CTCF, histone acetyltransferase p300 and histone mark H3K36me3 (Figure 4). Steps to download the raw data and generate peak summits are found in GitHub (https://github.com/Vihervaara/ChIP-seq_analyses).

Figure 3.

From identification of functional genomic regions to analyses of nascent transcription and transcription regulation

(A) Schematic illustrations of the pattern of engaged Pol II at genes and enhancers. Sites of divergent transcription are identified de novo using dREG (Wang et al., 2019). Divergent transcription that does not overlap with any annotated TSS (transcription start sites) of any gene is counted as an enhancer. Annotated genes with divergent transcription at the TSS are actively transcribed genes.

(B) Active genes are divided into: upstream divergent transcription (−750 to −250 nt from the TSS; purple bar), promoter-proximal region (−249 to +250 nt from the TSS; orange bar), gene body (+251 from the TSS to −500 from the CPS; black bar), CPS (−499 to +500 nt from the CPS; light blue bar) and termination window (+501 to +10500 nt from the CPS).

(C) Engaged transcription complexes are counted at each identified functional genomic region. The count can be a raw count in the given library (exemplified here) or a normalized count that enables comparison between libraries (not shown).

(D) The count of engaged Pol II complexes and (E) transcription factor binding sites uncovers the distribution of nascent transcription and its regulators across the genome. See the graphical abstract for illustration of the percentages in (D) and (E). TBP: TATA box-binding protein. The TBP ChIP-seq data is from Consortium EP (2011).

Figure 4.

Quantifying nascent transciption complexes and regulatory protein enrichments across functional genomic regions.

Expected results include (A) identification of active genes and enhancers, (B) quantification of nascent transcription complexes at functional genomic regions, and (C) tracking genomic distribution of transcription regulators

(A) Functional genomic regions identified at the UBE2T and PPP1R12B genes.

(B) Counts of nascent transcription complexes at hemoglobin beta 1 (HBE1) gene and the locus control region (LCR) of beta globin genes for two different conditions: non heat shocked cells, NHS and 30 min heat shocked cells, HS30. The identified enhancers (green bars) represent the four functionally verified enhancers at the LCR in K562 cells.

(C) Localization of selected chromatin-associating factors across the functional genomic regions. The brackets above the bars indicate the proportion of the factor peaks at transcribed regulatory elements (enhancers, upstream divergent transcription, and promoter-proximal regions). The color coding of the bar is as in (A), the additional gray regions indicates regions that were neither called as genes nor as enhancers (untranscribed / unknown). NELFe: negative elongation factor, subunit e. p300: histone acetyltransferase p300. RAD21: RAD21 Cohesin Complex Component. ChIP-seq data in figure C is from Consortium EP (2011).

Obtaining data-analytical tools

The data-analyses reported here are conducted in the command line environment of the Apple OS X operating system. Lists of genomic coordinates are processed in R (https://www.r-project.org) and coordinates of functional genomic regions are compared to active sites of nascent transcription using bedtools ((Quinlan and Hall, 2010); https://bedtools.readthedocs.io/en/latest/).

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Deposited data
PRO-seq	Raw data Vihervaara et al. (2021). Remapping to hg38 in this study.	Raw data: GSE127844. Remapped files: GSE181161
TBP ChIP-seq raw data	Consortium EP, 2011	https://www.ncbi.nlm.nih.gov/sra?term=SRX150574
CTCF ChIP-seq raw data	Consortium EP, 2011	https://www.ncbi.nlm.nih.gov/sra?term=SRX150487
RAD21 ChIP-seq raw data	Consortium EP, 2011	https://www.ncbi.nlm.nih.gov/sra/?term=SRR502023
H3K36me3 ChIP-seq raw data	Consortium EP, 2011	https://www.ncbi.nlm.nih.gov/sra?term=SRX067465
p300 ChIP-seq raw data	Consortium EP, 2011	https://www.ncbi.nlm.nih.gov/sra/?term=SRR502192
NELFe ChIP-seq raw data	Consortium EP, 2011	https://www.ncbi.nlm.nih.gov/sra/?term=SRR502172
GATA1 ChIP-seq raw data	Consortium EP, 2011	https://www.ncbi.nlm.nih.gov/sra?term=SRX186613
Input ChIP-seq raw data	Consortium EP, 2011	https://www.ncbi.nlm.nih.gov/sra?term=SRX150443
Software and algorithms
Bedtools	(Quinlan and Hall, 2010)	v2.29.2
Sra tools (fastq-dump)	https://ncbi.github.io/sra-tools/fastq-dump.html	2.8.0
R	https://www.r-project.org	4.1.0
dREG	Wang et al. (2019)	https://django.dreg.scigap.org/
bedGraphToBigWig	https://www.encodeproject.org/software/bedgraphtobigwig/	v 4
IGV	Thorvaldsdóttir et al. (2013)	http://software.broadinstitute.org/software/igv/
kbro	Hojoong Kwak, Cornell University, NY, USA.	2011
Other
Human hg38 refGene file	UCSC downloads	http://hgdownload.soe.ucsc.edu/goldenPath/hg38/database/refGene.txt.gz
Output files generated in this study	GitHub	https://github.com/Vihervaara/functionalGenomicRegions

Step-by-step method details

Outline of the protocol

This protocol begins with identification of transcribed regulatory elements (regions initiating divergent transcription) de novo (Figure 3A). Subsequently, the coordinates of transcribed regulatory elements are intersected with annotated TSSs of gene transcripts (Figure 3A). Sites of divergent transcription that do overlap with TSSs of annotated genes indicate actively transcribed genes in the investigated condition. Sites of divergent transcription that do not occur in the vicinity of any annotated TSS of a gene are distal Transcribed Regulatory Elements (dTREs), also called enhancer candidates. We and others have previously verified that enhancer candidates identified from PRO-seq data contain marks of active enhancers, loop to gene promoters, and capture functionally verified enhancers (see Vihervaara et al., 2017, 2021; Wang et al., 2019). For simplicity, we hereon refer to the enhancer candidates as enhancers.

Enhancer coordinates reported here are the dREG-identified sites of divergent transcription that do not overlap with any annotated promoter of a gene. Active genes, instead, are further divided into distinct regions (Figure 3B). A browser-compatible bed file is generated to visualize every functional genomic region, color-code each region according to the functional category, and display the count of engaged Pol II molecules at the region (Figure 3C). The count obtained here is the raw count of 3′-nts of PRO-seq reads (active sites) in the given dataset. This raw count can be normalized to allow comparison between datasets and account for different sequencing depths. Finally, distribution of engaged Pol II (Figure 3D) and selected regulatory proteins (Figure 3E) across the functional genomic regions are analyzed. For simplicity, the pipeline reported here uses a single condition of human K562 cells cultured in optimal growth conditions. The pipeline as shell and R scripts, as well as the output files generated here, are available via GitHub (https://github.com/Vihervaara/functionalGenomicRegions).

Run dREG to localize transcribed enhancers and active promoters

Timing: 1–3 h

This section identifies transcribed regulatory elements in the investigated cell line and condition.

• 1.
  Create an account at https://django.dreg.scigap.org/. Log in.

  • a.
    Choose dREG peak calling.
  • b.
    Upload the unnormalized 3′-coverage bigWig plus strand file to the correct box.
  • c.
    Upload the unnormalized 3′-coverage bigWig minus strand file to the correct box.
    Please note that the bigWig files need to be unnormalized (minimum value +1 for the plus strand and -1 for the minus strand).
  • d.
    Choose a prefix (here ’K562_hg38′) to describe your run and press Launch. The run time depends on the size of the file and available processing capacity, commonly ranging 1–3h.

• 2.When the run is complete, download the prefix.dREG.peak.full.bed.gz file and gunzip it.
• 3.Move the downloaded file to the working directory and rename it to a simpler form:

mv ∼/Downloads/K562_hg38.dREG.peak.full.bed /pathToWorkingDirectory/dREGcalls_hg38_K562.bed

Generate coordinates of functional regions

Timing: 30 min

These steps (4–6) divide genes into functional regions.

• 4.Download RefGene datafile, gunzip it and open it in R.

wget -c -O hg38.refGene.txt.gz http://hgdownload.soe.ucsc.edu/goldenPath/hg38/database/refGene.txt.gz

gunzip hg38.refGene.txt.gz

R

refGene = read.table("hg38.refGene.txt", header=F)

dim(refGene) #gives the number of rows and columns in the refGene dataframe

head(refGene)  #shows the first six rows of the dataframe

str(refGene) #shows information on each dimension

• 5.Name the refGene columns and remove unnecessary columns and chromosome entries.

names(refGene) =c("bin", "txID", "chr", "strand", "txStart", "txEnd","cdsStart", "cdsEnd", "exonCount", "exonStarts", "exonEnds", "score", "geneName", "cdsStartStat", "cdsEnsStat", "exonFrames")

refGene = refGene[-grep("_", refGene$chr),]  #maintains chromosomes 1-22, X, Y and M.

refGene$chr = factor(refGene$chr)  #drops the extra levels removed above.

refGene = refGene[,c(3,5:6,4,13,2)]

head(refGene)  #look at the dataframe again

• 6.Generate coordinates of functional regions for every annotated gene transcript.

In the RefGene data file, the ‘txStart’ is smaller than the same transcript’s ‘txEnd’. This is convenient when working with the coordinates of genes, but it leads to different columns reporting different functional sites depending on whether the plus or the minus strand encodes the gene. For example, the annotated TSS for genes on the plus strand is reported as txStart, while the TSS for genes on the minus strand is reported as txEnd. In the following steps, we add a column ‘TSS’ that reports the annotated TSS for each transcript. We then output a file that reports a 1000-nt window around the TSS. These windows of 1000 nt are intersected with the coordinates of dREG-identified sites of divergent transcription in step 7 to identify genes with active transcription and distal sites of divergent transcription, i.e., enhancers. We also obtain transcript-specific coordinates of promoter-proximal region (PP), divergent transcription (DIV), gene body (GB), CPS, and termination window (TW) according to the scheme in Figure 3B.

### Subset the genes based on the strand:

refGene_pl = subset(refGene, strand=="+")

refGene_mn = subset(refGene, strand=="-")

### Genes on the plus strand:

refGene_pl$TSS = refGene_pl$txStart # TSS

refGene_pl$CPS = refGene_pl$txEnd  # CPS

refGene_pl$DIVs = refGene_pl$txStart-750 # region of divergent transcription

refGene_pl$DIVe = refGene_pl$txStart-251

refGene_pl$PPs = refGene_pl$TSS-250  # promoter-proximal region

refGene_pl$PPe = refGene_pl$TSS+249

refGene_pl$GBs = refGene_pl$TSS+250  # genebody

refGene_pl$GBe = refGene_pl$CPS-501

refGene_pl$CPSs = refGene_pl$CPS-500  # CPS region

refGene_pl$CPSe = refGene_pl$CPS+499

refGene_pl$TWs = refGene_pl$CPS+500  # termination window

refGene_pl$TWe = refGene_pl$CPS+10499

#### Genes on the minus strand:

refGene_mn$TSS = refGene_mn$txEnd  # TSS

refGene_mn$CPS = refGene_mn$txStart  # CPS

refGene_mn$DIVs = refGene_mn$txEnd+251  # divergent transcription region

refGene_mn$DIVe = refGene_mn$txEnd+750

refGene_mn$PPs = refGene_mn$TSS-249  # promoter-proximal region

refGene_mn$PPe = refGene_mn$TSS+250

refGene_mn$GBs = refGene_mn$CPS+501  # genebody

refGene_mn$GBe = refGene_mn$TSS-250

refGene_mn$CPSs = refGene_mn$CPS-499  # CPS region

refGene_mn$CPSe = refGene_mn$CPS+500

refGene_mn$TWs = refGene_mn$CPS-10499  # termination window

refGene_mn$TWe = refGene_mn$CPS-500

#### combine the data of plus and minus stands:

refGene = rbind(refGene_pl, refGene_mn)

refGene$promC1 = refGene$TSS-500

refGene$promC2 = refGene$TSS+500

#### generate data files:

write.table(refGene, file="hg38_refGene_allTranscripts.txt", col.names=F, row.names=F, quote=F, sep="\t")

write.table(refGene[,c("chr","promC1","promC2","txID","geneName")], file="hg38_refGenes_TSSpm500.txt", col.names=F, row.names=F, quote=F, sep="\t")

save.image()      # saves the above entries in the R

q()       # exits R

y        # answers ‘yes’ for saving the workspace image

Please note that an R script comprising the steps from reading the refGene file in step 4 to the end of step 6 is provided in GitHub (https://github.com/Vihervaara/functionalGenomicRegions).

Identify active promoters and transcribed enhancers

Timing: 10 min

This step sorts transcribed regulatory elements into active genes and transcribed enhancers.

• 7.
  Intersect the dREG-identified regulatory regions with the 1000-nt window around TSSs.

  • a.
    Identify transcribed enhancers. These regulatory elements do not localize to any annotated TSS of a gene in the genome. The option -v directs the bedtools to output only entries in the file -a that do not show any overlap with coordinates given in file -b. The generated file contains the coordinates of transcribed enhancers.

    bedtools intersect -v -a dREGcalls_hg38_K562.bed -b hg38_refGenes_TSSpm500.txt > enhancers.bed
  • b.
    Identify annotated genes with divergent transcription at the promoter. These are genes that have transcriptional activity in the investigated condition. The argument -wa maintains the entries in -a that have any overlap with -b. The argument -u controls that each entry in -a is written only once even if multiple dREG peaks would overlap with the same promoter. The generated file contains coordinates of gene TSSs that have transcriptional activity.

    bedtools intersect -u -wa -a hg38_refGenes_TSSpm500.txt -b dREGcalls_hg38_K562.bed > activeGenes_hg38_K562.bed

Write files of functional genomic coordinates

Timing: 10 min

This section generates files that contain the coordinates of functional genomic regions.

• 8.Return to R and read in the file with the TSSs of transcribed genes

R

Active = read.table("activeGenes_hg38_K562.bed")

• 9.Generate a new data frame in R that contains only actively transcribed genes. In essence, the ‘refGene’ data frame generated in step 6 is reduced here to contain only gene transcripts which initiate transcription. The ‘txID’ column contains an individual identification code for each transcript variant.

refGeneAct = subset(refGene, txID %in% Active[,4])

• 10.Write files that contain the coordinates of promoter-proximal and divergent transcription regions. These coordinates were generated in step 6.

write.table(refGeneAct[,c("chr","PPs","PPe","geneName","txID","strand")], file="ppPolII.txt", col.names=F, row.names=F, quote=F, sep="\t")

write.table(refGeneAct[,c("chr","DIVs","DIVe","geneName","txID","strand")], file="divTx.txt", col.names=F, row.names=F, quote=F, sep="\t")

• 11.Remove short genes before generating files with the gene body coordinates. This stage is needed to omit gene transcripts, where, due to shortness of the gene, the gene body would overlap with the promoter-proximal region (stretching to +500 from TSS) and CPS (starting from −500 from CPS) windows.

shortGenes = subset(refGene, txEnd-txStart<=750)

refGeneAct_ = subset(refGeneAct, txEnd-txStart>750)

#in K562 cells mapped against hg38, 351 active genes are removed at this stage.

• 12.Write the files for the CPSs, gene body coordinates and transcription windows.

write.table(refGeneAct_[,c("chr","CPSs","CPSe","geneName","txID","strand")], file="CPS.txt", col.names=F, row.names=F, quote=F, sep="\t")

write.table(refGeneAct_[,c("chr","TWs","TWe","geneName","txID","strand")], file="TW.txt", col.names=F, row.names=F, quote=F, sep="\t")

refGeneAct_ = subset(refGeneAct_,GBe-GBs>1) #ensuring no negative gene body lengths remain.

write.table(refGeneAct_[,c("chr","GBs","GBe","geneName","txID","strand")], file="geneBody.txt", col.names=F, row.names=F, quote=F, sep="\t")

save.image()

q()

y

Of note: In the RefGen for hg38 file, short genes constitute 3,575 transcripts. Of these, only 351 genes were uniquely mappable, identified as ‘Active’ and, therefore, removed from our list of active gene transcripts in K562 cells.

The steps 8–12 can also be run via an R script provided in GitHub (https://github.com/Vihervaara/functionalGenomicRegions).

Allocate nascent transcription complexes to functional genomic regions

Timing: 20 min

In steps 13 and 14, active sites of transcription are allocated to individual functional genomic regions. The active sites of transcription are derived from the nascent transcription sequencing data.

• 13.
  Generate a bed file that only contains the 3′-nucleotide (active site of transcription) of each read (Figure 2B). In the generated 3pnt.bed file (right panel in Figure 2B), each RNA Polymerase position is reported as an individual row.

  • a.
    Split the file based on the strand of the mapped read

    echo retaining 3prime most coordinate of the bed file

     awk ‘$6 == "+"’ PROseq_K562_hg38.bed > tempPL.bed

     awk ‘$6 == "-"’ PROseq_K562_hg38.bed > tempMN.bed
  • b.
    Active site of transcription (3′-most nt) is the coordinate given in the third column of plus strand reads and the second column for minus strand reads. In this step, the active site of transcription (single nt) will be placed both to the second and the third column.

     awk ‘{$2 = $3; print}’ tempPL.bed > tempPL_3p.bed

     awk ‘{$3 = $2; print}’ tempMN.bed > tempMN_3p.bed
  • c.
    The reads from plus and minus strands are combined, the data is converted to tab-delimited, and the reads sorted based on genomic coordinates. Intermediary files are removed.

     cat tempPL_3p.bed tempMN_3p.bed | tr ‘ ‘ ‘\t‘ > temp_3p.bed

     sortBed -i temp_3p.bed > PROseq_K562_hg38_3pnt.bed

     rm ∗temp∗

• 14.Intersect the coordinates of nascent transcription with the coordinates of functional genomic regions.

The coordinates of genomic regions were saved above as individual .txt files. Here, the number of engaged Pol II molecules (rows in the 3pnt.bed file) are allocated to distinct functional categories.

Note: To allocate each engaged Pol II complex only once, we use the -u option in the bedtools intersect command and sequentially allocate the coordinates of active transcription to the distinct genomic categories. In each round, two different files are generated: File 1 retains active sites that localize to the given functional category (options -u and -wa). File 2 reports the active sites that do not localize to the given functional category (controlled with option -v). The file 2 will be used in the subsequent round to ensure that each engaged Pol II is allocated only to one genomic region. The order of the intersections is: i) promoter-proximal regions, ii) sites of divergent transcription, iii) enhancers, iv) CPSs, v) gene bodies, and vi) termination windows. In this order, promoter-associated Pol II molecules are not counted into enhancer transcription. Furthermore, transcription at intragenic enhancers is allocated to enhancers instead of gene bodies. Finally, termination windows can be relatively short or extend over several kilobases (Vilborg et al., 2017). In this strategy, long (10 kb) termination windows are queried, but only Pol II complexes that do not overlap with any other functional genomic region are allocated to a gene’s termination window.

##counting engaged Pol II at promoter-proximal regions

bedtools intersect -u -wa -a PROseq_K562_hg38_3pnt.bed -b ppPolII.txt > PROseq_K562_ppPolII.bed

bedtools intersect -v -a PROseq_K562_hg38_3pnt.bed -b ppPolII.txt > ppRemoved.bed

##counting engaged Pol II at the sites of divergent transcription

bedtools intersect -u -wa -a ppRemoved.bed -b divTx.txt > PROseq_K562_ppDiv.bed

bedtools intersect -v -a ppRemoved.bed -b divTx.txt > ppdivRemoved.bed

##counting engaged Pol II at enhancers

bedtools intersect -u -wa -a ppdivRemoved.bed -b enhancers.bed > PROseq_K562_enhancers.bed

bedtools intersect -v -a ppdivRemoved.bed -b enhancers.bed > ppdivEnhRemoved.bed

##counting engaged Pol II at CPS

bedtools intersect -u -wa -a ppdivEnhRemoved.bed -b CPS.txt > PROseq_K562_CPS.bed

bedtools intersect -v -a ppdivEnhRemoved.bed -b CPS.txt > ppdivEnhCPSRemoved.bed

##counting engaged Pol II at GB

bedtools intersect -u -wa -a ppdivEnhCPSRemoved.bed -b geneBody.txt > PROseq_K562_GB.bed

bedtools intersect -v -a ppdivEnhCPSRemoved.bed -b geneBody.txt > ppdivEnhCPSgbRemoved.bed

##counting engaged Pol II at termination windows

bedtools intersect -u -wa -a ppdivEnhCPSgbRemoved.bed -b TW.txt > PROseq_K562_TW.bed

bedtools intersect -v -a ppdivEnhCPSgbRemoved.bed -b TW.txt > PROseq_K562_noGene_noEnh.bed

rm ∗Removed.bed

Quantify distribution of nascent transcription across functional genomic regions

Timing: 10 min

The code in step 15 calculates engaged Pol II molecules in each category of functional genomic regions (enhancer, div, pp, gb, CPS, tw). Engaged Pol II that does not occur in any of the categories is indicated as unannotated. Step 15 further analyses the distribution of engaged Pol II across the functional genomic regions (see Figure 3D).

• 15.
  Plot the counts of engaged Pol II molecules at distinct categories of functional genomic regions.

  • a.
    Initiate a script that collects counts of engaged Pol II molecules at the distinct genomic regions. The counts will be printed in the .txt file as well as on the terminal window.

    script counts_at_functional_regions.txt
  • b.
    Plot the counts of rows in each intersected bed file. The number of rows in each file corresponds to the number of active sites of transcription in the given category of functional genomic regions.

    

  • c.
    Terminate the log script by pressing control + D in the terminal window.
    The file ‘counts_at_functional_regions.txt’ now reports the number of engaged Pol II molecules at each category of functional genomic regions. Figure 3D shows the counts in a table format and includes percentages of engaged Pol II in each category. The pie chart in the graphical abstract illustrates the distribution of engaged Pol II complexes across the functional genomic regions.

    Note: The counts generated in this protocol correspond to sites of active transcription (3′-nt of a read) in the dataset. Here, we only have a single sample and, therefore, focus on the distribution (proportions) of active sites across the genomic regions. With PRO-seq data, a normalization factor that accounts for differences in data handling and sequencing depth can be computed. Detailed description of normalization factors is out of the scope of this study. In brief, invariant whole-genome spike-in from a distinct organism can be added to all samples before the run-on reaction. This equal amount of foreign chromatin provides a count of nascent transcription against which the samples can be normalized (Booth et al., 2018; Vihervaara et al., 2021). Alternatively, genomic regions where no changes are detected during a time course can be utilized to generate a normalization factor between samples (Mahat et al., 2016b; Vihervaara et al., 2017).

Quantify nascent transcription complexes at individual genomic regions

Timing: 15 min

This section computes a bed file that can be visualized in a genome browser. The generated bed file shows color-coded functional genomic regions, as well as the raw count of engaged Pol II at each region.

• 16.Intersect coordinates of the active sites of transcription with the coordinates of individual functional genomic regions.

bedtools intersect -c -wa -a ppPolII.txt -b PROseq_K562_ppPolII.bed > ppPolCounts.tmp

bedtools intersect -c -wa -a divTx.txt -b PROseq_K562_ppDiv.bed > ppDivCounts.tmp

bedtools intersect -c -wa -a enhancers.bed -b PROseq_K562_enhancers.bed > enhancerCounts.tmp

bedtools intersect -c -wa -a geneBody.txt -b PROseq_K562_GB.bed > geneBodyCounts.tmp

bedtools intersect -c -wa -a CPS.txt -b PROseq_K562_CPS.bed > CPSCounts.tmp

bedtools intersect -c -wa -a TW.txt -b PROseq_K562_TW.bed > TerminationWinCounts.tmp

• 17.Color code the distinct categories of functional genomic regions. The color coding is in bed-compatible rgb format (0–255,0–255,0–255). If desired, the color-code for each category of functional genomic regions can be adjusted here by changing the rgb values.

awk -F ‘\t’ -v OFS=‘\t’ ‘{ $(NF+1) ="243,132,0"; print }’ ppPolCounts.tmp > ppPolCounts.bed

awk -F ‘\t’ -v OFS=‘\t’ ‘{ $(NF+1) ="178,59,212"; print }’ ppDivCounts.tmp > ppDivCounts.bed

awk -F ‘\t’ -v OFS=‘\t’ ‘{ $(NF+1) ="115,212,122"; print }’ enhancerCounts.tmp > enhancerCounts.bed

awk -F ‘\t’ -v OFS=‘\t’ ‘{ $(NF+1) ="0,0,0"; print }’ geneBodyCounts.tmp > geneBodyCounts.bed

awk -F ‘\t’ -v OFS=‘\t’ ‘{ $(NF+1) ="103,200,249"; print }’ CPSCounts.tmp > CPSCounts.bed

awk -F ‘\t’ -v OFS=‘\t’ ‘{ $(NF+1) ="255,54,98"; print }’ TerminationWinCounts.tmp > TerminationWinCounts.bed

• 18.Combine the files. Add an extra column "." to obtain a genome browser compatible bed file for visualization.

cat ppPolCounts.bed ppDivCounts.bed enhancerCounts.bed geneBodyCounts.bed CPSCounts.bed TerminationWinCounts.bed > catRegions.temp

awk -F ‘\t’ -v OFS=‘\t’ ‘{ $(NF+1) ="."; print }’ catRegions.temp > catRegions2.temp

• 19.Reorganize the columns and add a header track.

awk ‘{print $1 "\t" $2 "\t" $3 "\t" $7 "\t" $5 "\t" $6 "\t" $2 "\t" $3 "\t" $8}’ catRegions2.temp > catRegions3.temp

awk ‘!seen[$1,$2,$3,$6]++’ catRegions3.temp | sortBed > catRegions4.temp

touch headerLine.txt

echo track name="functional_genomic_regions" itemRgb="On" >> headerLine.txt

cat headerLine.txt catRegions4.temp > functionalGenomicRegions.bed

rm ∗.temp

rm headerLine.txt

The generated ’functionalGenomicregions.bed’ file can be read in genome browsers to show the identified functional genomic regions as well as the count of engaged Pol II at each indicated region (exemplified in Figures 3C, 4A, and 4B).

Note: As mentioned above, the number of engaged Pol II complexes at the distinct genomic regions generated in this protocol originates from raw read counts of the data. This count depends on the sequencing depth. To get a normalized count of engaged Pol II, the column 4 (reporting raw count) in the ’functionalGenomicregions.bed’ file can be multiplied with a normalization factor.

Analyze the distribution of chromatin-associated factors to functional genomic regions

Timing: 30 min if summit files are available, up to hours if they need to be generated from raw data

The steps 20–22 input summit coordinates of chromatin-associated factors. The summit coordinates are annotated to the functional genomic regions. The distribution of each factor across the functional genomic regions is obtained (see Figures 3E and 4C).

• 20.
  Count genome-associating factors at distinct functional genomic regions.

  • a.
    Obtain or generate datasets of interest. Place them in the folder of your working directory. Here, we used TBP, GATA1, CTCF, H3K36me3, NELFe, p300 and RAD21 ChIP-seq data (Consortium EP, 2011). We remapped the ChIP-seq data to hg38 (https://github.com/Vihervaara/ChIP-seq_analyses). As starting material, please use a file that reports the summit coordinate of each peak, named with '_summits.bed', for example: K562_TBP_summits.bed, K562_GATA1_summits.bed, K562_CTCF_summits.bed, K562_H3K36me3_summits.bed .
  • b.
    The peak summits for each given chromatin-associated factor are intersected with the distinct genomic regions. The strategy described in step 14 is used to ensure that each identified enrichment of a factor at the genome (peak) is counted once. For efficiency, we use a loop function that takes one bed file of mapped peak summits at a time. To define which datasets are analyzed in the loop function, please place names of factors in quotation marks, separated by a space, after the for x in the code below. In the loop, the ${x} will be replaced with the factor name, one listed factor after another.
  • c.
    Run the code below using the factors of your choice. Please, ensure that the files are placed in the correct folder (working directory) and that the file names correspond to the names in the code. The files listing the coordinates of functional genomic regions were generated in steps 1–12.

for x in "TBP" "GATA1" "CTCF" "H3K36me3"

do

## Factor-derived reads at promoter-proximal regions

bedtools intersect -u -wa -a K562_${x}_summits.bed -b ppPolII.txt > ${x}_K562_ppPolII.bed

bedtools intersect -v -a K562_${x}_summits.bed -b ppPolII.txt > ${x}_ppRemoved.bed

## Factor-derived reads at the sites of divergent transcription

bedtools intersect -u -wa -a ${x}_ppRemoved.bed -b divTx.txt > ${x}_K562_ppDiv.bed

bedtools intersect -v -a ${x}_ppRemoved.bed -b divTx.txt > ${x}_ppdivRemoved.bed

## Factor-derived reads at enhancers

bedtools intersect -u -wa -a ${x}_ppdivRemoved.bed -b enhancers.bed > ${x}_K562_enhancers.bed

bedtools intersect -v -a ${x}_ppdivRemoved.bed -b enhancers.bed > ${x}_ppdivEnhRemoved.bed

## Factor-derived reads at CPS

bedtools intersect -u -wa -a ${x}_ppdivEnhRemoved.bed -b CPS.txt > ${x}_K562_CPS.bed

bedtools intersect -v -a ${x}_ppdivEnhRemoved.bed -b CPS.txt > ${x}_ppdivEnhCPSRemoved.bed

## Factor-derived reads at GB

bedtools intersect -u -wa -a ${x}_ppdivEnhCPSRemoved.bed -b geneBody.txt > ${x}_K562_GB.bed

bedtools intersect -v -a ${x}_ppdivEnhCPSRemoved.bed -b geneBody.txt > ${x}_ppdivEnhCPSgbRemoved.bed

## Factor-derived reads at termination windows

bedtools intersect -u -wa -a ${x}_ppdivEnhCPSgbRemoved.bed -b TW.txt > ${x}_K562_TW.bed

bedtools intersect -v -a ${x}_ppdivEnhCPSgbRemoved.bed -b TW.txt > ${x}_K562_noGene_noEnh.bed

rm ∗Removed.bed

done

• 21.
  Plot the counts of chromatin-associated factors at distinct categories of functional regions.

  • a.
    Initiate the script that collects factor counts at the functional genomic regions.

    script Factor_counts_at_functional_regions.txt
  • b.
    Plot the counts of rows in each intersected bed file.

    

  • c.
    Terminate the log script by pressing control + D in the terminal window.

• 22.The file ‘Factor_peaks_at_functional_regions.txt’ reports the number of ChIP-seq peaks at each functional category. Figure 3E exemplifies the counts for TBP peaks in a table format, including the percentages of binding sites at each category of functional genomic regions. The bar charts in the graphical abstract illustrates the distribution of TBP, GATA1, CTCF and H3K36me3 across the functional genomic regions. In Figure 4E, bar charts for additional factors (NELFe, p300 and RAD21) are included.

Expected outcomes

Counting nascent RNA synthesis at functional genomic regions

This protocol inputs active sites of transcription, and outputs coordinates of transcribed genomic regions. A bed file that lists and visualizes individual functional genomic regions is generated in steps 1–19. In this functional_genomic_regions.bed file, each block represents the coordinates of an individual functional genomic region. The block color codes for the category of the region (green: enhancer; purple: divergent transcription; orange: promoter-proximal region; black: gene body; light blue: cleavage, and polyA site; pink: termination window). In Figure 4A, transcribed isoforms and functional genomic regions of UBET2 and PPP1R12B genes are shown, and two intergenic enhancer candidates identified on the longer isoform of PPP1R12B. Furthermore, the protocol counts engaged Pol II complexes that localize to each identified functional genomic region. The raw count of active sites of transcription is given in column 4 of the functional_genomic_regions.bed file, appearing above the color-coded block when visualized in a genome browser (Figure 4B). The raw count can be multiplied with a normalization factor to allow comparison of distinct PRO-seq datasets and/or to account for sequencing depth. In Figure 4B, the count of engaged transcription machineries is exemplified at the hemoglobin beta locus, spanning from HBE1 gene to the region’s upstream locus control element (LCR). In PRO-seq used here (Vihervaara et al., 2021), the same amount of Drosophila S2 cells were spiked into each human K562 cell sample as a means for normalization. In Figure 4B, the raw count of reads in untreated (NHS) K562 cells is shown above the identified functional genomic regions. The PRO-seq sample of 30-min heat shocked cells (HS30) was then normalized against the NHS by comparing the spike-in reads (normalization factor 0.94 derived from: 36,872 spike-in reads in NHS / 39,407 spike-in reads in HS30). The counts could be further normalized to take into account the sequencing depth, reporting counts of engaged Pol II per millions of mapped reads. Here, the sequencing depth in the NHS is outputted in step 15. Taken together, the comparison of engaged Pol II complexes at functional genomic regions allows analyzing changes in the distribution of nascent transcription complexes at individual genomic regions. As an example, the hemoglobin beta locus (Figure 4B) demonstrates the heat-induced accumulation of transcription machineries at promoter-proximal regions and enhancers (reviewed in Vihervaara et al., 2018).

Identifying chromatin-associated factors at transcribed genomic regions

The rate-limiting steps of transcription occur at specific functional genomic regions. Through the cycle of transcription, Pol II is coordinated by transcription factors, co-factors, elongation factors, chromatin remodelers, histone marks, architectural proteins, RNA-processing factors, and regulatory RNA. Subsequently, tracking interactions at the genome is essential for understanding the regulatory logic of transcription. In steps 20–22 of the protocol, ChIP-seq data from the ENCODE (Consortium EP, 2011) is utilized to map binding sites of chromatin-associating proteins across the functional genomic regions. The summit coordinate of each ChIP-seq peak, representing the highest point of factor enrichment, is queried against the coordinates of functional genomic regions. The code outputs the distribution of each factor’s binding sites across the functional genomic regions (Figure 4C). TBP is a component of the Pre-Initiation Complex. It localizes primarily to promoter-proximal regions but is also abundant at enhancers and sites of divergent transcription (Figure 4C). GATA1 and p300, instead, bind more often enhancers than promoters, and architectural protein CTCF and RAD21 have most of their enrichments in untranscribed regions (Figure 4C). Histone mark H3K36me3 displays a striking 98% of its binding sites at gene bodies (93%), downstream CPSs (2%) or termination windows (3%). These patterns of factor localizations reflect the steps of transcription they coordinate and can reveal regulatory stages at individual genomic regions.

Limitations

This protocol is limited to genomes where active genes and enhancers initiate transcription to diverging orientations. Consequently, to identify active genes and enhancers de novo this protocol requires strand-specific mapping of nascent transcription. Furthermore, precise positional information of active sites of transcription is essential for correct quantification of engaged Pol II complexes at the identified functional genomic regions. A good quality and adequate sequencing depth of nascent transcription data is also required. With shallow sequencing, pause-peaks are poorly visible and divergent transcription becomes less evident, particularly at weakly transcribed genes and enhancers. In the human genome, >15 million uniquely mapping reads derived from >5 million cells, provides a robust identification of divergently transcribed genes and enhancers. This protocol can be run with a lower sequencing depth, but please note that weakly transcribed genes and enhancers might not be called active regulatory elements. It is also worth noting that a small amount of cells as starting material can cause a low coverage of the genome by sequenced reads. Also in this case, the low representation of genomic regions due to technical limitations can impair the sensitivity for calling functional genomic regions at weakly transcribed parts of the genome.

Troubleshooting

Problem 1

Terminal shell gives a ‘file not found’ argument.

Potential solution

Check your working directory by typing 'pwd' in the terminal shell. If the files you are attempting to use are not in your working directory, change the working directory by typing 'cd ' and dragging the correct folder after it in the terminal shell window. Ensure also that all the files you are attempting to use are in the working directory and that the names of the files exactly match the names given in the code. For example, the ‘K562_’ in the file names provided here need to be removed or replaced when analyzing PRO-seq data from another cell type.

Problem 2

Terminal shell does not recognize the tools or packages.

Potential solution

Install correct software. In Mac OS, bedtools and wget can be installed for example via homebrew (https://brew.sh):

/bin/bash -c "$(curl -fsSLhttps://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

brew install bedtools

brew install wget

For installing R, see https://www.r-project.org for instructions to download the correct R.app. R, wget and bedtools can be run via a terminal shell.

Problem 3

dREG finds only a small number of sites with divergent transcription.

Potential solution

Ensure that unnormalized bigWig files are uploaded in dREG. Ensure also that the PRO-seq library is of adequate quality and has a good sequencing depth. If the nascent RNA-seq library has a low count of uniquely mapped reads or low complexity, the Pol II pause peaks might not become visible. In these cases, the pattern of divergent transcription might not be adequate for calling active genes and transcribed enhancers.

Problem 4

Only a small number of active genes are identified.

Potential solution

The genomes of human and mouse are well-annotated. In other species, a large fraction of the genes might remain to be identified. If working with a poorly annotated genome, the active genes are likely counted as enhancers since the reference genome does not report a gene TSS at the site of divergent transcription. As a possible solution, check that the latest version of gene annotation is used. Furthermore, tools such as dREG.HD (Chu et al., 2018), Tfit (Azofeifa and Dowell, 2017) and primaryTranscriptAnnotation in R (Anderson et al., 2020) could predict whether the transcription initiates from a gene or an enhancer, or identify the length of the transcript unit.

Problem 5

The code needs to be modified to accommodate a different set of samples than used here as examples.

Potential solution

Please find the code as shell and R script files in the GitHub repository https://github.com/Vihervaara/functionalGenomicRegions. These scripts can be modified to download a refGene file that matches the organism and genome version against which the user-specific nascent RNA-seq samples have been mapped (see shell script lines 5–7). Likewise, the names of the files in the scripts should be updated to match the user-specific input files.

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Anniina Vihervaara (viher@kth.se).

Materials availability

This study did not generate new unique reagents.

Data and code availability

The PRO-seq data utilized in this study is described in Vihervaara et al. (2021) and accessible via GEO (GSE127844). The input data files used here were remapped to the hg38 genome and are available via GEO accession code GSE181161. The ChIP-seq datasets were generated by the ENCODE (Consortium EP, 2011), the raw data of which is available via accession code GSE31477. The code to generate input files used here has been deposited to GitHub (https://github.com/Vihervaara/PRO-seq-analyses; https://github.com/Vihervaara/ChIP-seq_analyses).