1. Chromosomes and Gene Expression
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Transcriptome maps of general eukaryotic RNA degradation factors

Research Article
Cite this article as: eLife 2019;8:e47040 doi: 10.7554/eLife.47040
8 figures, 1 table, 5 data sets and 2 additional files

Figures

Figure 1 with 2 supplements
Overview of PAR-CLIP experiments performed in this study.

(A) Overview of degradation pathways studied. (B) Number of high-confidence PAR-CLIP cross-link sites obtained for each factor after merging data from replicates.

https://doi.org/10.7554/eLife.47040.003
Figure 1—figure supplement 1
Biological replicate PAR-CLIP experiments have high correlation.

(A) Total transcript occupancy of factors in replicate experiments are plotted (in log2 space) and Spearman correlation values are shown for each pair. Each dot corresponds to a transcript. The color indicates dot density. (B) Comparison of coverage profiles obtained from CRAC experiments of Xrn1, Mtr4, Trf4, and Ski2 in S. cerevisiae (Tuck and Tollervey, 2013) with occupancy profiles from our PAR-CLIP experiments highlights reproducibility of transcriptome profiles across different methods. These profiles show the averaged binding of degradation factors over mRNAs (sense strand: left and anti-sense strand: right) in a window of [±700 nt] around their transcription start site (TSS) and their poly-adenylation (pA) site in a window of [±700 nt]. Regions that have neighboring transcripts on the same strand were removed to avoid contaminating profiles (Materials and methods).

https://doi.org/10.7554/eLife.47040.004
Figure 1—figure supplement 2
Western Blot analysis for all degradation factors analyzed in this study show IP efficiency.

IP using the TAP-tag is detected by Western Blot analysis with tag specific antibody to show the IP quality of the different PAR-CLIP experiments representative for one replicate per factor. The factors are sorted according to their complexes: deadenylation (Ccr4, Pop2, Not1, Caf40, Pan3, and Pan3), decapping (Dcp1, Dcp2, Edc2, Edc3, and Dhh1), 5´→3´ exonuclease (Xrn1), exosome (Rrp6, Csl4, Rrp40, Rrp4, and Rrp44), TRAMP (Air1, Trf5, Mtr4, Air2, and Trf4), Ski (Ski2, Ski3, Ski7, and Ski8), and NMD (Upf1, Upf2, Upf3, and Nmd4). The molecular weight including the weight of the TAP tag (in kDa) is indicated for each factor. The band at ~25 kDa is caused by cross-reactivity of the light chain of the used antibodies for IP and Western Blot. A shift to higher molecular weight than indicated can be caused by UV-crosslinking of proteins to RNA.

https://doi.org/10.7554/eLife.47040.005
Figure 2 with 2 supplements
Distribution of degradation factor cross-link sites over the yeast transcriptome.

(A) Fractions of high confidence PAR-CLIP sequencing reads of 30 yeast degradation factors fall into various transcript classes. Depicted classes are the following: messenger RNA (mRNA) in turquoise (n = 4,928), ribosomal RNA (rRNA) in antique pink (n = 24), transfer RNA (tRNA) in dark blue (n = 299), small nucleolar RNA (snoRNA) in yellow (n = 77), small nuclear RNA (snRNA) in green (n = 6), stable unannotated transcripts (SUTs) in red (n = 318), cryptic unstable transcripts (CUTs) in light brown (n = 637), Nrd1-unterminated transcripts (NUTs) in dark brown (n = 298)(Materials and methods). (B) Enrichment z-scores of high confidence PAR-CLIP cross-link sites of 30 yeast degradation factors (rows) in various segments of mRNA transcripts (left columns; UTR: untranslated region; intron; CDS: coding sequence), or other transcript classes as in A (other columns). The color-coded enrichment score shows the column and row normalized enrichment values of binding preferences of each factor for each transcript class (color encoded, depleted in blue and enriched in red). The coefficient of variation on top is the standard deviation divided by the mean for each transcript class.

https://doi.org/10.7554/eLife.47040.006
Figure 2—figure supplement 1
Metagene profiles for subunits of the TRAMP complexes on snoRNA genes.

Transcript averaged PAR-CLIP occupancy profiles are shown for Air1, Trf5, Mtr4, Air2, and Trf4. snoRNA genes are aligned either at their 5´ end or at their 3´ end (n = 77). Occupancy profiles are shown over the range of ±50 nt.

https://doi.org/10.7554/eLife.47040.007
Figure 2—figure supplement 2
Different transcript classes have comparable U-content.

Fraction of U over all bases in transcript classes studied in Figure 2 (untranslated region (UTR); intron; coding sequence (CDS), ribosomal RNA (rRNA), transfer RNA (tRNA), small nucleolar RNA (snoRNA), small nuclear RNA (snRNA), stable unannotated transcripts (SUTs), cryptic unstable transcripts (CUTs), Nrd1- unterminated transcripts (NUTs)).

https://doi.org/10.7554/eLife.47040.008
Figure 3 with 3 supplements
Metagene analysis of degradation factor binding on mRNAs.

Averaged occupancy profiles of degradation factors over mRNAs aligned around their transcription start site (TSS) (n = 3,193, left) and around their poly-adenylation (pA) site (n = 3,193, right) in a window of [±700 nt]. Regions that have neighboring transcripts on the same strand were removed to avoid contaminating profiles (Materials and methods). Factors are grouped according to their functional role; from top to bottom: deadenylation, decapping, Xrn1, exosome, TRAMP complex, Ski complex, or NMD. The color code shows the average occupancy normalized between the minimum (blue) and maximum (red) values per profile.

https://doi.org/10.7554/eLife.47040.009
Figure 3—figure supplement 1
Metagene profiles of yeast RNA degradation factors centered on translation start and stop sites in comparison to TIF-annotated TSS and pA sites.

Transcript-averaged PAR-CLIP occupancy profiles are shown for RNA degradation factors involved in (A) deadenylation, (B) decapping, (C) 5´→3´ exonuclease Xrn1, (D) exosome, (E) TRAMP, (F) Ski, and (G) NMD. Transcripts are aligned either at transcript start site (TSS) and poly-adenylation (pA) site (marked with blue) or at their start and stop codons (marked with green). TIF-seq based annotation is shown in blue (n = 3193 for TSS and pA site profiles) (Pelechano et al., 2013). Open reading frames (ORF) annotated in the SGD (version 64.2.1) are shown in green (n = 4012 for TSS, and n = 3965 for pA site selected transcripts). To avoid contaminating signals from neighboring genes, we filtered out regions that had annotations upstream and downstream of the centered gene (up to 700 nt) (Materials and methods). Shaded areas (in blue TIF-seq annotation, or in green for ORF annotation) depict 95% confidence intervals derived from bootstrapping genes. Comparison between these two profiles highlights preferences for end binding degradation factors in binding to untranslated regions at the two sides of the transcript.

https://doi.org/10.7554/eLife.47040.010
Figure 3—figure supplement 2
Comparison of binding profiles on genes containing annotated upstream sense NUTs with all mRNAs.

(A) Binding enrichment of degradation factors around the TSS of genes with an upstream sense NUT. Enrichment is defined as the ratio of the average occupancy in the interval [±300 nt] of the TSS on these genes that contain an upstream NUT (n = 459) (Schulz et al., 2013) divided by the average occupancy on all genes. (B) Transcript-averaged PAR-CLIP occupancy profiles for all mRNAs (black) is compared to patterns derived from genes with upstream sense NUTs (blue). Transcripts were aligned at their TSS and averaged over the interval of [±600 nt]. We compared Nrd1 and Nab3 profiles, known to process NUTs, with subunits of the TRAMP complex. 95% confidence intervals obtained from bootstrapping genes are shown with gray and blue shades.

https://doi.org/10.7554/eLife.47040.011
Figure 3—figure supplement 3
Metagene analysis of degradation factor binding on mRNAs after removing signals from known NUTs and CUTs.

Cross-link sites were filtered to exclude regions that were previously annotated as NUTs and CUTs (Neil et al., 2009; Schulz et al., 2013). Averaged occupancy profiles of degradation factors are then shown over mRNAs aligned around their transcription start site (TSS) (n = 3,193, left) and around their poly-adenylation (pA) site (n = 3,193, right) in a window of [±700 nt]. Regions that have neighboring transcripts on the same strand were removed to avoid contaminating profiles (Materials and methods). Factors are grouped according to their functional role; from top to bottom: deadenylation, decapping, Xrn1, exosome, TRAMP complex, Ski complex, or NMD. The color code shows the average occupancy normalized between the minimum (blue) and maximum (red) values per profile.

https://doi.org/10.7554/eLife.47040.012
Figure 4 with 2 supplements
Surveillance of aberrant nuclear antisense RNAs by the exosome and the TRAMP4 complex.

Averaged occupancy profiles of degradation factors binding to transcripts antisense of mRNAs aligned around transcription start site (TSS) (n = 3,076, left) and around their poly-adenylation (pA) site (n = 2,705, right) in a window of [±700 nt]. Regions with annotated genes on the antisense strand are removed to avoid contaminating the profiles (Materials and methods). The color code shows the average occupancy normalized between the minimum (blue) and maximum (yellow) values per profile. On top, previously published PAR-CLIP profiles for Nrd1 and Nab3 are included for comparison (Schulz et al., 2013).

https://doi.org/10.7554/eLife.47040.013
Figure 4—figure supplement 1
Motif enrichment analysis shows enrichment of Nrd1/Nab3 motifs for the TRAMP4 and the exosome complex.

Motif analysis was performed for all degradation factors in this study. Nrd1 and Nab3 are included for comparison (Schulz et al., 2013). Occurrences of Nrd1 motif (GTAG) and Nab3 motif (CTTG) are highlighted with red. The color code shows the log2 enrichment factor of top five enriched and top 3 depleted 4-mers around PAR-CLIP cross-link sites [±8 nt]. Dark red represents strong enrichment and dark blue shows strong depletion of a 4-mer. Infeasible combinations are shown with gray.

https://doi.org/10.7554/eLife.47040.014
Figure 4—figure supplement 2
The aberrant nuclear ncRNAs bound by components of the exosome and the TRAMP4 complex are primarily NUTs and CUTs.

Cross-link sites were filtered to exclude regions that were previously annotated as NUTs and CUTs (Neil et al., 2009; Schulz et al., 2013). Averaged occupancy profiles of degradation factors are shown on transcripts antisense of mRNAs aligned around the transcription start site (TSS) (n = 3,076, left) and around their poly-adenylation (pA) site (n = 2,705, right) in a window of [±700 nt]. Regions with annotated genes on the antisense strand are removed to avoid contaminating the profiles (Materials and methods). The color code shows the average occupancy normalized between the minimum (blue) and maximum (yellow) values per profile. On top, previously published PAR-CLIP profiles for Nrd1 and Nab3 are included for comparison (Schulz et al., 2013).

https://doi.org/10.7554/eLife.47040.015
Figure 5 with 3 supplements
Global co-occupancy and co-localization analysis reveals unexpected cooperation between factors from different complexes and pathways.

(A) Matrix of pairwise correlation coefficients of factor occupancies evaluated over all transcripts. (B) Matrix of co-localization based on the enrichment of factor x binding within 40 nt of the cross-link site of factor x´ (Materials and methods). (C) Two-dimensional embedding of the co-occupancies in (A) analyzed for 74 RNA processing factors with tSNE, including 30 factors from this study (highlighted in bold), and 44 factors from previous studies (Baejen et al., 2017; Baejen et al., 2014; Battaglia et al., 2017; Schulz et al., 2013) (Supplementary file 1). Factors that are plotted in close proximity show a preference for binding to the same transcripts. Clusters present factors involved in RNA synthesis (1), splicing (2), 3´ processing (3), deadenylation (4), decapping (5), nuclear ncRNA processing (6), and surveillance (7).

https://doi.org/10.7554/eLife.47040.016
Figure 5—figure supplement 1
Co-occupancy for 74 RNA processing factors.

Matrix of pairwise correlation coefficients of factor occupancies evaluated over all transcripts. Analysis for 74 RNA processing factors, including 30 factors from this study, and 44 factors from previous studies (Baejen et al., 2017; Baejen et al., 2014; Battaglia et al., 2017; Schulz et al., 2013) (see Supplementary file 1). Dark blue represents high correlation in binding across all transcripts. Factors are sorted and color coded (left and upper border) according to their general function.

https://doi.org/10.7554/eLife.47040.017
Figure 5—figure supplement 2
Co-localization coefficients for all 74 RNA processing factors.

Pairwise correlation between normalized co-localization profiles of factors in a window of 40 nt centered at PAR-CLIP cross-link sites. Analysis for 74 RNA processing factors, including 30 factors from this study, and 44 factors from previous studies (Baejen et al., 2017; Baejen et al., 2014; Battaglia et al., 2017; Schulz et al., 2013) (see Supplementary file 1). High co-localization represents binding to the same position on transcripts (marked with dark red). Factors are sorted and color coded (left and upper border) according to their general function.

https://doi.org/10.7554/eLife.47040.018
Figure 5—figure supplement 3
Two-dimensional embedding of co-localization between 74 RNA processing factors.

tSNE plot visualizes similarities in co-localization profiles between RNA processing factors (factors are color-coded based on their function). Analysis for 74 RNA processing factors, including 30 factors from this study (marked in bold), and 44 factors from previous studies (Baejen et al., 2017; Baejen et al., 2014; Battaglia et al., 2017; Schulz et al., 2013) (see Supplementary file 1).

https://doi.org/10.7554/eLife.47040.019
Figure 6 with 9 supplements
Binding preferences reveal a link between decapping-mediated degradation and translation.

(A) Total occupancy per mRNA (according to TIF-seq annotation) for six factors as a function of the average mRNA codon optimality (transcript optimality). The occupancy of factors from the 5´→3´ degradation machinery (decapping and Xrn1, left) decreases with increasing transcript optimality, whereas the occupancy of factors from the 3´→5´ degradation machinery (Ccr4 and Caf40 deadenylation complex subunits and exosome subunit Rrp44, right) increases with increasing average codon optimality. (Gray shading: 95% confidence intervals generated by bootstrapping mRNAs). (B) Codon enrichment in transcripts bound by Dcp2 and Ccr4 compared to the average frequency over all mRNAs. The bar colors represent codon optimality, with highly optimal codons shown in dark red. (Thin gray lines: 90% confidence intervals generated by bootstrapping coding sequences.) (C) Significance of correlations between the binding strength of degradation factors and transcript length, transcript optimality (Pechmann and Frydman, 2013), expression level (Baejen et al., 2017), and half-life derived by multivariate linear regression analysis (Materials and methods). Bars are separated according to the direction of correlation with positive correlation marked by a red background and negative correlation marked by a blue background.

https://doi.org/10.7554/eLife.47040.020
Figure 6—figure supplement 1
Occupancies of deadenylation factors (Ccr4, Pop2, Not1, Caf40, Pan2, and Pan3) compared to transcript length, optimality, expression level, and half-life.

(A) To understand binding specificity of deadenylation factors, the total occupancy of each factor on a transcript is plotted against various transcript features (Gray shading: 95% confidence intervals generated by bootstrapping transcripts). (B) Same analysis as in Figure 6B: Codon enrichment shows deviations in codon frequencies of transcripts bound by a degradation factor compared to each codon’s frequency on all coding sequences. Each bar is colored according to its codon-optimality with highly optimal codons in dark red and highly non-optimal codons in dark blue. (Gray lines: 90% confidence intervals generated by bootstrapping coding sequences).

https://doi.org/10.7554/eLife.47040.021
Figure 6—figure supplement 2
Occupancies of decapping factors (Dcp2, Dcp1, Edc2, Edc3, and Dhh1) compared to transcript length, optimality, expression level, and half-life.

(A) To understand binding specificity of decapping factors, the total occupancy of each factor on a transcript is plotted against various transcript features (Gray shading: 95% confidence intervals generated by bootstrapping transcripts). (B) Same analysis as in Figure 6B: Codon enrichment shows deviations in codon frequencies of transcripts bound by a degradation factor compared to each codon’s frequency on all coding sequences. Each bar is colored according to its codon-optimality with highly optimal codons in dark red and highly non-optimal codons in dark blue. (Gray lines: 90% confidence intervals generated by bootstrapping coding sequences).

https://doi.org/10.7554/eLife.47040.022
Figure 6—figure supplement 3
Occupancy of Xrn1 compared to transcript length, optimality, expression level, and half-life.

(A) To understand binding specificity of Xrn1 on various mRNAs, the total occupancy of Xrn1 on a transcript is plotted against various transcript features (Gray shading: 95% confidence intervals generated by bootstrapping transcripts). (B) Same analysis as in Figure 6B: Codon enrichment shows deviations in codon frequencies of transcripts bound by a degradation factor compared to each codon’s frequency on all coding sequences. Each bar is colored according to its codon-optimality with highly optimal codons in dark red and highly non-optimal codons in dark blue. (Gray lines: 90% confidence intervals generated by bootstrapping coding sequences).

https://doi.org/10.7554/eLife.47040.023
Figure 6—figure supplement 4
Occupancies of exosome components (Rrp6, Csl4, Rrp40, Rrp4, and Rrp44) compared to transcript length, optimality, expression level, and half-life.

(A) To understand binding specificity of exosome components, the total occupancy of each factor on a transcript is plotted against various transcript features (Gray shading: 95% confidence intervals generated by bootstrapping transcripts). (B) Same analysis as in Figure 6B: Codon enrichment shows deviations in codon frequencies of transcripts bound by a degradation factor compared to each codon’s frequency on all coding sequences. Each bar is colored according to its codon-optimality with highly optimal codons in dark red and highly non-optimal codons in dark blue. (Gray lines: 90% confidence intervals generated by bootstrapping coding sequences).

https://doi.org/10.7554/eLife.47040.024
Figure 6—figure supplement 5
Occupancies for components of the TRAMP complex (Air1, Trf5, Mtr4, Air2, and Trf4) compared to transcript length, optimality, expression level, and half-life.

(A) To understand binding specificity of TRAMP components, the total occupancy of each factor on a transcript is plotted against various transcript features (Gray shading: 95% confidence intervals generated by bootstrapping transcripts). (B) Same analysis as in Figure 6B: Codon enrichment shows deviations in codon frequencies of transcripts bound by a degradation factor compared to each codon’s frequency on all coding sequences. Each bar is colored according to its codon-optimality with highly optimal codons in dark red and highly non-optimal codons in dark blue. (Gray lines: 90% confidence intervals generated by bootstrapping coding sequences).

https://doi.org/10.7554/eLife.47040.025
Figure 6—figure supplement 6
Occupancies for components of the Ski complex (Ski2, Ski3, Ski7, and Ski8) compared to transcript length, optimality, expression level, and half-life.

(A) To understand binding specificity of factors in the Ski complex, the total occupancy of each factor on a transcript is plotted against various transcript features (Gray shading: 95% confidence intervals generated by bootstrapping transcripts). (B) Same analysis as in Figure 6B: Codon enrichment shows deviations in codon frequencies of transcripts bound by a degradation factor compared to each codon’s frequency on all coding sequences. Each bar is colored according to its codon-optimality with highly optimal codons in dark red and highly non-optimal codons in dark blue. (Gray lines: 90% confidence intervals generated by bootstrapping coding sequences).

https://doi.org/10.7554/eLife.47040.026
Figure 6—figure supplement 7
Occupancies for components of the NMD pathway (Upf1, Upf2, Upf3, and Nmd4) compared to transcript length, optimality, expression level, and half-life.

(A) To understand binding specificity of factors in the NMD pathway, the total occupancy of each factor on a transcript is plotted against various transcript features (Gray shading: 95% confidence intervals generated by bootstrapping transcripts). (B) Same analysis as in Figure 6B: Codon enrichment shows deviations in codon frequencies of transcripts bound by a degradation factor compared to each codon’s frequency on all coding sequences. Each bar is colored according to its codon-optimality with highly optimal codons in dark red and highly non-optimal codons in dark blue. (Gray lines: 90% confidence intervals generated by bootstrapping coding sequences).

https://doi.org/10.7554/eLife.47040.027
Figure 6—figure supplement 8
Distributions of transcript length, half-life, expression level and transcript optimality for yeast mRNAs.

Histograms on the diagonal show distributions of length, half-life (Materials and methods), expression level (Baejen et al., 2017) and transcript optimality (Pechmann and Frydman, 2013). Pairwise comparisons of features are shown as scatter plots (top right) and kernel density estimates (KDEs) of bivariate densities are shown in the bottom with Pearson correlation values (r) (Materials and methods).

https://doi.org/10.7554/eLife.47040.028
Figure 6—figure supplement 9
Correlation between binding to degradation factors and transcript length, codon-optimality, expression, and half-life.

Pearson correlation values between the binding strength of degradation factors (total occupancy over each transcript) and transcript length, transcript optimality (Pechmann and Frydman, 2013), expression level (Baejen et al., 2017), and half-life derived by multivariate linear regression analysis (Materials and methods).

https://doi.org/10.7554/eLife.47040.029
Location and recruitment of the decapping complex Dcp1/Dcp2 and decapping enhancers Edc3, Dhh1, and Edc2.

(A) Smoothed, transcript-averaged PAR-CLIP occupancy profiles aligned at TSS and pA sites [±750 nt] of unstable and stable transcripts (first and fourth quantile of half-life distribution, respectively). (B) Dependence of total occupancy of factors on the transcripts half-life. The fitting function is plotted in red and the fitted value for b is marked with a dashed gray line. (Gray shade: 95% confidence intervals generated by bootstrapping transcripts). (C) Sequence binding preference for the catalytically active subunit of decapping complex (Dcp2), illustrated with the five most enriched and the 3 most depleted 4-mers. The color code shows the log2 enrichment factor of 4-mers around PAR-CLIP cross-link sites [±8 nt]. Dark red represents strong enrichment and dark blue shows strong depletion of a 4-mer. Infeasible combinations are shown with gray. The most highly enriched field is binding AAAAU with the cross-link at the U, which is enriched over random expectation approximately 22.3 = 5-fold.

https://doi.org/10.7554/eLife.47040.030
Author response image 1

Tables

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional information
Strain, strain background (S. cerevisiae, BY4741)Ccr4_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000000019
Strain, strain background (S. cerevisiae, BY4741)Pop2_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000005335
Strain, strain background (S. cerevisiae, BY4741)Not1_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000000689
Strain, strain background (S. cerevisiae, BY4741)Caf40_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000005232
Strain, strain background (S. cerevisiae, BY4741)Pan2_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000003062
Strain, strain background (S. cerevisiae, BY4741)Pan3_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000001508
Strain, strain background (S. cerevisiae, BY4741)Dcp1_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000005509
Strain, strain background (S. cerevisiae, BY4741)Dcp2_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000005062
Strain, strain background (S. cerevisiae, BY4741)Edc2_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000000837
Strain, strain background (S. cerevisiae, BY4741)Edc3_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000000741
Strain, strain background (S. cerevisiae, BY4741)Dhh1_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000002319
Strain, strain background (S. cerevisiae, BY4741)Xrn1_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000003141
Strain, strain background (S. cerevisiae, BY4741)Rrp6_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000005527
Strain, strain background (S. cerevisiae, BY4741)Csl4_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000005176
Strain, strain background (S. cerevisiae, BY4741)Rrp40-TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000005502
Strain, strain background (S. cerevisiae, BY4741)Rrp4_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000001111
Strain, strain background (S. cerevisiae, BY4741)Rrp44_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000005381
Strain, strain background (S. cerevisiae, BY4741)Air1_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000001341
Strain, strain background (S. cerevisiae, BY4741)Trf5_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000005243
Strain, strain background (S. cerevisiae, BY4741)Mtr4_TAPC-terminally tagged
gene (Open Biosystems, Germany).
SGD: S000003586
Strain, strain background (S. cerevisiae, BY4741)Air2_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000002334
Strain, strain background (S. cerevisiae, BY4741)Trf4_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000005475
Strain, strain
background (S. cerevisiae, BY4741)
Ski2_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000004390
Strain, strain background (S. cerevisiae, BY4741)Ski3_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000006393
Strain, strain background (S. cerevisiae, BY4741)Ski7_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000005602
Strain, strain background (S. cerevisiae, BY4741)Ski8_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000003181
Strain, strain background (S. cerevisiae, BY4741)Upf1_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000004685
Strain, strain background (S. cerevisiae, BY4741)Upf2_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000001119
Strain, strain background (S. cerevisiae, BY4741)Upf3_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000003304
Strain, strain background (S. cerevisiae, BY4741)Nmd4_TAPC-terminally tagged gene (Open Biosystems, Germany).SGD: S000004355
AntibodyIgGSigma-AldrichCat#: I5006, RRID:AB_1163659IP: 0.1 mg per IP
AntibodyPAP anti-TAPSigma AldrichCat#: P1291, RRID:AB_1079562WB (1:2000)
Commercial assay or kitDynabeads Protein GInvitrogenCat#: 10003D330 µl per IP
Commercial assay or kitRNase T1Thermo Fisher ScientificCat#: EN0541
Commercial assay or kitAntarctic PhosphataseNEBCat#: M0289S
Commercial
assay or kit
RNase OUTInvitrogenCat#: 10777019
Commercial assay or kitT4 Polynucleotide KinaseInvitrogenCat#: EK0032
Commercial assay or kitT4 RNA ligase 2, truncated KQNEBCat#: M0373S
Commercial assay or kitT4 RNA ligase 1NEBCat#: M0437M
Commercial assay or kitProteinase KNEBCat#: P8107S
Commercial assay or kitSuperScript III RTThermo Fisher ScientificCat#: 18080093
Commercial
assay or kit
Phusion High-Fidelity
PCR Master Mix
Thermo Fisher ScientificCat#: F531S
Chemical compound, drug4-thiouracilCarbosynthCat#:
591-28-6
1 mM final conc.
Software, algorithmmockinbirdRoth and Torkler, 2018;https://github.com/soedinglab/Degradation_scripts
Software, algorithmUMI-toolsSmith et al., 2017; doi:10.1101/gr.209601.116
Software, algorithmSkewerJiang et al., 2014; doi: 10.1186/1471-2105-15-182
Software, algorithmBowtieLangmead et al., 2009; doi: 10.1186/gb-2009-10-3-r25
Software, algorithmtSNEVan Der Maaten and Hinton, 2008; doi: 10.1007/s10479-011-0841-3

Data availability

Sequencing data have been deposited in GEO under accession codes GSE 128312.

The following data sets were generated
  1. 1
    NCBI Gene Expression Omnibus
    1. S Sohrabi-Johromi
    2. KB Hofmann
    3. A Boltendahl
    4. C Roth
    5. S Gressel
    6. C Baejen
    7. J Soeding
    8. P Cramer
    (2019)
    ID GSE8128312. Transcriptome maps of general eukaryotic RNA degradation factors.
The following previously published data sets were used
  1. 1
    Array Express
    1. D Schulz
    2. B Schwalb
    3. A Kiesel
    4. C Baejen
    5. P Torkler
    6. J Gagneur
    7. J Soeding
    8. P Cramer
    (2013)
    ID E-MTAB-1766. Nuclear depletion of the essential transcription termination factor Nrd1 in Saccharomyces cerevisiae was studied using a combination of RNA-Seq, ChIP-Seq of Pol II and PAR-CLIP of Nrd1.
  2. 2
    NCBI Gene Expression Omnibus
    1. C Baejen
    2. P Torkler
    3. S Gressel
    4. K Essig
    5. J Söding
    6. P Cramer
    (2014)
    ID GSE59676. Transcriptome maps of mRNP biogenesis factors define pre-mRNA recognition.
  3. 3
    NCBI Gene Expression Omnibus
    1. C Baejen
    2. J Andreani
    3. P Torkler
    4. S Battaglia
    5. B Schwalb
    6. M Lidschreiber
    7. KC Maier
    8. A Boltendahl
    9. P Rus
    10. S Esslinger
    11. J Soeding
    12. P Cramer
    (2017)
    ID GSE79222. Genome-wide analysis of RNA polymerase II termination at protein-coding genes.
  4. 4
    NCBI Gene Expression Omnibus
    1. S Battaglia
    2. M Lidschreiber
    3. C Baejen
    4. P Torkler
    5. S Vos
    6. P Cramer
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    ID GSE81822. RNA-dependent chromatin association of transcription elongation factors and Pol II CTD kinases.

Additional files

Supplementary file 1

Overview of RNA processing factors and their respective PAR-CLIP experiments used in this study.

https://doi.org/10.7554/eLife.47040.031
Transparent reporting form
https://doi.org/10.7554/eLife.47040.032

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