Scripts for analysing PAR-CLIP data of RNA degradation factors in yeast

Sohrabi-Jahromi, Salma, et al. "Transcriptome maps of general eukaryotic RNA degradation factors." eLife 8 (2019): e47040.

Summary:

RNA degradation pathways enable RNA processing, the regulation of RNA levels, and the surveillance of aberrant or poorly functional RNAs in cells. Here we provide transcriptome-wide RNA-binding profiles of 30 general RNA degradation factors in the yeast Saccharomyces cerevisiae. The profiles reveal the distribution of degradation factors between different RNA classes. They are consistent with the canonical degradation pathway for closed-loop forming mRNAs after deadenylation. Modeling based on mRNA half-lives suggests that most degradation factors bind intact mRNAs, whereas decapping factors are recruited only for mRNA degradation, consistent with decapping being a rate-limiting step. Decapping factors preferentially bind mRNAs with non-optimal codons, consistent with rapid degradation of inefficiently translated mRNAs. Global analysis suggests that the nuclear surveillance machinery, including the complexes Nrd1/Nab3 and TRAMP4, targets aberrant nuclear RNAs and processes snoRNAs.

A guide on the scripts and notebooks based on the figures in our paper:

Mockinbird config files:

preprocess.yaml
postprocess.yaml

Figure 1—figure supplement 1. Biological replicate PAR-CLIP experiments have high correlation.

replicate_similarity.ipynb

Figure 2. Distribution of degradation factor cross-link sites over the yeast transcriptome.

enrichment_normalized.ipynb

Figure 2—figure supplement 1. Different transcript classes have comparable U- content.

U_content_check.ipynb

Figure 2—figure supplement 2. Metagene profiles for subunits of the TRAMP complexes on snoRNA genes.

metagene_density_graph.ipynb

Figure 3. Metagene analysis of degradation factor binding on mRNAs.

heatmap_metageneplots.ipynb

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.

heatmap_metageneplots.ipynb

Figure 4. Surveillance of aberrant nuclear antisense RNAs by the exosome and the TRAMP4 complex.

heatmap_metageneplots.ipynb

Figure 4—figure supplement 1. Motif enrichment analysis shows enrichment of Nrd1/Nab3 motifs for the TRAMP4 and the exosome complex.

kmer_counting.ipynb

Figure 5. Global co-occupancy and co-localization analysis reveals unexpected cooperation between factors from different complexes and pathways.

co_occupancy.py
co_localization.py
co_occupancy.ipynb
co_localization.ipynb

Figure 5—figure supplement 1. Co-occupancy for 74 RNA processing factors.

co_occupancy.py
co_occupancy.ipynb

Figure 5—figure supplement 2. Co-localization coefficients for all 74 RNA processing factors. Figure 5—figure supplement 3. Two-dimensional embedding of co-localization between 74 RNA processing factors.

co_localization.py
co_localization.ipynb

Figure 6. Binding preferences reveal a link between decapping-mediated degradation and translation.

occupancy_by_features.ipynb
codon_freq_bar.ipynb
linear_regression.R

Figure 6—figure supplement 1-9.

occupancy_by_features.ipynb

Figure 7. Location and recruitment of the decapping complex Dcp1/Dcp2 and decapping enhancers Edc3, Dhh1, and Edc2.

metagene_by_halflife.ipynb
occupancy_by_features.ipynb
kmer_counting.ipynb

License

soedinglab/Degradation_scripts

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