Upf1-ribosomes

Analysis scripts and processed data for Ganesan et al.
DOI: 10.1261/rna.079416.122

Data availability

• Raw sequencing data generated in this study have been deposited and are available at NCBI's Gene Expression Omnibus (GEO) under accession number GSE186795.
• The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD029577 and 10.6019/PXD029577.
• Numerical data underlying the plots and the R scripts used to generate them are in the folder Figures or within the analysis scripts (below)

Mass spectrometry

• Processed data (exported from Scaffold) include normalized iBAQs (abundance quantification), Protein Identification Probability, and Total Spectrum Count for each sample, available in Processed data/Mass spec
• Criteria for valid identification of protein in a sample:
  • Protein Identification Probability >= 99%
  • Total Spectrum Count >= 2
  • normalized iBAQ > 0 for at least 2 biological replicates (if applicable)
• Analysis scripts/Mass spec analysis/Mass_spec_analysis_limma.Rmd consolidates data, identifies differentially recovered proteins, and plots Figure 1 and Figure S5.

RNA-Seq and Ribo-Seq sequence alignment and transcript abundance quantification

• Transcriptome used for sequence alignment is available at https://github.com/Jacobson-Lab/yeast_transcriptome_v5
• Transcript abundance results are combined and available in the folder Processed data/RSEM output
  • "expected_count" column from isoforms.results files
  • "FPKM" column from isoforms.results files
  • "TPM" column from isoforms.results files

Analyses of changes in transcript abundance using DESeq2

• Analysis scripts are in Analysis scripts/Sequencing data analyses
• Differential expression (RNA-Seq) between yeast strains
  • RNAseq_cormatrix_PCA.Rmd shows reprodicibility between replicates and produces Figure S12.
  • RNAseq_analysis_DESeq2.Rmd performs differential expression analysis and produces Figure S4.
• IP vs Total ribosomes (Ribo-Seq)
  • Riboseq_cormatrix_PCA.Rmd shows reprodicibility between replicates and produces Figure S11.
  • Riboseq_analysis_DESeq2.Rmd performs differential expression analysis, comparative analysis, and produces Figure 5.
• Ribosome occupancy (Total Ribo-Seq / RNA-Seq)
  • RNA-vs-RiboTotal.Rmd produces ribosome occupancy data used for Figure 5B.

Metagene analyses

• Initial processing of bam files was done using riboWaltz package (https://github.com/LabTranslationalArchitectomics/riboWaltz). Either reads_list or reads_psite_list data tables were used in further analysis steps.
• Analysis scripts are in Analysis scripts/Metagene analyses
• Footprint length distribution
  • rl_dist.R
  • Figure 2
• Mapping of the 5’ and 3’ ends of footprints
  • Calculated by riboWaltz's rends_heat function.
  • Figure 3
• Distribution of footprint abundance across the coding region
  • Metagene:
    • binning.R
    • Figure 4
  • Individual gene:
    • binning2.R
    • Figure S8
• Ribo-Seq diagnostic
  • 3-nucleotide periodicity:
    • Calculated by riboWaltz's metaprofile_psite function.
    • Figure S7
  • Footprint's P-sites
    • psite_region_frame_fraction.R
    • Fraction in mRNA regions: Figure S10 A-B, Figure 7A
    • Fraction of footprint's reading frames in an mRNA region: Figure S10 C-D, Figure 7B
• Unless otherwise indicated/shown, the output table of replicate libraries were averaged.
• The results and scripts for plotting them are available in Figures/data and Figures/scripts, respectively.

Codon optimality

• Codon optimality value for each coding sequence is calculated based on https://github.com/mariodosreis/tai
• Analysis scripts/Codon optimality/codon_optimality_tAI_CDS.R calculates codon optimality scores used for Figure 5D

A-site codon occupancy

• Analysis scripts and required files for processing are in Analysis scripts/A-site codon occupancy/
• Calculation of mean relative occupancy:
  1. write_rpl_by_size.R prepares riboWaltz's reads_psite_list data tables for the analysis and export them as txt files.
  2. codon_window_count_by_codonpos_from_rpl.py counts number of footprints whose A-site (or P- or E-site, as specified) are within a specified window (in codons) of a specified codon of interest.
  3. calc_REV_window_codonpos_v2.R calculates mean relative occupancy.
  • automate_codon_window_count_by_codonpos_from_rpl.sh automates steps 2 and 3 for multiple codons in a given codon list (such as codons_list.txt, which contains all 64 codons)
  • automate_automate_codon_window_count_by_codonpos_from_rpl.sh submits the above script as multiple jobs to process multiple samples in parallel.
  • Results of all samples for A-site occupancy in 30 codon window are combined and provided as A-site_codon_occupancy_window30_bycodon_allsamples.txt.
• codon_occupancy_analysis.Rmd further analyzes the results, plots Figure 6 and Figure S9.