This repository contains extended data for the manuscript:
Yeast Ssd1 is a non-enzymatic member of the RNase II family with an alternative RNA recognition interface. Rosemary A. Bayne, Uma Jayachandran, Aleksandra Kasprowicz, Stefan Bresson, David Tollervey, Edward W. J. Wallace, and Atlanta G. Cook. Nucleic Acids Research, 2021 doi: 10.1093/nar/gkab615
This repository concentrates on analysis of CRAC data measuring the RNA-binding of yeast Ssd1/YDR293C, 2020. The nearly-raw demultiplexed CRAC sequencing data is archived on Gene Expression Omnibus, accession GSE159835.
This repository also contains some related analysis and supporting information that is not directly analysing the CRAC data, see the table of contents below.
Please address questions to [email protected], at the Wallace lab, University of Edinburgh.
Structural data is available on PDB, PDBid 7AM1.
The main data here is CRAC, an Illumina short-read protocol related to HITS-CLIP, that detects RNA crosslinking sites for specific proteins. We analyse CRAC data using the python script, src/CRAC_pipeline_SE_demult_dedup.py. This script uses the ruffus computational pipeline library to organise running of many 3rd-party tools. This script was based on an earlier version by Sander Granneman for the paper:
van Nues R, Schweikert G, de Leau E, Selega A, Langford A, Franklin R, Iosub I, Wadsworth P, Sanguinetti G, Granneman S. Kinetic CRAC uncovers a role for Nab3 in determining gene expression profiles during stress. Nat Commun. 2017 Apr 11;8(1):12. doi: 10.1038/s41467-017-00025-5. PMID: 28400552; PMCID: PMC5432031.
The script makes extensive use of his pyCRAC software:
Webb S, Hector RD, Kudla G, Granneman S. PAR-CLIP data indicate that Nrd1-Nab3-dependent transcription termination regulates expression of hundreds of protein coding genes in yeast. Genome Biol. 2014 Jan 7;15(1):R8. doi: 10.1186/gb-2014-15-1-r8. PMID: 24393166; PMCID: PMC4053934.
This repository is "as self-contained as possible". Most files are kept locally, with the exception of the
- .fastq data (very large, some is available on GEO)
- .novoindex aligner index (large, created from the genome sequence in
input_annotation
The pipeline reads as inputs:
- one multiplexed .fastq file with all raw reads (available on request because GEO will not accept multiplexed files).
- annotation files, in
input_annotation - barcode files, in
input_barcodes
The pipeline writes as outputs:
- demultiplexed reads in .fastq format (stored on GEO)
- various intermediate files of trimmed or unsorted reads (very large, not stored)
- reads aligned to the genome and sorted, in .bam format (very large, not stored in repository)
- read counts for each annotated transcript, in a .gff-like format
- pileups of local read density on the genome in .bedgraph format
- locations and hit counts for peaks in the CRAC data, in a .gff-like format
To run the pipeline as used here and in our paper, use this code:
python src/CRAC_pipeline_SE_demult_dedup.py \
-f /homes/ewallac2/mount/datastore/wallace_rna/bigdata/2019/Ssd1CRAC-2019-01-15/20190114_Ssd1_CRAC.fastq \
-c input_annotation/Saccharomyces_cerevisiae.EF4.74.dna.toplevel.shortChrNames.lengths \
--gtf input_annotation/Saccharomyces_cerevisiae.EF4.74_SGDv64_CUTandSUT_withUTRs_noEstimates_antisense_intergenic_4xlncRNAs_final.pyCheckGTFfile.output.quotefix.gtf \
--novoindex /homes/rbayne2/CRACAnalysisJan2019_1/Saccharomyces_cerevisiae.EF4.74.dna.toplevel.shortChrNames.novoindex \
-p 5 \
--name Ssd1_CRAC_demult_dedup_20190114_all \
-a input_barcodes/3primeadapter.fasta \
-b input_barcodes/Ssd1CRACSampleBarcodes.txt \
--genelist input_annotation/Ssd1TargetGeneNamesOnly.txt \
--genometab input_annotation/Saccharomyces_cerevisiae.EF4.74.dna.toplevel.shortChrNames.fa.tab \
--transcriptgff input_annotation/abundant_verified_full-ORF_ypd_plus_other_fixed_UTR_length_transcripts.gff
Unfortunately, due to a bug that we have not been able to fix, this pipeline script needs to be run twice for all tasks to complete. We think the bug arises from the ruffus pipeline not waiting for novoalign to finish running, so that the first time the script runs all steps before genomic alignment, but those post-alignment return errors. Then the second time the pipeline runs, the post-alignment tasks run to completion (#overlyhonestmethods).
Data and code is organised into subdirectories, whose contents are briefly mentioned in this section.
Each directory has its own README.md file that describes the contents in more detail.
We start with the contents that are directly part of to the CRAC data analysis.
Source code that runs the pipeline, CRAC_pipeline_SE_demult_dedup.py.
Annotation files (genome sequence, transcript maps) needed to run CRAC pipeline for data from S288C yeast.
Sequencing adapters and barcodes needed to run CRAC pipeline.
This contains all the outputs generated by src/CRAC_pipeline_SE_demult_dedup.py.
Published data on Ssd1 targets from other studies. Because these data are relatively large and publicly available, we did not directly include them in the repository.
They can be downloaded from the links provided in input_targets/README.md.
Analysis scripts in Rmarkdown (.Rmd) format, that produce many figures in the manuscript. These run after the CRAC pipeline has completed.
Figures that are used for the manuscript, generated by scripts in rmarkdown.
Tabular data and other results generated by scripts in directory rmarkdown.
Motif conservation analysis in alternative fungi.
This analysis takes the CNYTCNYT motif found via CRAC analysis,as calculate by code in rmarkdown/peakanalysis_Ssd1.html.
It then searches for that in 5'UTR regulatory sequences in C. albicans, S. pombe, and A. fumigatus, and reports gene ontology analysis.
Analysis of S. cerevisiae SUN family (SIM1/UTH1/NCA3/SUN4) homologs in ascomycetes in C. albicans, S. pombe, A. fumigatus, and N. crassa, to look for conservation of Ssd1 regulation by CNYTCNYT and CCAACT motifs.
Information on yeast strain construction for Ssd1 analysis. Including construction of the Ssd1-HTP strain that we used for the CRAC analysis, and other yeast strains used in the paper but not directly for CRAC analysis.
It focuses on:
- Ssd1 locus maps detailing sequence and edits
- Plasmid maps for guide RNA plasmids for CRISPR