GRAND-SLAM analysis of mESCs treated with 4sU for 0, 0.75, 3, 12 h from Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435)

This is the processed SLAM-seq data from the progressive labeling data set  0, 0.75, 3, 12 h published in Herzog et al., Nature Methods 2017 (https://doi.org/10.1038/nmeth.4435). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The GRAND-SLAM output was generated using fixed conversion rates (conv 0.025, err 0.00035). The file progressive.tsv.gz is the GRAND-SLAM output table.

To generate the GRAND-SLAM output, first download the mus musculus genome (ensembl v90 FASTA).

https://ftp.ensembl.org/pub/release-102/fasta/mus_musculus/dna/Mus_musculus.GRCm38.dna.primary_assembly.fa.gz

Next index the mouse genome using the here provided mESC-end.gtf file:

gedi -e IndexGenome -s mus_musculus.90.fasta -a mESC-end.gtf -n mESC-ends

Finally call grandslam:

gedi -e Slam -trim5p 15 -reads progressive.cit -genomic mESC-ends -prefix grandslam_t15_fixedparam/progressive -plot -conv 0.025 -err 0.00035 -D -modelall -allGenes

To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.

You can also start from scratch (i.e., the json file):

Prepare the mouse genome (ensembl v90) and the murine rRNA sequence
Run: gedi -e Pipeline -r parallel -j progressive.json rnaseq_mapping.sh report.sh grandslam.sh

To create the grandslam output with fixed parameters follow the steps above.

gedi toolkit 1.0.4     GRAND-SLAM 2.0.7     STAR version 2.5.3a