TT-seq and RNA-seq reads were aligned using STAR and filtered with Samtools. HTSeq was used to calculate the read counts for different featues (please see https://github.com/cramerlab/TT-seq_analysis)
We included in our analysis only major isoforms with 70% or higher prevalenve per gene in both DMSO and Pla-B. In order to create the major isoforms annotation:
Download ncbi Refseq hg38 genome assembly from https://genome.ucsc.edu/cgi-bin/hgTables
(clade: Mammal, genome: Human, assembly: Dex.2013 GRCh38/h38, group: Gene and Gene Predictions, track: NCBI RefSeq, table: RefSeq Curated)
R_scripts/create_anno.R
R_scripts/create_extended.R
bash/salmon.sh
R_scripts/major_isoform_selection.R.
R_scripts/intronless_annotation.R
- Create Exon based spliced junction read count lists using bam files:
R_scripts/exon_based_spliced_junction.R - Calculate the splicing ratio and plot the results:
R_scripts/splicing_ratio.R
R_scripts/splicing_affected_unaffected.R
- Create Unique Transcribed Bases Rle Tracks for TT-seq and Fragment End Rle Tracks for mNET-seq:
R_scripts/TTseq_coverage_rle_tracks.R
R_scripts/mNETseq_coverage_rle_tracks.R - Create coverage files from rle tracks:
(for mNET seq create coverage using ProcessedData/mNETseq/FragmentEndRleTracks)
R_scripts/coverage.R - Calculate the RNA amount pre cell (based on TT-seq spike-ins coverage):
R_scripts/RNA_amount_per_cell.R - Calculate the elongation velocity using TT-seq and mNET-seq normalized coverage:
R_scripts/elong_velocity.R