Processing splicing inhibition and DMS-MaPseq data for introns and analyzing DMS-guided secondary structure predictions.
This repository contains:
- Data from DMS-MaPseq after splicing inhibition:
- Normalized reactivity data, raw mutational frequencies, and read coverage across different sequence sets in
analyze_run/combined_1221 - DMS-guided secondary structure predictions across different sequence sets in
secstruct_features/rnastructure_sherlock_1221/
- Normalized reactivity data, raw mutational frequencies, and read coverage across different sequence sets in
- Evaluating and processing DMS-MaPseq data in
analyze_run/- Checking DMS modification patterns and DMS reactivity for rRNA
- Evaluating the correlation between replicate experiments
- Analyzing the impact of splicing inhibition by pladB treatment
- Writing raw DMS-MaPseq output to convenient files
- Evaluating DMS structure predictions for control structures in
control_structs/- Control structure native structures in
control_structs/native_secstructs/ - Predicted structures in
control_structs/control_secstruct_bpps/ - Evaluating PPV and sensitivity for structure predictions with different approaches
- Control structure native structures in
- Sequence files for different intron / control sequence sets in
intron_annot/- .fa for fasta format
- .dat for sequence + branchpoint and strand direction)
- Secondary structure analysis for DMS-guided secondary structure predictions in
secstruct_features/- Compute features: Gini index, zipper stems and end stems, longest stem, maximum path length, average DMS accessibility
- Compare features between introns and coding regions: statistical comparisons and plots
- Cluster introns by secondary structure features: hierarchical clustering, tSNE, PCA
- Write table compiling intron information, secondary structures, and structural features
Each subdirectory includes a README detailing how to produce analysis figures, along with example figures from each command.
- scipy v1.10.1
- numpy v1.24.3
- matplotlib v3.7.1
- seaborn v0.12.2
- pandas v2.0.2
- arnie: https://github.com/DasLab/arnie
- Vienna2: https://www.tbi.univie.ac.at/RNA/
Installation: Install all requirements (expected time: 1-2 hours) and then clone this repo.