OncMTR

Oncology-specific MTR score calculated from the gnomAD (v2) cohort:

• Citation
• MTR
• OncMTR

---

Citation

Cancer-driving mutations are enriched in genic regions intolerant to germline variation

Dimitrios Vitsios^†, Ryan S Dhindsa, Dorota Matelska, Jonathan Mitchell, Xuequing Zou, Joshua Armenia, Fengyuan Hu, Quanli Wang, Ben Sidders, Andrew R Harper, Slavé Petrovski^†

Science Advances Vol 8, Issue 34 (2022) 10.1126/sciadv.abo6371

MTR

Calculate original and AB_MEDIAN-filtered versions of MTR

• Pre-generated data: Snpeff annotations on all exons for all possible mutations

out/snpeff_results/all_exons_mutations.snpeff_annotation.vcf

• Step 1.

cd modules/snpeff_annotation
sbatch ./submit.snpEff.gnomad_mutations.sh

# Note - Expected input VCF (e.g. from gnomAD)]:
data/gnomad_GRCh38/snpeff_input.gnomad_exons_mutations.pass.vcf

• Step 2.

cd modules/snpeff_post_process
sbatch ./submit_parse_snpeff_output.sh 

• Step 3. (same dir as in step 2)

python collate_hits_per_transcript.py  

• Step 4.

cd modules/mtr

./run_all.sh

or step by step

python -u calculate_mtr_scores.py -d gnomad -w 31 -s mtr -f  no
python -u calculate_mtr_scores.py -d gnomad -w 31 -s mtr-ab -f  yes

# submitted via
sbatch submit_mtr_calc.sh

OncMTR

Compile OncMTR from MTR and MTR-AB results

• compile_oncmtr.py

# e.g.
python compile_oncmtr.py -d gnomad -w 31

Annotate OncMTR signals with variants (e.g. clinvar/CGC) and create plots

• annotate_oncmtr_signals.py

# e.g.
python annotate_oncmtr_signals.py -d gnomad -w 31

# or with sbatch
sbatch submit_annotate_oncmtr_signals.sh

Get genomic coordinates for each OncMTR score:

• merge_oncmtr_scores_with_genomic_coords.py

# e.g.
python merge_oncmtr_scores_with_genomic_coords.py -d gnomad -w 31

# or with sbatch
sbatch submit_merge_with_genomic_coords.sh

Get variant level percentile scores

sbatch submit_get_pct_scores.sh

Get OncMTR distribution (based on various metrics) in CGC Tier 1/2 vs rest of exome

python plot_cgc_geneset_distributions.py