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Simulation scripts for "Mutational bias and the co-evolution of protein and splicing code" paper

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Mutational bias and the protein-code shape the evolution of splicing enhancers

Stephen Rong, Luke Buerer, Christy L. Rhine, Jing Wang, Kamil J. Cygan, and William G. Fairbrother

Scripts for the evolutionary simulations and mathematical model described in the paper. Simulations are initialized on a genome of random sequences or human exon sequences. Substitutions are drawn in proportion to estimated relative mutation (ERM) rates from Carlson et al. (Nature Communiations 2018) based on 7-mer sequence contexts. Simulations are run under models with purifying selection (exons) or without purifying selection (introns)

Scripts written by Stephen Rong (PhD Candidate, Fairbrother Lab, Brown University). Have questions? Contact stephen[underscore]rong[at]brown[dot]edu or post a git issue.

Last updated: March 25th, 2020

Contents:

data/ contains data files required for performing simulations with the exception of the file data/hg19-unipAliSwissprot-introns.txt.gz (292 Mb), which can instead be downloaded and gzipped from the UCSC Table Browser (GRCh37/hg19 assembly, Genes and Gene Predictions group, UniProt track, SwissProt Aln. table, introns only, accessed on 2018/07/05).

results/ contains simulation output:

  • figures/ contains plots of rescaled ERM rates generated by scripts/mu_sims_matrix.R, and example output generated by scripts/analytical_model_double.R

  • simulations/ is a placeholder for simulation output, and includes example simulation output from running scripts/temp_scripts/test.sh.

scripts/ contains the following files:

  • mu_sims_matrix.R is used to rescale ERM rates from Carlson et al. (Nat. Comm. 2018) in order to vary overall levels of mutational bias as described in the paper. Output already included in data/.

  • mu_sims_module.py is a module containing functions for loading ERM rates, EI scores, Rosenberg scores, and Grantham scores, initializing random sequences, initializing from user-supplied sequences, running the simulations, and tracking summary statistics.

  • mu_sims_random.py, mu_sims_exon.py, mu_sims_intron.py specify the simulations initialized with random sequences, human exonic sequences, and human intronic sequences, respectively (mu_sims_intron.py is only used to compute genome-wide means for introns).

  • mu_sims_exon_sh.py, mu_sims_intron_sh.py, mu_sims_random_sh.py are used to generate slurm job files in scripts/temp_scripts/, splitting runs into multiple jobs, and also to generate mu_sims_exon.sh, mu_sims_intron.sh, mu_sims_random.sh for submitting all jobs. Run *_sh.py and then *.sh to output simulations to results/simulations/.

  • mu_sims_random_scaled_0.py, mu_sims_random_scaled_50.py, mu_sims_random_scaled_100.py, mu_sims_random_scaled_200.py, mu_sims_random_scaled_0_sh.py, mu_sims_random_scaled_50_sh.py, mu_sims_random_scaled_100_sh.py, mu_sims_random_scaled_200_sh.py are variations of the mu_sims_random.py and mu_sims_random_sh.py files for vaying levels of mutational bias. Run *_sh.py and then *.sh to output simulations to results/simulations/.

  • temp_scripts/test.sh produces example simulation output. Output already included in results/simulations/.

  • analytical_model_double.R solves for the mutation-selection balance of exon frequency, intron frequency, and exon vs intron motif ratio for mutation rate matrices with different levels of mutational bias, and for different levels of purifying selection, used to create Supplementary Fig. 7a-c.

Dependencies:

Python (>=2.7.14), with NumPy (>=1.14.2), pandas (>=0.22.0), and Biopython (>=1.68)

R (>=3.4.4), with tidyverse (>=1.2.1), ggthemr (>=1.1.0)

Simulations were performed on the Fairbrother Lab server running slurm.

MIT License:

Copyright 2020 Stephen Rong

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Simulation scripts for "Mutational bias and the co-evolution of protein and splicing code" paper

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