Allelic imbalance (AI) indicates the presence of functional variation in cis regulatory regions. Detecting cis regulatory differences using AI is widespread, yet there is no formal statistical methodology that tests whether AI differs between conditions. The testing for AI involves several complex bioinformatics steps. BayesASE is a complete bioinformatics pipeline that incorporates state-of-the-art error reduction techniques and a flexible Bayesian approach to estimating AI and formally comparing levels of AI between conditions. BayeASE is available in five different implementations:
- A python package
- A bioconda package,
- A Galaxy toolkit
- A Nextflow workflow, and
- A collection of scripts for the SLURM workload manager
To help the user familiarize with the workflow, we include below a demo with a test data set. The demo is focused on bash scripts for the SLURM manager. They can be adapted to other workload managers of be run interactively on Linux machines.
The model included with the package can formally test AI within one condition for three or more replicates and can statistically compare differences in AI across conditions. This includes reciprocal crosses, test-crosses, and comparisons of GxE for the same genotype in replicated experiments. As gene expression affects power for detection of AI, and as expression may vary between conditions, the model explicitly takes coverage into account. The proposed model has low type I and II error under several scenarios, and is robust to large differences in coverage between conditions. The model included with the package reports estimates of AI for each condition, and the corresponding Bayesian evidence as well as a formal statistical evaluation of AI between conditions. The package is completely modular and the bioinformatics steps needed to map reads in a genotype specific manner can be used as input for other statistical models of AI and other methods for read counting can be used and the model described in Novelo et al. 2018 deployed. This model represents an update to the R code provided with the publication as the MCMC algorithm is now implemented in RSTAN (Stan Development Team (2020). "RStan: the R interface to Stan." R package version 2.21.2 and bias is allowed to vary between conditions and more than 2 conditions can be compared. This is a very general implementation.
The workflow is summarized in this figure.
If you would like to try if you have all the needed software, you can run this few lines of code.
We suggest that at first you clone our repo in one folder of your choice. This will ensure you have all the needed test input files and scripts in the correct place.
Download the repository in your system; this will result in a BayesASE folder containing the whole project. Enter the directory with
cd BayesASE
The input files needed to run the test data are in the example_in folder. The results will be stored in the example_out folder.
Run the demo steps in the specified order.
sbatch hpc/sbatch/run_ase_genotype_specific_references_testData.sbatch
sbatch hpc/sbatch/run_ase_align_and_count_testData.sbatch
sbatch hpc/sbatch/run_ase_summarize_counts_testData.sbatch
sbatch hpc/sbatch/run_ase_create_design_file_4_prior_calc.sbatch
sbatch hpc/sbatch/run_ase_prior_calculation_testData.sbatch
sbatch hpc/sbatch/run_ase_merge_priors_2_comparate_testData.sbatch
sbatch hpc/sbatch/run_ase_bayesian.sbatch
Additional details can be found in the guide for the Galaxy implementation.