BayesASE

BayesASE estimates allele-specific expression and tests for allelic imbalance using a Bayesian framework to detect cis-acting regulatory variation.

Key Features:

• Error Reduction Techniques: Incorporates methods to minimize errors in allele-specific sequence data to improve reliability of AI estimates.
• Bayesian Inference: Uses a flexible Bayesian methodology for probabilistic estimation of allelic imbalance and formal hypothesis testing between conditions.
• Modular Workflow Integration: Provides a modular structure enabling integration with computational environments such as Galaxy, Nextflow, and SLURM.
• Testcross Comparative Framework: Implements a testcross design with hypothesis testing of AI between testcrosses to compare multiple alleles with a reduced number of crosses.
• Data-Type Support: Accepts allele-specific sequence counts from sequencing technologies and assays for chromatin accessibility.
• Validation and Performance: Demonstrated validation on a mouse dataset with >90% validation when parent-of-origin effects are present and general validation rates of 60%–80%, with power comparable to reciprocal crosses.

Scientific Applications:

• Allelic Imbalance Detection: Estimating allele-specific expression and testing allelic imbalance in diploid organisms to identify cis-regulatory variation.
• Chromatin Accessibility Allele-Specific Analysis: Applying allele-specific count analysis to chromatin accessibility data to assess regulatory differences.
• Comparative Studies Across Conditions: Comparing allele-specific expression across tissues or environmental conditions within the same genotype.

Methodology:

Applies error reduction techniques, Bayesian probabilistic estimation of allelic imbalance, and a testcross design followed by hypothesis testing of AI between testcrosses.