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Bayesian modelling of high-throughput sequencing assays with malacoda.

Andrew R Ghazi1, Xianguo Kong2, Ed S Chen3

  • 1Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, Texas, United States of America.

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|July 22, 2020
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Summary

A new R package, malacoda, provides a robust Bayesian framework for analyzing next-generation sequencing (NGS) functional genomics screens. This tool addresses statistical challenges in massively parallel reporter assays (MPRAs) to accurately characterize genetic variant function.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Next-generation sequencing (NGS) has expanded the catalog of human genetic variation, but experimental characterization of variant function remains a challenge.
  • High-throughput screens accelerate variant functionalization, yet comprehensive statistical methods for analyzing screen data have lagged.
  • Massively parallel reporter assays (MPRAs) measure transcriptional changes from thousands of genetic variants but present statistical complexities like overdispersion and depth dependence.

Purpose of the Study:

  • To develop and present an extensive statistical framework for analyzing NGS functionalization screens.
  • To introduce the R package 'malacoda' that implements a probabilistic, fully Bayesian model for MPRA data analysis.
  • To enable the integration of external annotations for more informative prior estimations and improved data interpretation.

Main Methods:

  • Developed a probabilistic, fully Bayesian model using the negative binomial distribution with gamma priors to analyze sequencing counts.
  • The model accounts for experimental factors such as input library preparation and sequencing depth.
  • Incorporated empirical prior estimation leveraging high-throughput data and allowed integration of external annotations (e.g., ENCODE, DeepSea).

Main Results:

  • The 'malacoda' R package offers automated barcode counting, quality control, and visualization functions.
  • Validation using literature data, simulated assays, and primary MPRA data demonstrated the method's robustness.
  • Luciferase assays confirmed experimental validation of selected variants, including those with method disagreements and those uniquely identified using external annotations.

Conclusions:

  • The 'malacoda' package provides a statistically rigorous and comprehensive framework for analyzing NGS functionalization screens, particularly MPRAs.
  • This approach addresses key statistical challenges, improves the accuracy of variant function characterization, and facilitates data integration.
  • The developed software enhances the utility of high-throughput screening for understanding genetic variation and its functional impact.