malacoda

malacoda implements a Bayesian framework to model next-generation sequencing (NGS) counts from massively parallel reporter assays (MPRA) and CRISPR screens to infer functional effects of genetic variants.


Key Features:

  • Probabilistic Bayesian Modeling: Implements a fully Bayesian framework to model sequencing data from high-throughput assays and quantify uncertainty.
  • Negative Binomial with Gamma Priors: Models sequencing counts using a negative binomial distribution with gamma priors to address overdispersion and sequencing depth dependence.
  • Empirical Prior Estimation: Estimates prior distributions empirically from high-throughput assay data.
  • Integration of External Annotations: Incorporates external annotations such as ENCODE and DeepSea to inform and refine prior distributions.
  • Comprehensive Statistical Framework: Models experimental and technical structure of assays rather than relying on simple transformations of count-level data.
  • Quality Control and Utility Functions: Provides quality-control routines and automated barcode counting plus visualization functions for data inspection.

Scientific Applications:

  • MPRA variant characterization: Measures transcriptional shifts induced by thousands of genetic variants in MPRA experiments to functionally characterize variants.
  • CRISPR screen analysis: Analyzes CRISPR screens using sequencing counts to infer functional effects of perturbations.
  • Integrative variant interpretation: Combines assay data with external annotations to refine inference of variant function and link genomic variation to phenotypic outcomes.
  • Experimental validation support: Produces candidate variant effect estimates that can be validated with experimental assays such as luciferase reporter assays.

Methodology:

Uses Bayesian statistics with a negative binomial likelihood and gamma priors to model sequencing counts, performs empirical prior estimation from high-throughput data, accounts for input library preparation and sequencing depth, and optionally incorporates external annotations such as ENCODE and DeepSea.

Topics

Details

Tool Type:
library
Programming Languages:
R, C++
Added:
1/18/2021
Last Updated:
2/19/2021

Operations

Publications

Ghazi AR, Kong X, Chen ES, Edelstein LC, Shaw CA. Bayesian modelling of high-throughput sequencing assays with malacoda. PLOS Computational Biology. 2020;16(7):e1007504. doi:10.1371/journal.pcbi.1007504. PMID:32692749. PMCID:PMC7394446.

PMID: 32692749
PMCID: PMC7394446
Funding: - Foundation for the National Institutes of Health: R01HL128234