Bayesian modelling of high-throughput sequencing assays with malacoda

PLoS Computational Biology
Andrew R. GhaziChad A. Shaw

Abstract

NGS studies have uncovered an ever-growing catalog of human variation while leaving an enormous gap between observed variation and experimental characterization of variant function. High-throughput screens powered by NGS have greatly increased the rate of variant functionalization, but the development of comprehensive statistical methods to analyze screen data has lagged. In the massively parallel reporter assay (MPRA), short barcodes are counted by sequencing DNA libraries transfected into cells and the cell's output RNA in order to simultaneously measure the shifts in transcription induced by thousands of genetic variants. These counts present many statistical challenges, including overdispersion, depth dependence, and uncertain DNA concentrations. So far, the statistical methods used have been rudimentary, employing transformations on count level data and disregarding experimental and technical structure while failing to quantify uncertainty in the statistical model. We have developed an extensive framework for the analysis of NGS functionalization screens available as an R package called malacoda (available from github.com/andrewGhazi/malacoda). Our software implements a probabilistic, fully Bayesian model of screen data. T...Continue Reading

References

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Jul 28, 2018·Bioinformatics·Andrew R GhaziChad A Shaw
Sep 4, 2019·Genome Biology·Tal AshuachNir Yosef

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Methods Mentioned

BETA
transfection
ChIP-seq
PCR

Software Mentioned

mpradesigntools
- Toolkit
R
Stan
MPRAnalyze
GitHub
Automatic Differentiation Variational Inference
RData
FASTX
QuASAR

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