BayFish: Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells.

PMID:28870226

IF: 17.906

Cited by: 27

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Abstract

Single-molecule RNA fluorescence in situ hybridization (smFISH) provides unparalleled resolution in the measurement of the abundance and localization of nascent and mature RNA transcripts in fixed, single cells. We developed a computational pipeline (BayFish) to infer the kinetic parameters of gene expression from smFISH data at multiple time points after gene induction. Given an underlying model of gene expression, BayFish uses a Monte Carlo method to estimate the Bayesian posterior probability of the model parameters and quantify the parameter uncertainty given the observed smFISH data. We tested BayFish on synthetic data and smFISH measurements of the neuronal activity-inducible gene Npas4 in primary neurons.

Keywords

Gene Expression

smFISH

Bayesian posterior probability

Chemical master equation

Gene expression

Likelihood methods

Monte Carlo sampling

Stochastic process

MeSH terms

Animals

Basic Helix-Loop-Helix Transcription Factors

Bayes Theorem

Cells, Cultured

Computational Biology

Female

In Situ Hybridization, Fluorescence

Male

Mice

Models, Genetic

Neurons

Probability

RNA

Software

Stochastic Processes

Transcription, Genetic

Authors

Gómez-Schiavon, Mariana

Chen, Liang-Fu

West, Anne E

Buchler, Nicolas E

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