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

Genome Biol. 2017 Sep 4;18(1):164. doi: 10.1186/s13059-017-1297-9.

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: Bayesian posterior probability; Chemical master equation; Gene expression; Likelihood methods; Monte Carlo sampling; Stochastic process.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

MeSH terms

  • Animals
  • Basic Helix-Loop-Helix Transcription Factors / genetics
  • Bayes Theorem
  • Cells, Cultured
  • Computational Biology / methods*
  • Female
  • In Situ Hybridization, Fluorescence*
  • Male
  • Mice
  • Models, Genetic
  • Neurons / metabolism
  • Probability
  • RNA / genetics*
  • Software*
  • Stochastic Processes
  • Transcription, Genetic

Substances

  • Basic Helix-Loop-Helix Transcription Factors
  • Npas4 protein, mouse
  • RNA