PMID- 28870226 OWN - NLM STAT- MEDLINE VI - 18 IP - 1 TI - BayFish: Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells. PG - 164 LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't PT - Research Support, N.I.H., Extramural PL - England TA - Genome Biol JT - Genome biology JID - 100960660 IS - 1474-760X (Electronic) LID - 10.1186/s13059-017-1297-9 [doi] FAU - Gómez-Schiavon, Mariana AU - Gómez-Schiavon M AD - Program in Computational Biology & Bioinformatics, Duke University, Durham, NC, USA. AD - Present address: Department of Biochemistry & Biophysics, University of California, San Francisco, CA, USA. FAU - Chen, Liang-Fu AU - Chen LF AD - Department of Neurobiology, Duke University, Durham, NC, USA. FAU - West, Anne E AU - West AE AD - Department of Neurobiology, Duke University, Durham, NC, USA. west@neuro.duke.edu. FAU - Buchler, Nicolas E AU - Buchler NE AUID- ORCID: 0000-0003-3940-3432 AD - Department of Biology, Duke University, Durham, NC, USA. nicolas.buchler@duke.edu. AD - Department of Physics, Duke University, Durham, NC, USA. nicolas.buchler@duke.edu. AD - Center for Genomic & Computational Biology, Duke University, Durham, NC, USA. nicolas.buchler@duke.edu. IS - 1474-7596 (Linking) RN - 0 (Basic Helix-Loop-Helix Transcription Factors) RN - 0 (Npas4 protein, mouse) RN - 63231-63-0 (RNA) SB - IM MH - Animals MH - Basic Helix-Loop-Helix Transcription Factors/genetics MH - Bayes Theorem MH - Cells, Cultured MH - Computational Biology/*methods MH - Female MH - *In Situ Hybridization, Fluorescence MH - Male MH - Mice MH - Models, Genetic MH - Neurons/metabolism MH - Probability MH - RNA/*genetics MH - *Software MH - Stochastic Processes MH - Transcription, Genetic OTO - NOTNLM OT - *Bayesian posterior probability OT - *Chemical master equation OT - *Gene expression OT - *Likelihood methods OT - *Monte Carlo sampling OT - *Stochastic process PMC - PMC5582403 DCOM- 20180507 LR - 20181113 DP - 20170904 DEP - 20170904 AB - 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.