ArticleBioinformatics (Oxford, England)2022
Scalable inference of transcriptional kinetic parameters from MS2 time series data.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions.Bulletin of mathematical biology · 2024Article
- Context-dependent TGFβ family signalling in cell fate regulation.Nature reviews. Molecular cell biology · 2023Review
- Article
- BurstDECONV: a signal deconvolution method to uncover mechanisms of transcriptional bursting in live cells.Nucleic acids research · 2023Article
- Competing constraints shape the nonequilibrium limits of cellular decision-making.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage.PLoS computational biology · 2021Article
- Live imaging and quantitation of nascent transcription using the MS2/MCP system in theSTAR protocols · 2021Article
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4 authors.
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Abstract
motivationThe MS2-MCP (MS2 coat protein) live imaging system allows for visualization of transcription dynamics through the introduction of hairpin stem-loop sequences into a gene. A fluorescent signal at the site of nascent transcription in the nucleus quantifies mRNA production. Computational modelling can be used to infer the promoter states along with the kinetic parameters governing transcription, such as promoter switching frequency and polymerase loading rate. However, modelling of the fluorescent trace presents a challenge due its persistence; the observed fluorescence at a given time point depends on both current and previous promoter states. A compound state Hidden Markov Model (cpHMM) was recently introduced to allow inference of promoter activity from MS2-MCP data. However, the computational time for inference scales exponentially with gene length and the cpHMM is therefore not currently practical for application to many eukaryotic genes.
resultsWe present a scalable implementation of the cpHMM for fast inference of promoter activity and transcriptional kinetic parameters. This new method can model genes of arbitrary length through the use of a time-adaptive truncated compound state space. The truncated state space provides a good approximation to the full state space by retaining the most likely set of states at each time during the forward pass of the algorithm. Testing on MS2-MCP fluorescent data collected from early Drosophila melanogaster embryos indicates that the method provides accurate inference of kinetic parameters within a computationally feasible timeframe. The inferred promoter traces generated by the model can also be used to infer single-cell transcriptional parameters. AVAILABILITY AND IMPLEMENTATION: Python implementation is available at https://github.com/ManchesterBioinference/burstInfer, along with code to reproduce the examples presented here. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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