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Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model
Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain tr...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10073913/ https://www.ncbi.nlm.nih.gov/pubmed/37035293 http://dx.doi.org/10.1098/rsos.221057 |
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author | Luo, Songhao Zhang, Zhenquan Wang, Zihao Yang, Xiyan Chen, Xiaoxuan Zhou, Tianshou Zhang, Jiajun |
author_facet | Luo, Songhao Zhang, Zhenquan Wang, Zihao Yang, Xiyan Chen, Xiaoxuan Zhou, Tianshou Zhang, Jiajun |
author_sort | Luo, Songhao |
collection | PubMed |
description | Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain transcriptional bursting with Markovian assumptions. However, growing evidence suggests that the gene-state dwell times are generally non-exponential, as gene-state switching is a multi-step process in organisms. Therefore, interpretable non-Markovian mathematical models and efficient statistical inference methods are urgently required in investigating transcriptional burst kinetics. We develop an interpretable and tractable model, the generalized telegraph model (GTM), to characterize transcriptional bursting that allows arbitrary dwell-time distributions, rather than exponential distributions, to be incorporated into the ON and OFF switching process. Based on the GTM, we propose an inference method for transcriptional bursting kinetics using an approximate Bayesian computation framework. This method demonstrates an efficient and scalable estimation of burst frequency and burst size on synthetic data. Further, the application of inference to genome-wide data from mouse embryonic fibroblasts reveals that GTM would estimate lower burst frequency and higher burst size than those estimated by CTM. In conclusion, the GTM and the corresponding inference method are effective tools to infer dynamic transcriptional bursting from static single-cell snapshot data. |
format | Online Article Text |
id | pubmed-10073913 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-100739132023-04-06 Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model Luo, Songhao Zhang, Zhenquan Wang, Zihao Yang, Xiyan Chen, Xiaoxuan Zhou, Tianshou Zhang, Jiajun R Soc Open Sci Biochemistry, Cellular and Molecular Biology Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain transcriptional bursting with Markovian assumptions. However, growing evidence suggests that the gene-state dwell times are generally non-exponential, as gene-state switching is a multi-step process in organisms. Therefore, interpretable non-Markovian mathematical models and efficient statistical inference methods are urgently required in investigating transcriptional burst kinetics. We develop an interpretable and tractable model, the generalized telegraph model (GTM), to characterize transcriptional bursting that allows arbitrary dwell-time distributions, rather than exponential distributions, to be incorporated into the ON and OFF switching process. Based on the GTM, we propose an inference method for transcriptional bursting kinetics using an approximate Bayesian computation framework. This method demonstrates an efficient and scalable estimation of burst frequency and burst size on synthetic data. Further, the application of inference to genome-wide data from mouse embryonic fibroblasts reveals that GTM would estimate lower burst frequency and higher burst size than those estimated by CTM. In conclusion, the GTM and the corresponding inference method are effective tools to infer dynamic transcriptional bursting from static single-cell snapshot data. The Royal Society 2023-04-05 /pmc/articles/PMC10073913/ /pubmed/37035293 http://dx.doi.org/10.1098/rsos.221057 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Biochemistry, Cellular and Molecular Biology Luo, Songhao Zhang, Zhenquan Wang, Zihao Yang, Xiyan Chen, Xiaoxuan Zhou, Tianshou Zhang, Jiajun Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model |
title | Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model |
title_full | Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model |
title_fullStr | Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model |
title_full_unstemmed | Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model |
title_short | Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model |
title_sort | inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model |
topic | Biochemistry, Cellular and Molecular Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10073913/ https://www.ncbi.nlm.nih.gov/pubmed/37035293 http://dx.doi.org/10.1098/rsos.221057 |
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