Statistical inference for a quasi birth-death model of RNA transcription.

Computer Simulation Likelihood Functions RNA / genetics Sequence Analysis, RNA / methods
Erlangization technique Maximum likelihood estimation Quasi birth–death process RNA transcription

Journal

BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194

Informations de publication

Date de publication:
26 Mar 2022
Historique:
received: 28 09 2021
accepted: 11 03 2022
entrez: 29 3 2022
pubmed: 30 3 2022
medline: 31 3 2022
Statut: epublish

Résumé

A birth-death process of which the births follow a hypoexponential distribution with L phases and are controlled by an on/off mechanism, is a population process which we call the on/off-seq-L process. It is a suitable model for the dynamics of a population of RNA molecules in a single living cell. Motivated by this biological application, our aim is to develop a statistical method to estimate the model parameters of the on/off-seq-L process, based on observations of the population size at discrete time points, and to apply this method to real RNA data. It is shown that the on/off-seq-L process can be seen as a quasi birth-death process, and an Erlangization technique can be used to approximate the corresponding likelihood function. An extensive simulation-based numerical study is carried out to investigate the performance of the resulting estimation method. A statistical method is presented to find maximum likelihood estimates of the model parameters for the on/off-seq-L process. Numerical complications related to the likelihood maximization are identified and analyzed, and solutions are presented. The proposed estimation method is a highly accurate method to find the parameter estimates. Based on real RNA data, the on/off-seq-3 process emerges as the best model to describe RNA transcription.

Sections du résumé

BACKGROUND BACKGROUND
A birth-death process of which the births follow a hypoexponential distribution with L phases and are controlled by an on/off mechanism, is a population process which we call the on/off-seq-L process. It is a suitable model for the dynamics of a population of RNA molecules in a single living cell. Motivated by this biological application, our aim is to develop a statistical method to estimate the model parameters of the on/off-seq-L process, based on observations of the population size at discrete time points, and to apply this method to real RNA data.
METHODS METHODS
It is shown that the on/off-seq-L process can be seen as a quasi birth-death process, and an Erlangization technique can be used to approximate the corresponding likelihood function. An extensive simulation-based numerical study is carried out to investigate the performance of the resulting estimation method.
RESULTS AND CONCLUSION CONCLUSIONS
A statistical method is presented to find maximum likelihood estimates of the model parameters for the on/off-seq-L process. Numerical complications related to the likelihood maximization are identified and analyzed, and solutions are presented. The proposed estimation method is a highly accurate method to find the parameter estimates. Based on real RNA data, the on/off-seq-3 process emerges as the best model to describe RNA transcription.

Identifiants

DOI: 10.1186/s12859-022-04638-6 PMID: 35346020 PMC: PMC8961911
pubmed: 35346020
doi: 10.1186/s12859-022-04638-6
pii: 10.1186/s12859-022-04638-6
pmc: PMC8961911
doi:

Substances chimiques

RNA 63231-63-0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105

Informations de copyright

© 2022. The Author(s).

Références

PLoS Comput Biol. 2016 Oct 28;12(10):e1005174
pubmed: 27792724
J Mol Biol. 2011 Oct 7;412(5):754-71
pubmed: 21371479
Nat Rev Genet. 2005 Jun;6(6):451-64
pubmed: 15883588
Annu Rev Biochem. 1985;54:171-204
pubmed: 3896120
BMC Syst Biol. 2011 Sep 25;5:149
pubmed: 21943372
Bioinformatics. 2016 May 1;32(9):1346-52
pubmed: 26722120

Auteurs

Mathisca de Gunst (M)

Department of Mathematics, Vrije Universiteit Amsterdam, de Boelelaan 1111, 1081 HV, Amsterdam, The Netherlands.

Michel Mandjes (M)

Korteweg-de Vries Institute for Mathematics, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands.
Eurandom, Eindhoven University of Technology, Eindhoven, The Netherlands.
Amsterdam Business School, Faculty of Economics and Business, University of Amsterdam, Amsterdam, The Netherlands.

Birgit Sollie (B)

Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands. birgit.corporaal@gmail.com.

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Simulation numérique › Statistical inference for a quasi birth-death...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Probabilité › Fonctions de vraisemblance › Statistical inference for a quasi birth-death...
questionsmedicales.fr › Acides nucléiques, nucléotides et nucléosides › Acides nucléiques › ARN › Statistical inference for a quasi birth-death...
questionsmedicales.fr › Techniques d'investigation › Techniques génétiques › Analyse de séquence › Analyse de séquence d'ARN › Statistical inference for a quasi birth-death...