Nonlinear physics opens a new paradigm for accurate transcription start site prediction

BMC Bioinformatics

Research

José Antonio Barbero-Aparicio¹  César Ignacio García-Osorio¹  Javier Pérez-Rodríguez²  Nicolás García-Pedrajas³  Santiago Cuesta-Lopez⁴ 

[1] Departamento de Informática, Universidad de Burgos, Avda. de Cantabria s/n, 09006, Burgos, Spain;Departamento de Métodos Cuantitativos, Universidad de Loyola Andalucía, Escritor Castilla Aguayo, 4, 14004, Córdoba, Spain;Department of Computing and Numerical Analysis, University of Córdoba, Edificio Albert Einstein, Campus de Rabanales, 14071, Córdoba, Spain;Universidad de Burgos, Hospital del Rey, s/n, 09001, Burgos, Spain;ICAMCyL Foundation, Internacional Center for Advanced Materials and Raw Materials of Castilla y León, León Technology Park, main building, first floor, offices 106-108, C/Julia Morros s/n, Armunia, 24009, León, Spain;

关键词: DNA modelling;    DNA breathing;    Machine Learning;    TSS prediction;    SVM;    String kernels;   

DOI  :  10.1186/s12859-022-05129-4

 received in 2022-09-29, accepted in 2022-12-27,  发布年份 2022

来源: Springer

【 摘 要 】

There is evidence that DNA breathing (spontaneous opening of the DNA strands) plays a relevant role in the interactions of DNA with other molecules, and in particular in the transcription process. Therefore, having physical models that can predict these openings is of interest. However, this source of information has not been used before either in transcription start sites (TSSs) or promoter prediction. In this article, one such model is used as an additional information source that, when used by a machine learning (ML) model, improves the results of current methods for the prediction of TSSs. In addition, we provide evidence on the validity of the physical model, as it is able by itself to predict TSSs with high accuracy. This opens an exciting avenue of research at the intersection of statistical mechanics and ML, where ML models in bioinformatics can be improved using physical models of DNA as feature extractors.

【 授权许可】

CC BY   
© The Author(s) 2022

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