The assessment of histone acetylation marks in the vicinity of transcription factor binding sites in human CD4 + T cells using information theory methods. (June 2020)

• Record Type: Journal Article
• Authors: Banirazi Motlagh, Nafiseh; Mohammadpour Esfahani, Bahram; Ashrafi, Behnoosh; Zare-Mirakabad, Fatemeh
• Is Part Of: Computational biology and chemistry. Volume 86(2020)
• Publication Date: 2020-06
• Subjects: Transcription factor -- Histone acetylation -- Information theory -- Multilayer perceptron neural network; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107232
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Finding the correlation between histone acetylation marks (HAms) and transcription factor binding sites (TFBSs). Using information theory methods to evaluate the importance of HAms in distinguishing TFBSs and random positions. Using the selected HAms as input features for multilayer perceptron neural network to predict TFBSs. Abstract: The genetic information encoded in structural genes is decoded by an intracellular process called gene expression. This mechanism is regulated by epigenetic processes such as histone acetylation. Histone acetylation, which happens in nucleosomes, exposes DNA (genome) to transcription factors. Therefore, the correlation between histone acetylation and gene expression has been assessed as a fundamental issue in many previous studies. In the proposed research, we investigate which marks of histone acetylation are informative and which ones are redundant in the vicinity of SP1 transcription factor binding sites, in human CD4 + T cell. To achieve this, we use information theory methods. Subsequently, we apply a multilayer perceptron neural network to show that the selected histone acetylation marks by information theory methods are sufficiently informative. Finally, we use the neural network to predict binding sites of 17 other transcription factors on chromosomes 1 and 2. The results suggest that information conveyed by the selected histone acetylation marks are equivalent to that of all 18 marks associated with SP1 transcription factor binding sites on chromosome 1. Furthermore, almost 91.75 % of SP1 binding sites of chromosome 2 are predicted by the selected histone acetylation marks while all 18 marks predict 90.56 % correctly. Moreover, the selected histone acetylation marks are efficient at predicting 17 other types of transcription factor binding sites.