TEMPLE: analysing population genetic variation at transcription factor binding sites

2016 ◽  
Vol 16 (6) ◽  
pp. 1428-1434
Author(s):  
Maria Litovchenko ◽  
Stefan Laurent
PLoS Genetics ◽  
2020 ◽  
Vol 16 (11) ◽  
pp. e1009189
Author(s):  
Alejandro Martin-Trujillo ◽  
Nihir Patel ◽  
Felix Richter ◽  
Bharati Jadhav ◽  
Paras Garg ◽  
...  

Although DNA methylation is the best characterized epigenetic mark, the mechanism by which it is targeted to specific regions in the genome remains unclear. Recent studies have revealed that local DNA methylation profiles might be dictated by cis-regulatory DNA sequences that mainly operate via DNA-binding factors. Consistent with this finding, we have recently shown that disruption of CTCF-binding sites by rare single nucleotide variants (SNVs) can underlie cis-linked DNA methylation changes in patients with congenital anomalies. These data raise the hypothesis that rare genetic variation at transcription factor binding sites (TFBSs) might contribute to local DNA methylation patterning. In this work, by combining blood genome-wide DNA methylation profiles, whole genome sequencing-derived SNVs from 247 unrelated individuals along with 133 predicted TFBS motifs derived from ENCODE ChIP-Seq data, we observed an association between the disruption of binding sites for multiple TFs by rare SNVs and extreme DNA methylation values at both local and, to a lesser extent, distant CpGs. While the majority of these changes affected only single CpGs, 24% were associated with multiple outlier CpGs within ±1kb of the disrupted TFBS. Interestingly, disruption of functionally constrained sites within TF motifs lead to larger DNA methylation changes at nearby CpG sites. Altogether, these findings suggest that rare SNVs at TFBS negatively influence TF-DNA binding, which can lead to an altered local DNA methylation profile. Furthermore, subsequent integration of DNA methylation and RNA-Seq profiles from cardiac tissues enabled us to observe an association between rare SNV-directed DNA methylation and outlier expression of nearby genes. In conclusion, our findings not only provide insights into the effect of rare genetic variation at TFBS on shaping local DNA methylation and its consequences on genome regulation, but also provide a rationale to incorporate DNA methylation data to interpret the functional role of rare variants.


2021 ◽  
Vol 11 (11) ◽  
pp. 5123
Author(s):  
Maiada M. Mahmoud ◽  
Nahla A. Belal ◽  
Aliaa Youssif

Transcription factors (TFs) are proteins that control the transcription of a gene from DNA to messenger RNA (mRNA). TFs bind to a specific DNA sequence called a binding site. Transcription factor binding sites have not yet been completely identified, and this is considered to be a challenge that could be approached computationally. This challenge is considered to be a classification problem in machine learning. In this paper, the prediction of transcription factor binding sites of SP1 on human chromosome1 is presented using different classification techniques, and a model using voting is proposed. The highest Area Under the Curve (AUC) achieved is 0.97 using K-Nearest Neighbors (KNN), and 0.95 using the proposed voting technique. However, the proposed voting technique is more efficient with noisy data. This study highlights the applicability of the voting technique for the prediction of binding sites, and highlights the outperformance of KNN on this type of data. The study also highlights the significance of using voting.


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