scholarly journals Enriched transcription factor binding sites in hypermethylated gene promoters in drug resistant cancer cells

2008 ◽  
Vol 24 (16) ◽  
pp. 1745-1748 ◽  
Author(s):  
Meng Li ◽  
Hyun-il Henry Paik ◽  
Curt Balch ◽  
Yoosung Kim ◽  
Lang Li ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Cancer Cells ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Gene Promoters ◽  
Drug Resistant ◽  
Factor Binding

scholarly journals L1PA2 transposons contribute abundant regulatory sequences in MCF7 breast cancer cell line

2020 ◽  
Author(s):  
Jiayue-Clara Jiang ◽  
Joseph Rothnagel ◽  
Kyle Upton
Keyword(s):  
Breast Cancer ◽  
Transcription Factor ◽  
Cancer Cells ◽  
Binding Sites ◽  
Breast Cancer Cells ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Regulatory Sequences ◽  
Factor Binding ◽  
Regulatory Potential

ABSTRACTTransposons, a type of repetitive DNA elements, can contribute cis-regulatory sequences and regulate the expression of human genes. L1PA2 is a hominoid-specific subfamily of LINE1 transposons, with approximately 4,940 copies in the human genome. Individual transposons have been demonstrated to contribute specific biological functions, such as cancer-specific alternate promoter activity for the MET oncogene, which is correlated with enhanced malignancy and poor prognosis in cancer. Given the sequence similarity between L1PA2 elements, we hypothesise that transposons within the L1PA2 subfamily likely have a common regulatory potential and may provide a mechanism for global genome regulation. Here we show that in breast cancer, the regulatory potential of L1PA2 is not limited to single transposons, but is common within the subfamily. We demonstrate that the L1PA2 subfamily is an abundant reservoir of transcription factor binding sites, the majority of which cluster in the LINE1 5’UTR. In MCF7 breast cancer cells, over 27% of L1PA2 transposons harbour binding sites of functionally interacting, cancer-associated transcription factors. The ubiquitous and replicative nature of L1PA2 makes them an exemplary vector to disperse co-localised transcription factor binding sites, facilitating the co-ordinated regulation of genes. In MCF7 cells, L1PA2 transposons also supply transcription start sites to up-regulated transcripts. These transcriptionally active L1PA2 transposons display a cancer-specific active epigenetic profile, and likely play an oncogenic role in breast cancer aetiology. Overall, we show that the L1PA2 subfamily contributes abundant regulatory sequences in breast cancer cells, and likely plays a global role in modulating the tumorigenic state in breast cancer.

scholarly journals Whole-Genome rVISTA: a tool to determine enrichment of transcription factor binding sites in gene promoters from transcriptomic data

2013 ◽  
Vol 29 (16) ◽  
pp. 2059-2061 ◽  
Author(s):  
Inna Dubchak ◽  
Matthew Munoz ◽  
Alexandre Poliakov ◽  
Nathan Salomonis ◽  
Simon Minovitsky ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Whole Genome ◽  
Gene Promoters ◽  
Transcriptomic Data ◽  
Factor Binding

scholarly journals Prediction of Transcription Factor Binding Sites of SP1 on Human Chromosome1

2021 ◽  
Vol 11 (11) ◽  
pp. 5123
Author(s):  
Maiada M. Mahmoud ◽  
Nahla A. Belal ◽  
Aliaa Youssif
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Messenger Rna ◽  
Area Under The Curve ◽  
Noisy Data ◽  
Transcription Factor Binding Sites ◽  
Classification Problem ◽  
Transcription Factor Binding ◽  
K Nearest Neighbors ◽  
Factor Binding

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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