Mahmoud Ahmed

Postdoc - Cancer Genomics

target: an R package to predict combined function of transcription factors


Journal article


Mahmoud Ahmed, Deok Ryong Kim
F1000Research, vol. 10, 2021


View PDF View Online
Cite

Cite

APA   Click to copy
Ahmed, M., & Kim, D. R. (2021). target: an R package to predict combined function of transcription factors. F1000Research, 10. https://doi.org/10.12688/f1000research.52173.1


Chicago/Turabian   Click to copy
Ahmed, Mahmoud, and Deok Ryong Kim. “Target: an R Package to Predict Combined Function of Transcription Factors.” F1000Research 10 (2021).


MLA   Click to copy
Ahmed, Mahmoud, and Deok Ryong Kim. “Target: an R Package to Predict Combined Function of Transcription Factors.” F1000Research, vol. 10, 2021, doi:10.12688/f1000research.52173.1.


BibTeX   Click to copy

@article{mahmoud2021a,
  title = {target: an R package to predict combined function of transcription factors},
  year = {2021},
  journal = {F1000Research},
  volume = {10},
  doi = {10.12688/f1000research.52173.1},
  author = {Ahmed, Mahmoud and Kim, Deok Ryong}
}

Abstract

Researchers use ChIP binding data to identify potential transcription factor binding sites. Similarly, they use gene expression data from sequencing or microarrays to quantify the effect of the transcription factor overexpression or knockdown on its targets. Therefore, the integration of the binding and expression data can be used to improve the understanding of a transcription factor function. Here, we implemented the binding and expression target analysis (BETA) in an R/Bioconductor package. This algorithm ranks the targets based on the distances of their assigned peaks from the transcription factor ChIP experiment and the signed statistics from gene expression profiling with transcription factor perturbation. We further extend BETA to integrate two sets of data from two transcription factors to predict their targets and their combined functions. In this article, we briefly describe the workings of the algorithm and provide a workflow with a real dataset for using it. The gene targets and the aggregate functions of transcription factors YY1 and YY2 in HeLa cells were identified. Using the same datasets, we identified the shared targets of the two transcription factors, which were found to be, on average, more cooperatively regulated.

Share


Follow this website


You need to create an Owlstown account to follow this website.


Sign up

Already an Owlstown member?

Log in