IDENTIFICATION OF TRANSCRIPTION FACTOR BINDING SITES IN PROMOTER DATABASES

被引：0

作者：

Durast, Ilknur Melis ^{[1
]}

Dag, Ugur ^{[2
]}

Gungor, Burcu Bakzr ^{[1
,3
]}

Erdogan, Burcu ^{[2
]}

Kurnaz, Isil Aksan ^{[2
]}

Sezerman, O. Ugur ^{[1
]}

机构：

[1] Sabanci Univ, Dept Biol Sci & Bioengn, Istanbul, Turkey

[2] Yeditepe Univ, Dept Genet & Bioengn, Istanbul, Turkey

[3] Bahcesehir Univ, Dept Comp Sci & Engn, Istanbul, Turkey

来源：

PROCEEDINGS OF THE 6TH INTERNATIONAL SYMPOSIUM ON HEALTH INFORMATICS AND BIOINFORMATICS (HIBIT'11) | 2011年

关键词：

GENE-REGULATION;

D O I：

暂无

中图分类号：

R-058 [];

学科分类号：

摘要：

Transcription factors (TFs) are the proteins which regulates the expression of their target genes either in a positive or negative manner. TFs realize this task by binding to a specific DNA sequence contained in promoter regions, via their DNA binding motifs. Among ETS family TFs, Pea3 proteins are involved in the regulation of expression of genes, which are important for cell growth, development, differentiation, oncogenic transformation and apoptosis. In silica studies should be done to find out the novel target genes for this TF. Even though a few bioinformatics tools are available for this purpose, the user needs to go back and forth between different tools, and to repeat these steps for each of their candidate gene. Here we combined these tools and constituted a new tool which examines the affinity of any TF towards the selected target genes' promoter sequences. The tool is tested on several genes, which are predicted to be regulated by Pea3 TF.

引用

页码：68 / 73

页数：6

共 50 条

[21] Identification of transcription factor binding sites using Gaussian mixture models
Karabulut, Mustafa
Ibrikci, Turgay
EXPERT SYSTEMS, 2014, 31 (01) : 70 - 80
[22] Identification of transcription factor binding sites using ATAC-seq
Zhijian Li
Marcel H. Schulz
Thomas Look
Matthias Begemann
Martin Zenke
Ivan G. Costa
Genome Biology, 20
[23] Efficient Identification of Transcription Factor Binding Sites with a Graph Theoretic Approach
Song, Jia
Xu, Li
Sun, Hong
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2013, 2013
[24] Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning
Lin, Tzu-Chieh
Tsai, Cheng-Hung
Shiau, Cheng-Kai
Huang, Jia-Hsin
Tsai, Huai-Kuang
BMC GENOMICS, 2024, 25 (SUPPL 3):
[25] Mass spectrometric identification of phosphorylation sites of rRNA transcription factor upstream binding factor
Lin, C. Huie
Platt, Mark D.
Ficarro, Scott B.
Hoofnagle, Mark H.
Shabanowitz, Jeffrey
Comai, Lucio
Hunt, Donald F.
Owens, Gary K.
AMERICAN JOURNAL OF PHYSIOLOGY-CELL PHYSIOLOGY, 2007, 292 (05): : C1617 - C1624
[26] CONSTRAINTS ON SPACING BETWEEN TRANSCRIPTION FACTOR BINDING-SITES IN A SIMPLE ADENOVIRUS PROMOTER
WU, L
BERK, A
GENES & DEVELOPMENT, 1988, 2 (04) : 403 - 411
[27] Pseudocounts for transcription factor binding sites
Nishida, Keishin
Frith, Martin C.
Nakai, Kenta
NUCLEIC ACIDS RESEARCH, 2009, 37 (03) : 939 - 944
[28] Computational identification of transcription factor binding sites via a transcription-factor-centric clustering (TFCC) algorithm
Zhu, Z
Pilpel, Y
Church, GM
JOURNAL OF MOLECULAR BIOLOGY, 2002, 318 (01) : 71 - 81
[29] Identification of estrogen-responsive genes using a genome-wide analysis of promoter elements for transcription factor binding sites
Kamalakaran, S
Radhakrishnan, SK
Beck, WT
JOURNAL OF BIOLOGICAL CHEMISTRY, 2005, 280 (22) : 21491 - 21497
[30] Genome-Wide Identification of Transcription Start Sites, Promoters and Transcription Factor Binding Sites in E. coli
Mendoza-Vargas, Alfredo
Olvera, Leticia
Olvera, Maricela
Grande, Ricardo
Vega-Alvarado, Leticia
Taboada, Blanca
Jimenez-Jacinto, Veronica
Salgado, Heladia
Juarez, Katy
Contreras-Moreira, Bruno
Huerta, Araceli M.
Collado-Vides, Julio
Morett, Enrique
PLOS ONE, 2009, 4 (10):

← 1 2 3 4 5 →