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Journal articles on the topic 'TRANSCRIPTION FACTOR DATABASE'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Isles, Anthony. "Transcription factor database extended." Trends in Genetics 17, no. 3 (2001): 131. http://dx.doi.org/10.1016/s0168-9525(01)02252-1.

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2

Kummerfeld, S. K. "DBD: a transcription factor prediction database." Nucleic Acids Research 34, no. 90001 (2006): D74—D81. http://dx.doi.org/10.1093/nar/gkj131.

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3

Cai, Bin, Cheng-Hui Li, Ai-Sheng Xiong, et al. "DGTF: A Database of Grape Transcription Factors." Journal of the American Society for Horticultural Science 133, no. 3 (2008): 459–61. http://dx.doi.org/10.21273/jashs.133.3.459.

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The database of grape transcription factors (DGTF) is a plant transcription factor (TF) database comprehensively collecting and annotating grape (Vitis L.) TF. The DGTF contains 1423 putative grape TF in 57 families. These TF were identified from the predicted wine grape (Vitis vinifera L.) proteins from the grape genome sequencing project by means of a domain search. The DGTF provides detailed annotations for individual members of each TF family, including sequence feature, domain architecture, expression information, and orthologs in other plants. Cross-links to other public databases make i

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4

Shameer, K., S. Ambika, Susan Mary Varghese, N. Karaba, M. Udayakumar, and R. Sowdhamini. "STIFDB—Arabidopsis Stress Responsive Transcription Factor DataBase." International Journal of Plant Genomics 2009 (October 18, 2009): 1–8. http://dx.doi.org/10.1155/2009/583429.

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Elucidating the key players of molecular mechanism that mediate the complex stress-responses in plants system is an important step to develop improved variety of stress tolerant crops. Understanding the effects of different types of biotic and abiotic stress is a rapidly emerging domain in the area of plant research to develop better, stress tolerant plants. Information about the transcription factors, transcription factor binding sites, function annotation of proteins coded by genes expressed during abiotic stress (for example: drought, cold, salinity, excess light, abscisic acid, and oxidati

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5

Wang, Juan, Ming Lu, Chengxiang Qiu, and Qinghua Cui. "TransmiR: a transcription factor–microRNA regulation database." Nucleic Acids Research 38, suppl_1 (2009): D119—D122. http://dx.doi.org/10.1093/nar/gkp803.

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6

Zhang, H. M., H. Chen, W. Liu, et al. "AnimalTFDB: a comprehensive animal transcription factor database." Nucleic Acids Research 40, no. D1 (2011): D144—D149. http://dx.doi.org/10.1093/nar/gkr965.

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7

Guo, A. Y., X. Chen, G. Gao, et al. "PlantTFDB: a comprehensive plant transcription factor database." Nucleic Acids Research 36, Database (2007): D966—D969. http://dx.doi.org/10.1093/nar/gkm841.

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8

Bulow, L., Y. Brill, and R. Hehl. "AthaMap-assisted transcription factor target gene identification in Arabidopsis thaliana." Database 2010 (December 21, 2010): baq034. http://dx.doi.org/10.1093/database/baq034.

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9

Wu, Wei-Sheng, Fu-Jou Lai, Bor-Wen Tu, and Darby Tien-Hao Chang. "CoopTFD: a repository for predicted yeast cooperative transcription factor pairs." Database 2016 (2016): baw092. http://dx.doi.org/10.1093/database/baw092.

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10

Schaefer, U., S. Schmeier, and V. B. Bajic. "TcoF-DB: dragon database for human transcription co-factors and transcription factor interacting proteins." Nucleic Acids Research 39, Database (2010): D106—D110. http://dx.doi.org/10.1093/nar/gkq945.

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11

Aghamirzaie, Delasa, Karthik Raja Velmurugan, Shuchi Wu, Doaa Altarawy, Lenwood S. Heath, and Ruth Grene. "Expresso: A database and web server for exploring the interaction of transcription factors and their target genes in Arabidopsis thaliana using ChIP-Seq peak data." F1000Research 6 (March 28, 2017): 372. http://dx.doi.org/10.12688/f1000research.10041.1.

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Motivation: The increasing availability of chromatin immunoprecipitation sequencing (ChIP-Seq) data enables us to learn more about the action of transcription factors in the regulation of gene expression. Even though in vivo transcriptional regulation often involves the concerted action of more than one transcription factor, the format of each individual ChIP-Seq dataset usually represents the action of a single transcription factor. Therefore, a relational database in which available ChIP-Seq datasets are curated is essential. Results: We present Expresso (database and webserver) as a tool fo

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12

Tong, Zhan, Qinghua Cui, Juan Wang, and Yuan Zhou. "TransmiR v2.0: an updated transcription factor-microRNA regulation database." Nucleic Acids Research 47, no. D1 (2018): D253—D258. http://dx.doi.org/10.1093/nar/gky1023.

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13

Kanamori, Mutsumi, Hideaki Konno, Naoki Osato, Jun Kawai, Yoshihide Hayashizaki, and Harukazu Suzuki. "A genome-wide and nonredundant mouse transcription factor database." Biochemical and Biophysical Research Communications 322, no. 3 (2004): 787–93. http://dx.doi.org/10.1016/j.bbrc.2004.07.179.

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14

Kolmykov, Semyon, Ivan Yevshin, Mikhail Kulyashov, et al. "GTRD: an integrated view of transcription regulation." Nucleic Acids Research 49, no. D1 (2020): D104—D111. http://dx.doi.org/10.1093/nar/gkaa1057.

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Abstract The Gene Transcription Regulation Database (GTRD; http://gtrd.biouml.org/) contains uniformly annotated and processed NGS data related to gene transcription regulation: ChIP-seq, ChIP-exo, DNase-seq, MNase-seq, ATAC-seq and RNA-seq. With the latest release, the database has reached a new level of data integration. All cell types (cell lines and tissues) presented in the GTRD were arranged into a dictionary and linked with different ontologies (BRENDA, Cell Ontology, Uberon, Cellosaurus and Experimental Factor Ontology) and with related experiments in specialized databases on transcrip

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15

Wang, Guohua, Fang Wang, Qian Huang, Yu Li, Yunlong Liu, and Yadong Wang. "Understanding Transcription Factor Regulation by Integrating Gene Expression and DNase I Hypersensitive Sites." BioMed Research International 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/757530.

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Transcription factors are proteins that bind to DNA sequences to regulate gene transcription. The transcription factor binding sites are short DNA sequences (5–20 bp long) specifically bound by one or more transcription factors. The identification of transcription factor binding sites and prediction of their function continue to be challenging problems in computational biology. In this study, by integrating the DNase I hypersensitive sites with known position weight matrices in the TRANSFAC database, the transcription factor binding sites in gene regulatory region are identified. Based on the

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16

de Boer, C. G., and T. R. Hughes. "YeTFaSCo: a database of evaluated yeast transcription factor sequence specificities." Nucleic Acids Research 40, no. D1 (2011): D169—D179. http://dx.doi.org/10.1093/nar/gkr993.

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17

Cole, S. W., W. Yan, Z. Galic, J. Arevalo, and J. A. Zack. "Expression-based monitoring of transcription factor activity: the TELiS database." Bioinformatics 21, no. 6 (2004): 803–10. http://dx.doi.org/10.1093/bioinformatics/bti038.

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18

Schmeier, Sebastian, Tanvir Alam, Magbubah Essack, and Vladimir B. Bajic. "TcoF-DB v2: update of the database of human and mouse transcription co-factors and transcription factor interactions." Nucleic Acids Research 45, no. D1 (2016): D145—D150. http://dx.doi.org/10.1093/nar/gkw1007.

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19

Hehl, Reinhard. "From experiment-driven database analyses to database-driven experiments in Arabidopsis thaliana transcription factor research." Plant Science 262 (September 2017): 141–47. http://dx.doi.org/10.1016/j.plantsci.2017.06.011.

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20

Romeuf, Isabelle, Dominique Tessier, Mireille Dardevet, Gérard Branlard, Gilles Charmet, and Catherine Ravel. "wDBTF: an integrated database resource for studying wheat transcription factor families." BMC Genomics 11, no. 1 (2010): 185. http://dx.doi.org/10.1186/1471-2164-11-185.

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21

Sandelin, A. "JASPAR: an open-access database for eukaryotic transcription factor binding profiles." Nucleic Acids Research 32, no. 90001 (2004): 91D—94. http://dx.doi.org/10.1093/nar/gkh012.

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22

Xu, Xianjin, Zhiwei Ma, Hongmin Sun, and Xiaoqin Zou. "SM-TF: A structural database of small molecule-transcription factor complexes." Journal of Computational Chemistry 37, no. 17 (2016): 1559–64. http://dx.doi.org/10.1002/jcc.24370.

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23

Lin, C. Huie, Mark D. Platt, Scott B. Ficarro, et al. "Mass spectrometric identification of phosphorylation sites of rRNA transcription factor upstream binding factor." American Journal of Physiology-Cell Physiology 292, no. 5 (2007): C1617—C1624. http://dx.doi.org/10.1152/ajpcell.00176.2006.

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rRNA transcription is a fundamental requirement for all cellular growth processes and is activated by the phosphorylation of the upstream binding factor (UBF) in response to growth stimulation. Even though it is well known that phosphorylation of UBF is required for its activation and is a key step in activation of rRNA transcription, as yet, there has been no direct mapping of the UBF phosphorylation sites. The results of the present studies employed sophisticated nano-flow HPLC-microelectrospray-ionization tandem mass spectrometry (nHPLC-μESI-MS/MS) coupled with immobilized metal affinity ch

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24

Espinal-Enríquez, Jesús, Daniel González-Terán, and Enrique Hernández-Lemus. "The Transcriptional Network Structure of a Myeloid Cell: A Computational Approach." International Journal of Genomics 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/4858173.

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Understanding the general principles underlying genetic regulation in eukaryotes is an incomplete and challenging endeavor. The lack of experimental information regarding the regulation of the whole set of transcription factors and their targets in different cell types is one of the main reasons to this incompleteness. So far, there is a small set of curated known interactions between transcription factors and their downstream genes. Here, we built a transcription factor network for human monocytic THP-1 myeloid cells based on the experimentally curated FANTOM4 database where nodes are genes a

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25

Zhang, He, Jinpu Jin, Liang Tang, et al. "PlantTFDB 2.0: update and improvement of the comprehensive plant transcription factor database." Nucleic Acids Research 39, suppl_1 (2010): D1114—D1117. http://dx.doi.org/10.1093/nar/gkq1141.

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26

Pérez-Rodríguez, Paulino, Diego Mauricio Riaño-Pachón, Luiz Gustavo Guedes Corrêa, Stefan A. Rensing, Birgit Kersten, and Bernd Mueller-Roeber. "PlnTFDB: updated content and new features of the plant transcription factor database." Nucleic Acids Research 38, suppl_1 (2009): D822—D827. http://dx.doi.org/10.1093/nar/gkp805.

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27

Pfreundt, Ulrike, Daniel P. James, Susan Tweedie, Derek Wilson, Sarah A. Teichmann, and Boris Adryan. "FlyTF: improved annotation and enhanced functionality of the Drosophila transcription factor database." Nucleic Acids Research 38, suppl_1 (2009): D443—D447. http://dx.doi.org/10.1093/nar/gkp910.

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28

Singh, Akshay, Ajay Kumar Sharma, Nagendra Kumar Singh, and Tilak Raj Sharma. "PpTFDB: A pigeonpea transcription factor database for exploring functional genomics in legumes." PLOS ONE 12, no. 6 (2017): e0179736. http://dx.doi.org/10.1371/journal.pone.0179736.

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29

Kılıç, Sefa, Elliot R. White, Dinara M. Sagitova, Joseph P. Cornish, and Ivan Erill. "CollecTF: a database of experimentally validated transcription factor-binding sites in Bacteria." Nucleic Acids Research 42, no. D1 (2013): D156—D160. http://dx.doi.org/10.1093/nar/gkt1123.

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30

Klees, Selina, Felix Heinrich, Armin Otto Schmitt, and Mehmet Gültas. "agReg-SNPdb: A Database of Regulatory SNPs for Agricultural Animal Species." Biology 10, no. 8 (2021): 790. http://dx.doi.org/10.3390/biology10080790.

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Transcription factors (TFs) govern transcriptional gene regulation by specifically binding to short DNA motifs, known as transcription factor binding sites (TFBSs), in regulatory regions, such as promoters. Today, it is well known that single nucleotide polymorphisms (SNPs) in TFBSs can dramatically affect the level of gene expression, since they can cause a change in the binding affinity of TFs. Such SNPs, referred to as regulatory SNPs (rSNPs), have gained attention in the life sciences due to their causality for specific traits or diseases. In this study, we present agReg-SNPdb, a database

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31

Riva, A. "The MAPPER2 Database: a multi-genome catalog of putative transcription factor binding sites." Nucleic Acids Research 40, no. D1 (2011): D155—D161. http://dx.doi.org/10.1093/nar/gkr1080.

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32

Portales-Casamar, Elodie, Supat Thongjuea, Andrew T. Kwon, et al. "JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles." Nucleic Acids Research 38, suppl_1 (2009): D105—D110. http://dx.doi.org/10.1093/nar/gkp950.

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33

Yang, Lin, Tianyin Zhou, Iris Dror, et al. "TFBSshape: a motif database for DNA shape features of transcription factor binding sites." Nucleic Acids Research 42, no. D1 (2013): D148—D155. http://dx.doi.org/10.1093/nar/gkt1087.

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34

Kumar, Sunil, Giovanna Ambrosini, and Philipp Bucher. "SNP2TFBS – a database of regulatory SNPs affecting predicted transcription factor binding site affinity." Nucleic Acids Research 45, no. D1 (2016): D139—D144. http://dx.doi.org/10.1093/nar/gkw1064.

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35

Yevshin, Ivan, Ruslan Sharipov, Tagir Valeev, Alexander Kel, and Fedor Kolpakov. "GTRD: a database of transcription factor binding sites identified by ChIP-seq experiments." Nucleic Acids Research 45, no. D1 (2016): D61—D67. http://dx.doi.org/10.1093/nar/gkw951.

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36

Wang, Shuyuan, Wei Li, Baofeng Lian, et al. "TMREC: A Database of Transcription Factor and MiRNA Regulatory Cascades in Human Diseases." PLOS ONE 10, no. 5 (2015): e0125222. http://dx.doi.org/10.1371/journal.pone.0125222.

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37

Marinescu, V. D. "The MAPPER database: a multi-genome catalog of putative transcription factor binding sites." Nucleic Acids Research 33, Database issue (2004): D91—D97. http://dx.doi.org/10.1093/nar/gki103.

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38

Jagannathan, V. "HTPSELEX--a database of high-throughput SELEX libraries for transcription factor binding sites." Nucleic Acids Research 34, no. 90001 (2006): D90—D94. http://dx.doi.org/10.1093/nar/gkj049.

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39

Chen, Iuan-bor D., Vinay K. Rathi, Diana S. DeAndrade, and Patrick Y. Jay. "Association of genes with physiological functions by comparative analysis of pooled expression microarray data." Physiological Genomics 45, no. 2 (2013): 69–78. http://dx.doi.org/10.1152/physiolgenomics.00116.2012.

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The physiological functions of a tissue in the body are carried out by its complement of expressed genes. Genes that execute a particular function should be more specifically expressed in tissues that perform the function. Given this premise, we mined public microarray expression data to build a database of genes ranked by their specificity of expression in multiple organs. The database permitted the accurate identification of genes and functions known to be specific to individual organs. Next, we used the database to predict transcriptional regulators of brown adipose tissue (BAT) and validat

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40

Reddy, D. Ashok, B. V. L. S. Prasad, and Chanchal K. Mitra. "Functional classification of transcription factor binding sites: Information content as a metric." Journal of Integrative Bioinformatics 3, no. 1 (2006): 32–44. http://dx.doi.org/10.1515/jib-2006-20.

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Summary The information content (relative entropy) of transcription factor binding sites (TFBS) is used to classify the transcription factors (TFs). The TF classes are clustered based on the TFBS clustering using information content. Any TF belonging to the TF class cluster has a chance of binding to any TFBS of the clustered group. Thus, out of the 41 TFBS (in humans), perhaps only 5 -10 TFs may be actually needed and in case of mouse instead of 13 TFs, we may have actually 5 or so TFs. The JASPAR database of TFBS are used in this study. The experimental data on TFs of specific gene expressio

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41

Keenan, Alexandra B., Denis Torre, Alexander Lachmann, et al. "ChEA3: transcription factor enrichment analysis by orthogonal omics integration." Nucleic Acids Research 47, W1 (2019): W212—W224. http://dx.doi.org/10.1093/nar/gkz446.

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AbstractIdentifying the transcription factors (TFs) responsible for observed changes in gene expression is an important step in understanding gene regulatory networks. ChIP-X Enrichment Analysis 3 (ChEA3) is a transcription factor enrichment analysis tool that ranks TFs associated with user-submitted gene sets. The ChEA3 background database contains a collection of gene set libraries generated from multiple sources including TF–gene co-expression from RNA-seq studies, TF–target associations from ChIP-seq experiments, and TF–gene co-occurrence computed from crowd-submitted gene lists. Enrichmen

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42

Tzeng, David T. W., Yu-Ting Tseng, Matthew Ung, I.-En Liao, Chun-Chi Liu, and Chao Cheng. "DPRP: a database of phenotype-specific regulatory programs derived from transcription factor binding data." Nucleic Acids Research 42, no. D1 (2013): D178—D183. http://dx.doi.org/10.1093/nar/gkt1254.

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43

Yang, Lin, Iris Dror, Tianyin Zhou, et al. "15 TFBSshape: a motif database for DNA shape features of transcription factor binding sites." Journal of Biomolecular Structure and Dynamics 33, sup1 (2015): 9. http://dx.doi.org/10.1080/07391102.2015.1032555.

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44

Ning, Shangwei, Zuxianglan Zhao, Jingrun Ye, et al. "SNP@lincTFBS: An Integrated Database of Polymorphisms in Human LincRNA Transcription Factor Binding Sites." PLoS ONE 9, no. 7 (2014): e103851. http://dx.doi.org/10.1371/journal.pone.0103851.

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45

Nikitin, Daniil, Andrew Garazha, Maxim Sorokin, et al. "Retroelement—Linked Transcription Factor Binding Patterns Point to Quickly Developing Molecular Pathways in Human Evolution." Cells 8, no. 2 (2019): 130. http://dx.doi.org/10.3390/cells8020130.

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Background: Retroelements (REs) are transposable elements occupying ~40% of the human genome that can regulate genes by providing transcription factor binding sites (TFBS). RE-linked TFBS profile can serve as a marker of gene transcriptional regulation evolution. This approach allows for interrogating the regulatory evolution of organisms with RE-rich genomes. We aimed to characterize the evolution of transcriptional regulation for human genes and molecular pathways using RE-linked TFBS accumulation as a metric. Methods: We characterized human genes and molecular pathways either enriched or de

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46

Gomes, Ignatius, Tiffany T. Sharma, Seby Edassery, Noreen Fulton, Brenton G. Mar, and Carol A. Westbrook. "Novel transcription factors in human CD34 antigen–positive hematopoietic cells." Blood 100, no. 1 (2002): 107–19. http://dx.doi.org/10.1182/blood.v100.1.107.

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Abstract Transcription factors (TFs) and the regulatory proteins that control them play key roles in hematopoiesis, controlling basic processes of cell growth and differentiation; disruption of these processes may lead to leukemogenesis. Here we attempt to identify functionally novel and partially characterized TFs/regulatory proteins that are expressed in undifferentiated hematopoietic tissue. We surveyed our database of 15 970 genes/expressed sequence tags (ESTs) representing the normal human CD34+ cells transcriptosome (http://westsun.hema.uic.edu/cd34.html), using the UniGene annotation te

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47

Yu, Zhun, Qi He, and Guoping Xu. "Screening of Prognostic Factors in Early-Onset Breast Cancer." Technology in Cancer Research & Treatment 19 (January 1, 2020): 153303381989367. http://dx.doi.org/10.1177/1533033819893670.

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Background: Gene expression profiles from early-onset breast cancer and normal tissues were analyzed to explore the genes and prognostic factors associated with breast cancer. Methods: GSE109169 and GSE89116 were obtained from the database of Gene Expression Omnibus. We firstly screened the differentially expressed genes between tumor samples and normal samples from patients with early-onset breast cancer. Based on database for annotation, visualization and intergrated discovery (DAVID) tool, functional analysis was calculated. Transcription factor-target regulation and microRNA-target gene ne

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48

Choi, Claudia, Mathias Krull, Alexander Kel, et al. "TRANSPATH®—A High Quality Database Focused on Signal Transduction." Comparative and Functional Genomics 5, no. 2 (2004): 163–68. http://dx.doi.org/10.1002/cfg.386.

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TRANSPATH®can either be used as an encyclopedia, for both specific and general information on signal transduction, or can serve as a network analyser. Therefore, three modules have been created: the first one is the data, which have been manually extracted, mostly from the primary literature; the second is PathwayBuilder™, which provides several different types of network visualization and hence faciliates understanding; the third is ArrayAnalyzer™, which is particularly suited to gene expression array interpretation, and is able to identify key molecules within signalling networks (potential

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49

Lee, Alison P., Yuchen Yang, Sydney Brenner, and Byrappa Venkatesh. "TFCONES: A database of vertebrate transcription factor-encoding genes and their associated conserved noncoding elements." BMC Genomics 8, no. 1 (2007): 441. http://dx.doi.org/10.1186/1471-2164-8-441.

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50

Mathelier, Anthony, Xiaobei Zhao, Allen W. Zhang, et al. "JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles." Nucleic Acids Research 42, no. D1 (2013): D142—D147. http://dx.doi.org/10.1093/nar/gkt997.

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