Relevant bibliographies by topics / TRANSCRIPTION FACTOR DATABASE

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  2. Dissertations / Theses
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Academic literature on the topic 'TRANSCRIPTION FACTOR DATABASE'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "TRANSCRIPTION FACTOR DATABASE"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "TRANSCRIPTION FACTOR DATABASE"

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Riaño-Pachón, Diego Mauricio. "Identification of transcription factor genes in plants." Phd thesis, Universität Potsdam, 2008. http://opus.kobv.de/ubp/volltexte/2008/2700/.

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In order to function properly, organisms have a complex control mechanism, in which a given gene is expressed at a particular time and place. One way to achieve this control is to regulate the initiation of transcription. This step requires the assembly of several components, i.e., a basal/general machinery common to all expressed genes, and a specific/regulatory machinery, which differs among genes and is the responsible for proper gene expression in response to environmental or developmental signals. This specific machinery is composed of transcription factors (TFs), which can be grouped int
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Stigliani, Arnaud. "Modélisation de la liaison à l'ADN et des mécanismes d'action de facteurs de transcription floraux Building Transcription Factor Binding Site Models to Understand Gene Regulation in Plants JASPAR 2018: Update of the open-access database of transcription factor binding profiles and its web framework." Thesis, Université Grenoble Alpes (ComUE), 2019. https://thares.univ-grenoble-alpes.fr/2019GREAV032.pdf.

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Chez les angiospermes, la floraison est un processus qui prend part en plusieurs étapes. Le méristème caulinaire, un réservoir de cellule souche d’où émergent la totalité des organes aériens de la plante, va d’abord se différencier en méristème d’inflorescence. Des méristèmes floraux vont alors émerger des flancs du méristème d’inflorescence pour donner naissance aux différents organes qui composent la fleur : les pétales, les sépales, les étamines et le carpelle. Chacune de ces phases est régulée avec finesse par des facteurs de transcription, une famille de protéines se liant à l’ADN pour in
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Mwangi, Sarah Wambui. "In silico investigation of glossina morsitans promoters." University of the Western Cape, 2013. http://hdl.handle.net/11394/3990.

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Philosophiae Doctor - PhD<br>Tsetse flies (Glossina spp) are the biological vectors for Trypanosomes, the causative magents of Human African Trypanosomiasis (HAT). HAT is a debilitating disease that continues to present a major public health problem and a key factor limiting rural development in vast regions of tropical Africa. To augment vector control efforts, the International Glossina Genome Initiative (IGGI) was established in 2004 with the ultimate goal of generating a fully annotated whole genome sequence for Glossina morsitans. A working draft genome of Glossina morsitans was availed i
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Hanschen, Erik R., Tara N. Marriage, Patrick J. Ferris, et al. "The Gonium pectorale genome demonstrates co-option of cell cycle regulation during the evolution of multicellularity." NATURE PUBLISHING GROUP, 2016. http://hdl.handle.net/10150/614763.

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The transition to multicellularity has occurred numerous times in all domains of life, yet its initial steps are poorly understood. The volvocine green algae are a tractable system for understanding the genetic basis of multicellularity including the initial formation of cooperative cell groups. Here we report the genome sequence of the undifferentiated colonial alga, Gonium pectorale, where group formation evolved by co-option of the retinoblastoma cell cycle regulatory pathway. Significantly, expression of the Gonium retinoblastoma cell cycle regulator in unicellular Chlamydomonas causes it
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Adam, Muhammed Saleem. "A knowledgebase of stress reponsive gene regulatory elements in arabidopsis Thaliana." Thesis, University of the Western Cape, 2011. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_9599_1362393100.

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<p>Stress responsive genes play a key role in shaping the manner in which plants process and respond to environmental stress. Their gene products are linked to DNA transcription and its consequent translation into a response product. However, whilst these genes play a significant role in manufacturing responses to stressful stimuli, transcription factors coordinate access to these genes, specifically by accessing a gene&rsquo<br>s promoter region which houses transcription factor binding sites. Here transcriptional elements play a key role in mediating responses to environmental stress where e
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Bleda, Latorre Marta 1986. "Identification of altered regulatory interactions in disease." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/402191.

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Gene regulation is a complex biological process that requires the coordinated interaction of different molecules. The integrity of the underlying mechanisms ensures the correct expression of genes that maintain cell differentiation and stability in a healthy cell. Alterations in the regulatory elements involved can disrupt the process and unbalance gene products causing diseases such as cancer, cardiovascular problems or autoimmune disorders. Although high-throughput sequencing technologies have allowed a better understanding of the gene regulatory mechanisms, there is still much uncertainty a
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Childress, Paul. "LymphTF Database- A Database of Transcription Factor Activity in Lymphocyte Development." Thesis, 2006. http://hdl.handle.net/1805/622.

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Submitted to the faculty of the Bioinformatics Graduate Program in partial fulfillment of the requirements for the degree Master of Science in the School of Informatics, Indiana University September 2005<br>Study of the transcriptional regulation of lymphocyte development has advanced greatly in the past 15 years. Owing to improved techniques and intense interest in the topic, a great many interactions between transcription factors and their target genes have been described. For these B and T cells, a more clear picture is beginning to emerge of how they start with a common progenitor cell,
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Essack and Magbubah. "Transcription Regulation and Candidate Diagnostic Markers of Esophageal Cancer." Thesis, 2009. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_6738_1266806306.

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<p>This thesis reports on the development of a novel comprehensive database (Dragon Database of Genes Implicated in Esophageal Cancer, DDEC) as an integrated knowledge database aimed at representing a gateway to esophageal cancer related data. More importantly, it illustrates how the biocurated genes in the database may represent a reliable starting point for divulging transcriptional regulation, diagnostic markers and the biology related to esophageal cancer.</p>
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Pin-HanChen and 陳品翰. "Construction of a database for yeast cooperative transcription factor sets and their targeting gene modules." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/9bkf2g.

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Yu-ChengHung and 洪彧丞. "Construction of a database for transcription factor binding sites identified by plant chromatin immunoprecipitation sequencing (ChIP-seq) experiments." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/ss24mj.

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Book chapters on the topic "TRANSCRIPTION FACTOR DATABASE"

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Mochida, Keiichi, Chien Van Ha, Saad Sulieman, Nguyen Van Dong, and Lam-Son Phan Tran. "Databases of Transcription Factors in Legumes." In Biological Nitrogen Fixation. John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781119053095.ch81.

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Filiz, Ertugrul, Recep Vatansever, and Ibrahim Ilker Ozyigit. "Bioinformatics Database Resources for Plant Transcription Factors." In Plant Bioinformatics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67156-7_5.

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Zhang, Zhuo, Merlin Veronika, See-Kiong Ng, and Vladimir B. Bajic. "Intelligent Extraction Versus Advanced Query: Recognize Transcription Factors from Databases." In Pattern Recognition in Bioinformatics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11818564_15.

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He, Kun, An-Yuan Guo, Ge Gao, et al. "Computational Identification of Plant Transcription Factors and the Construction of the PlantTFDB Database." In Methods in Molecular Biology. Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-854-6_21.

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Dai, Yang, Eyad Almasri, Peter Larsen, and Guanrao Chen. "Structural Learning of Genetic Regulatory Networks Based on Prior Biological Knowledge and Microarray Gene Expression Measurements." In Handbook of Research on Computational Methodologies in Gene Regulatory Networks. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-685-3.ch012.

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The reconstruction of genetic regulatory networks from microarray gene expression measurements has been a challenging problem in bioinformatics. Various methods have been proposed for this problem including the Bayesian Network (BN) approach. In this chapter, we provide a comprehensive survey of the current development of using structure priors derived from high-throughput experimental results such as protein-protein interactions, transcription factor binding location data, evolutionary relationships, and literature database in learning regulatory networks.
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Wingender, Edgar, Alexander Kel, and Mathias Krull. "Transcription Factor Databases." In Encyclopedia of Bioinformatics and Computational Biology. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-809633-8.20216-1.

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Bailey, Timothy L. "MEME, MAST, and Meta-MEME: New Tools for Motif Discovery in Protein Sequences." In Pattern Discovery in Biomolecular Data. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195119404.003.0008.

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We are in the midst of an explosive increase in the number of DNA and protein sequences available for study, as various genome projects come on line. This wealth of information offers important opportunities for understanding many biological processes and developing new plant and animal models, and ultimately drugs, for human diseases, in addition to other applications of modern biotechnology. Unfortunately, sequences are accumulating at a pace that strains present methods for extracting significant biological information from them. A consequence of this explosion in the sequence databases is that there is much interest and effort in developing tools that can efficiently and automatically extract the relevant biological information in sequence data and make it available for use in biology and medicine. In this chapter, we describe one such method that we have developed based on algorithms from artificial intelligence research. We call this software tool MEME (Multiple Expectation-maximization for Motif Elicitation). It has the attractive property that it is an “unsupervised” discovery tool: it can identify motifs, such as regulatory sites in DNA and functional domains in proteins, from large or small groups of unaligned sequences. As we show below, motifs are a rich source of information about a dataset; they can be used to discover other homologs in a database, to identify protein subsets that contain one or more motifs, and to provide information for mutagenesis studies to elucidate structure and function in the protein family as well as its evolution. Learning tools are used to extract higher level biological patterns from lower level DNA and protein sequence data. In contrast, search tools such as BLAST (Basic Local Alignment Search Tool) take a given higher level pattern and find all items in a database that possess the pattern. Searching for items that have a certain pattern is a problem intrinsically easier than discovering what the pattern is from items that possess it. The patterns considered here are motifs, which for DNA data can be subsequences that interact with transcription factors, polymerases, and other proteins.
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Schlotzig, Vanessa, Kevin Kornrumpf, Alexander König, et al. "Predicting the Effect of Variants of Unknown Significance in Molecular Tumor Boards with the VUS-Predict Pipeline." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210562.

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Precision oncology utilizing molecular biomarkers for targeted therapies is one of the hopes to treat cancer. The availability of patient specific molecular profiling through next-generation sequencing, though, increases the amount of available data per patient to an extent that computational support is required to identify potential driver alterations for targeted therapies and rational decision-making in molecular tumor boards (MTBs). For some genetic variants evidence-based drug recommendations are available in public databases, but for the majority, the variants of unknown significance (VUS), this clinical information is missing. Additionally, for most of these variants no information about the functional impact on the protein is accessible. To acquire maximal functional evidence for VUS, the VUS-Predict pipeline collects estimations about the effect of a VUS by integrating multiple pre-existing tools. Pre-existing tools implement different approaches for their predictions, which are summarized by our newly developed tool with a common score and classification in neutral or deleterious variants. The primary tools are chosen based on their sensitivity and specificity on well-known variants of the transcription factor TP53. Resulting negative and positive predictive values are used to calibrate the VUS-Predict pipeline. Further, the pipeline is evaluated using data from public cancer databases and cases of the MTB in Göttingen, both also in comparison with the ensemble method REVEL. The results show that VUS-Predict has clear advantages in a clinical setting due to clear and traceable predictions. In particular, VUS outperforms REVEL in the real-life setting of a MTB. Likewise, an evaluation on variants of public cancer databases confirms the good results of VUS-Predict and shows the need for a reliable gold standard and unambiguous results of the tools under test.
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Chen, Bor-Sen. "Global screening of potential Candida albicans biofilm-related transcription factors by network comparison via big database mining and genome-wide microarray data identification." In Systems Immunology and Infection Microbiology. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-12-816983-4.00016-x.

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Conference papers on the topic "TRANSCRIPTION FACTOR DATABASE"

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Yang, Yang, Wei Wang, Hongfei Chen, and Jinke Wang. "TFDB: a new transcription factor database." In International Conference on Medical Engineering and Bioinformatics. WIT Press, 2014. http://dx.doi.org/10.2495/meb140531.

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Yang, Zhenping, Wei Wang, Yang Yang, Hongfei Chen, and Jinke Wang. "Construction of transcription factor mutual regulatory network based on a new database." In International Conference on Medical Engineering and Bioinformatics. WIT Press, 2014. http://dx.doi.org/10.2495/meb140541.

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"AD ASTRA: the database of Allelic Dosage-corrected Allele-Specific TRAnscription factor binding suggests causal regulatory sequence variants of pathologies." In Bioinformatics of Genome Regulation and Structure/ Systems Biology. institute of cytology and genetics siberian branch of the russian academy of science, Novosibirsk State University, 2020. http://dx.doi.org/10.18699/bgrs/sb-2020-001.

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Durasi, Ilknur Melis, Ugur Dag, Burcu Bakir Gungor, Burcu Erdogan, Isil Aksan Kurnaz, and O. Ugur Sezerman. "Identification of transcription factor binding sites in promoter databases." In 2011 6th International Symposium on Health Informatics and Bioinformatics (HIBIT). IEEE, 2011. http://dx.doi.org/10.1109/hibit.2011.6450811.

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Yilmaz, Alper, Ramana Davuluri, Saranyan Palaniswamy, and Erich Grotewold. "Discovery of Regulatory Networks in Plants by Linking Promoter and Transcription Factor Databases." In 2009 Ohio Collaborative Conference on Bioinformatics (OCCBIO). IEEE, 2009. http://dx.doi.org/10.1109/occbio.2009.13.

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