1,721,071 research outputs found
The TRANSFAC project as an example of framework technology that supports the analysis of genomic regulation
No full textSince its beginning as a data collection more than 20 years ago, the TRANSFAC project underwent an evolution to become the basis for a complex platform for the description and analysis of gene regulatory events and networks. In the following, I describe what the original concepts were, what their present status is and how they may be expected to contribute to future system biology approaches
CRITERIA FOR AN UPDATED CLASSIFICATION OF HUMAN TRANSCRIPTION FACTOR DNA-BINDING DOMAINS
No full text<p>By binding to cis-regulatory elements in a sequence-specific manner, transcription factors regulate the activity of nearby genes. Here, we discuss the criteria for a comprehensive classification of human TFs based on their DNA-binding domains. In particular, classification of basic leucine zipper (bZIP) and zinc finger factors is exemplarily discussed. The resulting classification can be used as a template for TFs of other biological species.</p>
Construction of predictive promoter models on the example of antibacterial response of human epithelial cells
No full textBackground: Binding of a bacteria to a eukaryotic cell triggers a complex network of interactions in and between both cells. P. aeruginosa is a pathogen that causes acute and chronic lung infections by interacting with the pulmonary epithelial cells. We use this example for examining the ways of triggering the response of the eukaryotic cell(s), leading us to a better understanding of the details of the inflammatory process in general. Results: Considering a set of genes co-expressed during the antibacterial response of human lung epithelial cells, we constructed a promoter model for the search of additional target genes potentially involved in the same cell response. The model construction is based on the consideration of pair-wise combinations of transcription factor binding sites (TFBS). It has been shown that the antibacterial response of human epithelial cells is triggered by at least two distinct pathways. We therefore supposed that there are two subsets of promoters activated by each of them. Optimally, they should be "complementary" in the sense of appearing in complementary subsets of the (+)-training set. We developed the concept of complementary pairs, i.e., two mutually exclusive pairs of TFBS, each of which should be found in one of the two complementary subsets. Conclusions: We suggest a simple, but exhaustive method for searching for TFBS pairs which characterize the whole (+)-training set, as well as for complementary pairs. Applying this method, we came up with a promoter model of antibacterial response genes that consists of one TFBS pair which should be found in the whole training set and four complementary pairs. We applied this model to screening of 13,000 upstream regions of human genes and identified 430 new target genes which are potentially involved in antibacterial defense mechanisms
Topological peculiarities of mammalian networks with different functionalities: transcription, signal transduction and metabolic networks
No full textWe have comparatively investigated three different mammalian networks – on transcription, signal transduction and metabolic processes - with respect to their common and individual topological traits. The networks have been constructed based on genome-wide data collected from human, mouse and rat. None of these three networks exhibits a pure power-law degree distribution and, therefore, could be considered scalefree. Rather, the degree distributions of all three networks were best fitted by mixed models of a power law with an exponential tail. The networks differ from one another in the quantitative parameters of the models. Moreover, the transcription network can also be very well approximated by an exponential law. The connectivity within each network is rather robust, as is seen when removing individual nodes and computing the values of their pairwise disconnectivity index (PDI), which characterizes the topological significance of each node v by the number of direct or indirect connections in the network that critically depend on the presence of v. The results evidence that the networks are not centralized: none of nodes globally controls the integrity of each network. Just a few vertices appeared to strongly affect the coherence of the networks. These nodes are characterized by a broad range of degrees, thereby indicating that the degree alone is not the decisive criteria of a node’s importance. The networks reveal distinct architectures: The transcriptional network exhibits a hierarchical modularity, whereas the signaling network is mainly comprised of semi-autonomous modules. The metabolic network seems to be made by a more complex mixture of substructures. Thus, despite being encoded by the same genomes, the networks significantly differ from one another in their general architectural design. Altogether, our results indicate that the subsets of genes and relationships that constitute these networks have co-evolved very differently and through multiple mechanisms
TFClass: an expandable hierarchical classification of human transcription factors
No full textTFClass (http://tfclass.bioinf.med.uni-goettingen.de/) provides a comprehensive classification of human transcription factors based on their DNA-binding domains. Transcription factors constitute a large functional family of proteins directly regulating the activity of genes. Most of them are sequence-specific DNA-binding proteins, thus reading out the information encoded in cis-regulatory DNA elements of promoters, enhancers and other regulatory regions of a genome. TFClass is a database that classifies human transcription factors by a six-level classification schema, four of which are abstractions according to different criteria, while the fifth level represents TF genes and the sixth individual gene products. Altogether, nine superclasses have been identified, comprising 40 classes and 111 families. Counted by genes, 1558 human TFs have been classified so far or > 2900 different TFs when including their isoforms generated by alternative splicing or protein processing events. With this classification, we hope to provide a basis for deciphering protein-DNA recognition codes; moreover, it can be used for constructing expanded transcriptional networks by inferring additional TF-target gene relations.Open-Access-Publikationsfonds 201
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