相关论文
2011 - BMC Bioinformatics
Context-dependent transcriptional regulations between signal transduction pathways

BackgroundCells coordinate their metabolism, proliferation, and cellular communication according to environmental cues through signal transduction. Because signal transduction has a primary role in cellular processes, many experimental techniques and approaches have emerged to discover the molecular components and dynamics that are dependent on cellular contexts. However, omics approaches based on genome-wide expression analysis data comparing one differing condition (e.g. complex disease patients and normal subjects) did not investigate the dynamics and inter-pathway cross-communication that are dependent on cellular contexts. Therefore, we introduce a new computational omics approach for discovering signal transduction pathways regulated by transcription and transcriptional regulations between pathways in signaling networks that are dependent on cellular contexts, especially focusing on a transcription-mediated mechanism of inter-pathway cross-communication.ResultsApplied to dendritic cells treated with lipopolysaccharide, our analysis well depicted how dendritic cells respond to the treatment through transcriptional regulations between signal transduction pathways in dendritic cell maturation and T cell activation.ConclusionsOur new approach helps to understand the underlying biological phenomenon of expression data (e.g. complex diseases such as cancer) by providing a graphical network which shows transcriptional regulations between signal transduction pathways. The software programs are available upon request.

2018 - IEEE/ACM Transactions on Computational Biology and Bioinformatics
Sequence-Based Prediction of Putative Transcription Factor Binding Sites in DNA Sequences of Any Length

A transcription factor (TF) is a protein that regulates gene expression by binding to specific DNA sequences. Despite recent advances in experimental techniques for identifying transcription factor binding sites (TFBS) in DNA sequences, a large number of TFBS are to be unveiled in many species. Several computational methods developed for predicting TFBS in DNA are tissue- or species-specific methods, and therefore cannot be used without prior knowledge of tissue or species. Some computational methods are applicable to identifying TFBS in short DNA sequences only. In this paper, we propose a new learning method for predicting TFBS in DNA of any length using the composition, transition, and distribution of nucleotides and amino acids in DNA and TF sequences. In independent testing of the method on datasets that were not used in training the method, the accuracy and MCC were as high as 81.84 percent and 0.634, respectively. The proposed method can be a useful aid for selecting potential TFBS in a large amount of DNA sequences before conducting biochemical experiments to empirically determine TFBS. The program and data sets are available at http://bclab.inha.ac.kr/TFbinding.

2010 - The 2010 International Joint Conference on Neural Networks (IJCNN)
Evolving Spiking Neural Networks for predicting transcription factor binding sites

Interdisciplinary problem solving in computational biology requires a fundamental understanding of complex biological adaptive systems, from cellular to molecular level in order to tackle challenging problems such as neurodegenerative diseases. In this work we present a description and an initial evaluation of a Spiking Neural Network-Genetic Algorithm (SNN-GA) system we are developing for the computational prediction of transcription factor binding sites (TFBS). The SNN-GA approach is based on modeling information processing of biological neurons through evolutionary processes. The goal of our work is to reduce the number of false positives in the predicted TFBSs, through a more precise modeling of information contained in the alignments in the training data. We show an evaluation of four proposed network topologies that represent TFBS data. We use real TFBS data from the TRANSFACê database and appropriately generated negative samples. We evaluated the network topologies one three well-known models for transcription factors: RSRFC4, ZID and p53. Benchmark performances for these models are given using MAPPER and MATCHTM. The results show that our method has the potential to attain very high classification accuracy.

2014 - Expert Syst. J. Knowl. Eng.
Identification of transcription factor binding sites using Gaussian mixture models

Identification of transcription factor binding sites still remains a challenging problem even though many computational tools have been proposed in the literature for this specific task. In this study, a method to discover such DNA subsequences, that is, motifs, is proposed. The method uses Gaussian mixture models with expectation-maximization algorithm. In order to show the potential of the proposed method, experiments are conducted by use of data sets extracted from the DNA sequences of various organisms. The proposed method is also compared with four other methods: MEME, MDScan, SOMBRERO and the fuzzy C-means based motif finder. As a result, the proposed method proves itself as a promising tool in identifying over-represented DNA motifs.

2014 - Nucleic Acids Research
Structural and functional basis of transcriptional regulation by TetR family protein CprB from S. coelicolor A3(2)

Antibiotic production and resistance pathways in Streptomyces are dictated by the interplay of transcriptional regulatory proteins that trigger downstream responses via binding to small diffusible molecules. To decipher the mode of DNA binding and the associated allosteric mechanism in the sub-class of transcription factors that are induced by γ-butyrolactones, we present the crystal structure of CprB in complex with the consensus DNA element to a resolution of 3.25 Å. Binding of the DNA results in the restructuring of the dimeric interface of CprB, inducing a pendulum-like motion of the helix-turn-helix motif that inserts into the major groove. The crystal structure revealed that, CprB is bound to DNA as a dimer of dimers with the mode of binding being analogous to the broad spectrum multidrug transporter protein QacR from the antibiotic resistant strain Staphylococcus aureus. It was demonstrated that the CprB displays a cooperative mode of DNA binding, following a clamp and click model. Experiments performed on a subset of DNA sequences from Streptomyces coelicolor A3(2) suggest that CprB is most likely a pleiotropic regulator. Apart from serving as an autoregulator, it is potentially a part of a network of proteins that modulates the γ-butyrolactone synthesis and antibiotic regulation pathways in S. coelicolor A3(2).

1994 - DNA and cell biology
The Surf-1 and Surf-2 genes and their essential bidirectional promoter elements are conserved between mouse and human.

The organization of the Surfeit locus and the juxtaposition of at least five of the Surfeit genes (Surf-1 to -5) are conserved between mouse and human (Williams et al., 1988; Yon et al., 1993). In the mouse, the heterogeneous transcription start sites of the divergent Surf-1 and Surf-2 genes are separated by a maximum of only 73 bp (Williams and Fried, 1986). This region contains a bidirectional promoter composed of three major factor binding sites required for the efficient expression of both the Surf-1 and Surf-2 genes (Lennard and Fried, 1991). Here we report the isolation and characterization of the human Surf-1 and Surf-2 genes and their intergenic region. Although the major Surf-1 and Surf-2 transcription start sites are separated by 97 bp in the human and there are multiple differences in the mouse and human sequence between and around the transcriptional start sites, there is high conservation of the sequence specifying the three major factor binding sites of the bidirectional promoter. The three factor binding sites (HSu1, 2, and 3) present within the human promoter bind nuclear factors, of which the binding of HSu1 and HSu2 are competed by oligonucleotides carrying the corresponding mouse factor binding sites. The HSu3 site binds factors that are similar but apparently not direct homologs of those that bind to the equivalent mouse sequences. Human Surf-1 and Surf-2 cDNAs have been cloned and sequenced. The putative human Surf-1 and Surf-2 proteins are 77% and 69% identical to the corresponding mouse proteins.

2020 - Nucleic Acids Research
GTRD: an integrated view of transcription regulation

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 transcription regulation (FANTOM5, ENCODE and GTEx). The updated version of the GTRD provides an integrated view of transcription regulation through a dedicated web interface with advanced browsing and search capabilities, an integrated genome browser, and table reports by cell types, transcription factors, and genes of interest.

2011 - Genomics
Transcriptional regulation of co-expressed microRNA target genes.

MicroRNAs play pivotal roles in gene regulation. Despite various research efforts on microRNAs, how microRNA target genes are transcriptionally regulated and how the transcriptional regulation of microRNA target genes relates to that of the microRNA genes are not well studied. By investigating the transcriptional regulation of microRNA target genes, we found that different groups of target genes of the same microRNA are co-expressed under different conditions, and these groups rarely overlap with each other for the majority of microRNAs. We also discovered that co-expressed microRNA target genes are often co-regulated, and different groups of target genes of the same microRNA are often regulated differently. In addition, we observed that transcription factors regulating a microRNA gene often regulate its target genes. Our study sheds light on the regulation of microRNA target genes, which will facilitate the prediction of microRNA target genes and the understanding of the transcriptional regulation of microRNA genes.

2008 - Nucleic acids research
A high-throughput percentage-of-binding strategy to measure binding energies in DNA–protein interactions: application to genome-scale site discovery

Quantifying the binding energy in DNA–protein interactions is of critical importance to understand transcriptional regulation. Based on a simple computational model, this study describes a high-throughput percentage-of-binding strategy to measure the binding energy in DNA–protein interactions between the Shewanella oneidensis ArcA two-component transcription factor protein and a systematic set of mutants in an ArcA-P (phosphorylated ArcA) binding site. The binding energies corresponding to each of the 4 nt at each position in the 15-bp binding site were used to construct a position-specific energy matrix (PEM) that allowed a reliable prediction of ArcA-P binding sites not only in Shewanella but also in related bacterial genomes.

2008 - Nucleic acids research
Algorithm for prediction of tumour suppressor p53 affinity for binding sites in DNA

The tumour suppressor p53 is a transcription factor that binds DNA in the vicinity of the genes it controls. The affinity of p53 for specific binding sites relative to other DNA sequences is an inherent driving force for specificity, all other things being equal. We measured the binding affinities of systematically mutated consensus p53 DNA-binding sequences using automated fluorescence anisotropy titrations. Based on measurements of the effects of every possible single base-pair substitution of a consensus sequence, we defined the DNA sequence with the highest affinity for full-length p53 and quantified the effects of deviation from it on the strength of protein–DNA interaction. The contributions of individual nucleotides were to a first approximation independent and additive. But, in some cases we observed significant deviations from additivity. Based on affinity data, we constructed a binding predictor that mirrored the existing p53 consensus sequence definition. We used it to search for high-affinity binding sites in the genome and to predict the effects of single-nucleotide polymorphisms in these sites. Although there was some correlation between the Kd and biological function, the spread of the Kds by itself was not sufficient to explain the activation of different pathways by changes in p53 concentration alone.

2011 - BMC Bioinformatics
Novel computational analysis of protein binding array data identifies direct targets of Nkx2.2 in the pancreas

BackgroundThe creation of a complete genome-wide map of transcription factor binding sites is essential for understanding gene regulatory networks in vivo. However, current prediction methods generally rely on statistical models that imperfectly model transcription factor binding. Generation of new prediction methods that are based on protein binding data, but do not rely on these models may improve prediction sensitivity and specificity.ResultsWe propose a method for predicting transcription factor binding sites in the genome by directly mapping data generated from protein binding microarrays (PBM) to the genome and calculating a moving average of several overlapping octamers. Using this unique algorithm, we predicted binding sites for the essential pancreatic islet transcription factor Nkx2.2 in the mouse genome and confirmed >90% of the tested sites by EMSA and ChIP. Scores generated from this method more accurately predicted relative binding affinity than PWM based methods. We have also identified an alternative core sequence recognized by the Nkx2.2 homeodomain. Furthermore, we have shown that this method correctly identified binding sites in the promoters of two critical pancreatic islet β-cell genes, NeuroD1 and insulin2, that were not predicted by traditional methods. Finally, we show evidence that the algorithm can also be applied to predict binding sites for the nuclear receptor Hnf4α.ConclusionsPBM-mapping is an accurate method for predicting Nkx2.2 binding sites and may be widely applicable for the creation of genome-wide maps of transcription factor binding sites.

2010 - Eukaryotic Cell
Candida albicans Hap43 Is a Repressor Induced under Low-Iron Conditions and Is Essential for Iron-Responsive Transcriptional Regulation and Virulence

ABSTRACT Candida albicans is an opportunistic fungal pathogen that exists as normal flora in healthy human bodies but causes life-threatening infections in immunocompromised patients. In addition to innate and adaptive immunities, hosts also resist microbial infections by developing a mechanism of “natural resistance” that maintains a low level of free iron to restrict the growth of invading pathogens. C. albicans must overcome this iron-deprived environment to cause infections. There are three types of iron-responsive transcriptional regulators in fungi; Aft1/Aft2 activators in yeast, GATA-type repressors in many fungi, and HapX/Php4 in Schizosaccharomyces pombe and Aspergillus species. In this study, we characterized the iron-responsive regulator Hap43, which is the C. albicans homolog of HapX/Php4 and is repressed by the GATA-type repressor Sfu1 under iron-sufficient conditions. We provide evidence that Hap43 is essential for the growth of C. albicans under low-iron conditions and for C. albicans virulence in a mouse model of infection. Hap43 was not required for iron acquisition under low-iron conditions. Instead, it was responsible for repression of genes that encode iron-dependent proteins involved in mitochondrial respiration and iron-sulfur cluster assembly. We also demonstrated that Hap43 executes its function by becoming a transcriptional repressor and accumulating in the nucleus in response to iron deprivation. Finally, we found a connection between Hap43 and the global corepressor Tup1 in low-iron-induced flavinogenesis. Taken together, our data suggest a complex interplay among Hap43, Sfu1, and Tup1 to coordinately regulate iron acquisition, iron utilization, and other iron-responsive metabolic activities.

2004 - Acta biochimica et biophysica Sinica
Predicting polymerase II core promoters by cooperating transcription factor binding sites in eukaryotic genes.

Several discriminate functions for predicting core promoters that based on the potential cooperation between transcription factor binding sites (TFBSs) are discussed. It is demonstrated that the promoter predicting accuracy is improved when the cooperation among TFBSs is taken into consideration. The core promoter region of a newly discovered gene CKLFSF1 is predicted to locate more than 1.5 kb far away from the 5' end of the transcript and in the last intron of its upstream gene, which is experimentally confirmed later. The core promoters of 3402 human RefSeq sequences, obtained by extending the mRNAs in human genome sequences, are predicted by our algorithm, and there are about 60% of the predicted core promoters locating within the +/- 500 bp region relative to the annotated transcription start site.

2010 - PLoS ONE
A Novel Alignment-Free Method for Comparing Transcription Factor Binding Site Motifs

Background Transcription factor binding site (TFBS) motifs can be accurately represented by position frequency matrices (PFM) or other equivalent forms. We often need to compare TFBS motifs using their PFMs in order to search for similar motifs in a motif database, or cluster motifs according to their binding preference. The majority of current methods for motif comparison involve a similarity metric for column-to-column comparison and a method to find the optimal position alignment between the two compared motifs. In some applications, alignment-free methods might be preferred; however, few such methods with high accuracy have been described. Methodology/Principal Findings Here we describe a novel alignment-free method for quantifying the similarity of motifs using their PFMs by converting PFMs into k-mer vectors. The motifs could then be compared by measuring the similarity among their corresponding k-mer vectors. Conclusions/Significance We demonstrate that our method in general achieves similar performance or outperforms the existing methods for clustering motifs according to their binding preference and identifying similar motifs of transcription factors of the same family.

2007 - Algorithms for Molecular Biology
PhyloScan: identification of transcription factor binding sites using cross-species evidence

BackgroundWhen transcription factor binding sites are known for a particular transcription factor, it is possible to construct a motif model that can be used to scan sequences for additional sites. However, few statistically significant sites are revealed when a transcription factor binding site motif model is used to scan a genome-scale database.MethodsWe have developed a scanning algorithm, PhyloScan, which combines evidence from matching sites found in orthologous data from several related species with evidence from multiple sites within an intergenic region, to better detect regulons. The orthologous sequence data may be multiply aligned, unaligned, or a combination of aligned and unaligned. In aligned data, PhyloScan statistically accounts for the phylogenetic dependence of the species contributing data to the alignment and, in unaligned data, the evidence for sites is combined assuming phylogenetic independence of the species. The statistical significance of the gene predictions is calculated directly, without employing training sets.ResultsIn a test of our methodology on synthetic data modeled on seven Enterobacteriales, four Vibrionales, and three Pasteurellales species, PhyloScan produces better sensitivity and specificity than MONKEY, an advanced scanning approach that also searches a genome for transcription factor binding sites using phylogenetic information. The application of the algorithm to real sequence data from seven Enterobacteriales species identifies novel Crp and PurR transcription factor binding sites, thus providing several new potential sites for these transcription factors. These sites enable targeted experimental validation and thus further delineation of the Crp and PurR regulons in E. coli.ConclusionBetter sensitivity and specificity can be achieved through a combination of (1) using mixed alignable and non-alignable sequence data and (2) combining evidence from multiple sites within an intergenic region.

2005 - Genome research
Identification of functional transcription factor binding sites using closely related Saccharomyces species.

Comparative genomics provides a rapid means of identifying functional DNA elements by their sequence conservation between species. Transcription factor binding sites (TFBSs) may constitute a significant fraction of these conserved sequences, but the annotation of specific TFBSs is complicated by the fact that these short, degenerate sequences may frequently be conserved by chance rather than functional constraint. To identify intergenic sequences that function as TFBSs, we calculated the probability of binding site conservation between Saccharomyces cerevisiae and its two closest relatives under a neutral model of evolution. We found that this probability is <5% for 134 of 163 transcription factor binding motifs, implying that we can reliably annotate binding sites for the majority of these transcription factors by conservation alone. Although our annotation relies on a number of assumptions, mutations in five of five conserved Ume6 binding sites and three of four conserved Ndt80 binding sites show Ume6- and Ndt80-dependent effects on gene expression. We also found that three of five unconserved Ndt80 binding sites show Ndt80-dependent effects on gene expression. Together these data imply that although sequence conservation can be reliably used to predict functional TFBSs, unconserved sequences might also make a significant contribution to a species' biology.

2006 - Journal of computational biology : a journal of computational molecular cell biology
Multiple Testing Methods For ChIP - Chip High Density Oligonucleotide Array Data

Cawley et al. (2004) have recently mapped the locations of binding sites for three transcription factors along human chromosomes 21 and 22 using ChIP-Chip experiments. ChIP-Chip experiments are a new approach to the genomewide identification of transcription factor binding sites and consist of chromatin (Ch) immunoprecipitation (IP) of transcription factor-bound genomic DNA followed by high density oligonucleotide hybridization (Chip) of the IP-enriched DNA. We investigate the ChIP-Chip data structure and propose methods for inferring the location of transcription factor binding sites from these data. The proposed methods involve testing for each probe whether it is part of a bound sequence using a scan statistic that takes into account the spatial structure of the data. Different multiple testing procedures are considered for controlling the familywise error rate and false discovery rate. A nested-Bonferroni adjustment, which is more powerful than the traditional Bonferroni adjustment when the test statistics are dependent, is discussed. Simulation studies show that taking into account the spatial structure of the data substantially improves the sensitivity of the multiple testing procedures. Application of the proposed methods to ChIP-Chip data for transcription factor p53 identified many potential target binding regions along human chromosomes 21 and 22. Among these identified regions, 18% fall within a 3 kb vicinity of the 5'UTR of a known gene or CpG island and 31% fall between the codon start site and the codon end site of a known gene but not inside an exon. More than half of these potential target sequences contain the p53 consensus binding site or very close matches to it. Moreover, these target segments include the 13 experimentally verified p53 binding regions of Cawley et al. (2004), as well as 49 additional regions that show higher hybridization signal than these 13 experimentally verified regions.

2004 - Briefings in bioinformatics
In silico representation and discovery of transcription factor binding sites

Understanding the complex mechanisms governing basic biological processes requires the characterisation of regulatory motifs modulating gene expression at transcriptional and post-transcriptional level. In particular, extent, chronology and cell-specificity of transcription are modulated by the interaction of transcription factors with their corresponding binding sites, mostly located near (or sometimes quite far away from) the transcription start site of the gene. The constantly growing amount of genomic data, complemented by other sources of information such as expression data derived from microarray experiments, has opened new opportunities to researchers in this field. Many different methods have been proposed for the identification of transcription factor binding sites in the regulatory regions of co-expressed genes: unfortunately this is a very challenging problem both from the computational and the biological viewpoint. This paper provides a survey of existing methods proposed for the problem, focusing both on the ideas underlying them and their availability to the scientific community.

2004 - Nucleic acids research
JASPAR: an open-access database for eukaryotic transcription factor binding profiles

The analysis of regulatory regions in genome sequences is strongly based on the detection of potential transcription factor binding sites. The preferred models for representation of transcription factor binding specificity have been termed position-specific scoring matrices. JASPAR is an open-access database of annotated, high-quality, matrix-based transcription factor binding site profiles for multicellular eukaryotes. The profiles were derived exclusively from sets of nucleotide sequences experimentally demonstrated to bind transcription factors. The database is complemented by a web interface for browsing, searching and subset selection, an online sequence analysis utility and a suite of programming tools for genome-wide and comparative genomic analysis of regulatory regions. JASPAR is available at http://jaspar. cgb.ki.se.

2003 - Current protocols in bioinformatics
Using TESS to Predict Transcription Factor Binding Sites in DNA Sequence

This unit describes how to use the Transcription Element Search System (TESS). This Web site predicts transcription factor binding sites (TFBS) in DNA sequence using two different kinds of models of sites, strings and positional weight matrices. The binding of transcription factors to DNA is a major part of the control of gene expression. Transcription factors exhibit sequence‐specific binding; they form stronger bonds to some DNA sequences than to others. Identification of a good binding site in the promoter for a gene suggests the possibility that the corresponding factor may play a role in the regulation of that gene. However, the sequences transcription factors recognize are typically short and allow for some amount of mismatch. Because of this, binding sites for a factor can typically be found at random every few hundred to a thousand base pairs. TESS has features to help sort through and evaluate the significance of predicted sites. Curr. Protoc. Bioinform. 21:2.6.1‐2.6.15. © 2008 by John Wiley & Sons, Inc.

TRANSFAC®: transcriptional regulation, from patterns to profiles

Alexander E. Kel
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Olga V. Kel-Margoulis
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Ingmar Reuter
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Edgar Wingender
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Ellen Fricke
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Martin Haubrock
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Klaus Hornischer
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Holger Michael
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Reinhard Hehl
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Maurice Scheer
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V. Matys
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Sigrid Land
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Birgit Lewicki-Potapov
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H. Saxel
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Ellen Gößling
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Stella Rotert
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Dagmar Karas
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Dorothee-U. Kloos
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R. Geffers
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Richard Münch
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S. Thiele
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E. Wingender
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R. Geffers
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K. Hornischer
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A. Kel
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E. Gößling
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I. Reuter
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O. Kel-Margoulis
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V. Matys
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E. Fricke
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Martin Haubrock
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R. Hehl
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D. Karas
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D. Kloos
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S. Land
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Birgit Lewicki-Potapov
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H. Michael
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R. Münch
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S. Rotert
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H. Saxel
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Maurice Scheer
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S. Thiele
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B. Lewicki-Potapov
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M. Scheer

Abstract:The TRANSFAC database on eukaryotic transcriptional regulation, comprising data on transcription factors, their target genes and regulatory binding sites, has been extended and further developed, both in number of entries and in the scope and structure of the collected data. Structured fields for expression patterns have been introduced for transcription factors from human and mouse, using the CYTOMER database on anatomical structures and developmental stages. The functionality of Match, a tool for matrix-based search of transcription factor binding sites, has been enhanced. For instance, the program now comes along with a number of tissue-(or state-)specific profiles and new profiles can be created and modified with Match Profiler. The GENE table was extended and gained in importance, containing amongst others links to LocusLink, RefSeq and OMIM now. Further, (direct) links between factor and target gene on one hand and between gene and encoded factor on the other hand were introduced. The TRANSFAC public release is available at http://www.gene-regulation.com. For yeast an additional release including the latest data was made available separately as TRANSFAC Saccharomyces Module (TSM) at http://transfac.gbf.de. For CYTOMER free download versions are available at http://www.biobase.de:8080/index.html.

摘要：TRANSFAC关于真核转录调控的数据库，包括关于转录因子、其目标基因和调控结合位点的数据，在条目数量以及收集的数据的范围和结构方面都得到了扩展和进一步发展。利用有关解剖结构和发育阶段的CyTOMER数据库，为人类和小鼠的转录因子引入了表达模式的结构化领域。Match是一种基于基质的转录因子结合位点搜索工具，其功能已得到增强。例如，该程序现在附带了许多特定于组织(或状态)的配置文件，并且可以使用Match Profiler创建和修改新的配置文件。基因表得到了扩展并获得了重要性，其中包括与LocusLink、RefSeq和OMIM的链接。此外，还介绍了因子与靶基因之间以及基因与编码因子之间的(直接)联系。TRANSFAC的公开发布可在http://www.gene-regulation.com.上获得对于酵母菌，包括最新数据的额外版本在http://transfac.gbf.de.上作为TRANSFAC酵母模块(TSM)单独提供如需免费下载，请访问：http://www.biobase.de:8080/index.html.

参考文献

[1]  E. Wingender,et al.  Compilation of transcription regulating proteins. , 1988, Nucleic acids research.

[2]  R. KNÜPPEL,et al.  TRANSFAC Retrieval Program: A Network Model Database of Eukaryotic Transcription Regulating Sequences and Proteins , 1994, J. Comput. Biol..

[3]  T. Werner,et al.  MatInd and MatInspector: new fast and versatile tools for detection of consensus matches in nucleotide sequence data. , 1995, Nucleic acids research.

[4]  Holger Karas,et al.  TRANSFAC: a database on transcription factors and their DNA binding sites , 1996, Nucleic Acids Res..

[5]  S. Smale,et al.  Multiple control elements mediate activation of the murine and human interleukin 12 p40 promoters: evidence of functional synergy between C/EBP and Rel proteins , 1997, Molecular and cellular biology.

[6]  T. Heinemeyer,et al.  TRANSFAC, TRRD and COMPEL: towards a federated database system on transcriptional regulation , 1997, Nucleic Acids Res..

[7]  E. Wingender.  CLASSIFICATION SCHEME OF EUKARYOTIC TRANSCRIPTION FACTORS , 1997 .

[8]  T. Heinemeyer,et al.  Databases on transcriptional regulation: TRANSFAC, TRRD and COMPEL , 1998, Nucleic Acids Res..

[9]  Dmitrij Frishman,et al.  MIPS: a database for genomes and protein sequences , 1999, Nucleic Acids Res..

[10]  Xin Chen,et al.  A database framework for mapping expression patterns , 1999, German Conference on Bioinformatics.

[11]  T. Heinemeyer,et al.  Expanding the TRANSFAC database towards an expert system of regulatory molecular mechanisms , 1999, Nucleic Acids Res..

[12]  Xin Chen,et al.  TRANSFAC: an integrated system for gene expression regulation , 2000, Nucleic Acids Res..

[13]  Edgar Wingender,et al.  Cytomer: A database on gene expression sources , 2001, German Conference on Bioinformatics.

[14]  Xin Chen,et al.  The TRANSFAC system on gene expression regulation , 2001, Nucleic Acids Res..

[15]  Frank Schacherer,et al.  The TRANSPATH signal transduction database: a knowledge base on signal transduction networks , 2001, Bioinform..

[16]  Alexander E. Kel,et al.  MATCHTM: a tool for searching transcription factor binding sites in DNA sequences , 2003, Nucleic Acids Res..

[17]  M. Neurath,et al.  Regulation of IL-12 p40 Promoter Activity in Primary Human Monocytes: Roles of NF-κB, CCAAT/Enhancer-Binding Protein β, and PU.1 and Identification of a Novel Repressor Element (GA-12) That Responds to IL-4 and Prostaglandin E21 , 2001, The Journal of Immunology.

[18]  B. Akache,et al.  Phenotypic analysis of genes encoding yeast zinc cluster proteins. , 2001, Nucleic acids research.

[19]  Ines Liebich,et al.  S/MARt DB: a database on scaffold/matrix attached regions , 2002, Nucleic Acids Res..

[20]  Antje Chang,et al.  BRENDA, enzyme data and metabolic information , 2002, Nucleic Acids Res..

[21]  Gregory D. Schuler,et al.  Database resources of the National Center for Biotechnology Information: 2002 update , 2002, Nucleic Acids Res..

[22]  Alexander E. Kel,et al.  TRANSCompel®: a database on composite regulatory elements in eukaryotic genes , 2002, Nucleic Acids Res..

[23]  Gregory D. Schuler,et al.  Database resources of the National Center for Biotechnology Information: update , 2004, Nucleic acids research.

引用
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DIANA-miRGen v3.0: accurate characterization of microRNA promoters and their regulators
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