相关论文
2007
PlasmoTFBM: An Intelligent Queriable Database for Predicted Transcription Factor Binding Motifs in Plasmodium falciparum

There is very little information available with regard to gene regulatory circuitries in Plasmodium falciparum. In an attempt to discover transcription factor binding motifs (TFBMs) in P. falciparum, we considered two approaches. In the first approach, gene expression data from asexual intraerythrocytic developmental cycle generated every hour for 48 hour post-infection were fed into the ISA (Iterative Signature Algorithm), which outputs modules composed of sets of genes associated with co-regulating conditions. Putative TFBMs were discovered by applying the AlignACE program on the resulting gene sets. In the second approach, the MotifRegressor program was used to predict potential motifs associated with induced and repressed genes for each time point and then clustered based on the strength of their correlation to the gene expression (i.e., motif coefficients) across different time points. A total of 637 and 840 putative motifs were predicted by the MotifRegressor and ISA-AlignACE programs, respectively. All this information was uploaded into a database, thus making it easy to devise complex queries. Using published information on known motifs, we were able to validate some of our results. In addition, modules consisting of putative transcription factors and related genes were also investigated. This work provides a bioinformatics methodology to analyze transcription regulation and TFBMs across the whole genome. By constructing a comprehensive relational database and an intelligent, user-friendly query system, biologically meaningful conclusions can be drawn easily even by an investigator with no prior knowledge of databases.

2006 - Blood
Differentially methylated alleles in a distinct region of the human interleukin-1alpha promoter are associated with allele-specific expression of IL-1alpha in CD4+ T cells.

Cytokine secretion profiles of activated T cells are critical for maintaining the immunologic balance between protection and tolerance. In mice, several cytokines have been reported to exhibit monoallelic expression. Previously, we found that the human interleukin-1 alpha (IL1A) gene exhibits a stable allele-specific expression pattern in CD4+ T-cell clones. We investigated whether DNA methylation is involved in the allele-specific expression of IL-1alpha. Here, we show that differential methylation of CpGs in the proximal promoter region is associated with allele-specific expression of IL-1alpha in CD4+ T cells. The differential methylation pattern is already observed in naive T cells. In keratinocytes, which constitutively produce IL-1alpha, the proximal promoter is hypomethylated. CpGs located further upstream and in intron 4 were almost all methylated, irrespective of expression. Treatment of nonexpressing cells and of T-cell clones with 5-aza-2'deoxycytidine induced IL-1alpha expression in the nonexpressing cells and induced expression of the formerly silent allele in T-cell clones. In addition, electrophoretic mobility shift assays showed that methylation of CpGs in the proximal promoter resulted in direct inhibition of binding of nuclear factor(s). Taken together, these results suggest that allele-specific expression of IL-1alpha in CD4+ cells is achieved, at least in part, by differential methylation of the promoter.

2004 - Regulatory Genomics
Application of Kernel Method to Reveal Subtypes of TF Binding Motifs

Transcription factor binding sites often contain several subtypes of sequences that follow not just one but several different patterns. We developed a novel sensitive method based on kernel estimations that is able to reveal subtypes of TF binding sites. The developed method produces patterns in form of positional weight matrices for the individual subtypes and has been tested on simulated data and compared with several other methods of pattern discovery (Gibbs sampling, MEME, CONSENSUS, MULTIPROFILER and PROJECTION). The kernel method showed the best performance in terms of how close the revealed weight matrices are to the original ones. We applied the Kernel method to several TFs including nuclear receptors and ligand-activated transcription factors AhR. The revealed patterns were applied to analyze gene expression data. In promoters of differentially expressed genes we found specific combinations of different types of TF binding patterns that correlate with the level of up or down regulation.

2017 - 2017 Iranian Conference on Electrical Engineering (ICEE)
Transcription factor binding sites identification on human genome using an artificial neural network

Transcription factor binding sites on human DNA are the target locations of specific proteins called transcription factors. Gene expression process begins when a transcription factor binds to its target location in the genome. Expensive experimental methods are used to identify a limited number of these binding sites, hence there is essential need for computational algorithms. In this paper, we train a back propagation neural network to identify SP1 factor binding sites on human chromosome1. Biological data have been extracted from NCBI database which includes a wide variety of genetic information of human and other species. In order to compare the performance of our trained neural network with other classification algorithms, we use Support Vector Machine, Discriminant Analysis and K-Nearest Neighbor algorithm to classify same data. Results show that our trained neural network outperforms other classification algorithms.

1999 - Bioinformatics
Integrated databases and computer systems for studying eukaryotic gene expression

MOTIVATION The goal of the work was to develop a WWW-oriented computer system providing a maximal integration of informational and software resources on the regulation of gene expression and navigation through them. Rapid growth of the variety and volume of information accumulated in the databases on regulation of gene expression necessarily requires the development of computer systems for automated discovery of the knowledge that can be further used for analysis of regulatory genomic sequences. RESULTS The GeneExpress system developed includes the following major informational and software modules: (1) Transcription Regulation (TRRD) module, which contains the databases on transcription regulatory regions of eukaryotic genes and TRRD Viewer for data visualization; (2) Site Activity Prediction (ACTIVITY), the module for analysis of functional site activity and its prediction; (3) Site Recognition module, which comprises (a) B-DNA-VIDEO system for detecting the conformational and physicochemical properties of DNA sites significant for their recognition, (b) Consensus and Weight Matrices (ConsFrec) and (c) Transcription Factor Binding Sites Recognition (TFBSR) systems for detecting conservative contextual regions of functional sites and their recognition; (4) Gene Networks (GeneNet), which contains an object-oriented database accumulating the data on gene networks and signal transduction pathways, and the Java-based Viewer for exploration and visualization of the GeneNet information; (5) mRNA Translation (Leader mRNA), designed to analyze structural and contextual properties of mRNA 5'-untranslated regions (5'-UTRs) and predict their translation efficiency; (6) other program modules designed to study the structure-function organization of regulatory genomic sequences and regulatory proteins. AVAILABILITY GeneExpress is available at http://wwwmgs.bionet.nsc. ru/systems/GeneExpress/ and the links to the mirror site(s) can be found at http://wwwmgs.bionet.nsc.ru/mgs/links/mirrors.html+ ++.

2016 - Bioinformatics
Structure-based prediction of transcription factor binding specificity using an integrative energy function

Transcription factors (TFs) regulate gene expression through binding to specific target DNA sites. Accurate annotation of transcription factor binding sites (TFBSs) at genome scale represents an essential step toward our understanding of gene regulation networks. In this article, we present a structure-based method for computational prediction of TFBSs using a novel, integrative energy (IE) function. The new energy function combines a multibody (MB) knowledge-based potential and two atomic energy terms (hydrogen bond and π interaction) that might not be accurately captured by the knowledge-based potential owing to the mean force nature and low count problem. We applied the new energy function to the TFBS prediction using a non-redundant dataset that consists of TFs from 12 different families. Our results show that the new IE function improves the prediction accuracy over the knowledge-based, statistical potentials, especially for homeodomain TFs, the second largest TF family in mammals. Contact: jguo4@uncc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

1996 - Biochemistry
Inhibition of transcription factor binding by ultraviolet-induced pyrimidine dimers.

The formation of DNA photoproducts by ultraviolet (UV) light is responsible for the induction of mutations and the development of skin cancer. Cis-syn cyclobutane pyrimidine dimers (pyrimidine dimers) are the most frequent lesions produced in DNA by UV irradiation. Besides being mutagenic, pyrimidine dimers may interfere with other important DNA-dependent processes. To analyze the effects of pyrimidine dimers on the ability of DNA sequences to be recognized by trans-acting factors, we have incorporated site-specific T-T dimers into oligonucleotides containing the recognition sequences of the sequence-specific transcription factors E2F, NF-Y, AP-1, NF kappa B, and p53. In each case, presence of the photodimer strongly inhibited binding of the respective transcription factor complex. Reduction of binding varied between 11- and 60-fold. The results indicate that the most common UV-induced DNA lesion can interfere severely with binding of several important cell cycle regulatory and DNA damage responsive transcription factors. We suggest that inhibition of transcription factor binding may be a major biological effect of UV radiation since promoter regions are known to be repaired inefficiently and since UV damage can deregulate the function of a large number of different factors.

2009 - BMC Bioinformatics
FISim: A new similarity measure between transcription factor binding sites based on the fuzzy integral

BackgroundRegulatory motifs describe sets of related transcription factor binding sites (TFBSs) and can be represented as position frequency matrices (PFMs). De novo identification of TFBSs is a crucial problem in computational biology which includes the issue of comparing putative motifs with one another and with motifs that are already known. The relative importance of each nucleotide within a given position in the PFMs should be considered in order to compute PFM similarities. Furthermore, biological data are inherently noisy and imprecise. Fuzzy set theory is particularly suitable for modeling imprecise data, whereas fuzzy integrals are highly appropriate for representing the interaction among different information sources.ResultsWe propose FISim, a new similarity measure between PFMs, based on the fuzzy integral of the distance of the nucleotides with respect to the information content of the positions. Unlike existing methods, FISim is designed to consider the higher contribution of better conserved positions to the binding affinity. FISim provides excellent results when dealing with sets of randomly generated motifs, and outperforms the remaining methods when handling real datasets of related motifs. Furthermore, we propose a new cluster methodology based on kernel theory together with FISim to obtain groups of related motifs potentially bound by the same TFs, providing more robust results than existing approaches.ConclusionFISim corrects a design flaw of the most popular methods, whose measures favour similarity of low information content positions. We use our measure to successfully identify motifs that describe binding sites for the same TF and to solve real-life problems. In this study the reliability of fuzzy technology for motif comparison tasks is proven.

2003 - Molecular and Cellular Biology
Activation of the Early B-Cell-Specific mb-1 (Ig-α) Gene by Pax-5 Is Dependent on an Unmethylated Ets Binding Site

ABSTRACT Methylation of cytosine in CpG dinucleotides promotes transcriptional repression in mammals by blocking transcription factor binding and recruiting methyl-binding proteins that initiate chromatin remodeling. Here, we use a novel cell-based system to show that retrovirally expressed Pax-5 protein activates endogenous early B-cell-specific mb-1 genes in plasmacytoma cells, but only when the promoter is hypomethylated. CpG methylation does not directly affect binding of the promoter by Pax-5. Instead, methylation of an adjacent CpG interferes with assembly of ternary complexes comprising Pax-5 and Ets proteins. In electrophoretic mobility shift assays, recruitment of Ets-1 is blocked by methylation of the Ets site (5′CCGGAG) on the antisense strand. In transfection assays, selective methylation of a single CpG within the Pax-5-dependent Ets site greatly reduces mb-1 promoter activity. Prior demethylation of the endogenous mb-1 promoter is required for its activation by Pax-5 in transduced cells. Although B-lineage cells have only unmethylated mb-1 genes and do not modulate methylation of the mb-1 promoter during development, other tissues feature high percentages of methylated alleles. Together, these studies demonstrate a novel DNA methylation-dependent mechanism for regulating transcriptional activity through the inhibition of DNA-dependent protein-protein interactions.

2009 - BMC Evolutionary Biology
Statistical tests for natural selection on regulatory regions based on the strength of transcription factor binding sites

BackgroundAlthough cis-regulatory changes play an important role in evolution, it remains difficult to establish the contribution of natural selection to regulatory differences between species. For protein coding regions, powerful tests of natural selection have been developed based on comparisons of synonymous and non-synonymous substitutions, and analogous tests for regulatory regions would be of great utility.ResultsHere, tests for natural selection on regulatory regions are proposed based on nucleotide substitutions that occur in characterized transcription factor binding sites (an important type functional element within regulatory regions). In the absence of selection, these substitutions will tend to reduce the strength of existing binding sites. On the other hand, purifying selection will act to preserve the binding sites in regulatory regions, while positive selection can act to create or destroy binding sites, as well as change their strength. Using standard models of binding site strength and molecular evolution in the absence of selection, this intuition can be used to develop statistical tests for natural selection. Application of these tests to two well-characterized regulatory regions in Drosophila provides evidence for purifying selection.ConclusionThis demonstrates that it is possible to develop tests for selection on regulatory regions based on the specific functional constrains on these sequences.

2006 - BMC Bioinformatics
Design of a combinatorial DNA microarray for protein-DNA interaction studies

BackgroundDiscovery of precise specificity of transcription factors is an important step on the way to understanding the complex mechanisms of gene regulation in eukaryotes. Recently, double-stranded protein-binding microarrays were developed as a potentially scalable approach to tackle transcription factor binding site identification.ResultsHere we present an algorithmic approach to experimental design of a microarray that allows for testing full specificity of a transcription factor binding to all possible DNA binding sites of a given length, with optimally efficient use of the array. This design is universal, works for any factor that binds a sequence motif and is not species-specific. Furthermore, simulation results show that data produced with the designed arrays is easier to analyze and would result in more precise identification of binding sites.ConclusionIn this study, we present a design of a double stranded DNA microarray for protein-DNA interaction studies and show that our algorithm allows optimally efficient use of the arrays for this purpose. We believe such a design will prove useful for transcription factor binding site identification and other biological problems.

2008 - BMC Bioinformatics
CORE_TF: a user-friendly interface to identify evolutionary conserved transcription factor binding sites in sets of co-regulated genes

BackgroundThe identification of transcription factor binding sites is difficult since they are only a small number of nucleotides in size, resulting in large numbers of false positives and false negatives in current approaches. Computational methods to reduce false positives are to look for over-representation of transcription factor binding sites in a set of similarly regulated promoters or to look for conservation in orthologous promoter alignments.ResultsWe have developed a novel tool, "CORE_TF" (Conserved and Over-REpresented Transcription Factor binding sites) that identifies common transcription factor binding sites in promoters of co-regulated genes. To improve upon existing binding site predictions, the tool searches for position weight matrices from the TRANSFACRdatabase that are over-represented in an experimental set compared to a random set of promoters and identifies cross-species conservation of the predicted transcription factor binding sites. The algorithm has been evaluated with expression and chromatin-immunoprecipitation on microarray data. We also implement and demonstrate the importance of matching the random set of promoters to the experimental promoters by GC content, which is a unique feature of our tool.ConclusionThe program CORE_TF is accessible in a user friendly web interface at http://www.LGTC.nl/CORE_TF. It provides a table of over-represented transcription factor binding sites in the users input genes' promoters and a graphical view of evolutionary conserved transcription factor binding sites. In our test data sets it successfully predicts target transcription factors and their binding sites.

2001 - Bioinformatics
Combining frequency and positional information to predict transcription factor binding sites

MOTIVATION Even though a number of genome projects have been finished on the sequence level, still only a small proportion of DNA regulatory elements have been identified. Growing amounts of gene expression data provide the possibility of finding coregulated genes by clustering methods. By analysis of the promoter regions of those genes, rather weak signals of transcription factor binding sites may be detected. RESULTS We introduce the new algorithm ITB, an Integrated Tool for Box finding, which combines frequency and positional information to predict transcription factor binding sites in upstream regions of coregulated genes. Motifs are extracted by exhaustive analysis of regular expression-like patterns and by estimating probabilities of positional clusters of motifs. ITB detects consensus sequences of experimentally verified transcription factor binding sites of the yeast Saccharomyces cerevisiae. Moreover, a number of new binding site candidates with significant scores are predicted. Besides applying ITB on yeast upstream regions, the program is run on human promoter sequences. AVAILABILITY ITB is available upon request.

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 - BMC Bioinformatics
Predicting transcription factor binding sites using local over-representation and comparative genomics

BackgroundIdentifying cis-regulatory elements is crucial to understanding gene expression, which highlights the importance of the computational detection of overrepresented transcription factor binding sites (TFBSs) in coexpressed or coregulated genes. However, this is a challenging problem, especially when considering higher eukaryotic organisms.ResultsWe have developed a method, named TFM-Explorer, that searches for locally overrepresented TFBSs in a set of coregulated genes, which are modeled by profiles provided by a database of position weight matrices. The novelty of the method is that it takes advantage of spatial conservation in the sequence and supports multiple species. The efficiency of the underlying algorithm and its robustness to noise allow weak regulatory signals to be detected in large heterogeneous data sets.ConclusionTFM-Explorer provides an efficient way to predict TFBS overrepresentation in related sequences. Promising results were obtained in a variety of examples in human, mouse, and rat genomes. The software is publicly available at http://bioinfo.lifl.fr/TFM-Explorer.

2004 - Nucleic acids research
AthaMap: an online resource for in silico transcription factor binding sites in the Arabidopsis thaliana genome

Gene expression is controlled mainly by the binding of transcription factors to regulatory sequences. To generate a genomic map for regulatory sequences, the Arabidopsis thaliana genome was screened for putative transcription factor binding sites. Using publicly available data from the TRANSFAC database and from publications, alignment matrices for 23 transcription factors of 13 different factor families were used with the pattern search program Patser to determine the genomic positions of more than 2.4 x 10(6) putative binding sites. Due to the dense clustering of genes and the observation that regulatory sequences are not restricted to upstream regions, the prediction of binding sites was performed for the whole genome. The genomic positions and the underlying data were imported into the newly developed AthaMap database. This data can be accessed by positional information or the Arabidopsis Genome Initiative identification number. Putative binding sites are displayed in the defined region. Data on the matrices used and on the thresholds applied in these screens are given in the database. Considering the high density of sites it will be a valuable resource for generating models on gene expression regulation. The data are available at http://www.athamap.de.

2015 - Genome research
Unraveling determinants of transcription factor binding outside the core binding site

Binding of transcription factors (TFs) to regulatory sequences is a pivotal step in the control of gene expression. Despite many advances in the characterization of sequence motifs recognized by TFs, our ability to quantitatively predict TF binding to different regulatory sequences is still limited. Here, we present a novel experimental assay termed BunDLE-seq that provides quantitative measurements of TF binding to thousands of fully designed sequences of 200 bp in length within a single experiment. Applying this binding assay to two yeast TFs, we demonstrate that sequences outside the core TF binding site profoundly affect TF binding. We show that TF-specific models based on the sequence or DNA shape of the regions flanking the core binding site are highly predictive of the measured differential TF binding. We further characterize the dependence of TF binding, accounting for measurements of single and co-occurring binding events, on the number and location of binding sites and on the TF concentration. Finally, by coupling our in vitro TF binding measurements, and another application of our method probing nucleosome formation, to in vivo expression measurements carried out with the same template sequences serving as promoters, we offer insights into mechanisms that may determine the different expression outcomes observed. Our assay thus paves the way to a more comprehensive understanding of TF binding to regulatory sequences and allows the characterization of TF binding determinants within and outside of core binding sites.

2005 - Proceedings of the National Academy of Sciences of the United States of America
Identifying tissue-selective transcription factor binding sites in vertebrate promoters.

We present a computational method aimed at systematically identifying tissue-selective transcription factor binding sites. Our method focuses on the differences between sets of promoters that are associated with differentially expressed genes, and it is effective at identifying the highly degenerate motifs that characterize vertebrate transcription factor binding sites. Results on simulated data indicate that our method detects motifs with greater accuracy than the leading methods, and its detection of strongly overrepresented motifs is nearly perfect. We present motifs identified by our method as the most overrepresented in promoters of liver- and muscle-selective genes, demonstrating that our method accurately identifies known transcription factor binding sites and previously uncharacterized motifs.

2009 - Nucleic Acids Research
Pscan: finding over-represented transcription factor binding site motifs in sequences from co-regulated or co-expressed genes

The first step in gene expression, transcription, is modulated by the interaction of transcription factors with their corresponding binding sites on the DNA sequence. Pscan is a software tool that scans a set of sequences (e.g. promoters) from co-regulated or co-expressed genes with motifs describing the binding specificity of known transcription factors and assesses which motifs are significantly over- or under-represented, providing thus hints on which transcription factors could be common regulators of the genes studied, together with the location of their candidate binding sites in the sequences. Pscan does not resort to comparisons with orthologous sequences and experimental results show that it compares favorably to other tools for the same task in terms of false positive predictions and computation time. The website is free and open to all users and there is no login requirement. Address: http://www.beaconlab.it/pscan.

2003 - Nucleic Acids Res.
MATCHTM: a tool for searching transcription factor binding sites in DNA sequences

MatchTM is a weight matrix-based tool for searching putative transcription factor binding sites in DNA sequences. MatchTM is closely interconnected and distributed together with the TRANSFAC® database. In particular, MatchTM uses the matrix library collected in TRANSFAC® and therefore provides the possibility to search for a great variety of different transcription factor binding sites. Several sets of optimised matrix cut-off values are built in the system to provide a variety of search modes of different stringency. The user may construct and save his/her specific user profiles which are selected subsets of matrices including default or user-defined cut-off values. Furthermore a number of tissue-specific profiles are provided that were compiled by the TRANSFAC® team. A public version of the MatchTM tool is available at: http://www.gene-regulation.com/pub/programs.html#match. The same program with a different web interface can be found at http://compel.bionet.nsc.ru/Match/Match.html. An advanced version of the tool called MatchTM Professional is available at http://www.biobase.de.

MatInspector and beyond: promoter analysis based on transcription factor binding sites

Thomas Werner
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Matthias Frisch
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Bernward Klocke
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Manuela Haltmeier
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Kornelie Frech
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Korbinian Grote
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M. Bayerlein
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Andreas Klingenhoff
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K. Cartharius
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T. Werner
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K. Frech
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A. Klingenhoff
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B. Klocke
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M. Frisch
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K. Grote
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K. Cartharius
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M. Haltmeier
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M. Bayerlein
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Cartharius K
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Frech K
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Grote K
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Klocke B
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Haltmeier M
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Klingenhoff A
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Frisch M
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Bayerlein M
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Werner T
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Kerstin Cartharius

Abstract:MOTIVATION Promoter analysis is an essential step on the way to identify regulatory networks. A prerequisite for successful promoter analysis is the prediction of potential transcription factor binding sites (TFBS) with reasonable accuracy. The next steps in promoter analysis can be tackled only with reliable predictions, e.g. finding phylogenetically conserved patterns or identifying higher order combinations of sites in promoters of co-regulated genes. RESULTS We present a new version of the program MatInspector that identifies TFBS in nucleotide sequences using a large library of weight matrices. By introducing a matrix family concept, optimized thresholds, and comparative analysis, the enhanced program produces concise results avoiding redundant and false-positive matches. We describe a number of programs based on MatInspector allowing in-depth promoter analysis (DiAlignTF, FrameWorker) and targeted design of regulatory sequences (SequenceShaper).

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引用
The Right Gene, Expressed at the Wrong Time, or at the Wrong Place
Molecular Syndromology
2018
Transforming growth factor β1 represses proximal tubular cell microRNA-192 expression through decreased hepatocyte nuclear factor DNA binding.
The Biochemical journal
2012
Wnt signaling inhibits adrenal steroidogenesis by cell-autonomous and non-cell-autonomous mechanisms.
Molecular Endocrinology
2014
Evolutionary Sound Matching: A Test Methodology and Comparative Study
ICMLA 2007
2007
BiblioSphere — Hypothesis Generation in Regulatory Network Analysis
2009
Sterol O-Acyltransferase 1 (SOAT1, ACAT) Is a Novel Target of Steroidogenic Factor-1 (SF-1, NR5A1, Ad4BP) in the Human Adrenal
The Journal of clinical endocrinology and metabolism
2011
A Word Counting Graph
2009
The HMGA1-IGF-I/IGFBP system: a novel pathway for modulating glucose uptake.
Molecular endocrinology
2012
A three-single-nucleotide polymorphism haplotype in intron 1 of OCA2 explains most human eye-color variation.
American journal of human genetics
2007
A single SNP in an evolutionary conserved region within intron 86 of the HERC2 gene determines human blue-brown eye color.
American journal of human genetics
2008
MACO: A Gapped-Alignment Scoring Tool for Comparing Transcription Factor Binding Sites
Silico Biol.
2006
BindSDb: A binding-information spatial database
2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)
2010
Association weight matrix for the genetic dissection of puberty in beef cattle
Proceedings of the National Academy of Sciences
2010
Bacterial promoter modeling and prediction for E. coli and B. subtilis with Beagle
2006
Urothelial carcinoma associated 1 is a hypoxia-inducible factor-1α-targeted long noncoding RNA that enhances hypoxic bladder cancer cell proliferation, migration, and invasion
Tumor Biology
2014
Urotensin II is a New Chemotactic Factor for UT Receptor-Expressing Monocytes1
The Journal of Immunology
2007
Large-scale modeling of condition-specific gene regulatory networks by information integration and inference
Nucleic acids research
2014
HUR protects NONO from degradation by mir320, which is induced by p53 upon UV irradiation
Oncotarget
2016
Improved predictions of transcription factor binding sites using physicochemical features of DNA
Nucleic acids research
2012
Maps of Open Chromatin Guide the Functional Follow-Up of Genome-Wide Association Signals: Application to Hematological Traits
PLoS genetics
2011