ライフサイエンスコーパス: TFBS

1 TFBS are proposed as novel independent features for use

2 TFBS prediction tools used to scan PWMs against DNA fall

3 TFBS were disrupted by site-directed mutagenesis (SDM) t

4 TFBSs are generally recognized by scanning a position we

5 CONFAC identified 12 TFBS that were statistically over-represented from our d

6 We calculated theoretical occurrences of 184 TFBS according to their position weight matrices and the

7 rid approach called LogicMotif composed of a TFBS identification method combined with the new regress

8 Epistatic capture is the stabilization of a TFBS that is ancestral but variable in outgroup lineages

9 is the identification of two boundaries of a TFBS with high resolution, whereas other methods only re

10 mpleted vertebrate genomes, then performed a TFBS prediction in the corresponding complete genomic se

11 ts contain foreign DNA sequences, additional TFBSs can be identified from the previously unaligned Ch

12 more evolutionarily conserved than adjacent TFBS positions.

13 ), implicating a mechanism involving altered TFBS occupancy.

14 osition-specific patterns of variation among TFBS to look for signs of functional constraint on TFBS

15 een novel NF-kappaB/RelA-regulated genes and TFBSs were experimentally validated, including TANK, a n

16 create a multi-organism catalog of annotated TFBSs.

17 quires a functional interaction with the AP1 TFBSs.

18 entify intergenic sequences that function as TFBSs, we calculated the probability of binding site con

19 sting of clusters of specifically-associated TFBSs and it also scores the association of individual t

20 this improved algorithm will greatly augment TFBS discovery.

21 g it to increase the accuracy of motif-based TFBS searching for an example TF.

22 A subset of these candidate TFBS was validated by measuring activation of correspond

23 only evolutionary conservation of candidate TFBSs and sets of strongly coexpressed genes but also th

24 found numerous experimentally characterized TFBS in the human genome, 7-10% of all mapped sites, whi

25 e of co-expression than those with no common TFBS in Drosophila.

26 it remained unclear whether or not composite TFBS elements, commonly found in higher organisms where

27 uate the rate of recurrence of computational TFBS predictions by commonly used sampling procedures.

28 emphasize the point that specific consensus TFBSs do not contribute to this effect.

29 ily diverged loci by searching for conserved TFBS arrangements.

30 ind improves the identification of conserved TFBSs by improving the alignment accuracy of TFBS famili

31 are all variants of six known CpG-containing TFBS: ETS, NRF-1, BoxA, SP1, CRE, and E-Box.

32 entify candidate promoters and corresponding TFBS and the activity of each was assessed by luciferase

33 1/HNF6 ChIP-exo data, MACE is able to define TFBSs with high sensitivity, specificity and spatial res

34 earched for promoters harboring user-defined TFBSs given as a consensus or a position weight matrix.

35 these TFs, referred to as highly-degenerate TFBSs, that are enriched around the cognate binding site

36 epetitive DNA to test whether repeat-derived TFBS are in fact rapidly evolving.

37 ue evolutionary properties of repeat-derived TFBS are perhaps even more intriguing.

38 l lines of evidence indicate that TE-derived TFBS are functionally constrained.

39 TE-derived TFBS in particular, while clearly functionally constrain

40 TE-derived TFBS sequences are far less conserved between species th

41 Finally, TE-derived TFBS show position-specific patterns of sequence variati

42 Most TE-derived TFBS would be missed using the kinds of sequence conserv

43 nctionally important positions in TE-derived TFBS, specifically those residues thought to physically

44 cause of epistatic interactions with derived TFBSs.

45 esian approach (ChIP-BIT) to reliably detect TFBSs and their target genes by jointly modeling binding

46 on factor binding sites (TFBSs) to determine TFBS-association signatures that can be used for discrim

47 nogaster, by using experimentally determined TFBS and microarray expression data.

48 ntial of 'positional regulomics' to discover TFBSs and determine their function remains unknown.

49 om a collection of experimentally discovered TFBSs; (ii) predict TFBSs in SNP sequences using the PWM

50 ms that multiple COPD eQTL lead SNPs disrupt TFBS, and enhancer enrichment analysis for loci with the

51 e (SVM) classifier is trained to distinguish TFBSs from background sequences based on local chemical

52 sites (TFBS) by comparing enrichment of each TFBS relative to a reference set using the Promoter Anal

53 Such rapidly evolving TFBS are likely to confer species-specific regulatory ph

54 s, we find that (i) the accuracy of existing TFBS motif patterns can be significantly improved; and (

55 DE ChIP-Seq data indicates that experimental TFBSs highly correlate with predicted sites.

56 t least three data sources: gene expression, TFBS, and ChIP-chip or/and mutant data.

57 st, scalable and sensitive method to extract TFBSs from ChIP-chip experiments on genome tiling arrays

58 tif Sampler (HMS), specifically designed for TFBS motif discovery in ChIP-Seq data.

59 milies, such as MIR and L2, are enriched for TFBS relative to younger families like Alu and L1.

60 We present a new method for TFBS prediction in metazoan genomes that utilizes both t

61 integrated into a user-friendly webtool for TFBS search and visualization called LASAGNA-Search.

62 Our results demonstrated that four TFBS were enriched in hypermethylated sequences.

63 od dependent on LASAGNA to an alignment-free TFBS search method.

64 SM models for 189 TFs and 133 TFs built from TFBSs in the TRANSFAC Public database (release 7.0) and

65 n can be reliably used to predict functional TFBSs, unconserved sequences might also make a significa

66 ation on experimentally validated functional TFBSs is limited and consequently there is a need for ac

67 point the way to improved accuracy in future TFBS predictions.

68 that a model that combined a GREF and a GATA TFBS was sufficient for predicting a class of functional

69 In general, TFBS that do not contain a CpG are involved in regulated

70 work reconstructed by integrating genotypic, TFBS and PPI data is the most predictive.

71 TFBStools provides a toolkit for handling TFBS profile matrices, scanning sequences and alignments

72 d a striking clustering of distinct homeobox TFBS.

73 studying cis-regulation is to understand how TFBS variants affect gene expression.

74 ndance of experimentally characterized human TFBS that are derived from repetitive DNA speaks to the

75 ional information were compared on two human TFBS datasets, each containing sequences corresponding t

76 ions, this program recognizes the identified TFBS as an evolutionary conserved motif (ECM).

77 for any given gene together with identified TFBSs located on its promoter.

78 ior of each individual probe, and identifies TFBSs using a hidden Markov model (HMM).

79 ysis revealed multiple potentially important TFBS for each promoter.

80 , we demonstrate the capabilities to improve TFBS prediction in microbes.

81 CF), also suggests that HHMM yields improved TFBS identification in comparison to analyses using indi

82 nomic features, such as CpG islands improves TFBS prediction in some TFCT.

83 cts on gene expression due to differences in TFBS affinity for cognate TFs and differences in TFBS sp

84 affinity for cognate TFs and differences in TFBS specificity for noncognate TFs.

85 the alignments through their performance in TFBS prediction; both methods show considerable improvem

86 were characterized by strong similarities in TFBS occurrences.

87 al system for discovering functional SNPs in TFBSs in the human genome and predicting their impact on

88 tivity, and the identification of individual TFBS in genome sequences is a major goal to inferring re

89 two classes - those that predict individual TFBSs and those that identify clusters.

90 AGNA, which is aware of the lengths of input TFBSs and utilizes position dependence.

91 utation and makes it possible to investigate TFBS functional constraints instance-by-instance as well

92 Here we investigate TFBS variability by combining transcription factor bindi

93 Through the integration of known TFBS obtained from the literature and experimental studi

94 rt sites and are strongly enriched for known TFBSs.

95 thus search with a profile model from known TFBSs produces many false positives.

96 n Arabidopsis thaliana, for which many known TFBSs are now available.

97 ich only 608 of the 1226 words matched known TFBSs.

98 nalyzed 66 TF regulons with previously known TFBSs in B. subtilis and projected them to other Bacilla

99 rthermore, near the TSS, RMs can co-localize TFBSs with each other and the TSS.

100 transcription factor-binding sites (PcG/MIR/TFBS), was associated with reduced survival (HR, 3.98; P

101 found in higher organisms where two or more TFBSs form functional complexes, could also be identifie

102 significantly increase the accuracy of motif TFBS searching, demonstrating that the TF-TF connections

103 significant over-representation of multiple TFBS was found in both repetitive and non-repetitive gen

104 Of note, TFBS could be predicted not only from the features at th

105 From these efforts, we predicted 1,340 novel TFBSs and 253 new TF-TFBS pairs in the maize genome, far

106 Studies have shown that groups of nucleotide TFBS variants (subtypes) can contribute to distinct mode

107 of the resolutions of assays used to obtain TFBSs, databases such as TRANSFAC, ORegAnno and PAZAR st

108 vation analysis at the level of co-occurring TFBS provides a valuable contribution to the translation

109 provides new insights into how co-occurring TFBSs and local chromatin context orchestrate activation

110 TFBSs by improving the alignment accuracy of TFBS families within orthologous DNA sequences.

111 we have performed a genome-wide analysis of TFBS-like sequences for the transcriptional repressor, R

112 how evidence for the functional buffering of TFBS mutations in both humans and flies.

113 can be combined to enhance the detection of TFBS.

114 re could improve PWM-based discrimination of TFBS from non-binding-sites.

115 We studied the in vivo effects of TFBS variants on cis-regulation using synthetic promoter

116 We analyzed the effects of TFBS variants using a thermodynamic framework that model

117 r statistical power to detect the effects of TFBS variants.

118 the expression correlation and the extent of TFBS sharing.

119 ion of these promoters and identification of TFBS has important implications for future studies in sp

120 s to improve computational identification of TFBS through these two types of approaches and conclude

121 ty made possible through this integration of TFBS data into REDfly, together with additional improvem

122 We introduce a metric of TFBS variability that takes into account changes in moti

123 enomes for evolutionary conserved modules of TFBS in a predefined configuration, and created a tool,

124 do not share significantly larger numbers of TFBS.

125 Additionally, the 'palindromicity' of TFBS footprint data of E. coli is characterised.

126 fication of SREs utilizing known patterns of TFBS in active regulatory elements (REs) as seeds for ge

127 The proportion of TFBS positional preferences due to TFBS co-localization

128 cillales genomes, resulting in refinement of TFBS motifs and identification of novel regulon members.

129 Distinct sets of TFBS could be identified that were significantly enriche

130 this article, sensitivity and specificity of TFBS search can be improved significantly.

131 estimates the in vivo relative affinities of TFBSs and predicts unexpected interactions between sever

132 , our web tool is useful for the analysis of TFBSs on so far unknown DNA regions identified through C

133 the occurrence of such homotypic clusters of TFBSs (HCTs) in the human genome has remained largely un

134 on TFBSs within HCTs, as the conservation of TFBSs is stronger than the conservation of sequences sep

135 re we studied the positional distribution of TFBSs in Arabidopsis thaliana, for which many known TFBS

136 Our study of the positional distribution of TFBSs seems to be the first in a plant.

137 ly, broadly surveying the co-localization of TFBSs with tight positional preferences relative to the

138 The co-localization of TFBSs within RMs therefore likely explains much of the t

139 eloped a method to identify the locations of TFBSs in the promoter sequences of genes in A. thaliana.

140 signal intensities and binding locations of TFBSs.

141 package for the analysis and manipulation of TFBSs and their associated transcription factor profile

142 ed dPattern that searches for occurrences of TFBSs in the promotor regions of up/down regulated or ra

143 y there is a need for accurate prediction of TFBSs for gene annotation and in applications such as ev

144 d originally developed for the prediction of TFBSs in Escherichia coli that minimises the need for pr

145 based method for computational prediction of TFBSs using a novel, integrative energy (IE) function.

146 tiate the biological functions of subsets of TFBSs sharing a common sequence motif.

147 into three definable subcategories based on TFBS location and usage.

148 o look for signs of functional constraint on TFBS derived from repetitive and non-repetitive DNA.

149 As work on TFBS alignment algorithms has been limited, it is highly

150 und evidence of negative selection acting on TFBSs within HCTs, as the conservation of TFBSs is stron

151 3% and 93% accuracy respectively, using only TFBS as delimiters of operons.

152 iscriminate analysis for identifying ordered TFBS pairs.

153 Several of the newly identified p53 TFBSs are in the promoter region of known genes or assoc

154 riminate analysis can simultaneously perform TFBS pattern recognition, TFBS pattern selection, and ta

155 rgets significantly (P-value < 0.01) possess TFBS pairs, (DBP, MYC), (DBP, MYC/MAX heterodimer), (DBP

156 genes significantly (P-value < 0.01) possess TFBS pairs, such as (DBP, c-ETS1-68), (DBP, USF2) and (D

157 If multiple species have this potential TFBS in homologous positions, this program recognizes th

158 xperimentally discovered TFBSs; (ii) predict TFBSs in SNP sequences using the PWM and map SNPs to the

159 Predicted TFBS located outside of the transcription associated are

160 The predicted TFBS profiles for each of the four promoters shared few

161 analysis of the genomic context of predicted TFBSs, functional assignment of target genes, and effect

162 ral collections of characterized prokaryotic TFBS motifs and shown to outperform EM and an alternativ

163 Multiple putative TFBS in gene promoters of placental mammals were found t

164 he presence or absence of different putative TFBSs between the novel alleles and the common L (16r) a

165 ne the evolutionary conservation of putative TFBSs by phylogenetic footprinting; (iv) prioritize cand

166 Consequently, the palindromicity of putative TFBSs predicted can also enhance operon predictions.

167 F-values and real TFBS occurrences calculated for human, chimp, mouse, rat

168 ltaneously perform TFBS pattern recognition, TFBS pattern selection, and target class prediction; suc

169 can be superior predictors as they recognize TFBSs in their functional context.

170 characteristic distances from NF-kappaB/RelA TFBSs.

171 Functionally relevant TFBSs are often highly conserved during evolution leavin

172 imated from a small number of representative TFBS sequences.

173 ackage was used to identify over-represented TFBS in the upstream promoter regions of ischemia-induce

174 for non-repeat derived sequences of the same TFBS.

175 f classifiers and show that our cross-sample TFBS prediction method outperforms several previously de

176 a novel multiple MSA methodology that scores TFBS DNA sequences by including the interdependence of n

177 on operators during the evolutionary search, TFBSs of different sizes and complexity can be identifie

178 motif enrichment analysis uncovers secondary TFBSs (AP1, SP1) at characteristic distances from NF-kap

179 sed mapping of in vivo TF binding sequences (TFBSs) using Chromatin ImmunoPrecipitation followed by m

180 icts unexpected interactions between several TFBSs.

181 ssed genes are more likely to contain shared TFBS and, thus, TFBS can be identified computationally.

182 Pairs of genes with shared TFBS show, on average, a higher degree of co-expression

183 Transcription factor binding site (TFBS) analysis confirms that multiple COPD eQTL lead SNP

184 addition, transcription factor binding site (TFBS) analysis was performed using MatInspector (Genomat

185 to reveal transcription factor binding site (TFBS) boundaries with near-single nucleotide resolution.

186 rtance of transcription factor binding site (TFBS) copies in effector genes, in regulating the transi

187 ), a transcription factor (TF)-binding site (TFBS) discovery assay that couples affinity-purified TFs

188 roblem of transcription factor binding site (TFBS) motif discovery and underlies the most widely used

189 tion of a transcription factor binding site (TFBS) sequence pattern because the PWM can be estimated

190 potential transcription factor-binding site (TFBS) to screen the homologous regions of a second and t

191 echanism, transcription factor binding site (TFBS) turnover, which relates sequence evolution to epig

192 , mutant, transcription factor binding site (TFBS), and cell cycle gene expression data.

193 pression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previo

194 tegrating transcription factor binding site (TFBS), mutant, ChIP-chip, and heat shock time series gen

195 possible transcription factor-binding site (TFBS)-putatively regulated gene pair, the relative dista

196 ns requires characterizing TF binding sites (TFBS) across multiple cell types and conditions.

197 improve transcription factor binding sites (TFBS) and gene regulatory network predictions based on g

198 ntifying transcription factor binding sites (TFBS) and has been widely used.

199 r shared transcription factor binding sites (TFBS) by comparing enrichment of each TFBS relative to a

200 ccurring transcription factor binding sites (TFBS) can identify cis-regulatory variants and elucidate

201 ictor of transcription factor-binding sites (TFBS) followed by features employed by DNAshape.

202 putative transcription factor binding sites (TFBS) found at these sites in multiple species indicate

203 ition of transcription factor binding sites (TFBS) in a regulatory element.

204 ction of transcription factor binding sites (TFBS) in genomic sequences is a basic task for elucidati

205 overy of transcription factor binding sites (TFBS) in promoter regions upstream of coexpressed genes.

206 onsensus transcription factor binding sites (TFBS) in the genes of the dataset.

207 ledge of transcription factor binding sites (TFBS) is important for a mechanistic understanding of tr

208 onserved transcription factor binding sites (TFBS) recognized by WT1, EGR1, SP1, SP2, AP2 and GATA1 w

209 ed human transcription factor binding sites (TFBS) that are derived from repetitive versus non-repeti

210 hat only transcription factor binding sites (TFBS) that contain the CpG dinucleotide are involved in

211 identify transcription factor binding sites (TFBS) via the presence of common sequence motifs.

212 Two key transcription factor binding sites (TFBS) were identified, corresponding to NF-kappaB and CC

213 of known transcription factor binding sites (TFBS), evolutionarily conserved mammalian promoter and 3

214 possible transcription factor binding sites (TFBS).

215 d refine transcription factor binding sites (TFBS).

216 certain transcription factor binding sites (TFBS).

217 between transcription factor binding sites (TFBSs) ancestral to MER39 and derived sites.

218 predict transcription factor binding sites (TFBSs) and their cognate transcription factors (TFs) usi

219 th optimal spacing between TF binding sites (TFBSs) and their distance from the TATA box.

220 Transcription factor binding sites (TFBSs) are most commonly characterized by the nucleotide

221 Transcription factor binding sites (TFBSs) are short DNA sequences interacting with transcri

222 Transcription factor binding sites (TFBSs) are the functional elements that determine transc

223 Transcription factor binding sites (TFBSs) are typically short in length, thus search with a

224 Experimentally identified TF binding sites (TFBSs) are usually similar enough to be summarized by a

225 ation of transcription factor binding sites (TFBSs) at genome scale represents an essential step towa

226 redicted transcription factor binding sites (TFBSs) by exploiting the position of genomic landmarks l

227 th which transcription factor binding sites (TFBSs) can be identified from genome-wide profiling stud

228 ntifying transcription factor binding sites (TFBSs) encoding complex regulatory signals in metazoan g

229 multiple transcription factor binding sites (TFBSs) for the same transcription factor (TF) is a commo

230 estigate transcription factor binding sites (TFBSs) genome-wide is central to computational studies o

231 a small number of specific TF binding sites (TFBSs) have been detected.

232 nnotated transcription factor binding sites (TFBSs) in evolutionary conserved and promoter elements.

233 identify transcription factor binding sites (TFBSs) in target genomes.

234 e listed transcription factor binding sites (TFBSs) in their upstream elements, using either regular

235 edicting transcription factor binding sites (TFBSs) involve use of a position-specific weight matrix

236 iption factor (TF) to its DNA binding sites (TFBSs) is a critical step to initiate the transcription

237 l map of transcription factor binding sites (TFBSs) is critical to understanding gene regulation and

238 ctors to transcription factor binding sites (TFBSs) is key to the mediation of transcriptional regula

239 SNPs) in transcription factor binding sites (TFBSs) may affect the binding of transcription factors,

240 Transcription factor binding sites (TFBSs) may constitute a significant fraction of these co

241 Some transcription factor binding sites (TFBSs) near the transcription start site (TSS) display t

242 ny known transcription factor binding sites (TFBSs) occur within an interval [-300, 0] bases upstream

243 recognize short, but specific binding sites (TFBSs) that are located within the promoter and enhancer

244 files of transcription factor binding sites (TFBSs) to determine TFBS-association signatures that can

245 terns of transcription factor binding sites (TFBSs) which distinguish genes directly regulated by est

246 tions of transcription factor binding sites (TFBSs) with high precision.

247 such as transcription factor binding sites (TFBSs), are frequently not related by common descent, an

248 ols, allowing inference of TF binding sites (TFBSs), comparative analysis of the genomic context of p

249 specific transcription factor binding sites (TFBSs), implicating a mechanism involving altered TFBS o

250 abase of transcription factor binding sites (TFBSs), into a single integrated database containing ext

251 resent outside of specific TF binding sites (TFBSs), statistically control TF-DNA binding preferences

252 edicting transcription factor-binding sites (TFBSs), turning publicly available gene expression sampl

253 However, transcription factor binding sites (TFBSs), typically found upstream of the first gene in an

254 multiple transcription factor binding sites (TFBSs), which may vary in affinity for their cognate tra

255 for transcription factor (TF) binding sites (TFBSs).

256 of transcription factor (TF) binding sites (TFBSs).

257 ells for transcription factor binding sites (TFBSs).

258 ome-wide transcription factor binding sites (TFBSs).

259 TFs) and transcription factor binding sites (TFBSs, also known as DNA motifs) are critical activities

260 experiments confirm co-localization of some TFBSs genome-wide, including near the TSS, but they typi

261 strate that through building genome specific TFBS position-specific-weight-matrices (PSWMs) it is pos

262 ed sequences, but the annotation of specific TFBSs is complicated by the fact that these short, degen

263 to assess statistical enrichment of specific TFBSs.

264 require the presence of consensus (specific) TFBSs in order to achieve genome-wide TF-DNA binding spe

265 in the maize genome, far exceeding the 30 TF-TFBS pairs currently known in maize.

266 o predict maize TF-TFBS pairs using known TF-TFBS pairs in Arabidopsis or rice.

267 developed another method to predict maize TF-TFBS pairs using known TF-TFBS pairs in Arabidopsis or r

268 e predicted 1,340 novel TFBSs and 253 new TF-TFBS pairs in the maize genome, far exceeding the 30 TF-

269 In vitro tests of 12 predicted TF-TFBS interactions showed that our methods perform well.

270 are far less conserved between species than TFBS derived from SSRs and non-repetitive DNA.

271 We argue that TFBS composition is often necessary to retain and suffic

272 on factor binding data to show evidence that TFBS mutations, particularly at evolutionarily conserved

273 We also show that TFBS footprint data in E. coli generally contains invert

274 proposed new algorithm clearly suggests that TFBSs are not randomly distributed within ERalpha target

275 ficant intra-motif dependency inside all the TFBS motifs we tested; modeling these dependencies furth

276 Unmethylated CpG dinucleotides in the TFBS in CpG islands allow the transcription factors to f

277 Evaluation procedures applicable to the TFBS prediction outputs need improvement.

278 We applied the new energy function to the TFBS prediction using a non-redundant dataset that consi

279 mparing with both genomic background and the TFBSs identified earlier.

280 ranscription start site (TSS) and 86% of the TFBSs are in the region from -1,000 bp to +200 bp with r

281 t is therefore interesting to know where the TFBSs of a gene are likely to locate in the promoter reg

282 These TFBS are not bound when the CpG is methylated.

283 ncies further improves the accuracy of these TFBS motif patterns.

284 e interestingly, overrepresentation of these TFBS was observed in hyper-/hypo-methylated sequences wh

285 ore likely to contain shared TFBS and, thus, TFBS can be identified computationally.

286 therefore likely explains much of the tight TFBS positional preferences near the TSS.

287 A TF(A) may bind to TFBS subtypes a(1) or a(2) depending on whether it assoc

288 ortion of TFBS positional preferences due to TFBS co-localization within RMs is unknown, however.

289 e to have an alignment algorithm tailored to TFBSs.

290 Motifs corresponding to two TFBSs in a RM should co-occur more than by chance alone,

291 mentally studied TFs with previously unknown TFBSs.

292 osely located motifs representing vertebrate TFBSs that are enriched in the training mixed set consis

293 Thus, the very TFBS that are most likely to yield human-specific charac

294 Weighted TFBS contributions to putative gene regulation are integ

295 nd orientation are calculated to learn which TFBSs are most likely to regulate a given gene.

296 involved in regulated gene expression while TFBS that contain a CpG are involved in constitutive gen

297 RNAP binding to housekeeping promoters while TFBS that do not contain a CpG are involved in regulated

298 ical Process) is much better associated with TFBS sharing, as compared to the expression correlation.

299 accessible chromatin and mRNA datasets with TFBS prediction and in vivo reporter assays can reveal t

300 This analysis located several potential WT1 TFBS in the PSA gene promoter and led to the rapid ident