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

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

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

9 more evolutionarily conserved than adjacent TFBS positions.

10 ), implicating a mechanism involving altered TFBS occupancy.

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

12 s, simultaneously identifying cell types and TFBS in those same cells.

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

14 quires a functional interaction with the AP1 TFBSs.

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

16 this improved algorithm will greatly augment TFBS discovery.

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

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

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

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

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

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

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

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

25 ily diverged loci by searching for conserved TFBS arrangements.

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

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

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

29 1/HNF6 ChIP-exo data, MACE is able to define TFBSs with high sensitivity, specificity and spatial res

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

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

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

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

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

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

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

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

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

39 cause of epistatic interactions with derived TFBSs.

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

41 ing algorithms have been developed to detect TFBSs by investigating chromatin accessibility patterns;

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

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

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

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

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

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

48 The structural profiles for each TFBS entry now include 13 shape features and minor groov

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

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

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

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

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

54 Supervised learning approaches for TFBS predictions require large amounts of labeled data.

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

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

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

58 The standard approaches used for TFBS prediction, such as position weight matrices (PWMs)

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

60 site affinity only affects TP53 binding for TFBSs located at the same nucleosomal positions; otherwi

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

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

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

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

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

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

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

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

69 ed to the larger-extent usage of heterotypic TFBS clusters in fragile enhancers.

70 d a striking clustering of distinct homeobox TFBS.

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

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

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

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

75 ysis revealed multiple potentially important TFBS for each promoter.

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

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

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

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

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

81 Moreover, recurrent mutations in TFBS of over-represented TFs such as EZH2 affected MCF2L

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

83 DNA shape-preserving nucleotide mutations in TFBSs.

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

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

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

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

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

89 Here we investigate TFBS variability by combining transcription factor bindi

90 ions as a pioneer factor that can target its TFBS within nucleosomes, but it remains unclear how TP53

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

92 rt sites and are strongly enriched for known TFBSs.

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

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

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

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

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

98 s, TF-transposase fusions can be used to map TFBS.

99 eously measuring gene expression and mapping TFBS in single cells.

100 he JASPAR and UniPROBE databases, methylated TFBSs derived from in vitro high-throughput EpiSELEX-seq

101 EX-seq binding assays and in vivo methylated TFBSs from the MeDReaders database.

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

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

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

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

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

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

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

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

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

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

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 enhancer categories and a large fraction of TFBS outside of the regulatory grammar.

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

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

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

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

124 do not share significantly larger numbers of TFBS.

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

126 lements (TRACE) to improve the prediction of TFBS footprints.

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 nd accuracy for the shape-based alignment of TFBSs and designed new tools to compare methylated and u

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

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

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

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

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

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

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

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

141 signal intensities and binding locations of TFBSs.

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

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

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

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

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

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

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

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

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

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

152 3% and 93% accuracy respectively, using only TFBS as delimiters of operons.

153 iscriminate analysis for identifying ordered TFBS pairs.

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

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

156 y, we demonstrated the presence of potential TFBS such as E-box in CRF22_01A, and Stat 6 in subtypes

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

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

161 common features are aimed to help predicting TFBSs for all cell types especially those cell types tha

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 characteristic distances from NF-kappaB/RelA TFBSs.

168 Functionally relevant TFBSs are often highly conserved during evolution leavin

169 imated from a small number of representative TFBS sequences.

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

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

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

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

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

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

176 icts unexpected interactions between several TFBSs.

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

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

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

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

181 integrate transcription factor binding site (TFBS) and microRNA target data to generate a gene intera

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

183 ant employment of homotypic TF binding site (TFBS) clusters, as opposed to the larger-extent usage of

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

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

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

187 higher in transcription factor binding site (TFBS) of regulatory elements specifically active in neur

188 inatorial transcription factor binding site (TFBS) patterns, including homotypic clusters, heterotypi

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 The ability to target a TF binding site (TFBS) within a nucleosome has been the defining characte

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

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

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

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

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

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

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

200 fects of transcription factor binding sites (TFBS) are influenced by the order and orientation of sit

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

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

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

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

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

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

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

208 ogy links cell identity to TF binding sites (TFBS) in those cell types.

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

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

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

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

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

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

215 possible transcription factor binding sites (TFBS).

216 d refine transcription factor binding sites (TFBS).

217 specific transcription factor binding sites (TFBS).

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

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

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

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

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

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

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

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

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

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

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

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

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

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

232 nctional transcription factor-binding sites (TFBSs) genome-wide.

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

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

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

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

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

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

239 ction of transcription factor binding sites (TFBSs) is crucial for gene expression analysis.

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

241 Thus, identification of TF binding sites (TFBSs) is key to understanding gene expression and whole

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

273 Results show that TFBSs predicted by MTTFsite alone can achieve good perfo

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

278 ffects of every possible mutation within the TFBS motif, SEMpl can predict the consequences of SNPs t

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

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

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

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

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

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

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

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

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

288 e to have an alignment algorithm tailored to TFBSs.

289 omes containing a high- or low-affinity TP53 TFBS located at differing translational and rotational p

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