ライフサイエンスコーパス: PWM

1 PWM is a common strategy used in electronics for informa

2 PWM was found to significantly enhance the susceptibilit

3 PWMs can then be used to predict the location of TF bind

4 oughly 200 TFs in yeast, there are over 1200 PWMs in the literature.

5 A PWM algorithm enabling permuted switching of the PV sour

6 em", using the proposed score for counting a PWM in the sequences.

7 hat allows us to infer binding energy from a PWM score.

8 We also introduce a PWM logo, which visually displays the implications of ob

9 analysis is to "count" the occurrences of a PWM in a DNA sequence.

10 A web server for predicting a PWM given a protein containing C2H2-ZF domains is availa

11 The first approach uses a PWM and background genomic sequence as input to estimate

12 We show that in most cases where a PWM is not sufficient, a BEM that includes energy parame

13 cy of A- and T-tracts, in combination with a PWM-based core promoter model, accurately predicted prom

14 ated evolution at any two positions within a PWM, based on a multiple alignment of 5 mammalian genome

15 mary monocytes failed to support PHA, Con A, PWM, or anti-CD3- induced T cell proliferation 1 wk afte

16 Both PCL and PWM comprise precursors displaying traits of juvenile as

17 In addition, PCL and PWM differ in the generated progeny.

18 CD62L MFI on PPD- and PWM-stimulated gammadelta T-cell receptor-positive (TCR(

19 urthermore, we tested if combining shape and PWM-based features provides better predictions than usin

20 ores, we developed two methods, Kmer-Sum and PWM (Position Weight Matrix) stacking, for full-length b

21 Our results show that both Kmer-Sum and PWM stacking in the new pentamer approach along with a s

22 gic interneurons are produced exclusively by PWM astroglial-like progenitors, whereas PCL precursors

23 se contradictory results can be explained by PWM and ChIP data reflecting primarily biophysical prope

24 centration was reduced, responses induced by PWM were restored while TSST-1 induced responses remaine

25 factor, which allows us to directly compare PWMs that were generated by different approaches.

26 stic score to solve this problem of counting PWM occurrences.

27 table for large-scale analyses) for creating PWMs from high-throughput ChIP-Seq data and for scanning

28 arity between user-entered data and database PWMs, and a function for locating putative binding sites

29 an reliably convert lambda between different PWMs of the same transcription factor, which allows us t

30 discriminative as well as non-discriminative PWM finding algorithms.

31 ensitivity/specificity of a poorly estimated PWM and further improved the quality of a good PWM.

32 simple method to improve a poorly estimated PWM using ChIP data.

33 Starting from an existing PWM, a set of ChIP sequences, and a set of background se

34 The majority of existing PWMs provide a low level of both sensitivity and specifi

35 Expanding PWMs to include sequence context-dependence will increas

36 The mean FA was 0.280 for plaques, 0.383 for PWM, 0.493 for NAWM, and 0.537 for control subject WM.

37 ec for plaques, 0.786 x 10(-3) mm(2)/sec for PWM, 0.739 x 10(-3) mm(2)/sec for NAWM, and 0.726 x 10(-

38 ion of discrete pulses answering the general PWM problem in terms of the manifold of all rational sol

39 of the best-performing tools for generating PWMs from ChIP-Seq data and for scanning PWMs against DN

40 (2) For any given PWM, this score can be computed while allowing for occur

41 M and further improved the quality of a good PWM.

42 specificity in their binding (despite having PWMs with higher information content).

43 e and tested whether the score could improve PWM-based discrimination of TFBS from non-binding-sites.

44 nces, our method, GAPWM, derives an improved PWM via a genetic algorithm that maximizes the area unde

45 n, and PGE2 restored both immunoglobulins in PWM-stimulated cultures.

46 nce will increase the information content in PWMs and facilitate a more efficient functional identifi

47 res only a user-specified FDR and an initial PWM.

48 nd problem we address is to find, ab initio, PWMs that have high counts in one set of sequences, and

49 ing for occurrences of other, a priori known PWMs, in a statistically sound framework.

50 stine are directly activated with the lectin PWM.

51 if information, including the sequence logo, PWM, consensus sequence and specific matching sites can

52 lity of predicting position weight matrices (PWM) for an entire protein family based on the structure

53 ion and identified position weight matrices (PWM), demonstrating that, in at least one case, deleting

54 By employing 99 position weight matrices (PWM), we systematically scanned the regulatory regions u

55 Position-weight matrices (PWMs) are broadly used to locate transcription factor bi

56 t is unclear which position weight matrices (PWMs) are most useful; for the roughly 200 TFs in yeast,

57 ChIP)-seq/chip and position weight matrices (PWMs) data, protein-protein interactions and kinase-subs

58 sequences against Position Weight Matrices (PWMs) is a widely adopted method to identify putative tr

59 es, for predicting position weight matrices (PWMs) representing DNA-binding specificities for C2H2-ZF

60 ntly modeled using position weight matrices (PWMs) that assume the positions within a binding site co

61 imilar k-mers into position weight matrices (PWMs).

62 based on position-specific weight matrices (PWMs).

63 gnized by scanning a position weight matrix (PWM) against DNA using one of a number of available comp

64 k-mers'), as well as position weight matrix (PWM) and graphical sequence logo representations of the

65 k-mers'), as well as position weight matrix (PWM) and graphical sequence logo representations of the

66 od of constructing a position weight matrix (PWM) by comparing the frequency of the preferred sequenc

67 The position weight matrix (PWM) derived for BqsR uncovered hundreds of likely bindi

68 , we (i) construct a position weight matrix (PWM) from a collection of experimentally discovered TFBS

69 The position weight matrix (PWM) is a popular method to model transcription factor b

70 The position-weight matrix (PWM) is a useful representation of a transcription facto

71 Positional weight matrix (PWM) is derived from a set of experimentally determined

72 largely rely on the position weight matrix (PWM) model for DNA binding, and the effect of alternativ

73 chnique based on the position weight matrix (PWM) model to locate conserved motifs in a given set of

74 The position-specific weight matrix (PWM) model, which assumes that each position in the DNA

75 toff threshold for a position weight matrix (PWM) of a motif identified from ChIP-chip data by ab ini

76 used to construct a position weight matrix (PWM) of the Ey protein.

77 sis using a TRANSFAC position weight matrix (PWM) search, 86% of non-specific TF sites were removed.

78 ve use of a position-specific weight matrix (PWM) to statistically characterize the sequences of the

79 When a position weight matrix (PWM) was constructed from the protein gene promoters, fa

80 TF in the form of a position weight matrix (PWM), DNA accessibility data (in the case of eukaryotes)

81 F-DNA binding--the positional weight matrix (PWM)--presumes independence between positions within the

82 atches to a sequence position weight matrix (PWM).

83 and represented in a position weight matrix (PWM).

84 then summarized by a position weight matrix (PWM).

85 l optimization [e.g. position weight matrix (PWM)] and significance testing at each step.

86 used to generate a position-weighted matrix (PWM) for EBNA1's DNA-binding sites.

87 nesis and from the prospective white matter (PWM) during postnatal development.

88 The prospective white matter (PWM) in the nascent cerebellum contains a transient germ

89 placed on plaques, periplaque white matter (PWM) regions, NAWM regions in the contralateral side of

90 rotein derivative (PPD) or pokeweed mitogen (PWM) and evaluated concurrently for proliferation and ac

91 drome toxin-1 (TSST-1) and pokeweed mitogen (PWM) were inhibited at high concentrations of bacterial

92 teers were stimulated with pokeweed mitogen (PWM), and the cultures were manipulated by adding PGE2,

93 ntiation agents, including pokeweed mitogen (PWM), to enhance the sensitivity of myeloma cells to cel

94 stimulated in culture with pokeweed mitogen (PWM); the levels of available IL-1 gene products were ma

95 underpinning of the pulse width modulation (PWM) technique lies in the attempt to represent "accurat

96 ntration range using pulse-width modulation (PWM).

97 ng a strategy termed pulse width modulation (PWM).

98 The comparison of old and new PWMs shows that the latter increase both sensitivity and

99 approach, we first developed a refined OCT4 PWM.

100 estis development, colocalizes with the OCT4 PWM.

101 vation and differentiation in the absence of PWM, in an MHC-unrestricted fashion.

102 directed against CD9 abrogated the effect of PWM.

103 Incubation of PWM-stimulated myeloma cells with either monoclonal anti

104 binding intensity rank-ordered collection of PWMs each of which spans a different region in the bindi

105 a reanalysis of these data with a mixture of PWMs approach.

106 e scale and suggests the use of a mixture of PWMs, instead of the current practice of using a single

107 r large-scale, structure-based prediction of PWMs is discussed.

108 Stimulation with PPD or PWM increased CD25 and CD44 mean fluorescence intensity

109 Our PWM module can combine up to six different inputs and se

110 Our PWM module produces robust and precise concentration pro

111 lyses we provide evidence that the postnatal PWM hosts a bipotent progenitor that gives rise to both

112 at for >85% of the proteins, their predicted PWMs are accurate in 50% of their nucleotide positions.

113 ities correlated with corresponding promoter PWM scores.

114 es an alternative for obtaining high-quality PWMs for genome-wide identification of transcription fac

115 IgG at the onset of cultures greatly reduced PWM-induced tissue injury, without inhibiting the increa

116 We then used the refined PWM and a ChIPModules approach to identify transcription

117 The resultant PWM may not reliably discriminate a true motif from a fa

118 The resulting PWMs were evaluated with respect to preferred conservati

119 vide computationally efficient ways to scale PWM scores and estimate the strength of transcription fa

120 TFBS prediction tools used to scan PWMs against DNA fall into two classes - those that pred

121 ing PWMs from ChIP-Seq data and for scanning PWMs against DNA has the potential to improve prediction

122 s approach is an advancement over the simple PWM model and accommodates position dependencies based o

123 city of most TFs is well fit with the simple PWM model, but in some cases more complex models are req

124 ad of the current practice of using a single PWM, for a transcription factor.

125 rmore, it still functioned when the starting PWM contained a major error.

126 for the target genes of one of the subclass-PWMs.

127 g sites and if the mixture of these subclass-PWMs can improve the binding site prediction.

128 sed the relative merit of using two subclass-PWMs.

129 uth appears to generally perform better than PWM-based methods.

130 pe-based models perform arguably better than PWM-based models.

131 The PWM+shape model was more accurate than the PWM-only mode

132 ing site (TFBS) sequence pattern because the PWM can be estimated from a small number of representati

133 However, because the PWM probability model assumes independence between indiv

134 BqsR operator affinity was predicted by the PWM well.

135 ional PWM model to a model that combines the PWM with a DNA shape feature-based regulatory potential

136 ndence to aid motif discovery, we extend the PWM model to include pairs of correlated positions and d

137 whether germinative sites different from the PWM originate inhibitory interneurons.

138 We further show how the PWM strategy extends the utility of bacterial optogeneti

139 he Staden-Bucher approach, that improves the PWM.

140 P-low) cells attenuates proliferation in the PWM, reducing both intermediate progenitor classes.

141 from distant Purkinje neurons maintains the PWM niche independently of its classical role in regulat

142 fied motifs are available, estimation of the PWM may be poor.

143 ibe in this paper a complete solution of the PWM problem using Pade approximations, orthogonal polyno

144 s to both strong and weak occurrences of the PWM, without using thresholds.

145 We applied the proposed technique on the PWM of the GC-box, binding site for Sp1.

146 atures, such as TF-TF interactions, than the PWM approach.

147 e PWM+shape model was more accurate than the PWM-only model, for 45% of TFs tested, with no significa

148 We show that the PWM based on our data more accurately predicts promoters

149 ndependently controllable, and show that the PWM module can execute rapid concentration changes as we

150 ficiently differentiable with respect to the PWM parameters, which has important consequences for des

151 pe captures information complimentary to the PWM, in a way that is useful for expression modeling.

152 ii) predict TFBSs in SNP sequences using the PWM and map SNPs to the upstream regions of genes; (iii)

153 NA interactions were not captured within the PWM or that the broader regulatory context at each promo

154 between individual nucleotide positions, the PWMs for some TFs poorly discriminate binding sites from

155 eting sequences containing a subset of these PWMs from one identified regulatory element abrogated it

156 This PWM helped identify additional DNA-binding sites for EBN

157 This PWM was then used for in silico prediction of potential

158 When TNFR-IgG was added to PWM-stimulated explants, there was a reduction in MMPs i

159 We compared a traditional PWM model to a model that combines the PWM with a DNA sh

160 The majority of commonly used PWMs are the 4-row mononucleotide matrices, although 16-

161 Based on 64 JASPAR vertebrate PWMs, 61-81% of the cases resulted in a higher conservat

162 tle compared to non-infected controls, while PWM-induced cytokine levels were similar between the two

163 hibit suppressor function when cultured with PWM- or rCD40 ligand (rCD40L)-activated non-T cells, whe

164 t prestimulation of non-T cells for 8 h with PWM or for 48 h for rCD40L results in non-T cells capabl

165 to predict gene expression better than with PWM models alone.

166 nction in the presence of TT with or without PWM or rCD40L.