ライフサイエンスコーパス: PWM

1 PWM flow for 2.19 um (targeted) and 7.32 um (untargeted)

2 PWM is a common strategy used in electronics for informa

3 PWM was found to significantly enhance the susceptibilit

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

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

6 tion FABIAN-variant uses 1224 TFFMs and 3790 PWMs to predict whether and to which degree DNA variants

7 A PWM algorithm enabling permuted switching of the PV sour

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

23 TRACE incorporates DNase-seq data and PWMs within a multivariate hidden Markov model (HMM) to

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

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

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

27 nd tools are required to efficiently cluster PWMs and assess quality of clusters.

28 here is a lack of efficient tools to cluster PWMs.

29 It efficiently clustered PWMs from multiple sources with or without using DNA-Bin

30 ally, and filtered out incorrectly clustered PWMs.

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

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

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

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

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

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

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

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

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

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

41 Expanding PWMs to include sequence context-dependence will increas

42 as or better than computationally expensive PWM-based methods.

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

44 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(-

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

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

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

48 h to predict human RBP-RNA interaction given PWM of a RBP and one RNA sequence.

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

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

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

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

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

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

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

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

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

58 stine are directly activated with the lectin PWM.

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

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

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

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

63 g. frequency-based position-weight matrices (PWMs) and attribution-based contribution-weight matrices

64 rediction, such as position weight matrices (PWMs) and chromatin immunoprecipitation followed by sequ

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

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

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

68 ifs represented as Position Weight Matrices (PWMs) in VGs.

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

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

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

72 be predicted using position weight matrices (PWMs).

73 quences in form of position weight matrices (PWMs).

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

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

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

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

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

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

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

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

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

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

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

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

86 the well-established position weight matrix (PWM) model of transcription factor binding affinity to t

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

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

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

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

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

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

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

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

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

96 a rank-dependent and position weight matrix (PWM)-independent manner.

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

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

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

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

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

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

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

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

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

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

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

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

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

110 Nearest level pulse width modulation (PWM) is used to generate switching pulses, while reliabi

111 We show a Pulse Width Modulation (PWM) process can periodically backflush the filter membr

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

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

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

115 n combination with a pulse-width-modulation (PWM) technique, to achieve programmable and automatic li

116 ival time (PAT); (ii) pulse wave morphology (PWM), and (iii) demographic data, can be combined with o

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

118 approach, we first developed a refined OCT4 PWM.

119 estis development, colocalizes with the OCT4 PWM.

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

121 directed against CD9 abrogated the effect of PWM.

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

123 Assessing quality of PWM clusters is yet another challenge.

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

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

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

127 sed as an algorithm for identifying pairs of PWMs whose similarity is statistically significant, but

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

129 Optimized PWM flow was then used to purify custom particles for im

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

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

132 Our PWM module produces robust and precise concentration pro

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

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

135 ities correlated with corresponding promoter PWM scores.

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

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

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

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

140 The resulting PWMs were evaluated with respect to preferred conservati

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

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

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

144 to cluster biologically relevant or similar PWMs, whether coming from experimental detection or in s

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

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

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

148 GRAFIMO extends the standard PWM scanning procedure by considering variations and alt

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

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

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

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

153 ase automatically, classified known human TF PWMs to the respective DBD family, and performed TF moti

154 ughput sequencing data using ~ 1770 human TF PWMs.

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

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

157 ntroduced and shown to be more accurate than PWMs.

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

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

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

161 BqsR operator affinity was predicted by the PWM well.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

187 vitro and in vivo, outperforming widely-used PWM models as well as recently developed deep learning m

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

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

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

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

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

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

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