Datasets:
vgainullin
/
xciting_data

Dataset card Data Studio Files
xet
 Community
1
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5
DISCUSSION
0
null
null
17,452,352
null
and hhf2+ genes are up-regulated by overproduction of Ams2, transcription of the hht2+ gene as well as the hhf2+ gene is probably promoted during S-phase.
null
154
7,700
0
false
null
null
and hhf2+ genes are up-regulated by overproduction of Ams2, transcription of the hht2+ gene as well as the hhf2+ gene is probably promoted during S-phase.
false
true
true
true
false
1,247
5
DISCUSSION
0
null
null
17,452,352
null
Thus, the transcript of hht2+ may be destabilized post-transcriptionally or may be suppressed by transcriptional repressors that abrogate Ams2-dependent activation.
null
164
7,701
0
false
null
null
Thus, the transcript of hht2+ may be destabilized post-transcriptionally or may be suppressed by transcriptional repressors that abrogate Ams2-dependent activation.
true
true
true
true
true
1,247
5
DISCUSSION
0
null
null
17,452,352
null
Further studies are required to determine how this asymmetric transcription of copy-2 genes is converted into meaningful physiological output.
null
142
7,702
0
false
null
null
Further studies are required to determine how this asymmetric transcription of copy-2 genes is converted into meaningful physiological output.
true
true
true
true
true
1,247
6
DISCUSSION
1
21
[ "B21", "B23", "B22" ]
17,452,352
pmid-16571659|pmid-12086617|pmid-16267050
Despite the identical amino acid sequences deduced from the three H3-H4 gene pairs, their differential transcription profiles may result in differences in timing of deposition of the histones encoded by these genes into the nucleosomes.
[ "21", "23", "22" ]
236
7,703
0
false
Despite the identical amino acid sequences deduced from the three H3-H4 gene pairs, their differential transcription profiles may result in differences in timing of deposition of the histones encoded by these genes into the nucleosomes.
[]
Despite the identical amino acid sequences deduced from the three H3-H4 gene pairs, their differential transcription profiles may result in differences in timing of deposition of the histones encoded by these genes into the nucleosomes.
true
true
true
true
true
1,248
6
DISCUSSION
1
21
[ "B21", "B23", "B22" ]
17,452,352
pmid-16571659|pmid-12086617|pmid-16267050
Although recent experiments in higher eukaryotes have demonstrated that differences in a few amino acids between H3 and H3.3 can specify which nucleosome assembly pathway is used (21,23), the timing of the expression of H3 may be a more critical determinant for the timing of deposition into the nucleosomes in lower euk...
[ "21", "23", "22" ]
328
7,704
0
false
Although recent experiments in higher eukaryotes have demonstrated that differences in a few amino acids between H3 and H3.3 can specify which nucleosome assembly pathway is used, the timing of the expression of H3 may be a more critical determinant for the timing of deposition into the nucleosomes in lower eukaryotes.
[ "21,23" ]
Although recent experiments in higher eukaryotes have demonstrated that differences in a few amino acids between H3 and H3.3 can specify which nucleosome assembly pathway is used, the timing of the expression of H3 may be a more critical determinant for the timing of deposition into the nucleosomes in lower eukaryotes.
true
true
true
true
true
1,248
6
DISCUSSION
1
22
[ "B21", "B23", "B22" ]
17,452,352
pmid-16571659|pmid-12086617|pmid-16267050
Recently, it has been reported that, although remarkably similar in amino acid sequence, H3.1, H3.2 and H3.3 differ in their patterns of expression and the post-transcriptional modification in human cells (22).
[ "21", "23", "22" ]
210
7,705
1
false
Recently, it has been reported that, although remarkably similar in amino acid sequence, H3.1, H3.2 and H3.3 differ in their patterns of expression and the post-transcriptional modification in human cells.
[ "22" ]
Recently, it has been reported that, although remarkably similar in amino acid sequence, H3.1, H3.2 and H3.3 differ in their patterns of expression and the post-transcriptional modification in human cells.
true
true
true
true
true
1,248
6
DISCUSSION
1
21
[ "B21", "B23", "B22" ]
17,452,352
pmid-16571659|pmid-12086617|pmid-16267050
It will be intriguing to investigate whether three histone H3 and H4 proteins in fission yeast are also differentially modified and whether the timing of their expression can influence their deposition.
[ "21", "23", "22" ]
202
7,706
0
false
It will be intriguing to investigate whether three histone H3 and H4 proteins in fission yeast are also differentially modified and whether the timing of their expression can influence their deposition.
[]
It will be intriguing to investigate whether three histone H3 and H4 proteins in fission yeast are also differentially modified and whether the timing of their expression can influence their deposition.
true
true
true
true
true
1,248
0
INTRODUCTION
1
1
[ "b1", "b3", "b4", "b5", "b6", "b8", "b9", "b11" ]
17,204,484
pmid-8879249|pmid-10591207|NA|pmid-2179676|pmid-9788350|pmid-11262934|pmid-16522208|pmid-16683017|pmid-15807889|pmid-14704356
Co-regulation is a basic mechanism to coordinately control expression of genes in modules, biochemical pathways and protein complexes (1–3).
[ "1", "3", "4", "5", "6", "8", "9", "11" ]
140
7,707
0
false
Co-regulation is a basic mechanism to coordinately control expression of genes in modules, biochemical pathways and protein complexes.
[ "1–3" ]
Co-regulation is a basic mechanism to coordinately control expression of genes in modules, biochemical pathways and protein complexes.
true
true
true
true
true
1,249
0
INTRODUCTION
1
4
[ "b1", "b3", "b4", "b5", "b6", "b8", "b9", "b11" ]
17,204,484
pmid-8879249|pmid-10591207|NA|pmid-2179676|pmid-9788350|pmid-11262934|pmid-16522208|pmid-16683017|pmid-15807889|pmid-14704356
In eukaryotes, expression is most often mediated by transcription factors (TFs) that bind upstream of the transcription start site (TSS) and recruit the polymerase assembly (4).
[ "1", "3", "4", "5", "6", "8", "9", "11" ]
177
7,708
1
false
In eukaryotes, expression is most often mediated by transcription factors (TFs) that bind upstream of the transcription start site (TSS) and recruit the polymerase assembly.
[ "4" ]
In eukaryotes, expression is most often mediated by transcription factors (TFs) that bind upstream of the transcription start site (TSS) and recruit the polymerase assembly.
true
true
true
true
true
1,249
0
INTRODUCTION
1
5
[ "b1", "b3", "b4", "b5", "b6", "b8", "b9", "b11" ]
17,204,484
pmid-8879249|pmid-10591207|NA|pmid-2179676|pmid-9788350|pmid-11262934|pmid-16522208|pmid-16683017|pmid-15807889|pmid-14704356
TFs bind, with varying affinity, to a set of similar, short (∼6–20 nt) sequences (5).
[ "1", "3", "4", "5", "6", "8", "9", "11" ]
85
7,709
1
false
TFs bind, with varying affinity, to a set of similar, short sequences.
[ "∼6–20 nt", "5" ]
TFs bind, with varying affinity, to a set of similar, short sequences.
true
true
true
true
true
1,249
0
INTRODUCTION
1
1
[ "b1", "b3", "b4", "b5", "b6", "b8", "b9", "b11" ]
17,204,484
pmid-8879249|pmid-10591207|NA|pmid-2179676|pmid-9788350|pmid-11262934|pmid-16522208|pmid-16683017|pmid-15807889|pmid-14704356
Computational binding site discovery focuses on finding significantly overrepresented sequences in upstream regions of co-regulated genes (6–8).
[ "1", "3", "4", "5", "6", "8", "9", "11" ]
144
7,710
0
false
Computational binding site discovery focuses on finding significantly overrepresented sequences in upstream regions of co-regulated genes.
[ "6–8" ]
Computational binding site discovery focuses on finding significantly overrepresented sequences in upstream regions of co-regulated genes.
true
true
true
true
true
1,249
0
INTRODUCTION
1
1
[ "b1", "b3", "b4", "b5", "b6", "b8", "b9", "b11" ]
17,204,484
pmid-8879249|pmid-10591207|NA|pmid-2179676|pmid-9788350|pmid-11262934|pmid-16522208|pmid-16683017|pmid-15807889|pmid-14704356
Thus, computational TFBS prediction algorithms must begin with an input set of promoters from genes hypothetically co-regulated by a shared TF.
[ "1", "3", "4", "5", "6", "8", "9", "11" ]
143
7,711
0
false
Thus, computational TFBS prediction algorithms must begin with an input set of promoters from genes hypothetically co-regulated by a shared TF.
[]
Thus, computational TFBS prediction algorithms must begin with an input set of promoters from genes hypothetically co-regulated by a shared TF.
true
true
true
true
true
1,249
0
INTRODUCTION
1
1
[ "b1", "b3", "b4", "b5", "b6", "b8", "b9", "b11" ]
17,204,484
pmid-8879249|pmid-10591207|NA|pmid-2179676|pmid-9788350|pmid-11262934|pmid-16522208|pmid-16683017|pmid-15807889|pmid-14704356
The algorithms aim to predict the binding positions and consequently the nucleotide specificity of the TF (9–11).
[ "1", "3", "4", "5", "6", "8", "9", "11" ]
113
7,712
0
false
The algorithms aim to predict the binding positions and consequently the nucleotide specificity of the TF.
[ "9–11" ]
The algorithms aim to predict the binding positions and consequently the nucleotide specificity of the TF.
true
true
true
true
true
1,249
1
INTRODUCTION
1
12
[ "b12", "b10", "b13", "b14", "b15", "b16", "b17" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
The first part of transcription factor binding site (TFBS) discovery, the input set, can be identified using either computational or experimental methods.
[ "12", "10", "13", "14", "15", "16", "17" ]
154
7,713
0
false
The first part of transcription factor binding site (TFBS) discovery, the input set, can be identified using either computational or experimental methods.
[]
The first part of transcription factor binding site (TFBS) discovery, the input set, can be identified using either computational or experimental methods.
true
true
true
true
true
1,250
1
INTRODUCTION
1
12
[ "b12", "b10", "b13", "b14", "b15", "b16", "b17" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
Experimental techniques, such as chromatin immunoprecipitation (ChIP) (12), have been successfully used to generate a genome scale mapping of approximate TF-binding positions (10,13,14).
[ "12", "10", "13", "14", "15", "16", "17" ]
186
7,714
1
false
Experimental techniques, such as chromatin immunoprecipitation (ChIP), have been successfully used to generate a genome scale mapping of approximate TF-binding positions.
[ "12", "10,13,14" ]
Experimental techniques, such as chromatin immunoprecipitation (ChIP), have been successfully used to generate a genome scale mapping of approximate TF-binding positions.
true
true
true
true
true
1,250
1
INTRODUCTION
1
12
[ "b12", "b10", "b13", "b14", "b15", "b16", "b17" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
Computational techniques, such as phylogenetic profiling (15,16) and artificial neural networks, can also be used to identify sets of co-regulated genes.
[ "12", "10", "13", "14", "15", "16", "17" ]
153
7,715
0
false
Computational techniques, such as phylogenetic profiling and artificial neural networks, can also be used to identify sets of co-regulated genes.
[ "15,16" ]
Computational techniques, such as phylogenetic profiling and artificial neural networks, can also be used to identify sets of co-regulated genes.
true
true
true
true
true
1,250
1
INTRODUCTION
1
12
[ "b12", "b10", "b13", "b14", "b15", "b16", "b17" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
Both experimental and computational approaches, however, suffer from a significant false positive (FP) prediction rate.
[ "12", "10", "13", "14", "15", "16", "17" ]
119
7,716
0
false
Both experimental and computational approaches, however, suffer from a significant false positive (FP) prediction rate.
[]
Both experimental and computational approaches, however, suffer from a significant false positive (FP) prediction rate.
true
true
true
true
true
1,250
1
INTRODUCTION
1
17
[ "b12", "b10", "b13", "b14", "b15", "b16", "b17" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
Inclusion of extraneous promoters in the input sets dilutes the enrichment of the shared TFBS sequences making computational TFBS discovery significantly more challenging (17).
[ "12", "10", "13", "14", "15", "16", "17" ]
176
7,717
1
false
Inclusion of extraneous promoters in the input sets dilutes the enrichment of the shared TFBS sequences making computational TFBS discovery significantly more challenging.
[ "17" ]
Inclusion of extraneous promoters in the input sets dilutes the enrichment of the shared TFBS sequences making computational TFBS discovery significantly more challenging.
true
true
true
true
true
1,250
1
INTRODUCTION
1
12
[ "b12", "b10", "b13", "b14", "b15", "b16", "b17" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
We term such erroneously included promoters decoy sequences (DSs).
[ "12", "10", "13", "14", "15", "16", "17" ]
66
7,718
0
false
We term such erroneously included promoters decoy sequences (DSs).
[]
We term such erroneously included promoters decoy sequences (DSs).
true
true
true
true
true
1,250
2
INTRODUCTION
1
6
[ "b6", "b8", "b18", "b19", "b20", "b17" ]
17,204,484
pmid-9788350|pmid-11262934|pmid-8211139|pmid-10812473|pmid-16246914|pmid-15807889|pmid-9679020
After receiving a set of upstream regions co-regulated by a shared TF as input, computational methods aim to predict the binding positions of that TF (6–8,18).
[ "6", "8", "18", "19", "20", "17" ]
159
7,719
0
false
After receiving a set of upstream regions co-regulated by a shared TF as input, computational methods aim to predict the binding positions of that TF.
[ "6–8,18" ]
After receiving a set of upstream regions co-regulated by a shared TF as input, computational methods aim to predict the binding positions of that TF.
true
true
true
true
true
1,251
2
INTRODUCTION
1
6
[ "b6", "b8", "b18", "b19", "b20", "b17" ]
17,204,484
pmid-9788350|pmid-11262934|pmid-8211139|pmid-10812473|pmid-16246914|pmid-15807889|pmid-9679020
Given a set of input promoters, motif detection algorithms identify a set of short, oligonucleotide segments hypothesized to bind to the TF of interest.
[ "6", "8", "18", "19", "20", "17" ]
152
7,720
0
false
Given a set of input promoters, motif detection algorithms identify a set of short, oligonucleotide segments hypothesized to bind to the TF of interest.
[]
Given a set of input promoters, motif detection algorithms identify a set of short, oligonucleotide segments hypothesized to bind to the TF of interest.
true
true
true
true
true
1,251
2
INTRODUCTION
1
19
[ "b6", "b8", "b18", "b19", "b20", "b17" ]
17,204,484
pmid-9788350|pmid-11262934|pmid-8211139|pmid-10812473|pmid-16246914|pmid-15807889|pmid-9679020
The predicted sequences can be used to construct a position weight matrix (PWM) representing the average nucleotide frequencies for each position in the site (19).
[ "6", "8", "18", "19", "20", "17" ]
163
7,721
1
false
The predicted sequences can be used to construct a position weight matrix (PWM) representing the average nucleotide frequencies for each position in the site.
[ "19" ]
The predicted sequences can be used to construct a position weight matrix (PWM) representing the average nucleotide frequencies for each position in the site.
true
true
true
true
true
1,251
2
INTRODUCTION
1
6
[ "b6", "b8", "b18", "b19", "b20", "b17" ]
17,204,484
pmid-9788350|pmid-11262934|pmid-8211139|pmid-10812473|pmid-16246914|pmid-15807889|pmid-9679020
Ideally, computational detection will return all sequences that bind to every TF with biologically relevant function in those upstream regions.
[ "6", "8", "18", "19", "20", "17" ]
143
7,722
0
false
Ideally, computational detection will return all sequences that bind to every TF with biologically relevant function in those upstream regions.
[]
Ideally, computational detection will return all sequences that bind to every TF with biologically relevant function in those upstream regions.
true
true
true
true
true
1,251
2
INTRODUCTION
1
20
[ "b6", "b8", "b18", "b19", "b20", "b17" ]
17,204,484
pmid-9788350|pmid-11262934|pmid-8211139|pmid-10812473|pmid-16246914|pmid-15807889|pmid-9679020
However, since the source of binding specificity for TFs is not well understood (20), heuristic approaches and ad hoc multiple alignment based scoring schemes are used to identify locally optimal solutions (17).
[ "6", "8", "18", "19", "20", "17" ]
211
7,723
1
false
However, since the source of binding specificity for TFs is not well understood, heuristic approaches and ad hoc multiple alignment based scoring schemes are used to identify locally optimal solutions.
[ "20", "17" ]
However, since the source of binding specificity for TFs is not well understood, heuristic approaches and ad hoc multiple alignment based scoring schemes are used to identify locally optimal solutions.
true
true
true
true
true
1,251
2
INTRODUCTION
1
6
[ "b6", "b8", "b18", "b19", "b20", "b17" ]
17,204,484
pmid-9788350|pmid-11262934|pmid-8211139|pmid-10812473|pmid-16246914|pmid-15807889|pmid-9679020
Each local optimum that exists in a given set of promoters may correspond to distinctly different motifs, and may score differently relative to each other according to different scoring schemes.
[ "6", "8", "18", "19", "20", "17" ]
194
7,724
0
false
Each local optimum that exists in a given set of promoters may correspond to distinctly different motifs, and may score differently relative to each other according to different scoring schemes.
[]
Each local optimum that exists in a given set of promoters may correspond to distinctly different motifs, and may score differently relative to each other according to different scoring schemes.
true
true
true
true
true
1,251
3
INTRODUCTION
1
17
[ "b17", "b21", "b22", "b23", "b23", "b24" ]
17,204,484
pmid-15807889|pmid-16722558|NA|pmid-15637633|pmid-15637633|pmid-9679020
Binding site prediction algorithms are generally confounded by several factors: degeneracy in the binding site; the unknown length of the binding site; the relatively large length of promoters; and the inclusion of DSs in the input sets (17,21,22).
[ "17", "21", "22", "23", "23", "24" ]
248
7,725
0
false
Binding site prediction algorithms are generally confounded by several factors: degeneracy in the binding site; the unknown length of the binding site; the relatively large length of promoters; and the inclusion of DSs in the input sets.
[ "17,21,22" ]
Binding site prediction algorithms are generally confounded by several factors: degeneracy in the binding site; the unknown length of the binding site; the relatively large length of promoters; and the inclusion of DSs in the input sets.
true
true
true
true
true
1,252
3
INTRODUCTION
1
23
[ "b17", "b21", "b22", "b23", "b23", "b24" ]
17,204,484
pmid-15807889|pmid-16722558|NA|pmid-15637633|pmid-15637633|pmid-9679020
As a result as few as 10% of predicted positions correspond to biologically functional binding sites (23).
[ "17", "21", "22", "23", "23", "24" ]
106
7,726
1
false
As a result as few as 10% of predicted positions correspond to biologically functional binding sites.
[ "23" ]
As a result as few as 10% of predicted positions correspond to biologically functional binding sites.
true
true
true
true
true
1,252
3
INTRODUCTION
1
23
[ "b17", "b21", "b22", "b23", "b23", "b24" ]
17,204,484
pmid-15807889|pmid-16722558|NA|pmid-15637633|pmid-15637633|pmid-9679020
Due, in part, to the low accuracy rate, computational binding site identification has been of limited use (23).
[ "17", "21", "22", "23", "23", "24" ]
111
7,727
1
false
Due, in part, to the low accuracy rate, computational binding site identification has been of limited use.
[ "23" ]
Due, in part, to the low accuracy rate, computational binding site identification has been of limited use.
true
true
true
true
true
1,252
3
INTRODUCTION
1
24
[ "b17", "b21", "b22", "b23", "b23", "b24" ]
17,204,484
pmid-15807889|pmid-16722558|NA|pmid-15637633|pmid-15637633|pmid-9679020
Problems identifying binding sites are further exacerbated in mammalian genomes by larger promoter regions (24) and scarcity of reliable information on co-regulation of genes.
[ "17", "21", "22", "23", "23", "24" ]
175
7,728
1
false
Problems identifying binding sites are further exacerbated in mammalian genomes by larger promoter regions and scarcity of reliable information on co-regulation of genes.
[ "24" ]
Problems identifying binding sites are further exacerbated in mammalian genomes by larger promoter regions and scarcity of reliable information on co-regulation of genes.
true
true
true
true
true
1,252
3
INTRODUCTION
1
17
[ "b17", "b21", "b22", "b23", "b23", "b24" ]
17,204,484
pmid-15807889|pmid-16722558|NA|pmid-15637633|pmid-15637633|pmid-9679020
Thus, the most demanding test of efficacy for TFBS identification approaches lies in their application to mammalian systems and subsequent validation of predictions.
[ "17", "21", "22", "23", "23", "24" ]
165
7,729
0
false
Thus, the most demanding test of efficacy for TFBS identification approaches lies in their application to mammalian systems and subsequent validation of predictions.
[]
Thus, the most demanding test of efficacy for TFBS identification approaches lies in their application to mammalian systems and subsequent validation of predictions.
true
true
true
true
true
1,252
4
INTRODUCTION
1
18
[ "b18" ]
17,204,484
pmid-8211139
Because of computational complexity of the problem, Gibbs sampling is often used to identify binding positions (18).
[ "18" ]
116
7,730
1
false
Because of computational complexity of the problem, Gibbs sampling is often used to identify binding positions.
[ "18" ]
Because of computational complexity of the problem, Gibbs sampling is often used to identify binding positions.
true
true
true
true
true
1,253
4
INTRODUCTION
1
18
[ "b18" ]
17,204,484
pmid-8211139
In this paper, we present a new strategy that clusters Gibbs sampling results at each input nucleotideβ€”a technique we term positional clusteringβ€”to improve accuracy of predicted TF binding.
[ "18" ]
189
7,731
0
false
In this paper, we present a new strategy that clusters Gibbs sampling results at each input nucleotideβ€”a technique we term positional clusteringβ€”to improve accuracy of predicted TF binding.
[]
In this paper, we present a new strategy that clusters Gibbs sampling results at each input nucleotideβ€”a technique we term positional clusteringβ€”to improve accuracy of predicted TF binding.
true
true
true
true
true
1,253
4
INTRODUCTION
1
18
[ "b18" ]
17,204,484
pmid-8211139
We evaluate the efficacy of our approach using known examples of binding and regulation in yeast and experimentally testing predicted TF-binding sites upstream of the subunit genes coding for the heteromeric mammalian neurotransmitter receptor system, the type A Ξ³-aminobutyric acid receptor (GABAAR).
[ "18" ]
301
7,732
0
false
We evaluate the efficacy of our approach using known examples of binding and regulation in yeast and experimentally testing predicted TF-binding sites upstream of the subunit genes coding for the heteromeric mammalian neurotransmitter receptor system, the type A Ξ³-aminobutyric acid receptor (GABAAR).
[]
We evaluate the efficacy of our approach using known examples of binding and regulation in yeast and experimentally testing predicted TF-binding sites upstream of the subunit genes coding for the heteromeric mammalian neurotransmitter receptor system, the type A Ξ³-aminobutyric acid receptor (GABAAR).
true
true
true
true
true
1,253
5
INTRODUCTION
1
25
[ "b25", "b26", "b27", "b28", "b29", "b31", "b32", "b29", "b30", "b33", "b35", "b36" ]
17,204,484
NA|NA|pmid-12746549|pmid-15758057|pmid-15031002|pmid-16091474|NA|pmid-15031002|pmid-11520315|pmid-16101898|pmid-9771750|pmid-15618944
The GABAAR is the major inhibitory neurotransmitter receptor in the central nervous system (CNS) (25,26) with important roles in development (27,28) and disease (29–31).
[ "25", "26", "27", "28", "29", "31", "32", "29", "30", "33", "35", "36" ]
169
7,733
0
false
The GABAAR is the major inhibitory neurotransmitter receptor in the central nervous system (CNS) with important roles in development and disease.
[ "25,26", "27,28", "29–31" ]
The GABAAR is the major inhibitory neurotransmitter receptor in the central nervous system (CNS) with important roles in development and disease.
true
true
true
true
true
1,254
5
INTRODUCTION
1
32
[ "b25", "b26", "b27", "b28", "b29", "b31", "b32", "b29", "b30", "b33", "b35", "b36" ]
17,204,484
NA|NA|pmid-12746549|pmid-15758057|pmid-15031002|pmid-16091474|NA|pmid-15031002|pmid-11520315|pmid-16101898|pmid-9771750|pmid-15618944
The receptor is believed to be a pentamer made up of multiple subunits that come from at least four different subunit classes (Ξ±, Ξ², Ξ³ and Ξ΄) (32).
[ "25", "26", "27", "28", "29", "31", "32", "29", "30", "33", "35", "36" ]
147
7,734
1
false
The receptor is believed to be a pentamer made up of multiple subunits that come from at least four different subunit classes (Ξ±, Ξ², Ξ³ and Ξ΄).
[ "32" ]
The receptor is believed to be a pentamer made up of multiple subunits that come from at least four different subunit classes (Ξ±, Ξ², Ξ³ and Ξ΄).
true
true
true
true
true
1,254
5
INTRODUCTION
1
25
[ "b25", "b26", "b27", "b28", "b29", "b31", "b32", "b29", "b30", "b33", "b35", "b36" ]
17,204,484
NA|NA|pmid-12746549|pmid-15758057|pmid-15031002|pmid-16091474|NA|pmid-15031002|pmid-11520315|pmid-16101898|pmid-9771750|pmid-15618944
At least 19 genes code for the various subunits that differentially combine to form numerous pharmacologically distinct GABAA receptor isoforms (29,30).
[ "25", "26", "27", "28", "29", "31", "32", "29", "30", "33", "35", "36" ]
152
7,735
0
false
At least 19 genes code for the various subunits that differentially combine to form numerous pharmacologically distinct GABAA receptor isoforms.
[ "29,30" ]
At least 19 genes code for the various subunits that differentially combine to form numerous pharmacologically distinct GABAA receptor isoforms.
true
true
true
true
true
1,254
5
INTRODUCTION
1
25
[ "b25", "b26", "b27", "b28", "b29", "b31", "b32", "b29", "b30", "b33", "b35", "b36" ]
17,204,484
NA|NA|pmid-12746549|pmid-15758057|pmid-15031002|pmid-16091474|NA|pmid-15031002|pmid-11520315|pmid-16101898|pmid-9771750|pmid-15618944
Isoform utilization depends in part on the relative abundance of the subunits, which may change under various conditions (33–35).
[ "25", "26", "27", "28", "29", "31", "32", "29", "30", "33", "35", "36" ]
129
7,736
0
false
Isoform utilization depends in part on the relative abundance of the subunits, which may change under various conditions.
[ "33–35" ]
Isoform utilization depends in part on the relative abundance of the subunits, which may change under various conditions.
true
true
true
true
true
1,254
5
INTRODUCTION
1
36
[ "b25", "b26", "b27", "b28", "b29", "b31", "b32", "b29", "b30", "b33", "b35", "b36" ]
17,204,484
NA|NA|pmid-12746549|pmid-15758057|pmid-15031002|pmid-16091474|NA|pmid-15031002|pmid-11520315|pmid-16101898|pmid-9771750|pmid-15618944
Understanding subunit regulatory mechanisms may provide insight into GABAA receptor isoform usage and related phenotypes (36).
[ "25", "26", "27", "28", "29", "31", "32", "29", "30", "33", "35", "36" ]
126
7,737
1
false
Understanding subunit regulatory mechanisms may provide insight into GABAA receptor isoform usage and related phenotypes.
[ "36" ]
Understanding subunit regulatory mechanisms may provide insight into GABAA receptor isoform usage and related phenotypes.
true
true
true
true
true
1,254
6
INTRODUCTION
1
35
[ "b35" ]
17,204,484
pmid-9771750
In the current study, we test the ability of positional clustering to detect known TF-binding sites in a series of increasingly noisy sets of yeast promoters, and found marked improvement in the percentage of correct predictions over Gibbs sampling alone.
[ "35" ]
255
7,738
0
false
In the current study, we test the ability of positional clustering to detect known TF-binding sites in a series of increasingly noisy sets of yeast promoters, and found marked improvement in the percentage of correct predictions over Gibbs sampling alone.
[]
In the current study, we test the ability of positional clustering to detect known TF-binding sites in a series of increasingly noisy sets of yeast promoters, and found marked improvement in the percentage of correct predictions over Gibbs sampling alone.
true
true
true
true
true
1,255
6
INTRODUCTION
1
35
[ "b35" ]
17,204,484
pmid-9771750
We also present de novo predictions of TF-binding sites in promoter regions of GABAA receptor subunit genes (GABRs) whose expression is altered (either up-regulated or down-regulated) in an animal model of temporal lobe epilepsy (35).
[ "35" ]
234
7,739
1
false
We also present de novo predictions of TF-binding sites in promoter regions of GABAA receptor subunit genes (GABRs) whose expression is altered (either up-regulated or down-regulated) in an animal model of temporal lobe epilepsy.
[ "35" ]
We also present de novo predictions of TF-binding sites in promoter regions of GABAA receptor subunit genes (GABRs) whose expression is altered (either up-regulated or down-regulated) in an animal model of temporal lobe epilepsy.
true
true
true
true
true
1,255
6
INTRODUCTION
1
35
[ "b35" ]
17,204,484
pmid-9771750
Positional clustering identified a number of putative cis-regulatory sites, many of which correspond to known binding elements for TFs found in the CNS.
[ "35" ]
152
7,740
0
false
Positional clustering identified a number of putative cis-regulatory sites, many of which correspond to known binding elements for TFs found in the CNS.
[]
Positional clustering identified a number of putative cis-regulatory sites, many of which correspond to known binding elements for TFs found in the CNS.
true
true
true
true
true
1,255
6
INTRODUCTION
1
35
[ "b35" ]
17,204,484
pmid-9771750
Mobility shift assays showed several predicted GABR-binding sequences specifically bind nuclear proteins derived from primary neocortical neurons kept in culture.
[ "35" ]
162
7,741
0
false
Mobility shift assays showed several predicted GABR-binding sequences specifically bind nuclear proteins derived from primary neocortical neurons kept in culture.
[]
Mobility shift assays showed several predicted GABR-binding sequences specifically bind nuclear proteins derived from primary neocortical neurons kept in culture.
true
true
true
true
true
1,255
6
INTRODUCTION
1
35
[ "b35" ]
17,204,484
pmid-9771750
Furthermore, a particular non-consensus GABR putative regulatory sequence was shown to have a functional role in cultured cortical neurons demonstrating the efficacy of positional clustering in detecting functional regulatory elements in mammals.
[ "35" ]
246
7,742
0
false
Furthermore, a particular non-consensus GABR putative regulatory sequence was shown to have a functional role in cultured cortical neurons demonstrating the efficacy of positional clustering in detecting functional regulatory elements in mammals.
[]
Furthermore, a particular non-consensus GABR putative regulatory sequence was shown to have a functional role in cultured cortical neurons demonstrating the efficacy of positional clustering in detecting functional regulatory elements in mammals.
true
true
true
true
true
1,255
0
DISCUSSION
1
17
[ "b17", "b54" ]
17,204,484
pmid-8879249|pmid-10591207|NA|pmid-2179676|pmid-9788350|pmid-11262934|pmid-16522208|pmid-16683017|pmid-15807889|pmid-14704356
How reliable are the binding site predictions returned by Gibbs sampling based TFBS identification algorithms?
[ "17", "54" ]
110
7,743
0
false
How reliable are the binding site predictions returned by Gibbs sampling based TFBS identification algorithms?
[]
How reliable are the binding site predictions returned by Gibbs sampling based TFBS identification algorithms?
true
true
true
true
true
1,256
0
DISCUSSION
1
17
[ "b17", "b54" ]
17,204,484
pmid-8879249|pmid-10591207|NA|pmid-2179676|pmid-9788350|pmid-11262934|pmid-16522208|pmid-16683017|pmid-15807889|pmid-14704356
We began by evaluating the stability of binding site predictions via repeated runs of Gibbs sampling.
[ "17", "54" ]
101
7,744
0
false
We began by evaluating the stability of binding site predictions via repeated runs of Gibbs sampling.
[]
We began by evaluating the stability of binding site predictions via repeated runs of Gibbs sampling.
true
true
true
true
true
1,256
0
DISCUSSION
1
17
[ "b17", "b54" ]
17,204,484
pmid-8879249|pmid-10591207|NA|pmid-2179676|pmid-9788350|pmid-11262934|pmid-16522208|pmid-16683017|pmid-15807889|pmid-14704356
To quantify the robustness of predictions, we counted the number of Gibbs sampling results at each nucleotide position in the input set (Figure 1) over a large number of repeated trials.
[ "17", "54" ]
186
7,745
0
false
To quantify the robustness of predictions, we counted the number of Gibbs sampling results at each nucleotide position in the input set (Figure 1) over a large number of repeated trials.
[]
To quantify the robustness of predictions, we counted the number of Gibbs sampling results at each nucleotide position in the input set over a large number of repeated trials.
true
true
true
true
true
1,256
0
DISCUSSION
1
17
[ "b17", "b54" ]
17,204,484
pmid-8879249|pmid-10591207|NA|pmid-2179676|pmid-9788350|pmid-11262934|pmid-16522208|pmid-16683017|pmid-15807889|pmid-14704356
We find that the most frequently returned positions better predict TF binding sites than the maximally scoring motifs from Gibbs sampling (Figures 2 and 3).
[ "17", "54" ]
156
7,746
0
false
We find that the most frequently returned positions better predict TF binding sites than the maximally scoring motifs from Gibbs sampling (Figures 2 and 3).
[]
We find that the most frequently returned positions better predict TF binding sites than the maximally scoring motifs from Gibbs sampling (Figures 2 and 3).
true
true
true
true
true
1,256
0
DISCUSSION
1
17
[ "b17", "b54" ]
17,204,484
pmid-8879249|pmid-10591207|NA|pmid-2179676|pmid-9788350|pmid-11262934|pmid-16522208|pmid-16683017|pmid-15807889|pmid-14704356
Since scoring functions are empirically derived and do not necessarily represent biological reality, the result is not altogether unexpected (17).
[ "17", "54" ]
146
7,747
1
false
Since scoring functions are empirically derived and do not necessarily represent biological reality, the result is not altogether unexpected.
[ "17" ]
Since scoring functions are empirically derived and do not necessarily represent biological reality, the result is not altogether unexpected.
true
true
true
true
true
1,256
0
DISCUSSION
1
17
[ "b17", "b54" ]
17,204,484
pmid-8879249|pmid-10591207|NA|pmid-2179676|pmid-9788350|pmid-11262934|pmid-16522208|pmid-16683017|pmid-15807889|pmid-14704356
However, we find that selecting frequently recurring positions allows filtering of up to 90% of spurious sampling results caused by convergence on biologically uninformative local minima.
[ "17", "54" ]
187
7,748
0
false
However, we find that selecting frequently recurring positions allows filtering of up to 90% of spurious sampling results caused by convergence on biologically uninformative local minima.
[]
However, we find that selecting frequently recurring positions allows filtering of up to 90% of spurious sampling results caused by convergence on biologically uninformative local minima.
true
true
true
true
true
1,256
0
DISCUSSION
1
54
[ "b17", "b54" ]
17,204,484
pmid-8879249|pmid-10591207|NA|pmid-2179676|pmid-9788350|pmid-11262934|pmid-16522208|pmid-16683017|pmid-15807889|pmid-14704356
Positional clustering allows unbiased aggregation of results from different motif widths, thus approximating the width of the binding site β€˜for free’ (54).
[ "17", "54" ]
155
7,749
1
false
Positional clustering allows unbiased aggregation of results from different motif widths, thus approximating the width of the binding site β€˜for free’.
[ "54" ]
Positional clustering allows unbiased aggregation of results from different motif widths, thus approximating the width of the binding site β€˜for free’.
true
true
true
true
true
1,256
1
DISCUSSION
1
17
[ "b17", "b22" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
Next we show that positional clustering improves robustness to the addition of DSs (Figures 2 and 3).
[ "17", "22" ]
101
7,750
0
false
Next we show that positional clustering improves robustness to the addition of DSs (Figures 2 and 3).
[]
Next we show that positional clustering improves robustness to the addition of DSs (Figures 2 and 3).
true
true
true
true
true
1,257
1
DISCUSSION
1
17
[ "b17", "b22" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
Such sequences arise from inclusion of promoter regions in input sets without direct binding to the TF either due to experimental error or computational mis-annotation (17,22).
[ "17", "22" ]
176
7,751
0
false
Such sequences arise from inclusion of promoter regions in input sets without direct binding to the TF either due to experimental error or computational mis-annotation.
[ "17,22" ]
Such sequences arise from inclusion of promoter regions in input sets without direct binding to the TF either due to experimental error or computational mis-annotation.
true
true
true
true
true
1,257
1
DISCUSSION
1
17
[ "b17", "b22" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
In the STE12 example studied, linear regression models indicate our approach will maintain an advantage over traditional Gibbs sampling through addition of up to 150% noise to the original signal (Supplementary Figure S4).
[ "17", "22" ]
222
7,752
0
false
In the STE12 example studied, linear regression models indicate our approach will maintain an advantage over traditional Gibbs sampling through addition of up to 150% noise to the original signal (Supplementary Figure S4).
[]
In the STE12 example studied, linear regression models indicate our approach will maintain an advantage over traditional Gibbs sampling through addition of up to 150% noise to the original signal (Supplementary Figure S4).
true
true
true
true
true
1,257
1
DISCUSSION
1
17
[ "b17", "b22" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
Empirical data, however, show a sharp decrease in improvement close to the addition of 45 DSs, or roughly double the input set (Figure 2).
[ "17", "22" ]
138
7,753
0
false
Empirical data, however, show a sharp decrease in improvement close to the addition of 45 DSs, or roughly double the input set (Figure 2).
[]
Empirical data, however, show a sharp decrease in improvement close to the addition of 45 DSs, or roughly double the input set (Figure 2).
true
true
true
true
true
1,257
1
DISCUSSION
1
17
[ "b17", "b22" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
Moreover, evaluations using promoters co-regulated by other TFs indicate positional clustering is less likely to improve predictions when Gibbs sampling identifies a correct site in <20% of repetitions (Figure 3).
[ "17", "22" ]
213
7,754
0
false
Moreover, evaluations using promoters co-regulated by other TFs indicate positional clustering is less likely to improve predictions when Gibbs sampling identifies a correct site in <20% of repetitions (Figure 3).
[]
Moreover, evaluations using promoters co-regulated by other TFs indicate positional clustering is less likely to improve predictions when Gibbs sampling identifies a correct site in <20% of repetitions (Figure 3).
true
true
true
true
true
1,257
1
DISCUSSION
1
17
[ "b17", "b22" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
Thus, it is possible the rather simplistic linear model overestimates improvement in robustness beyond what is practically achievable.
[ "17", "22" ]
134
7,755
0
false
Thus, it is possible the rather simplistic linear model overestimates improvement in robustness beyond what is practically achievable.
[]
Thus, it is possible the rather simplistic linear model overestimates improvement in robustness beyond what is practically achievable.
true
true
true
true
true
1,257
1
DISCUSSION
1
17
[ "b17", "b22" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
Moreover, when multiple motifs exist in the input promoters, preliminary evidence suggests positional clustering will uniquely identify a single dominant motif (Supplementary Figure S5).
[ "17", "22" ]
186
7,756
0
false
Moreover, when multiple motifs exist in the input promoters, preliminary evidence suggests positional clustering will uniquely identify a single dominant motif (Supplementary Figure S5).
[]
Moreover, when multiple motifs exist in the input promoters, preliminary evidence suggests positional clustering will uniquely identify a single dominant motif (Supplementary Figure S5).
true
true
true
true
true
1,257
1
DISCUSSION
1
17
[ "b17", "b22" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
With further refinement, however, it may be possible to recover subordinate motifs, enabling identification of cis-regulatory modules.
[ "17", "22" ]
134
7,757
0
false
With further refinement, however, it may be possible to recover subordinate motifs, enabling identification of cis-regulatory modules.
[]
With further refinement, however, it may be possible to recover subordinate motifs, enabling identification of cis-regulatory modules.
true
true
true
true
true
1,257
1
DISCUSSION
1
17
[ "b17", "b22" ]
17,204,484
pmid-10579938|pmid-15343339|pmid-11206552|pmid-12399584|pmid-10200254|pmid-12912833|pmid-15807889|pmid-15807889|NA
In spite of these limitations, using positional clustering of repeated runs, researchers can successfully apply sampling algorithms in identification of functional binding sites in datasets with a significant proportion of noise.
[ "17", "22" ]
229
7,758
0
false
In spite of these limitations, using positional clustering of repeated runs, researchers can successfully apply sampling algorithms in identification of functional binding sites in datasets with a significant proportion of noise.
[]
In spite of these limitations, using positional clustering of repeated runs, researchers can successfully apply sampling algorithms in identification of functional binding sites in datasets with a significant proportion of noise.
true
true
true
true
true
1,257
2
DISCUSSION
1
24
[ "b24" ]
17,204,484
pmid-9788350|pmid-11262934|pmid-8211139|pmid-10812473|pmid-16246914|pmid-15807889|pmid-9679020
Computational prediction of TF binding in mammalian genomes poses just such a challenge due to increased decoy sequence in large upstream regions (24).
[ "24" ]
151
7,759
1
false
Computational prediction of TF binding in mammalian genomes poses just such a challenge due to increased decoy sequence in large upstream regions.
[ "24" ]
Computational prediction of TF binding in mammalian genomes poses just such a challenge due to increased decoy sequence in large upstream regions.
true
true
true
true
true
1,258
2
DISCUSSION
1
24
[ "b24" ]
17,204,484
pmid-9788350|pmid-11262934|pmid-8211139|pmid-10812473|pmid-16246914|pmid-15807889|pmid-9679020
Thus, having established increased robustness to DSs in yeast, we applied our approach to identify potentially unknown GABAA receptor subunit gene regulatory sequences that may participate in the response of the genome to seizure activity.
[ "24" ]
239
7,760
0
false
Thus, having established increased robustness to DSs in yeast, we applied our approach to identify potentially unknown GABAA receptor subunit gene regulatory sequences that may participate in the response of the genome to seizure activity.
[]
Thus, having established increased robustness to DSs in yeast, we applied our approach to identify potentially unknown GABAA receptor subunit gene regulatory sequences that may participate in the response of the genome to seizure activity.
true
true
true
true
true
1,258
2
DISCUSSION
1
24
[ "b24" ]
17,204,484
pmid-9788350|pmid-11262934|pmid-8211139|pmid-10812473|pmid-16246914|pmid-15807889|pmid-9679020
We reasoned that GABAA receptor subunit genes either up-regulated or down-regulated in the animal model of epilepsy would share common binding motifs.
[ "24" ]
150
7,761
0
false
We reasoned that GABAA receptor subunit genes either up-regulated or down-regulated in the animal model of epilepsy would share common binding motifs.
[]
We reasoned that GABAA receptor subunit genes either up-regulated or down-regulated in the animal model of epilepsy would share common binding motifs.
true
true
true
true
true
1,258
2
DISCUSSION
1
24
[ "b24" ]
17,204,484
pmid-9788350|pmid-11262934|pmid-8211139|pmid-10812473|pmid-16246914|pmid-15807889|pmid-9679020
Using positional clustering, we predicted 13 TF-binding sites upstream of GABAA receptor subunit genes (Table 1).
[ "24" ]
113
7,762
0
false
Using positional clustering, we predicted 13 TF-binding sites upstream of GABAA receptor subunit genes (Table 1).
[]
Using positional clustering, we predicted 13 TF-binding sites upstream of GABAA receptor subunit genes (Table 1).
true
true
true
true
true
1,258
2
DISCUSSION
1
24
[ "b24" ]
17,204,484
pmid-9788350|pmid-11262934|pmid-8211139|pmid-10812473|pmid-16246914|pmid-15807889|pmid-9679020
Twelve of our predictions were verified by either comparison to known binding sites or experimental verification using in vitro binding assays.
[ "24" ]
143
7,763
0
false
Twelve of our predictions were verified by either comparison to known binding sites or experimental verification using in vitro binding assays.
[]
Twelve of our predictions were verified by either comparison to known binding sites or experimental verification using in vitro binding assays.
true
true
true
true
true
1,258
2
DISCUSSION
1
24
[ "b24" ]
17,204,484
pmid-9788350|pmid-11262934|pmid-8211139|pmid-10812473|pmid-16246914|pmid-15807889|pmid-9679020
Initially positive experimental results highlight the ability of computational techniques to direct research into transcriptional regulation in mammalian models.
[ "24" ]
161
7,764
0
false
Initially positive experimental results highlight the ability of computational techniques to direct research into transcriptional regulation in mammalian models.
[]
Initially positive experimental results highlight the ability of computational techniques to direct research into transcriptional regulation in mammalian models.
true
true
true
true
true
1,258
2
DISCUSSION
1
24
[ "b24" ]
17,204,484
pmid-9788350|pmid-11262934|pmid-8211139|pmid-10812473|pmid-16246914|pmid-15807889|pmid-9679020
As such, our approach may be applicable in the study of other protein complexes in the mammalian proteome.
[ "24" ]
106
7,765
0
false
As such, our approach may be applicable in the study of other protein complexes in the mammalian proteome.
[]
As such, our approach may be applicable in the study of other protein complexes in the mammalian proteome.
true
true
true
true
true
1,258
3
DISCUSSION
0
null
null
17,204,484
pmid-15807889|pmid-16722558|NA|pmid-15637633|pmid-15637633|pmid-9679020
The reported predictions may enable pharmacologically important downstream research.
null
84
7,766
0
false
null
null
The reported predictions may enable pharmacologically important downstream research.
true
true
true
true
true
1,259
3
DISCUSSION
0
null
null
17,204,484
pmid-15807889|pmid-16722558|NA|pmid-15637633|pmid-15637633|pmid-9679020
For example the predicted sites can be used as a starting point for quantifying in vivo effect on downstream transcription; for identifying the TFs bound; and even for the more complex task of understanding the role of each site in determining the relative abundance of GABAA receptor isoforms.
null
294
7,767
0
false
null
null
For example the predicted sites can be used as a starting point for quantifying in vivo effect on downstream transcription; for identifying the TFs bound; and even for the more complex task of understanding the role of each site in determining the relative abundance of GABAA receptor isoforms.
true
true
true
true
true
1,259
3
DISCUSSION
0
null
null
17,204,484
pmid-15807889|pmid-16722558|NA|pmid-15637633|pmid-15637633|pmid-9679020
Research along these lines may dramatically improve our understanding of GABAA receptor regulation and its role in disease and development.
null
139
7,768
0
false
null
null
Research along these lines may dramatically improve our understanding of GABAA receptor regulation and its role in disease and development.
true
true
true
true
true
1,259
3
DISCUSSION
0
null
null
17,204,484
pmid-15807889|pmid-16722558|NA|pmid-15637633|pmid-15637633|pmid-9679020
Additionally, a more comprehensive evaluation of the remaining GABAA receptor subunit genes may reveal additional TF-binding sites that uncover the evolutionary significance of Ξ³-Ξ±-Ξ² GABR clusters in the human genome.
null
217
7,769
0
false
null
null
Additionally, a more comprehensive evaluation of the remaining GABAA receptor subunit genes may reveal additional TF-binding sites that uncover the evolutionary significance of Ξ³-Ξ±-Ξ² GABR clusters in the human genome.
true
true
true
true
true
1,259
0
INTRODUCTION
1
25
[ "b1", "b9", "b10", "b14", "b15", "b20", "b21", "b24", "b25" ]
17,088,290
pmid-9630891|pmid-10656818|pmid-2201029|pmid-7535098|pmid-10051566|pmid-8303274|pmid-8568873|pmid-8609615|pmid-8254660
Phage display is a widely used method to select antibody fragments (1–9), peptides (10–14) and other scaffolds (15–20) from large libraries, as well as to increase the affinity of antibodies for their antigens (21–24) and other proteins for their receptors (25).
[ "1", "9", "10", "14", "15", "20", "21", "24", "25" ]
262
7,770
1
false
Phage display is a widely used method to select antibody fragments, peptides and other scaffolds from large libraries, as well as to increase the affinity of antibodies for their antigens and other proteins for their receptors.
[ "1–9", "10–14", "15–20", "21–24", "25" ]
Phage display is a widely used method to select antibody fragments, peptides and other scaffolds from large libraries, as well as to increase the affinity of antibodies for their antigens and other proteins for their receptors.
true
true
true
true
true
1,260
0
INTRODUCTION
1
1
[ "b1", "b9", "b10", "b14", "b15", "b20", "b21", "b24", "b25" ]
17,088,290
pmid-9630891|pmid-10656818|pmid-2201029|pmid-7535098|pmid-10051566|pmid-8303274|pmid-8568873|pmid-8609615|pmid-8254660
Polypeptides are displayed as phage coat protein fusions and the corresponding gene is contained within the particle.
[ "1", "9", "10", "14", "15", "20", "21", "24", "25" ]
117
7,771
0
false
Polypeptides are displayed as phage coat protein fusions and the corresponding gene is contained within the particle.
[]
Polypeptides are displayed as phage coat protein fusions and the corresponding gene is contained within the particle.
true
true
true
true
true
1,260
0
INTRODUCTION
1
1
[ "b1", "b9", "b10", "b14", "b15", "b20", "b21", "b24", "b25" ]
17,088,290
pmid-9630891|pmid-10656818|pmid-2201029|pmid-7535098|pmid-10051566|pmid-8303274|pmid-8568873|pmid-8609615|pmid-8254660
It is usually mediated by the fusion of the displayed polypeptide to a coat protein, and encapsulating the gene encoding the fusion protein within the phage particle.
[ "1", "9", "10", "14", "15", "20", "21", "24", "25" ]
166
7,772
0
false
It is usually mediated by the fusion of the displayed polypeptide to a coat protein, and encapsulating the gene encoding the fusion protein within the phage particle.
[]
It is usually mediated by the fusion of the displayed polypeptide to a coat protein, and encapsulating the gene encoding the fusion protein within the phage particle.
true
true
true
true
true
1,260
0
INTRODUCTION
1
1
[ "b1", "b9", "b10", "b14", "b15", "b20", "b21", "b24", "b25" ]
17,088,290
pmid-9630891|pmid-10656818|pmid-2201029|pmid-7535098|pmid-10051566|pmid-8303274|pmid-8568873|pmid-8609615|pmid-8254660
This coupling of phenotype and genotype ensures that selection of the displayed protein simultaneously selects the encoding gene, allowing further selection rounds and the eventual isolation of a population highly enriched in phage displaying polypeptides of interest.
[ "1", "9", "10", "14", "15", "20", "21", "24", "25" ]
268
7,773
0
false
This coupling of phenotype and genotype ensures that selection of the displayed protein simultaneously selects the encoding gene, allowing further selection rounds and the eventual isolation of a population highly enriched in phage displaying polypeptides of interest.
[]
This coupling of phenotype and genotype ensures that selection of the displayed protein simultaneously selects the encoding gene, allowing further selection rounds and the eventual isolation of a population highly enriched in phage displaying polypeptides of interest.
true
true
true
true
true
1,260
0
INTRODUCTION
1
1
[ "b1", "b9", "b10", "b14", "b15", "b20", "b21", "b24", "b25" ]
17,088,290
pmid-9630891|pmid-10656818|pmid-2201029|pmid-7535098|pmid-10051566|pmid-8303274|pmid-8568873|pmid-8609615|pmid-8254660
Vectors based on filamentous Ff phage are the most popular, and of the five coat proteins, the gene 3 protein (g3p) is most commonly used for display.
[ "1", "9", "10", "14", "15", "20", "21", "24", "25" ]
150
7,774
0
false
Vectors based on filamentous Ff phage are the most popular, and of the five coat proteins, the gene 3 protein (g3p) is most commonly used for display.
[]
Vectors based on filamentous Ff phage are the most popular, and of the five coat proteins, the gene 3 protein (g3p) is most commonly used for display.
true
true
true
true
true
1,260
1
INTRODUCTION
1
26
[ "b26", "b27" ]
17,088,290
pmid-2985470|pmid-9370269|pmid-2985470|pmid-9370269|pmid-9370269|pmid-7875580|pmid-12771223|pmid-10625396|pmid-11526909|pmid-10404162|pmid-16442560|pmid-11526909|pmid-10623552|pmid-15062775
There are two broad categories of vectors used for phage display: phage and phagemid.
[ "26", "27" ]
85
7,775
0
false
There are two broad categories of vectors used for phage display: phage and phagemid.
[]
There are two broad categories of vectors used for phage display: phage and phagemid.
true
true
true
true
true
1,261
1
INTRODUCTION
1
26
[ "b26", "b27" ]
17,088,290
pmid-2985470|pmid-9370269|pmid-2985470|pmid-9370269|pmid-9370269|pmid-7875580|pmid-12771223|pmid-10625396|pmid-11526909|pmid-10404162|pmid-16442560|pmid-11526909|pmid-10623552|pmid-15062775
When proteins are displayed using phage vectors, which are not of the 33 or 88 kind, the bacteria produce phage particles that all display recombinant protein.
[ "26", "27" ]
159
7,776
0
false
When proteins are displayed using phage vectors, which are not of the 33 or 88 kind, the bacteria produce phage particles that all display recombinant protein.
[]
When proteins are displayed using phage vectors, which are not of the 33 or 88 kind, the bacteria produce phage particles that all display recombinant protein.
true
true
true
true
true
1,261
1
INTRODUCTION
1
26
[ "b26", "b27" ]
17,088,290
pmid-2985470|pmid-9370269|pmid-2985470|pmid-9370269|pmid-9370269|pmid-7875580|pmid-12771223|pmid-10625396|pmid-11526909|pmid-10404162|pmid-16442560|pmid-11526909|pmid-10623552|pmid-15062775
The gene encoding the recombinant displayed protein is included in the phage genome and as a result the phage population produced by a single clone is phenotypically and genotypically homogenous, excluding the effects of proteolysis.
[ "26", "27" ]
233
7,777
0
false
The gene encoding the recombinant displayed protein is included in the phage genome and as a result the phage population produced by a single clone is phenotypically and genotypically homogenous, excluding the effects of proteolysis.
[]
The gene encoding the recombinant displayed protein is included in the phage genome and as a result the phage population produced by a single clone is phenotypically and genotypically homogenous, excluding the effects of proteolysis.
true
true
true
true
true
1,261
1
INTRODUCTION
1
26
[ "b26", "b27" ]
17,088,290
pmid-2985470|pmid-9370269|pmid-2985470|pmid-9370269|pmid-9370269|pmid-7875580|pmid-12771223|pmid-10625396|pmid-11526909|pmid-10404162|pmid-16442560|pmid-11526909|pmid-10623552|pmid-15062775
In contrast, phagemid make recombinant displayed protein, but require the additional proteins provided by helper phage to create phage particles that display recombinant protein.
[ "26", "27" ]
178
7,778
0
false
In contrast, phagemid make recombinant displayed protein, but require the additional proteins provided by helper phage to create phage particles that display recombinant protein.
[]
In contrast, phagemid make recombinant displayed protein, but require the additional proteins provided by helper phage to create phage particles that display recombinant protein.
true
true
true
true
true
1,261
1
INTRODUCTION
1
26
[ "b26", "b27" ]
17,088,290
pmid-2985470|pmid-9370269|pmid-2985470|pmid-9370269|pmid-9370269|pmid-7875580|pmid-12771223|pmid-10625396|pmid-11526909|pmid-10404162|pmid-16442560|pmid-11526909|pmid-10623552|pmid-15062775
Helper phage are essential for phagemid systems as they supply all the other proteins required to make functional phage.
[ "26", "27" ]
120
7,779
0
false
Helper phage are essential for phagemid systems as they supply all the other proteins required to make functional phage.
[]
Helper phage are essential for phagemid systems as they supply all the other proteins required to make functional phage.
true
true
true
true
true
1,261
1
INTRODUCTION
1
26
[ "b26", "b27" ]
17,088,290
pmid-2985470|pmid-9370269|pmid-2985470|pmid-9370269|pmid-9370269|pmid-7875580|pmid-12771223|pmid-10625396|pmid-11526909|pmid-10404162|pmid-16442560|pmid-11526909|pmid-10623552|pmid-15062775
Helper phage (26,27) are normal Ff phages with a number of modifications: they contain an additional origin of replication, they usually carry antibiotic resistance genes and their packaging signal is severely disabled.
[ "26", "27" ]
219
7,780
0
false
Helper phage are normal Ff phages with a number of modifications: they contain an additional origin of replication, they usually carry antibiotic resistance genes and their packaging signal is severely disabled.
[ "26,27" ]
Helper phage are normal Ff phages with a number of modifications: they contain an additional origin of replication, they usually carry antibiotic resistance genes and their packaging signal is severely disabled.
true
true
true
true
true
1,261
2
INTRODUCTION
0
null
null
17,088,290
pmid-9370269|pmid-9631287
When a bacterium is infected with helper phage, the disabled packaging signal does not prevent the production of phage particles.
null
129
7,781
0
false
null
null
When a bacterium is infected with helper phage, the disabled packaging signal does not prevent the production of phage particles.
true
true
true
true
true
1,262
2
INTRODUCTION
0
null
null
17,088,290
pmid-9370269|pmid-9631287
However, when a bacterium is infected with both phagemid and helper phage, the phagemid DNA (containing an optimal packaging signal) is packaged in preference.
null
159
7,782
0
false
null
null
However, when a bacterium is infected with both phagemid and helper phage, the phagemid DNA (containing an optimal packaging signal) is packaged in preference.
true
true
true
true
true
1,262
2
INTRODUCTION
0
null
null
17,088,290
pmid-9370269|pmid-9631287
As a result, phagemid preparations are both phenotypically and genotypically heterogeneous (Figure 1): the display protein may be either wild type (derived from the helper phage) or recombinant (derived from the phagemid), and the packaged genome may be either phage or phagemid.
null
279
7,783
0
false
null
null
As a result, phagemid preparations are both phenotypically and genotypically heterogeneous (Figure 1): the display protein may be either wild type (derived from the helper phage) or recombinant (derived from the phagemid), and the packaged genome may be either phage or phagemid.
true
true
true
true
true
1,262
2
INTRODUCTION
0
null
null
17,088,290
pmid-9370269|pmid-9631287
In theory, the disabled packaging signal should significantly reduce the number of helper phage particles in any phagemid preparation.
null
134
7,784
0
false
null
null
In theory, the disabled packaging signal should significantly reduce the number of helper phage particles in any phagemid preparation.
true
true
true
true
true
1,262
2
INTRODUCTION
0
null
null
17,088,290
pmid-9370269|pmid-9631287
However, the number of helper phage can sometimes equal, or exceed, the number of phagemid particles, which can significantly compromise subsequent selections.
null
159
7,785
0
false
null
null
However, the number of helper phage can sometimes equal, or exceed, the number of phagemid particles, which can significantly compromise subsequent selections.
true
true
true
true
true
1,262
3
INTRODUCTION
0
null
null
17,088,290
pmid-3886166|pmid-3872373|pmid-2009963|pmid-2804106|pmid-9244308|NA|pmid-9370269|pmid-6955583|pmid-2319594
The different antibiotic resistance genes carried by the helper phage and phagemid genomes allows the selection of bacteria that contain both, resulting in the recovery of functional phagemid particles displaying the recombinant protein of interest.
null
249
7,786
0
false
null
null
The different antibiotic resistance genes carried by the helper phage and phagemid genomes allows the selection of bacteria that contain both, resulting in the recovery of functional phagemid particles displaying the recombinant protein of interest.
true
true
true
true
true
1,263
4
INTRODUCTION
1
28
[ "b28", "b29" ]
17,088,290
pmid-1908075|pmid-11226299|pmid-9244308|NA
Practically, phage and phagemid libraries have a number of differences.
[ "28", "29" ]
71
7,787
0
false
Practically, phage and phagemid libraries have a number of differences.
[]
Practically, phage and phagemid libraries have a number of differences.
true
true
true
true
true
1,264
4
INTRODUCTION
1
28
[ "b28", "b29" ]
17,088,290
pmid-1908075|pmid-11226299|pmid-9244308|NA
At the DNA level (preparing DNA, cloning, transfection efficiency) it is easier to work with phagemids than phage.
[ "28", "29" ]
114
7,788
0
false
At the DNA level (preparing DNA, cloning, transfection efficiency) it is easier to work with phagemids than phage.
[]
At the DNA level (preparing DNA, cloning, transfection efficiency) it is easier to work with phagemids than phage.
true
true
true
true
true
1,264
4
INTRODUCTION
1
28
[ "b28", "b29" ]
17,088,290
pmid-1908075|pmid-11226299|pmid-9244308|NA
As a result, phagemid libraries can be made far larger than phage libraries.
[ "28", "29" ]
76
7,789
0
false
As a result, phagemid libraries can be made far larger than phage libraries.
[]
As a result, phagemid libraries can be made far larger than phage libraries.
true
true
true
true
true
1,264
4
INTRODUCTION
1
28
[ "b28", "b29" ]
17,088,290
pmid-1908075|pmid-11226299|pmid-9244308|NA
It is also easier to produce soluble proteins using phagemids by the insertion of an amber stop codon between the displayed protein and g3p (28).
[ "28", "29" ]
145
7,790
1
false
It is also easier to produce soluble proteins using phagemids by the insertion of an amber stop codon between the displayed protein and g3p.
[ "28" ]
It is also easier to produce soluble proteins using phagemids by the insertion of an amber stop codon between the displayed protein and g3p.
true
true
true
true
true
1,264
4
INTRODUCTION
1
29
[ "b28", "b29" ]
17,088,290
pmid-1908075|pmid-11226299|pmid-9244308|NA
Although soluble protein could theoretically be made in phage libraries using a similar genetic arrangement, the low copy number of the vector and the weakness of the g3p promoter and ribosome-binding site, results in levels of soluble protein that are too low for most practical purposes, requiring subsequent recloning...
[ "28", "29" ]
350
7,791
1
false
Although soluble protein could theoretically be made in phage libraries using a similar genetic arrangement, the low copy number of the vector and the weakness of the g3p promoter and ribosome-binding site, results in levels of soluble protein that are too low for most practical purposes, requiring subsequent recloning...
[ "29" ]
Although soluble protein could theoretically be made in phage libraries using a similar genetic arrangement, the low copy number of the vector and the weakness of the g3p promoter and ribosome-binding site, results in levels of soluble protein that are too low for most practical purposes, requiring subsequent recloning...
true
true
true
true
true
1,264
4
INTRODUCTION
1
28
[ "b28", "b29" ]
17,088,290
pmid-1908075|pmid-11226299|pmid-9244308|NA
Another advantage of phagemids concerns the relative resistance to deletions of extraneous genetic material.
[ "28", "29" ]
108
7,792
0
false
Another advantage of phagemids concerns the relative resistance to deletions of extraneous genetic material.
[]
Another advantage of phagemids concerns the relative resistance to deletions of extraneous genetic material.
true
true
true
true
true
1,264
4
INTRODUCTION
1
28
[ "b28", "b29" ]
17,088,290
pmid-1908075|pmid-11226299|pmid-9244308|NA
Filamentous phage vectors, in general, have a tendency to delete unnecessary DNA, due to the selective growth advantage that a smaller phage genome has over a larger one.
[ "28", "29" ]
170
7,793
0
false
Filamentous phage vectors, in general, have a tendency to delete unnecessary DNA, due to the selective growth advantage that a smaller phage genome has over a larger one.
[]
Filamentous phage vectors, in general, have a tendency to delete unnecessary DNA, due to the selective growth advantage that a smaller phage genome has over a larger one.
true
true
true
true
true
1,264
4
INTRODUCTION
1
28
[ "b28", "b29" ]
17,088,290
pmid-1908075|pmid-11226299|pmid-9244308|NA
Phagemids suffer far less from such deletions and as a result are more genetically stable.
[ "28", "29" ]
90
7,794
0
false
Phagemids suffer far less from such deletions and as a result are more genetically stable.
[]
Phagemids suffer far less from such deletions and as a result are more genetically stable.
true
true
true
true
true
1,264
5
INTRODUCTION
1
30
[ "b30", "b31" ]
17,088,290
pmid-11384684|pmid-12514927|pmid-10373366
Phage libraries have considerable operational advantages.
[ "30", "31" ]
57
7,795
0
false
Phage libraries have considerable operational advantages.
[]
Phage libraries have considerable operational advantages.
true
true
true
true
true
1,265
5
INTRODUCTION
1
30
[ "b30", "b31" ]
17,088,290
pmid-11384684|pmid-12514927|pmid-10373366
First, they do not require the use of helper phage for phage production.
[ "30", "31" ]
72
7,796
0
false
First, they do not require the use of helper phage for phage production.
[]
First, they do not require the use of helper phage for phage production.
true
true
true
true
true
1,265
5
INTRODUCTION
1
30
[ "b30", "b31" ]
17,088,290
pmid-11384684|pmid-12514927|pmid-10373366
As a result, the additional technical procedures associated with helper phage infection, such as monitoring the absorbance of bacterial cultures, are omitted from protocols.
[ "30", "31" ]
173
7,797
0
false
As a result, the additional technical procedures associated with helper phage infection, such as monitoring the absorbance of bacterial cultures, are omitted from protocols.
[]
As a result, the additional technical procedures associated with helper phage infection, such as monitoring the absorbance of bacterial cultures, are omitted from protocols.
true
true
true
true
true
1,265
5
INTRODUCTION
1
30
[ "b30", "b31" ]
17,088,290
pmid-11384684|pmid-12514927|pmid-10373366
To amplify phage libraries it is sufficient to grow bacteria containing phage genomes and phage particles are produced.
[ "30", "31" ]
119
7,798
0
false
To amplify phage libraries it is sufficient to grow bacteria containing phage genomes and phage particles are produced.
[]
To amplify phage libraries it is sufficient to grow bacteria containing phage genomes and phage particles are produced.
true
true
true
true
true
1,265
5
INTRODUCTION
1
30
[ "b30", "b31" ]
17,088,290
pmid-11384684|pmid-12514927|pmid-10373366
This makes phage far easier to use in selections, particularly in high-throughput selections where antibodies are selected against up to 96 targets simultaneously (30,31).
[ "30", "31" ]
171
7,799
0
false
This makes phage far easier to use in selections, particularly in high-throughput selections where antibodies are selected against up to 96 targets simultaneously.
[ "30,31" ]
This makes phage far easier to use in selections, particularly in high-throughput selections where antibodies are selected against up to 96 targets simultaneously.
true
true
true
true
true
1,265