Datasets:
vgainullin
/
xciting_data

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INTRODUCTION
1
3
[ "B3", "B7" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
(3) showed many interesting findings on the replication timing of human chromosomes, reporting that ∼60% of interrogated tiling array probes were evenly replicated across the four time periods.
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showed many interesting findings on the replication timing of human chromosomes, reporting that ∼60% of interrogated tiling array probes were evenly replicated across the four time periods.
[ "3" ]
showed many interesting findings on the replication timing of human chromosomes, reporting that ∼60% of interrogated tiling array probes were evenly replicated across the four time periods.
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1
INTRODUCTION
1
3
[ "B3", "B7" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
Here, significantly improving the analysis accuracy and fidelity by several novel statistical approaches, we reanalyze this data set to identify sequence probes and genes that are replicated at specific times in S phase.
[ "3", "7" ]
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Here, significantly improving the analysis accuracy and fidelity by several novel statistical approaches, we reanalyze this data set to identify sequence probes and genes that are replicated at specific times in S phase.
[]
Here, significantly improving the analysis accuracy and fidelity by several novel statistical approaches, we reanalyze this data set to identify sequence probes and genes that are replicated at specific times in S phase.
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1,580
1
INTRODUCTION
1
7
[ "B3", "B7" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
Specifically, overcoming the aforementioned difficulties, we apply an improved error estimation approach to small-sample tiling array data using a recent error-pooling method called local pooled error (LPE) (7).
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Specifically, overcoming the aforementioned difficulties, we apply an improved error estimation approach to small-sample tiling array data using a recent error-pooling method called local pooled error (LPE).
[ "7" ]
Specifically, overcoming the aforementioned difficulties, we apply an improved error estimation approach to small-sample tiling array data using a recent error-pooling method called local pooled error (LPE).
true
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1,580
1
INTRODUCTION
1
3
[ "B3", "B7" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
We also use several novel statistical methods that are well suited for analyzing tiling array data: a weighted ANOVA modeling for simultaneously identifying differentially replicated probes across the four time periods of the replication in cell-cycle S-phase, significant stretch analysis for testing the local proximit...
[ "3", "7" ]
544
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We also use several novel statistical methods that are well suited for analyzing tiling array data: a weighted ANOVA modeling for simultaneously identifying differentially replicated probes across the four time periods of the replication in cell-cycle S-phase, significant stretch analysis for testing the local proximit...
[]
We also use several novel statistical methods that are well suited for analyzing tiling array data: a weighted ANOVA modeling for simultaneously identifying differentially replicated probes across the four time periods of the replication in cell-cycle S-phase, significant stretch analysis for testing the local proximit...
true
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1,580
0
DISCUSSION
1
7
[ "B7" ]
17,430,969
pmid-15611667|pmid-15591350|pmid-15845769|NA|pmid-14993201|pmid-16046496|pmid-14555628
The statistical analysis of tiling array data is challenging due to the extremely large number of individual probe sequences, much higher noise level (than standard expression arrays) and the limited number of replicates.
[ "7" ]
221
9,904
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The statistical analysis of tiling array data is challenging due to the extremely large number of individual probe sequences, much higher noise level (than standard expression arrays) and the limited number of replicates.
[]
The statistical analysis of tiling array data is challenging due to the extremely large number of individual probe sequences, much higher noise level (than standard expression arrays) and the limited number of replicates.
true
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1,581
0
DISCUSSION
1
7
[ "B7" ]
17,430,969
pmid-15611667|pmid-15591350|pmid-15845769|NA|pmid-14993201|pmid-16046496|pmid-14555628
Unfortunately, many classical statistical methods assume homogeneity of variance and/or a relatively large sample size for their maximal performance, so that their statistical inference is severely underpowered and biased for tiling data analysis.
[ "7" ]
247
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Unfortunately, many classical statistical methods assume homogeneity of variance and/or a relatively large sample size for their maximal performance, so that their statistical inference is severely underpowered and biased for tiling data analysis.
[]
Unfortunately, many classical statistical methods assume homogeneity of variance and/or a relatively large sample size for their maximal performance, so that their statistical inference is severely underpowered and biased for tiling data analysis.
true
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1,581
0
DISCUSSION
1
7
[ "B7" ]
17,430,969
pmid-15611667|pmid-15591350|pmid-15845769|NA|pmid-14993201|pmid-16046496|pmid-14555628
The error variances of tiling array probes often greatly differ among different probes, yet are quite dependent on the underlying mean intensities of individual probes.
[ "7" ]
168
9,906
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false
The error variances of tiling array probes often greatly differ among different probes, yet are quite dependent on the underlying mean intensities of individual probes.
[]
The error variances of tiling array probes often greatly differ among different probes, yet are quite dependent on the underlying mean intensities of individual probes.
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1,581
0
DISCUSSION
1
7
[ "B7" ]
17,430,969
pmid-15611667|pmid-15591350|pmid-15845769|NA|pmid-14993201|pmid-16046496|pmid-14555628
An error-pooling method was thus critical and important for accurately estimating technical error variability of tiling array data with a small number of replicates across the entire intensity range.
[ "7" ]
199
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An error-pooling method was thus critical and important for accurately estimating technical error variability of tiling array data with a small number of replicates across the entire intensity range.
[]
An error-pooling method was thus critical and important for accurately estimating technical error variability of tiling array data with a small number of replicates across the entire intensity range.
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1,581
0
DISCUSSION
1
7
[ "B7" ]
17,430,969
pmid-15611667|pmid-15591350|pmid-15845769|NA|pmid-14993201|pmid-16046496|pmid-14555628
Taking this into account, the proposed LPE approach dramatically improved the accuracy of tiling array error estimates, which resulted in considerably higher statistical power in tiling array data analysis (7).
[ "7" ]
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Taking this into account, the proposed LPE approach dramatically improved the accuracy of tiling array error estimates, which resulted in considerably higher statistical power in tiling array data analysis.
[ "7" ]
Taking this into account, the proposed LPE approach dramatically improved the accuracy of tiling array error estimates, which resulted in considerably higher statistical power in tiling array data analysis.
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1,581
0
DISCUSSION
1
7
[ "B7" ]
17,430,969
pmid-15611667|pmid-15591350|pmid-15845769|NA|pmid-14993201|pmid-16046496|pmid-14555628
We also introduced a weighted ANOVA analysis on 10-kb sliding windows based on the LPE baseline error distributions.
[ "7" ]
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We also introduced a weighted ANOVA analysis on 10-kb sliding windows based on the LPE baseline error distributions.
[]
We also introduced a weighted ANOVA analysis on 10-kb sliding windows based on the LPE baseline error distributions.
true
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1,581
0
DISCUSSION
1
7
[ "B7" ]
17,430,969
pmid-15611667|pmid-15591350|pmid-15845769|NA|pmid-14993201|pmid-16046496|pmid-14555628
Our sliding-window approach further stabilized and reduced highly variable individual probe effects for our error estimation.
[ "7" ]
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Our sliding-window approach further stabilized and reduced highly variable individual probe effects for our error estimation.
[]
Our sliding-window approach further stabilized and reduced highly variable individual probe effects for our error estimation.
true
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1,581
1
DISCUSSION
1
1
[ "B1", "B2" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
Based on our improved analysis approach, we found many novel observations in replication timing on human chromosomes with a high resolution.
[ "1", "2" ]
140
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Based on our improved analysis approach, we found many novel observations in replication timing on human chromosomes with a high resolution.
[]
Based on our improved analysis approach, we found many novel observations in replication timing on human chromosomes with a high resolution.
true
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1,582
1
DISCUSSION
1
1
[ "B1", "B2" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
We first identified 26% probes as being differently replicated with a tight statistical cutoff criterion (5% FDR), which is somewhat smaller than the original study reported (∼30–35%).
[ "1", "2" ]
184
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We first identified 26% probes as being differently replicated with a tight statistical cutoff criterion (5% FDR), which is somewhat smaller than the original study reported (∼30–35%).
[]
We first identified 26% probes as being differently replicated with a tight statistical cutoff criterion (5% FDR), which is somewhat smaller than the original study reported (∼30–35%).
true
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1,582
1
DISCUSSION
1
1
[ "B1", "B2" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
Among these differentially replicated probes, 47% probes were from coding regions, which then matched 410 known genes.
[ "1", "2" ]
118
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Among these differentially replicated probes, 47% probes were from coding regions, which then matched 410 known genes.
[]
Among these differentially replicated probes, 47% probes were from coding regions, which then matched 410 known genes.
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1,582
1
DISCUSSION
1
1
[ "B1", "B2" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
We found that a much higher proportion of genic probes (25.0%) were differentially replicated than intergenic probes (18.6%) on chromosome 21, suggesting a gene-centric view of replication timing as opposed to one based on physical location along a chromosome.
[ "1", "2" ]
260
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We found that a much higher proportion of genic probes were differentially replicated than intergenic probes on chromosome 21, suggesting a gene-centric view of replication timing as opposed to one based on physical location along a chromosome.
[ "25.0%", "18.6%" ]
We found that a much higher proportion of genic probes were differentially replicated than intergenic probes on chromosome 21, suggesting a gene-centric view of replication timing as opposed to one based on physical location along a chromosome.
true
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1,582
1
DISCUSSION
1
1
[ "B1", "B2" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
More specifically, we observed that early replicating regions tended to be associated with gene-dense regions and late replicating sequences were found in relatively gene-poor loci similar to Jeon et al.
[ "1", "2" ]
203
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More specifically, we observed that early replicating regions tended to be associated with gene-dense regions and late replicating sequences were found in relatively gene-poor loci similar to Jeon et al.
[]
More specifically, we observed that early replicating regions tended to be associated with gene-dense regions and late replicating sequences were found in relatively gene-poor loci similar to Jeon et al.
true
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1,582
1
DISCUSSION
1
1
[ "B1", "B2" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
Assuming that (1) chromatin tends to be open in gene-dense regions and in a compacted state (i.e.
[ "1", "2" ]
97
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Assuming that chromatin tends to be open in gene-dense regions and in a compacted state (i.e.
[ "1" ]
Assuming that chromatin tends to be open in gene-dense regions and in a compacted state (i.e.
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1,582
1
DISCUSSION
1
2
[ "B1", "B2" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
heterochromatin) in gene-poor regions upon entry of S phase and (2) the replication machinery cannot access DNA in heterochromatic regions, many have formulated a chromatin-centric model of replication dynamics.
[ "1", "2" ]
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heterochromatin) in gene-poor regions upon entry of S phase and the replication machinery cannot access DNA in heterochromatic regions, many have formulated a chromatin-centric model of replication dynamics.
[ "2" ]
heterochromatin) in gene-poor regions upon entry of S phase and the replication machinery cannot access DNA in heterochromatic regions, many have formulated a chromatin-centric model of replication dynamics.
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1,582
1
DISCUSSION
1
1
[ "B1", "B2" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
Finally, we note that these two views are not necessarily mutually exclusive, given that nucleosome depletion tends to occur just 5′ of actively transcribed genes and that typical replication fork rates are ∼1 kb/min.
[ "1", "2" ]
217
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Finally, we note that these two views are not necessarily mutually exclusive, given that nucleosome depletion tends to occur just 5′ of actively transcribed genes and that typical replication fork rates are ∼1 kb/min.
[]
Finally, we note that these two views are not necessarily mutually exclusive, given that nucleosome depletion tends to occur just 5′ of actively transcribed genes and that typical replication fork rates are ∼1 kb/min.
true
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1,582
1
DISCUSSION
1
1
[ "B1", "B2" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
It is somewhat surprising that we found that replication timing seems to be relevant to certain molecular functions and biological processes.
[ "1", "2" ]
141
9,919
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false
It is somewhat surprising that we found that replication timing seems to be relevant to certain molecular functions and biological processes.
[]
It is somewhat surprising that we found that replication timing seems to be relevant to certain molecular functions and biological processes.
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1,582
1
DISCUSSION
1
1
[ "B1", "B2" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
Furthermore, even though the number of mutually validated genes between the tiling and cDNA microarrays was relatively small (and majority of these validated genes were sparsely distributed), we found that some of these were very closely located, e.g.
[ "1", "2" ]
251
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Furthermore, even though the number of mutually validated genes between the tiling and cDNA microarrays was relatively small (and majority of these validated genes were sparsely distributed), we found that some of these were very closely located, e.g.
[]
Furthermore, even though the number of mutually validated genes between the tiling and cDNA microarrays was relatively small (and majority of these validated genes were sparsely distributed), we found that some of these were very closely located, e.g.
true
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1,582
1
DISCUSSION
1
1
[ "B1", "B2" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
no other gene exists between two consecutive genes for the four areas highlighted in Supplementary Table S3, yet showed quite different replication times.
[ "1", "2" ]
154
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no other gene exists between two consecutive genes for the four areas highlighted in Supplementary Table S3, yet showed quite different replication times.
[]
no other gene exists between two consecutive genes for the four areas highlighted in Supplementary Table S3, yet showed quite different replication times.
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1,582
1
DISCUSSION
1
1
[ "B1", "B2" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
This small number of mutually confirming, switch-over cases cannot yet prove the existence of a large number of replication break points across the entire human genome.
[ "1", "2" ]
168
9,922
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false
This small number of mutually confirming, switch-over cases cannot yet prove the existence of a large number of replication break points across the entire human genome.
[]
This small number of mutually confirming, switch-over cases cannot yet prove the existence of a large number of replication break points across the entire human genome.
true
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1,582
1
DISCUSSION
1
1
[ "B1", "B2" ]
17,430,969
pmid-15845769|pmid-14555628|pmid-15611667|pmid-15591350
However, considering that >70% of tiling array genes’ replication times obtained by our proposed approach were consistent with and effectively confirmed by the cDNA array data (29/41), we believe that our observations on the frequent changes of replication times between adjacent genes should apply to the rest of the hu...
[ "1", "2" ]
331
9,923
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However, considering that >70% of tiling array genes’ replication times obtained by our proposed approach were consistent with and effectively confirmed by the cDNA array data, we believe that our observations on the frequent changes of replication times between adjacent genes should apply to the rest of the human geno...
[ "29/41" ]
However, considering that >70% of tiling array genes’ replication times obtained by our proposed approach were consistent with and effectively confirmed by the cDNA array data, we believe that our observations on the frequent changes of replication times between adjacent genes should apply to the rest of the human geno...
true
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1,582
2
DISCUSSION
1
5
[ "B5", "B5" ]
17,430,969
pmid-14993201|pmid-14993201
We used a sliding window strategy to reduce the noise level of individual sequence probes.
[ "5", "5" ]
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We used a sliding window strategy to reduce the noise level of individual sequence probes.
[]
We used a sliding window strategy to reduce the noise level of individual sequence probes.
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1,583
2
DISCUSSION
1
5
[ "B5", "B5" ]
17,430,969
pmid-14993201|pmid-14993201
As pointed by (5), the determination of a suitable window size is constrained by two factors: the spacing of the probes along the chromosomes and the characteristic size of the functional elements being assayed (e.g.
[ "5", "5" ]
216
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As pointed by, the determination of a suitable window size is constrained by two factors: the spacing of the probes along the chromosomes and the characteristic size of the functional elements being assayed (e.g.
[ "5" ]
As pointed by, the determination of a suitable window size is constrained by two factors: the spacing of the probes along the chromosomes and the characteristic size of the functional elements being assayed (e.g.
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1,583
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DISCUSSION
1
5
[ "B5", "B5" ]
17,430,969
pmid-14993201|pmid-14993201
exon for an mRNA assay) on these chromosomes.
[ "5", "5" ]
45
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exon for an mRNA assay) on these chromosomes.
[]
exon for an mRNA assay) on these chromosomes.
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1,583
2
DISCUSSION
1
5
[ "B5", "B5" ]
17,430,969
pmid-14993201|pmid-14993201
One additional fact to be mindful of when using a sliding-window-based approach is that although the false-positive rate was reduced in identifying the sites of transcription, the signal from the probe pairs was smoothed, making strict determination of the transcription boundaries problematic (5).
[ "5", "5" ]
298
9,927
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One additional fact to be mindful of when using a sliding-window-based approach is that although the false-positive rate was reduced in identifying the sites of transcription, the signal from the probe pairs was smoothed, making strict determination of the transcription boundaries problematic.
[ "5" ]
One additional fact to be mindful of when using a sliding-window-based approach is that although the false-positive rate was reduced in identifying the sites of transcription, the signal from the probe pairs was smoothed, making strict determination of the transcription boundaries problematic.
true
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1,583
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DISCUSSION
1
5
[ "B5", "B5" ]
17,430,969
pmid-14993201|pmid-14993201
We experimented with a few different strategies, including varying the window width and using disjoint windows.
[ "5", "5" ]
111
9,928
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false
We experimented with a few different strategies, including varying the window width and using disjoint windows.
[]
We experimented with a few different strategies, including varying the window width and using disjoint windows.
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1,583
2
DISCUSSION
1
5
[ "B5", "B5" ]
17,430,969
pmid-14993201|pmid-14993201
The results were generally consistent if the window width exceeded a certain minimum.
[ "5", "5" ]
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9,929
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The results were generally consistent if the window width exceeded a certain minimum.
[]
The results were generally consistent if the window width exceeded a certain minimum.
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1,583
2
DISCUSSION
1
5
[ "B5", "B5" ]
17,430,969
pmid-14993201|pmid-14993201
For example, we analyzed the data with a much shorter window size, 700 bp, in order to identify finer replication patterns and check the consistency of the results.
[ "5", "5" ]
164
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For example, we analyzed the data with a much shorter window size, 700 bp, in order to identify finer replication patterns and check the consistency of the results.
[]
For example, we analyzed the data with a much shorter window size, 700 bp, in order to identify finer replication patterns and check the consistency of the results.
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1,583
2
DISCUSSION
1
5
[ "B5", "B5" ]
17,430,969
pmid-14993201|pmid-14993201
This analysis resulted in a much smaller number of significantly differentially replicated probes and corresponding genes—6524 probes and 178 genes (64 genes in chromosome 21 and 114 genes in chromosome 22; see Supplementary Results).
[ "5", "5" ]
234
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This analysis resulted in a much smaller number of significantly differentially replicated probes and corresponding genes—6524 probes and 178 genes (64 genes in chromosome 21 and 114 genes in chromosome 22; see Supplementary Results).
[]
This analysis resulted in a much smaller number of significantly differentially replicated probes and corresponding genes—6524 probes and 178 genes (64 genes in chromosome 21 and 114 genes in chromosome 22; see Supplementary Results).
true
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1,583
2
DISCUSSION
1
5
[ "B5", "B5" ]
17,430,969
pmid-14993201|pmid-14993201
This was expected since longer windows result in greater power to detect long stretches of significant replication differences.
[ "5", "5" ]
127
9,932
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false
This was expected since longer windows result in greater power to detect long stretches of significant replication differences.
[]
This was expected since longer windows result in greater power to detect long stretches of significant replication differences.
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1,583
2
DISCUSSION
1
5
[ "B5", "B5" ]
17,430,969
pmid-14993201|pmid-14993201
Nevertheless, the main conclusions were quite consistent with those from the 10-kb window analysis.
[ "5", "5" ]
99
9,933
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false
Nevertheless, the main conclusions were quite consistent with those from the 10-kb window analysis.
[]
Nevertheless, the main conclusions were quite consistent with those from the 10-kb window analysis.
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1,583
2
DISCUSSION
1
5
[ "B5", "B5" ]
17,430,969
pmid-14993201|pmid-14993201
Twice as many genes were identified on chromosome 22 than chromosome 21; significant exon and intron probes were quite evenly distributed across the four time periods, as shown in Figures 2–5 for the 10-kb window case.
[ "5", "5" ]
218
9,934
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false
Twice as many genes were identified on chromosome 22 than chromosome 21; significant exon and intron probes were quite evenly distributed across the four time periods, as shown in Figures 2–5 for the 10-kb window case.
[]
Twice as many genes were identified on chromosome 22 than chromosome 21; significant exon and intron probes were quite evenly distributed across the four time periods, as shown in Figures 2–5 for the 10-kb window case.
true
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1,583
3
DISCUSSION
0
null
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17,430,969
null
A rigorous sampling-based method was applied to evaluate statistical significance for the stretch proximity of 50 consecutive differentially replicated probes at each time period based on 10 000 random samples.
null
210
9,935
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false
null
null
A rigorous sampling-based method was applied to evaluate statistical significance for the stretch proximity of 50 consecutive differentially replicated probes at each time period based on 10 000 random samples.
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1,584
3
DISCUSSION
0
null
null
17,430,969
null
From this analysis, we found that these stretches of replication are more or less evenly distributed among the four time periods and not concentrated in any local region at a particular time period.
null
198
9,936
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false
null
null
From this analysis, we found that these stretches of replication are more or less evenly distributed among the four time periods and not concentrated in any local region at a particular time period.
true
true
true
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1,584
3
DISCUSSION
0
null
null
17,430,969
null
This appears to contradict the existence of replication break points, with a considerable length between two adjacent break points.
null
131
9,937
0
false
null
null
This appears to contradict the existence of replication break points, with a considerable length between two adjacent break points.
true
true
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1,584
3
DISCUSSION
0
null
null
17,430,969
null
This may suggest that such break points may exist at much finer scales than has been assayed.
null
93
9,938
0
false
null
null
This may suggest that such break points may exist at much finer scales than has been assayed.
true
true
true
true
true
1,584
3
DISCUSSION
0
null
null
17,430,969
null
We believe these results provide a novel insight into the replication of genes, which awaits further confirmation.
null
114
9,939
0
false
null
null
We believe these results provide a novel insight into the replication of genes, which awaits further confirmation.
true
true
true
true
true
1,584
4
DISCUSSION
0
null
null
17,430,969
null
The use of GO analysis may provide useful functional insights regarding the replication timing of particular genes during the cell cycle.
null
137
9,940
0
false
null
null
The use of GO analysis may provide useful functional insights regarding the replication timing of particular genes during the cell cycle.
true
true
true
true
true
1,585
4
DISCUSSION
0
null
null
17,430,969
null
We detected some over-represented ontologies for differentially replicated genes by using GOstat after accounting for the biased representation of such GO terms on chromosomes 21 and 22.
null
186
9,941
0
false
null
null
We detected some over-represented ontologies for differentially replicated genes by using GOstat after accounting for the biased representation of such GO terms on chromosomes 21 and 22.
true
true
true
true
true
1,585
4
DISCUSSION
0
null
null
17,430,969
null
Genes with certain molecular functions such as hydrolase activity, transferase activity and receptor binding were found to show differential temporal expression patterns and tend to replicate at particular time periods.
null
219
9,942
0
false
null
null
Genes with certain molecular functions such as hydrolase activity, transferase activity and receptor binding were found to show differential temporal expression patterns and tend to replicate at particular time periods.
true
true
true
true
true
1,585
4
DISCUSSION
0
null
null
17,430,969
null
In the biological process category, lipid transport, glutathione biosynthesis and cyanate metabolism were over-represented with marginal significance.
null
150
9,943
0
false
null
null
In the biological process category, lipid transport, glutathione biosynthesis and cyanate metabolism were over-represented with marginal significance.
true
true
true
true
true
1,585
4
DISCUSSION
0
null
null
17,430,969
null
On the contrary, no significantly over-represented cellular component was found.
null
80
9,944
0
false
null
null
On the contrary, no significantly over-represented cellular component was found.
true
true
true
true
true
1,585
5
DISCUSSION
1
7
[ "B7" ]
17,430,969
pmid-14555628
In this study, we did not directly compare the performance of our approach with other approaches for the following reasons.
[ "7" ]
123
9,945
0
false
In this study, we did not directly compare the performance of our approach with other approaches for the following reasons.
[]
In this study, we did not directly compare the performance of our approach with other approaches for the following reasons.
true
true
true
true
true
1,586
5
DISCUSSION
1
7
[ "B7" ]
17,430,969
pmid-14555628
First, the results from a simulation study, which may be one of the standard ways of comparison, may heavily depend on its simulation setting, but no realistic setting was seemingly appropriate for comparing different approaches objectively on the tiling array data, especially since our error-pooling approach, which as...
[ "7" ]
497
9,946
0
false
First, the results from a simulation study, which may be one of the standard ways of comparison, may heavily depend on its simulation setting, but no realistic setting was seemingly appropriate for comparing different approaches objectively on the tiling array data, especially since our error-pooling approach, which as...
[]
First, the results from a simulation study, which may be one of the standard ways of comparison, may heavily depend on its simulation setting, but no realistic setting was seemingly appropriate for comparing different approaches objectively on the tiling array data, especially since our error-pooling approach, which as...
true
true
true
true
true
1,586
5
DISCUSSION
1
7
[ "B7" ]
17,430,969
pmid-14555628
Second, the improvement of LPE error estimation was demonstrated in (7), in which a dramatically higher statistical power of LPE estimation was shown than that of other within-probe (or within-gene) approaches when the number of replicated arrays was less than five.
[ "7" ]
266
9,947
1
false
Second, the improvement of LPE error estimation was demonstrated in, in which a dramatically higher statistical power of LPE estimation was shown than that of other within-probe (or within-gene) approaches when the number of replicated arrays was less than five.
[ "7" ]
Second, the improvement of LPE error estimation was demonstrated in, in which a dramatically higher statistical power of LPE estimation was shown than that of other within-probe (or within-gene) approaches when the number of replicated arrays was less than five.
true
true
true
true
true
1,586
5
DISCUSSION
1
7
[ "B7" ]
17,430,969
pmid-14555628
Also, evaluated by FDR, which is a recent statistical significance concept that simultaneously controls false positives and false negatives in a large-screening microarray data analysis, we found that our approach tightly controlled both false-positive and false-negative errors; for example, as shown in Table 1, our we...
[ "7" ]
390
9,948
0
false
Also, evaluated by FDR, which is a recent statistical significance concept that simultaneously controls false positives and false negatives in a large-screening microarray data analysis, we found that our approach tightly controlled both false-positive and false-negative errors; for example, as shown in Table 1, our we...
[]
Also, evaluated by FDR, which is a recent statistical significance concept that simultaneously controls false positives and false negatives in a large-screening microarray data analysis, we found that our approach tightly controlled both false-positive and false-negative errors; for example, as shown in Table 1, our we...
true
true
true
true
true
1,586
5
DISCUSSION
1
7
[ "B7" ]
17,430,969
pmid-14555628
Finally, as shown in the results, the probes significantly identified by our approach were quite biologically consistent, e.g.
[ "7" ]
126
9,949
0
false
Finally, as shown in the results, the probes significantly identified by our approach were quite biologically consistent, e.g.
[]
Finally, as shown in the results, the probes significantly identified by our approach were quite biologically consistent, e.g.
true
true
true
true
true
1,586
5
DISCUSSION
1
7
[ "B7" ]
17,430,969
pmid-14555628
many adjacent probes and the probes of the same gene tend to share the same statistical significance level and replication patterns.
[ "7" ]
132
9,950
0
false
many adjacent probes and the probes of the same gene tend to share the same statistical significance level and replication patterns.
[]
many adjacent probes and the probes of the same gene tend to share the same statistical significance level and replication patterns.
false
true
true
true
false
1,586
5
DISCUSSION
1
7
[ "B7" ]
17,430,969
pmid-14555628
From these observations, we believe that our approach has significantly improved tiling array data analysis.
[ "7" ]
108
9,951
0
false
From these observations, we believe that our approach has significantly improved tiling array data analysis.
[]
From these observations, we believe that our approach has significantly improved tiling array data analysis.
true
true
true
true
true
1,586
6
DISCUSSION
0
null
null
17,430,969
null
There still exist several remaining issues regarding our tiling array data analysis on cell cycle replication.
null
110
9,952
0
false
null
null
There still exist several remaining issues regarding our tiling array data analysis on cell cycle replication.
true
true
true
true
true
1,587
6
DISCUSSION
0
null
null
17,430,969
null
For example, the current normalization across all the tiling arrays was performed based on the assumption that the interquartile range of each array is the same.
null
161
9,953
0
false
null
null
For example, the current normalization across all the tiling arrays was performed based on the assumption that the interquartile range of each array is the same.
true
true
true
true
true
1,587
6
DISCUSSION
0
null
null
17,430,969
null
While this may be a conservative assumption in terms of temporal differential replication discovery, this may not be true if one particular time period is more active in replication than the others; this should be more carefully evaluated biologically in order to avoid a biased identification of differential replicatio...
null
331
9,954
0
false
null
null
While this may be a conservative assumption in terms of temporal differential replication discovery, this may not be true if one particular time period is more active in replication than the others; this should be more carefully evaluated biologically in order to avoid a biased identification of differential replicatio...
true
true
true
true
true
1,587
6
DISCUSSION
0
null
null
17,430,969
null
We also did not correct for the large differences in the hybridization affinity of tiling array probes.
null
103
9,955
0
false
null
null
We also did not correct for the large differences in the hybridization affinity of tiling array probes.
true
true
true
true
true
1,587
6
DISCUSSION
0
null
null
17,430,969
null
Despite these shortcomings, we believe that our current result demonstrates that a series of improved statistical analysis methods can yield novel insights at effectively higher resolution from genomic tiling array data.
null
220
9,956
0
false
null
null
Despite these shortcomings, we believe that our current result demonstrates that a series of improved statistical analysis methods can yield novel insights at effectively higher resolution from genomic tiling array data.
true
true
true
true
true
1,587
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-8107089|pmid-8804822
PANTHER is a publicly available database that relates protein sequence evolution to evolution of protein functions and biological roles (1,2).
[ "1", "2", "3", "4" ]
142
9,957
0
false
PANTHER is a publicly available database that relates protein sequence evolution to evolution of protein functions and biological roles.
[ "1,2" ]
PANTHER is a publicly available database that relates protein sequence evolution to evolution of protein functions and biological roles.
true
true
true
true
true
1,588
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-8107089|pmid-8804822
The data have been generated by computational algorithms and, crucially, expert biologist curation, using an extensive software system for associating ontology terms with phylogenetically-defined subfamilies of proteins.
[ "1", "2", "3", "4" ]
220
9,958
0
false
The data have been generated by computational algorithms and, crucially, expert biologist curation, using an extensive software system for associating ontology terms with phylogenetically-defined subfamilies of proteins.
[]
The data have been generated by computational algorithms and, crucially, expert biologist curation, using an extensive software system for associating ontology terms with phylogenetically-defined subfamilies of proteins.
true
true
true
true
true
1,588
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-8107089|pmid-8804822
Protein family trees are constructed computationally from sequence data.
[ "1", "2", "3", "4" ]
72
9,959
0
false
Protein family trees are constructed computationally from sequence data.
[]
Protein family trees are constructed computationally from sequence data.
true
true
true
true
true
1,588
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-8107089|pmid-8804822
Nodes in the tree, corresponding to common ancestors of extant family members, are annotated with their inferred functions and roles in biological processes and pathways, based on experiments performed on extant proteins.
[ "1", "2", "3", "4" ]
221
9,960
0
false
Nodes in the tree, corresponding to common ancestors of extant family members, are annotated with their inferred functions and roles in biological processes and pathways, based on experiments performed on extant proteins.
[]
Nodes in the tree, corresponding to common ancestors of extant family members, are annotated with their inferred functions and roles in biological processes and pathways, based on experiments performed on extant proteins.
true
true
true
true
true
1,588
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-8107089|pmid-8804822
These inferences are made by expert biologists.
[ "1", "2", "3", "4" ]
47
9,961
0
false
These inferences are made by expert biologists.
[]
These inferences are made by expert biologists.
true
true
true
true
true
1,588
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-8107089|pmid-8804822
These annotated nodes define protein subfamilies, each of which is represented by a hidden Markov model (HMM) (3,4) to allow classification of newly discovered protein sequences.
[ "1", "2", "3", "4" ]
178
9,962
0
false
These annotated nodes define protein subfamilies, each of which is represented by a hidden Markov model (HMM) to allow classification of newly discovered protein sequences.
[ "3,4" ]
These annotated nodes define protein subfamilies, each of which is represented by a hidden Markov model (HMM) to allow classification of newly discovered protein sequences.
true
true
true
true
true
1,588
1
INTRODUCTION
1
5
[ "b5", "b6", "b7", "b8", "b9" ]
17,130,144
pmid-12438188|pmid-16381885|pmid-16381923|pmid-15089754|pmid-15608231
One of the major recent updates is the improvement of the PANTHER pathway curation software module, resulting in a steady increase in the number of pathways available.
[ "5", "6", "7", "8", "9" ]
167
9,963
0
false
One of the major recent updates is the improvement of the PANTHER pathway curation software module, resulting in a steady increase in the number of pathways available.
[]
One of the major recent updates is the improvement of the PANTHER pathway curation software module, resulting in a steady increase in the number of pathways available.
true
true
true
true
true
1,589
1
INTRODUCTION
1
5
[ "b5", "b6", "b7", "b8", "b9" ]
17,130,144
pmid-12438188|pmid-16381885|pmid-16381923|pmid-15089754|pmid-15608231
This has provided PANTHER with a much richer description of protein function for an increasing number of proteins.
[ "5", "6", "7", "8", "9" ]
114
9,964
0
false
This has provided PANTHER with a much richer description of protein function for an increasing number of proteins.
[]
This has provided PANTHER with a much richer description of protein function for an increasing number of proteins.
true
true
true
true
true
1,589
1
INTRODUCTION
1
5
[ "b5", "b6", "b7", "b8", "b9" ]
17,130,144
pmid-12438188|pmid-16381885|pmid-16381923|pmid-15089754|pmid-15608231
There are various related efforts in curating biological pathways, such as the Signal Transduction Knowledge Environment (STKE) (5), Kyoto Encyclopedia of Genes and Genomics (KEGG) (6), MetaCyc (7), Functional Relationship Explorer (FREX) (8) and Reactome (9).
[ "5", "6", "7", "8", "9" ]
260
9,965
1
false
There are various related efforts in curating biological pathways, such as the Signal Transduction Knowledge Environment (STKE), Kyoto Encyclopedia of Genes and Genomics (KEGG), MetaCyc, Functional Relationship Explorer (FREX) and Reactome.
[ "5", "6", "7", "8", "9" ]
There are various related efforts in curating biological pathways, such as the Signal Transduction Knowledge Environment (STKE), Kyoto Encyclopedia of Genes and Genomics (KEGG), MetaCyc, Functional Relationship Explorer (FREX) and Reactome.
true
true
true
true
true
1,589
1
INTRODUCTION
1
5
[ "b5", "b6", "b7", "b8", "b9" ]
17,130,144
pmid-12438188|pmid-16381885|pmid-16381923|pmid-15089754|pmid-15608231
KEGG and MetaCyc have collected mostly metabolic pathways, while STKE and Reactome contain well curated, publicly available data on signaling pathways.
[ "5", "6", "7", "8", "9" ]
151
9,966
0
false
KEGG and MetaCyc have collected mostly metabolic pathways, while STKE and Reactome contain well curated, publicly available data on signaling pathways.
[]
KEGG and MetaCyc have collected mostly metabolic pathways, while STKE and Reactome contain well curated, publicly available data on signaling pathways.
true
true
true
true
true
1,589
1
INTRODUCTION
1
5
[ "b5", "b6", "b7", "b8", "b9" ]
17,130,144
pmid-12438188|pmid-16381885|pmid-16381923|pmid-15089754|pmid-15608231
STKE provides probably the most comprehensive description of signaling pathways, but these pathways are not connected to computationally-accessible data of the participating molecules, such as sequence accession numbers.
[ "5", "6", "7", "8", "9" ]
220
9,967
0
false
STKE provides probably the most comprehensive description of signaling pathways, but these pathways are not connected to computationally-accessible data of the participating molecules, such as sequence accession numbers.
[]
STKE provides probably the most comprehensive description of signaling pathways, but these pathways are not connected to computationally-accessible data of the participating molecules, such as sequence accession numbers.
true
true
true
true
true
1,589
2
INTRODUCTION
1
1
[ "b1", "b2", "b10" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-16912992
There are several common goals of these pathway efforts.
[ "1", "2", "10" ]
56
9,968
0
false
There are several common goals of these pathway efforts.
[]
There are several common goals of these pathway efforts.
true
true
true
true
true
1,590
2
INTRODUCTION
1
1
[ "b1", "b2", "b10" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-16912992
The first goal is to make pathways accessible to computation.
[ "1", "2", "10" ]
61
9,969
0
false
The first goal is to make pathways accessible to computation.
[]
The first goal is to make pathways accessible to computation.
true
true
true
true
true
1,590
2
INTRODUCTION
1
1
[ "b1", "b2", "b10" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-16912992
In the current post-genome-sequencing era, with the large amount of genomic information, data analysis largely relies on computation.
[ "1", "2", "10" ]
133
9,970
0
false
In the current post-genome-sequencing era, with the large amount of genomic information, data analysis largely relies on computation.
[]
In the current post-genome-sequencing era, with the large amount of genomic information, data analysis largely relies on computation.
true
true
true
true
true
1,590
2
INTRODUCTION
1
1
[ "b1", "b2", "b10" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-16912992
It is crucial to make pathways that were traditionally represented in the review literature as simple diagrams, readable by computers.
[ "1", "2", "10" ]
134
9,971
0
false
It is crucial to make pathways that were traditionally represented in the review literature as simple diagrams, readable by computers.
[]
It is crucial to make pathways that were traditionally represented in the review literature as simple diagrams, readable by computers.
true
true
true
true
true
1,590
2
INTRODUCTION
1
1
[ "b1", "b2", "b10" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-16912992
The second goal is to use a standard format to store the pathway information so that the data can be transferred among different databases or shared widely across different software packages.
[ "1", "2", "10" ]
191
9,972
0
false
The second goal is to use a standard format to store the pathway information so that the data can be transferred among different databases or shared widely across different software packages.
[]
The second goal is to use a standard format to store the pathway information so that the data can be transferred among different databases or shared widely across different software packages.
true
true
true
true
true
1,590
2
INTRODUCTION
1
1
[ "b1", "b2", "b10" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-16912992
The third goal is to make these pathways accessible to biologists.
[ "1", "2", "10" ]
66
9,973
0
false
The third goal is to make these pathways accessible to biologists.
[]
The third goal is to make these pathways accessible to biologists.
true
true
true
true
true
1,590
2
INTRODUCTION
1
1
[ "b1", "b2", "b10" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-16912992
The PANTHER pathway curation software module has been used to date by 20 expert biologists to represent 130 pathways.
[ "1", "2", "10" ]
117
9,974
0
false
The PANTHER pathway curation software module has been used to date by 20 expert biologists to represent 130 pathways.
[]
The PANTHER pathway curation software module has been used to date by 20 expert biologists to represent 130 pathways.
true
true
true
true
true
1,590
2
INTRODUCTION
1
1
[ "b1", "b2", "b10" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-16912992
All PANTHER pathways are associated to protein sequences in the PANTHER library of protein families and subfamilies.
[ "1", "2", "10" ]
116
9,975
0
false
All PANTHER pathways are associated to protein sequences in the PANTHER library of protein families and subfamilies.
[]
All PANTHER pathways are associated to protein sequences in the PANTHER library of protein families and subfamilies.
true
true
true
true
true
1,590
2
INTRODUCTION
1
1
[ "b1", "b2", "b10" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-16912992
In this way, the pathways are linked directly to molecular evolutionary data, including protein phylogenies, statistical models for protein functional conservation and divergence, and comparative genomics data (1,2).
[ "1", "2", "10" ]
216
9,976
0
false
In this way, the pathways are linked directly to molecular evolutionary data, including protein phylogenies, statistical models for protein functional conservation and divergence, and comparative genomics data.
[ "1,2" ]
In this way, the pathways are linked directly to molecular evolutionary data, including protein phylogenies, statistical models for protein functional conservation and divergence, and comparative genomics data.
true
true
true
true
true
1,590
2
INTRODUCTION
1
10
[ "b1", "b2", "b10" ]
17,130,144
pmid-15608197|pmid-12952881|pmid-16912992
Because PANTHER family and subfamily models have been used to classify all known and predicted protein coding genes in the human, mouse, rat and Drosophila genomes (and code is available for classification of genes from any other organism), users can make use of the PANTHER web services (10) to analyze genomic data in ...
[ "1", "2", "10" ]
344
9,977
1
false
Because PANTHER family and subfamily models have been used to classify all known and predicted protein coding genes in the human, mouse, rat and Drosophila genomes (and code is available for classification of genes from any other organism), users can make use of the PANTHER web services to analyze genomic data in the c...
[ "10" ]
Because PANTHER family and subfamily models have been used to classify all known and predicted protein coding genes in the human, mouse, rat and Drosophila genomes (and code is available for classification of genes from any other organism), users can make use of the PANTHER web services to analyze genomic data in the c...
true
true
true
true
true
1,590
0
INTRODUCTION
1
1
[ "b1", "b2" ]
16,936,310
pmid-14744438|pmid-15735639|pmid-15597548
Functional regulatory elements for gene expression reside in the genome in the form of short subsequences.
[ "1", "2" ]
106
9,978
0
false
Functional regulatory elements for gene expression reside in the genome in the form of short subsequences.
[]
Functional regulatory elements for gene expression reside in the genome in the form of short subsequences.
true
true
true
true
true
1,591
0
INTRODUCTION
1
1
[ "b1", "b2" ]
16,936,310
pmid-14744438|pmid-15735639|pmid-15597548
In the majority of the identified cases, these regulatory elements appear in promoter regions upstream of gene coding sequences.
[ "1", "2" ]
128
9,979
0
false
In the majority of the identified cases, these regulatory elements appear in promoter regions upstream of gene coding sequences.
[]
In the majority of the identified cases, these regulatory elements appear in promoter regions upstream of gene coding sequences.
true
true
true
true
true
1,591
0
INTRODUCTION
1
1
[ "b1", "b2" ]
16,936,310
pmid-14744438|pmid-15735639|pmid-15597548
They are often recognized by transcriptional factors which activate/suppress gene transcription.
[ "1", "2" ]
96
9,980
0
false
They are often recognized by transcriptional factors which activate/suppress gene transcription.
[]
They are often recognized by transcriptional factors which activate/suppress gene transcription.
true
true
true
true
true
1,591
0
INTRODUCTION
1
1
[ "b1", "b2" ]
16,936,310
pmid-14744438|pmid-15735639|pmid-15597548
In some other cases, regulatory elements appear in the 3′-untranslated region of a gene, and modulate the stability and translatability of the transcribed message through protein factors, or through RNA molecules, such as micro RNAs, as reviewed by Bartel (1).
[ "1", "2" ]
260
9,981
1
false
In some other cases, regulatory elements appear in the 3′-untranslated region of a gene, and modulate the stability and translatability of the transcribed message through protein factors, or through RNA molecules, such as micro RNAs, as reviewed by Bartel.
[ "1" ]
In some other cases, regulatory elements appear in the 3′-untranslated region of a gene, and modulate the stability and translatability of the transcribed message through protein factors, or through RNA molecules, such as micro RNAs, as reviewed by Bartel.
true
true
true
true
true
1,591
0
INTRODUCTION
1
1
[ "b1", "b2" ]
16,936,310
pmid-14744438|pmid-15735639|pmid-15597548
A recent study by Xie et al.
[ "1", "2" ]
28
9,982
0
false
A recent study by Xie et al.
[]
A recent study by Xie et al.
true
true
true
true
true
1,591
0
INTRODUCTION
1
2
[ "b1", "b2" ]
16,936,310
pmid-14744438|pmid-15735639|pmid-15597548
has shown the feasibility and significance of genome-wide analysis to extract common regulatory elements conserved in several species (2).
[ "1", "2" ]
138
9,983
1
false
has shown the feasibility and significance of genome-wide analysis to extract common regulatory elements conserved in several species.
[ "2" ]
has shown the feasibility and significance of genome-wide analysis to extract common regulatory elements conserved in several species.
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true
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true
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1,591
0
INTRODUCTION
1
1
[ "b1", "b2" ]
16,936,310
pmid-14744438|pmid-15735639|pmid-15597548
From a different perspective, common-sequence analysis may also be applied to identify common mechanisms governing the expression of co-regulated genes.
[ "1", "2" ]
152
9,984
0
false
From a different perspective, common-sequence analysis may also be applied to identify common mechanisms governing the expression of co-regulated genes.
[]
From a different perspective, common-sequence analysis may also be applied to identify common mechanisms governing the expression of co-regulated genes.
true
true
true
true
true
1,591
1
INTRODUCTION
1
3
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16,936,310
pmid-11807806|pmid-11553848|NA|pmid-14673175|pmid-12356738|pmid-10348704|pmid-10739394|pmid-12359731|pmid-8639837|pmid-11012226|pmid-11807806|pmid-12032776|pmid-14673175|pmid-11012226
This article presents a case study on the megakaryocytic promoter group.
[ "3", "4", "5", "6", "7", "8", "10", "11", "12", "13", "3", "14", "6", "13" ]
72
9,985
0
false
This article presents a case study on the megakaryocytic promoter group.
[]
This article presents a case study on the megakaryocytic promoter group.
true
true
true
true
true
1,592
1
INTRODUCTION
1
3
[ "b3", "b4", "b5", "b6", "b7", "b8", "b10", "b11", "b12", "b13", "b3", "b14", "b6", "b13" ]
16,936,310
pmid-11807806|pmid-11553848|NA|pmid-14673175|pmid-12356738|pmid-10348704|pmid-10739394|pmid-12359731|pmid-8639837|pmid-11012226|pmid-11807806|pmid-12032776|pmid-14673175|pmid-11012226
Megakaryocytes are hematopoietic cells that give rise to platelets, as discussed by Ravid et al.
[ "3", "4", "5", "6", "7", "8", "10", "11", "12", "13", "3", "14", "6", "13" ]
96
9,986
0
false
Megakaryocytes are hematopoietic cells that give rise to platelets, as discussed by Ravid et al.
[]
Megakaryocytes are hematopoietic cells that give rise to platelets, as discussed by Ravid et al.
true
true
true
true
true
1,592
1
INTRODUCTION
1
3
[ "b3", "b4", "b5", "b6", "b7", "b8", "b10", "b11", "b12", "b13", "b3", "b14", "b6", "b13" ]
16,936,310
pmid-11807806|pmid-11553848|NA|pmid-14673175|pmid-12356738|pmid-10348704|pmid-10739394|pmid-12359731|pmid-8639837|pmid-11012226|pmid-11807806|pmid-12032776|pmid-14673175|pmid-11012226
and Shivdasani (3,4).
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21
9,987
0
false
and Shivdasani.
[ "3,4" ]
and Shivdasani.
false
true
true
true
false
1,592
1
INTRODUCTION
1
5
[ "b3", "b4", "b5", "b6", "b7", "b8", "b10", "b11", "b12", "b13", "b3", "b14", "b6", "b13" ]
16,936,310
pmid-11807806|pmid-11553848|NA|pmid-14673175|pmid-12356738|pmid-10348704|pmid-10739394|pmid-12359731|pmid-8639837|pmid-11012226|pmid-11807806|pmid-12032776|pmid-14673175|pmid-11012226
During the differentiation of megakaryocytes, lineage-specific/selective activation of genes takes place as reviewed by Kaluzhny (5).
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133
9,988
1
false
During the differentiation of megakaryocytes, lineage-specific/selective activation of genes takes place as reviewed by Kaluzhny.
[ "5" ]
During the differentiation of megakaryocytes, lineage-specific/selective activation of genes takes place as reviewed by Kaluzhny.
true
true
true
true
true
1,592
1
INTRODUCTION
1
6
[ "b3", "b4", "b5", "b6", "b7", "b8", "b10", "b11", "b12", "b13", "b3", "b14", "b6", "b13" ]
16,936,310
pmid-11807806|pmid-11553848|NA|pmid-14673175|pmid-12356738|pmid-10348704|pmid-10739394|pmid-12359731|pmid-8639837|pmid-11012226|pmid-11807806|pmid-12032776|pmid-14673175|pmid-11012226
The following genes are selectively co-expressed in megakaryocytes: the platelet factor 4 (PF4) (6), glycoprotein IIb (GPIIb) (7), glycoprotein-V (GPV) (8–10), glycoprotein VI (GPVI) (11) and c-Mpl (12,13).
[ "3", "4", "5", "6", "7", "8", "10", "11", "12", "13", "3", "14", "6", "13" ]
206
9,989
1
false
The following genes are selectively co-expressed in megakaryocytes: the platelet factor 4, glycoprotein IIb (GPIIb), glycoprotein-V (GPV), glycoprotein VI (GPVI) and c-Mpl.
[ "PF4", "6", "7", "8–10", "11", "12,13" ]
The following genes are selectively co-expressed in megakaryocytes: the platelet factor 4, glycoprotein IIb (GPIIb), glycoprotein-V (GPV), glycoprotein VI (GPVI) and c-Mpl.
true
true
true
true
true
1,592
1
INTRODUCTION
1
3
[ "b3", "b4", "b5", "b6", "b7", "b8", "b10", "b11", "b12", "b13", "b3", "b14", "b6", "b13" ]
16,936,310
pmid-11807806|pmid-11553848|NA|pmid-14673175|pmid-12356738|pmid-10348704|pmid-10739394|pmid-12359731|pmid-8639837|pmid-11012226|pmid-11807806|pmid-12032776|pmid-14673175|pmid-11012226
The whole change in gene expression profile is important for megakaryocyte/platelet development and function (3).
[ "3", "4", "5", "6", "7", "8", "10", "11", "12", "13", "3", "14", "6", "13" ]
113
9,990
1
false
The whole change in gene expression profile is important for megakaryocyte/platelet development and function.
[ "3" ]
The whole change in gene expression profile is important for megakaryocyte/platelet development and function.
true
true
true
true
true
1,592
1
INTRODUCTION
1
3
[ "b3", "b4", "b5", "b6", "b7", "b8", "b10", "b11", "b12", "b13", "b3", "b14", "b6", "b13" ]
16,936,310
pmid-11807806|pmid-11553848|NA|pmid-14673175|pmid-12356738|pmid-10348704|pmid-10739394|pmid-12359731|pmid-8639837|pmid-11012226|pmid-11807806|pmid-12032776|pmid-14673175|pmid-11012226
However, the mechanism by which these genes are all selectively expressed in megakaryocytes is not fully understood.
[ "3", "4", "5", "6", "7", "8", "10", "11", "12", "13", "3", "14", "6", "13" ]
116
9,991
0
false
However, the mechanism by which these genes are all selectively expressed in megakaryocytes is not fully understood.
[]
However, the mechanism by which these genes are all selectively expressed in megakaryocytes is not fully understood.
true
true
true
true
true
1,592
1
INTRODUCTION
1
14
[ "b3", "b4", "b5", "b6", "b7", "b8", "b10", "b11", "b12", "b13", "b3", "b14", "b6", "b13" ]
16,936,310
pmid-11807806|pmid-11553848|NA|pmid-14673175|pmid-12356738|pmid-10348704|pmid-10739394|pmid-12359731|pmid-8639837|pmid-11012226|pmid-11807806|pmid-12032776|pmid-14673175|pmid-11012226
In other lineages of the hematopoietic system, often genes expressed in the same lineage share a common mechanism of control, such as common DNA binding sites to PU.1 factor in genes expressed in the myeloid lineage as reviewed by Friedman (14).
[ "3", "4", "5", "6", "7", "8", "10", "11", "12", "13", "3", "14", "6", "13" ]
245
9,992
1
false
In other lineages of the hematopoietic system, often genes expressed in the same lineage share a common mechanism of control, such as common DNA binding sites to PU.1 factor in genes expressed in the myeloid lineage as reviewed by Friedman.
[ "14" ]
In other lineages of the hematopoietic system, often genes expressed in the same lineage share a common mechanism of control, such as common DNA binding sites to PU.1 factor in genes expressed in the myeloid lineage as reviewed by Friedman.
true
true
true
true
true
1,592
1
INTRODUCTION
1
3
[ "b3", "b4", "b5", "b6", "b7", "b8", "b10", "b11", "b12", "b13", "b3", "b14", "b6", "b13" ]
16,936,310
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No unique, tissue-selective transcription factor has been identified in the megakaryocytic lineage.
[ "3", "4", "5", "6", "7", "8", "10", "11", "12", "13", "3", "14", "6", "13" ]
99
9,993
0
false
No unique, tissue-selective transcription factor has been identified in the megakaryocytic lineage.
[]
No unique, tissue-selective transcription factor has been identified in the megakaryocytic lineage.
true
true
true
true
true
1,592
1
INTRODUCTION
1
3
[ "b3", "b4", "b5", "b6", "b7", "b8", "b10", "b11", "b12", "b13", "b3", "b14", "b6", "b13" ]
16,936,310
pmid-11807806|pmid-11553848|NA|pmid-14673175|pmid-12356738|pmid-10348704|pmid-10739394|pmid-12359731|pmid-8639837|pmid-11012226|pmid-11807806|pmid-12032776|pmid-14673175|pmid-11012226
It is then reasonable to hypothesize that there is a common mechanism underlying the megakaryocyte-specific gene regulation, but this might rely on a, yet unidentified, unique combination of common sequences.
[ "3", "4", "5", "6", "7", "8", "10", "11", "12", "13", "3", "14", "6", "13" ]
208
9,994
0
false
It is then reasonable to hypothesize that there is a common mechanism underlying the megakaryocyte-specific gene regulation, but this might rely on a, yet unidentified, unique combination of common sequences.
[]
It is then reasonable to hypothesize that there is a common mechanism underlying the megakaryocyte-specific gene regulation, but this might rely on a, yet unidentified, unique combination of common sequences.
true
true
true
true
true
1,592
1
INTRODUCTION
1
3
[ "b3", "b4", "b5", "b6", "b7", "b8", "b10", "b11", "b12", "b13", "b3", "b14", "b6", "b13" ]
16,936,310
pmid-11807806|pmid-11553848|NA|pmid-14673175|pmid-12356738|pmid-10348704|pmid-10739394|pmid-12359731|pmid-8639837|pmid-11012226|pmid-11807806|pmid-12032776|pmid-14673175|pmid-11012226
In support of this contention, all megakaryocyte-expressing genes are regulated by DNA binding sites to Ets and GATA-1 transcription factors (6–13).
[ "3", "4", "5", "6", "7", "8", "10", "11", "12", "13", "3", "14", "6", "13" ]
148
9,995
0
false
In support of this contention, all megakaryocyte-expressing genes are regulated by DNA binding sites to Ets and GATA-1 transcription factors.
[ "6–13" ]
In support of this contention, all megakaryocyte-expressing genes are regulated by DNA binding sites to Ets and GATA-1 transcription factors.
true
true
true
true
true
1,592
1
INTRODUCTION
1
3
[ "b3", "b4", "b5", "b6", "b7", "b8", "b10", "b11", "b12", "b13", "b3", "b14", "b6", "b13" ]
16,936,310
pmid-11807806|pmid-11553848|NA|pmid-14673175|pmid-12356738|pmid-10348704|pmid-10739394|pmid-12359731|pmid-8639837|pmid-11012226|pmid-11807806|pmid-12032776|pmid-14673175|pmid-11012226
However, it is not clear whether these are the only factors regulating specific gene expression in this lineage.
[ "3", "4", "5", "6", "7", "8", "10", "11", "12", "13", "3", "14", "6", "13" ]
112
9,996
0
false
However, it is not clear whether these are the only factors regulating specific gene expression in this lineage.
[]
However, it is not clear whether these are the only factors regulating specific gene expression in this lineage.
true
true
true
true
true
1,592
1
INTRODUCTION
1
3
[ "b3", "b4", "b5", "b6", "b7", "b8", "b10", "b11", "b12", "b13", "b3", "b14", "b6", "b13" ]
16,936,310
pmid-11807806|pmid-11553848|NA|pmid-14673175|pmid-12356738|pmid-10348704|pmid-10739394|pmid-12359731|pmid-8639837|pmid-11012226|pmid-11807806|pmid-12032776|pmid-14673175|pmid-11012226
Thus, the application of the presented computational platform on this group of promoters may help in identifying new regulatory sites and confirming already described ones.
[ "3", "4", "5", "6", "7", "8", "10", "11", "12", "13", "3", "14", "6", "13" ]
172
9,997
0
false
Thus, the application of the presented computational platform on this group of promoters may help in identifying new regulatory sites and confirming already described ones.
[]
Thus, the application of the presented computational platform on this group of promoters may help in identifying new regulatory sites and confirming already described ones.
true
true
true
true
true
1,592
2
INTRODUCTION
1
15
[ "b15", "b17", "b18", "b22", "b15" ]
16,936,310
NA|NA|NA|pmid-7584402|NA|pmid-10552946
A vast volume of work is reported in the literature of Bioinformatics concerning analysis of DNA sequences, focusing primarily on statistical methods (15–17), and various statistical-scoring techniques, expressed in statistical matrices [see also (18–22), ].
[ "15", "17", "18", "22", "15" ]
258
9,998
0
false
A vast volume of work is reported in the literature of Bioinformatics concerning analysis of DNA sequences, focusing primarily on statistical methods, and various statistical-scoring techniques, expressed in statistical matrices.
[ "15–17", "see also (18–22)," ]
A vast volume of work is reported in the literature of Bioinformatics concerning analysis of DNA sequences, focusing primarily on statistical methods, and various statistical-scoring techniques, expressed in statistical matrices.
true
true
true
true
true
1,593
2
INTRODUCTION
1
15
[ "b15", "b17", "b18", "b22", "b15" ]
16,936,310
NA|NA|NA|pmid-7584402|NA|pmid-10552946
The methodology discussed in the open literature commonly utilizes web-based platforms that enable the user to develop applications based on computer resources out of their control (15).
[ "15", "17", "18", "22", "15" ]
186
9,999
1
false
The methodology discussed in the open literature commonly utilizes web-based platforms that enable the user to develop applications based on computer resources out of their control.
[ "15" ]
The methodology discussed in the open literature commonly utilizes web-based platforms that enable the user to develop applications based on computer resources out of their control.
true
true
true
true
true
1,593