Datasets:
vgainullin
/
xciting_data

Dataset card Data Studio Files
xet
 Community
1
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int64
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5
DISCUSSION
0
null
null
17,584,785
null
Using MEFs generated from Zimp10 knockout mice, we demonstrated that the disruption of Zimp10 inhibits p53-mediated transcription.
null
130
9,200
0
false
null
null
Using MEFs generated from Zimp10 knockout mice, we demonstrated that the disruption of Zimp10 inhibits p53-mediated transcription.
true
true
true
true
true
1,465
5
DISCUSSION
0
null
null
17,584,785
null
In MEFs with an intact wild-type Zimp10 allele, a clear dose-dependent induction of p53 transcriptional activity was observed in cells transfected with increasing amounts of p53.
null
178
9,201
0
false
null
null
In MEFs with an intact wild-type Zimp10 allele, a clear dose-dependent induction of p53 transcriptional activity was observed in cells transfected with increasing amounts of p53.
true
true
true
true
true
1,465
5
DISCUSSION
0
null
null
17,584,785
null
In contrast, no enhancement was observed in cells where both Zimp10 alleles were disrupted.
null
91
9,202
0
false
null
null
In contrast, no enhancement was observed in cells where both Zimp10 alleles were disrupted.
true
true
true
true
true
1,465
5
DISCUSSION
0
null
null
17,584,785
null
This perhaps provides the most convincing evidence that Zimp10 can indeed regulate p53 activity in an in vivo system.
null
117
9,203
0
false
null
null
This perhaps provides the most convincing evidence that Zimp10 can indeed regulate p53 activity in an in vivo system.
true
true
true
true
true
1,465
5
DISCUSSION
0
null
null
17,584,785
null
Further study using this in vivo system should help to elucidate the biological influence of Zimp10 on p53-mediated tumor repressive effects.
null
141
9,204
0
false
null
null
Further study using this in vivo system should help to elucidate the biological influence of Zimp10 on p53-mediated tumor repressive effects.
true
true
true
true
true
1,465
6
DISCUSSION
0
null
null
17,584,785
null
In conclusion, this study demonstrates for the first time that hZimp10, a novel PIAS-like protein, augments the transcriptional activity of the p53 tumor suppressor.
null
165
9,205
0
false
null
null
In conclusion, this study demonstrates for the first time that hZimp10, a novel PIAS-like protein, augments the transcriptional activity of the p53 tumor suppressor.
true
true
true
true
true
1,466
6
DISCUSSION
0
null
null
17,584,785
null
This interaction provides an additional line of evidence to demonstrate that Zimp10 is involved in transcriptional regulation.
null
126
9,206
0
false
null
null
This interaction provides an additional line of evidence to demonstrate that Zimp10 is involved in transcriptional regulation.
true
true
true
true
true
1,466
6
DISCUSSION
0
null
null
17,584,785
null
Further studies into the molecular mechanisms by which hZimp10 and other PIAS proteins regulate p53-mediated transcription may provide new insight into the biological role of PIAS and PIAS-like proteins in cell growth, apoptosis, differentiation and tumorigenesis.
null
264
9,207
0
false
null
null
Further studies into the molecular mechanisms by which hZimp10 and other PIAS proteins regulate p53-mediated transcription may provide new insight into the biological role of PIAS and PIAS-like proteins in cell growth, apoptosis, differentiation and tumorigenesis.
true
true
true
true
true
1,466
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4", "b12", "b11", "b13", "b9", "b11", "b14" ]
16,914,446
pmid-14529615|NA|pmid-15999435|pmid-3547329|pmid-16413165|pmid-15347738|pmid-10843847|pmid-11278070|pmid-15347738|pmid-15978695
In Escherichia coli, regions upstream of first transcribed genes contain higher densities of sigma-70 promoter-like signals than both coding regions and intergenic regions downstream of convergently-transcribed genes (1).
[ "1", "2", "3", "4", "12", "11", "13", "9", "11", "14" ]
221
9,208
1
false
In Escherichia coli, regions upstream of first transcribed genes contain higher densities of sigma-70 promoter-like signals than both coding regions and intergenic regions downstream of convergently-transcribed genes.
[ "1" ]
In Escherichia coli, regions upstream of first transcribed genes contain higher densities of sigma-70 promoter-like signals than both coding regions and intergenic regions downstream of convergently-transcribed genes.
true
true
true
true
true
1,467
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4", "b12", "b11", "b13", "b9", "b11", "b14" ]
16,914,446
pmid-14529615|NA|pmid-15999435|pmid-3547329|pmid-16413165|pmid-15347738|pmid-10843847|pmid-11278070|pmid-15347738|pmid-15978695
Thus, differences in promoter-like signals of upstream regions might help predict operons (2,3), stretches of genes in the same-strand transcribed into a single messenger RNA.
[ "1", "2", "3", "4", "12", "11", "13", "9", "11", "14" ]
175
9,209
0
false
Thus, differences in promoter-like signals of upstream regions might help predict operons, stretches of genes in the same-strand transcribed into a single messenger RNA.
[ "2,3" ]
Thus, differences in promoter-like signals of upstream regions might help predict operons, stretches of genes in the same-strand transcribed into a single messenger RNA.
true
true
true
true
true
1,467
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4", "b12", "b11", "b13", "b9", "b11", "b14" ]
16,914,446
pmid-14529615|NA|pmid-15999435|pmid-3547329|pmid-16413165|pmid-15347738|pmid-10843847|pmid-11278070|pmid-15347738|pmid-15978695
Sigma-70 is not the only sigma factor in Prokaryotes and examples of promoters for other sigma factors are scarce.
[ "1", "2", "3", "4", "12", "11", "13", "9", "11", "14" ]
114
9,210
0
false
Sigma-70 is not the only sigma factor in Prokaryotes and examples of promoters for other sigma factors are scarce.
[]
Sigma-70 is not the only sigma factor in Prokaryotes and examples of promoters for other sigma factors are scarce.
true
true
true
true
true
1,467
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4", "b12", "b11", "b13", "b9", "b11", "b14" ]
16,914,446
pmid-14529615|NA|pmid-15999435|pmid-3547329|pmid-16413165|pmid-15347738|pmid-10843847|pmid-11278070|pmid-15347738|pmid-15978695
Still, a consequence of the high concentration of sigma-70 or other promoter-like signals within promoter regions (PRs) might be a bias in oligonucleotide signatures.
[ "1", "2", "3", "4", "12", "11", "13", "9", "11", "14" ]
166
9,211
0
false
Still, a consequence of the high concentration of sigma-70 or other promoter-like signals within promoter regions (PRs) might be a bias in oligonucleotide signatures.
[]
Still, a consequence of the high concentration of sigma-70 or other promoter-like signals within promoter regions (PRs) might be a bias in oligonucleotide signatures.
true
true
true
true
true
1,467
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4", "b12", "b11", "b13", "b9", "b11", "b14" ]
16,914,446
pmid-14529615|NA|pmid-15999435|pmid-3547329|pmid-16413165|pmid-15347738|pmid-10843847|pmid-11278070|pmid-15347738|pmid-15978695
Oligonucleotide signatures might also be different at regions upstream of genes inside operons (see Figure 1).
[ "1", "2", "3", "4", "12", "11", "13", "9", "11", "14" ]
110
9,212
0
false
Oligonucleotide signatures might also be different at regions upstream of genes inside operons.
[ "see Figure 1" ]
Oligonucleotide signatures might also be different at regions upstream of genes inside operons.
true
true
true
true
true
1,467
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4", "b12", "b11", "b13", "b9", "b11", "b14" ]
16,914,446
pmid-14529615|NA|pmid-15999435|pmid-3547329|pmid-16413165|pmid-15347738|pmid-10843847|pmid-11278070|pmid-15347738|pmid-15978695
Furthermore, differences in oligonucleotide signatures might result from other characteristics of PRs, such as increased curvature (4–12), higher stacking energies (higher stacking energies mean the regions are easier to melt) (11,13), and higher AT content (9,11,14).
[ "1", "2", "3", "4", "12", "11", "13", "9", "11", "14" ]
268
9,213
0
false
Furthermore, differences in oligonucleotide signatures might result from other characteristics of PRs, such as increased curvature, higher stacking energies (higher stacking energies mean the regions are easier to melt), and higher AT content.
[ "4–12", "11,13", "9,11,14" ]
Furthermore, differences in oligonucleotide signatures might result from other characteristics of PRs, such as increased curvature, higher stacking energies (higher stacking energies mean the regions are easier to melt), and higher AT content.
true
true
true
true
true
1,467
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4", "b12", "b11", "b13", "b9", "b11", "b14" ]
16,914,446
pmid-14529615|NA|pmid-15999435|pmid-3547329|pmid-16413165|pmid-15347738|pmid-10843847|pmid-11278070|pmid-15347738|pmid-15978695
Signatures for upstream regions are available as soon as the genome annotation is ready.
[ "1", "2", "3", "4", "12", "11", "13", "9", "11", "14" ]
88
9,214
0
false
Signatures for upstream regions are available as soon as the genome annotation is ready.
[]
Signatures for upstream regions are available as soon as the genome annotation is ready.
true
true
true
true
true
1,467
0
INTRODUCTION
1
1
[ "b1", "b2", "b3", "b4", "b12", "b11", "b13", "b9", "b11", "b14" ]
16,914,446
pmid-14529615|NA|pmid-15999435|pmid-3547329|pmid-16413165|pmid-15347738|pmid-10843847|pmid-11278070|pmid-15347738|pmid-15978695
Thus, signatures might constitute an alternative method for overall operon predictions across Prokaryotes.
[ "1", "2", "3", "4", "12", "11", "13", "9", "11", "14" ]
106
9,215
0
false
Thus, signatures might constitute an alternative method for overall operon predictions across Prokaryotes.
[]
Thus, signatures might constitute an alternative method for overall operon predictions across Prokaryotes.
true
true
true
true
true
1,467
1
INTRODUCTION
1
15
[ "b15", "b17" ]
16,914,446
pmid-11275307|pmid-15477389
In this work we show that densities of sigma-70 promoter-like signals distinguish co-directional transcription unit boundaries (TUBs) from operon junctions (OJs) in the genomes of E.coli and Bacillus subtilis.
[ "15", "17" ]
209
9,216
0
false
In this work we show that densities of sigma-70 promoter-like signals distinguish co-directional transcription unit boundaries (TUBs) from operon junctions (OJs) in the genomes of E.coli and Bacillus subtilis.
[]
In this work we show that densities of sigma-70 promoter-like signals distinguish co-directional transcription unit boundaries (TUBs) from operon junctions (OJs) in the genomes of E.coli and Bacillus subtilis.
true
true
true
true
true
1,468
1
INTRODUCTION
1
15
[ "b15", "b17" ]
16,914,446
pmid-11275307|pmid-15477389
Then we show that oligonucleotide signatures have improved accuracies in operon predictions over those obtained with promoter-like signals.
[ "15", "17" ]
139
9,217
0
false
Then we show that oligonucleotide signatures have improved accuracies in operon predictions over those obtained with promoter-like signals.
[]
Then we show that oligonucleotide signatures have improved accuracies in operon predictions over those obtained with promoter-like signals.
true
true
true
true
true
1,468
1
INTRODUCTION
1
15
[ "b15", "b17" ]
16,914,446
pmid-11275307|pmid-15477389
We expand the work to genomes with no experimentally characterized operons using regions upstream of divergently transcribed genes, forcefully TUBs, and regions between highly conserved co-directional genes, most probably in operons (15–17) as training sets to learn oligonucleotide signatures.
[ "15", "17" ]
294
9,218
0
false
We expand the work to genomes with no experimentally characterized operons using regions upstream of divergently transcribed genes, forcefully TUBs, and regions between highly conserved co-directional genes, most probably in operons as training sets to learn oligonucleotide signatures.
[ "15–17" ]
We expand the work to genomes with no experimentally characterized operons using regions upstream of divergently transcribed genes, forcefully TUBs, and regions between highly conserved co-directional genes, most probably in operons as training sets to learn oligonucleotide signatures.
true
true
true
true
true
1,468
1
INTRODUCTION
1
15
[ "b15", "b17" ]
16,914,446
pmid-11275307|pmid-15477389
We evaluate the genome-wide predictions obtained by this approach using diverse functional genomics data and demonstrate the capability of this method to produce high-quality operon predictions across genomes.
[ "15", "17" ]
209
9,219
0
false
We evaluate the genome-wide predictions obtained by this approach using diverse functional genomics data and demonstrate the capability of this method to produce high-quality operon predictions across genomes.
[]
We evaluate the genome-wide predictions obtained by this approach using diverse functional genomics data and demonstrate the capability of this method to produce high-quality operon predictions across genomes.
true
true
true
true
true
1,468
1
INTRODUCTION
1
15
[ "b15", "b17" ]
16,914,446
pmid-11275307|pmid-15477389
(See Figure 1).
[ "15", "17" ]
15
9,220
0
false
(See Figure 1).
[]
.
false
false
true
true
false
1,468
0
INTRODUCTION
0
null
null
17,452,365
null
Ribosomes are the molecular machines which form the connection between nucleic acids and proteins in all living organisms.
null
122
9,221
0
false
null
null
Ribosomes are the molecular machines which form the connection between nucleic acids and proteins in all living organisms.
true
true
true
true
true
1,469
0
INTRODUCTION
0
null
null
17,452,365
null
The ribosome's dependence on ribosomal RNAs (rRNAs) for its function has caused them to be conserved at both the sequence and the structure level.
null
146
9,222
0
false
null
null
The ribosome's dependence on ribosomal RNAs (rRNAs) for its function has caused them to be conserved at both the sequence and the structure level.
true
true
true
true
true
1,469
0
INTRODUCTION
0
null
null
17,452,365
null
Because of this, rRNAs are often used in comparative studies such as phylogenetic inference.
null
92
9,223
0
false
null
null
Because of this, rRNAs are often used in comparative studies such as phylogenetic inference.
true
true
true
true
true
1,469
0
INTRODUCTION
0
null
null
17,452,365
null
Comparative studies have become more popular as more genomes have been completely sequenced, but can potentially become complicated when some of the genes they are based on are poorly annotated or not annotated at all.
null
218
9,224
0
false
null
null
Comparative studies have become more popular as more genomes have been completely sequenced, but can potentially become complicated when some of the genes they are based on are poorly annotated or not annotated at all.
true
true
true
true
true
1,469
0
INTRODUCTION
0
null
null
17,452,365
null
Unfortunately, this is often a problem with rRNAs as genome annotation pipelines usually do not include tools specific for rRNA detection.
null
138
9,225
0
false
null
null
Unfortunately, this is often a problem with rRNAs as genome annotation pipelines usually do not include tools specific for rRNA detection.
true
true
true
true
true
1,469
0
INTRODUCTION
0
null
null
17,452,365
null
Instead, rRNAs are often located by sequence similarity searches such as BLAST.
null
79
9,226
0
false
null
null
Instead, rRNAs are often located by sequence similarity searches such as BLAST.
true
true
true
true
true
1,469
0
INTRODUCTION
0
null
null
17,452,365
null
Although such searches may give reasonable answers due to the high level of sequence conservation in the core regions of the genes, using such results for annotation purposes can be problematic.
null
194
9,227
0
false
null
null
Although such searches may give reasonable answers due to the high level of sequence conservation in the core regions of the genes, using such results for annotation purposes can be problematic.
true
true
true
true
true
1,469
0
INTRODUCTION
0
null
null
17,452,365
null
The validity of the search results depends on the program and database used.
null
76
9,228
0
false
null
null
The validity of the search results depends on the program and database used.
true
true
true
true
true
1,469
0
INTRODUCTION
0
null
null
17,452,365
null
Changing one or both of these can drastically change the results.
null
65
9,229
0
false
null
null
Changing one or both of these can drastically change the results.
true
true
true
true
true
1,469
0
INTRODUCTION
0
null
null
17,452,365
null
Genomic databases have grown exponentially over the past two decades and search programs have as a consequence had to undergo constant revisions in order to meet the requirements of the research community.
null
205
9,230
0
false
null
null
Genomic databases have grown exponentially over the past two decades and search programs have as a consequence had to undergo constant revisions in order to meet the requirements of the research community.
true
true
true
true
true
1,469
0
INTRODUCTION
0
null
null
17,452,365
null
Thus, the results of a search done today are probably very different from those produced several years ago.
null
107
9,231
0
false
null
null
Thus, the results of a search done today are probably very different from those produced several years ago.
true
true
true
true
true
1,469
0
INTRODUCTION
0
null
null
17,452,365
null
An added complication is that the most commonly used database search methods have poor performance for noncoding RNAs.
null
118
9,232
0
false
null
null
An added complication is that the most commonly used database search methods have poor performance for noncoding RNAs.
true
true
true
true
true
1,469
0
INTRODUCTION
0
null
null
17,452,365
null
A recent study comparing several different methods for predicting noncoding RNAs, including rRNAs, found that the most commonly used methods gave the most inaccurate results (1).
null
178
9,233
0
false
null
null
A recent study comparing several different methods for predicting noncoding RNAs, including rRNAs, found that the most commonly used methods gave the most inaccurate results (1).
true
true
true
true
true
1,469
1
INTRODUCTION
0
null
null
17,452,365
null
Through our work on the GenomeAtlas database (2), we have seen the results of poor annotation of rRNAs.
null
103
9,234
0
false
null
null
Through our work on the GenomeAtlas database (2), we have seen the results of poor annotation of rRNAs.
true
true
true
true
true
1,470
1
INTRODUCTION
0
null
null
17,452,365
null
Some genomes do not have any rRNAs annotated at all, whereas other genomes seem to have rRNAs annotated on the wrong strand.
null
124
9,235
0
false
null
null
Some genomes do not have any rRNAs annotated at all, whereas other genomes seem to have rRNAs annotated on the wrong strand.
true
true
true
true
true
1,470
1
INTRODUCTION
0
null
null
17,452,365
null
We initially tried to do systematic BLAST (3) searches, but it proved difficult to maintain consistency throughout this process.
null
128
9,236
0
false
null
null
We initially tried to do systematic BLAST (3) searches, but it proved difficult to maintain consistency throughout this process.
true
true
true
true
true
1,470
1
INTRODUCTION
0
null
null
17,452,365
null
The high level of sequence conservation among the rRNAs enabled us to create hidden Markov models (HMMs) from structural alignments.
null
132
9,237
0
false
null
null
The high level of sequence conservation among the rRNAs enabled us to create hidden Markov models (HMMs) from structural alignments.
true
true
true
true
true
1,470
1
INTRODUCTION
0
null
null
17,452,365
null
Such models are more capable of capturing the sequence variation that is inherently present in the rRNA gene families than simple BLAST searches.
null
145
9,238
0
false
null
null
Such models are more capable of capturing the sequence variation that is inherently present in the rRNA gene families than simple BLAST searches.
true
true
true
true
true
1,470
1
INTRODUCTION
0
null
null
17,452,365
null
Using HMMs also simplifies the use of common criteria for prediction assessment.
null
80
9,239
0
false
null
null
Using HMMs also simplifies the use of common criteria for prediction assessment.
true
true
true
true
true
1,470
1
INTRODUCTION
0
null
null
17,452,365
null
A library of HMMs was constructed and the program RNAmmer was developed to make use of this library.
null
100
9,240
0
false
null
null
A library of HMMs was constructed and the program RNAmmer was developed to make use of this library.
true
true
true
true
true
1,470
1
INTRODUCTION
0
null
null
17,452,365
null
RNAmmer is available through the CBS web site, as a web service or as a stand-alone package.
null
92
9,241
0
false
null
null
RNAmmer is available through the CBS web site, as a web service or as a stand-alone package.
true
true
true
true
true
1,470
1
INTRODUCTION
0
null
null
17,452,365
null
It has been tested on all published genomes and gives accurate predictions of rRNAs.
null
84
9,242
0
false
null
null
It has been tested on all published genomes and gives accurate predictions of rRNAs.
true
true
true
true
true
1,470
1
INTRODUCTION
0
null
null
17,452,365
null
The program also has the added benefit of producing results that are comparable between genomes.
null
96
9,243
0
false
null
null
The program also has the added benefit of producing results that are comparable between genomes.
true
true
true
true
true
1,470
2
INTRODUCTION
0
null
null
17,452,365
null
Our work has focused on three of the major rRNA species.
null
56
9,244
0
false
null
null
Our work has focused on three of the major rRNA species.
true
true
true
true
true
1,471
2
INTRODUCTION
0
null
null
17,452,365
null
The ribosome consists of two subunits, the small and the large subunit, which pair up to form the functional ribosome.
null
118
9,245
0
false
null
null
The ribosome consists of two subunits, the small and the large subunit, which pair up to form the functional ribosome.
true
true
true
true
true
1,471
2
INTRODUCTION
0
null
null
17,452,365
null
The rRNAs present in prokaryotes are the 5S and 23S in the large subunit, and the 16S in the small subunit.
null
107
9,246
0
false
null
null
The rRNAs present in prokaryotes are the 5S and 23S in the large subunit, and the 16S in the small subunit.
true
true
true
true
true
1,471
2
INTRODUCTION
0
null
null
17,452,365
null
In eukaryotes, 5S, 5.8S and 28S rRNA exist in the large subunit, and 18S rRNA in the small subunit.
null
99
9,247
0
false
null
null
In eukaryotes, 5S, 5.8S and 28S rRNA exist in the large subunit, and 18S rRNA in the small subunit.
true
true
true
true
true
1,471
2
INTRODUCTION
0
null
null
17,452,365
null
The 5.8S is not considered in this work.
null
40
9,248
0
false
null
null
The 5.8S is not considered in this work.
true
true
true
true
true
1,471
2
INTRODUCTION
0
null
null
17,452,365
null
There are substantial sequence and secondary structure similarities between eukaryotic and prokaryotic rRNAs; however, the eukaryotic rRNAs commonly have longer stems and larger loops than those of the prokaryotes.
null
214
9,249
0
false
null
null
There are substantial sequence and secondary structure similarities between eukaryotic and prokaryotic rRNAs; however, the eukaryotic rRNAs commonly have longer stems and larger loops than those of the prokaryotes.
true
true
true
true
true
1,471
2
INTRODUCTION
0
null
null
17,452,365
null
The subunits are composed of both RNAs and proteins.
null
52
9,250
0
false
null
null
The subunits are composed of both RNAs and proteins.
true
true
true
true
true
1,471
2
INTRODUCTION
0
null
null
17,452,365
null
Since their discovery in the early 1950s, it has been debated whether ribosomal function should be credited to the rRNAs or the proteins.
null
137
9,251
0
false
null
null
Since their discovery in the early 1950s, it has been debated whether ribosomal function should be credited to the rRNAs or the proteins.
true
true
true
true
true
1,471
2
INTRODUCTION
0
null
null
17,452,365
null
Recent crystal studies have revealed that protein synthesis to a large extent is dependent on the rRNAs (4–7) and this has most likely been instrumental for their high level of conservation.
null
190
9,252
0
false
null
null
Recent crystal studies have revealed that protein synthesis to a large extent is dependent on the rRNAs (4–7) and this has most likely been instrumental for their high level of conservation.
true
true
true
true
true
1,471
3
INTRODUCTION
0
null
null
17,452,365
null
In prokaryotes, the 16S, 23S and 5S rRNAs are commonly transcribed together, while the 18S, 28S and 5.8S rRNAs form a transcriptional unit in eukaryotes.
null
153
9,253
0
false
null
null
In prokaryotes, the 16S, 23S and 5S rRNAs are commonly transcribed together, while the 18S, 28S and 5.8S rRNAs form a transcriptional unit in eukaryotes.
true
true
true
true
true
1,472
3
INTRODUCTION
0
null
null
17,452,365
null
Eukaryotic 5S rRNA commonly appear in highly duplicated tandem repeats (8).
null
75
9,254
0
false
null
null
Eukaryotic 5S rRNA commonly appear in highly duplicated tandem repeats (8).
true
true
true
true
true
1,472
3
INTRODUCTION
0
null
null
17,452,365
null
In most organisms, there are several copies of the rRNA transcription unit, and although as much as 11% sequence divergence has been observed between units within the same genome, the difference is usually less than 1% (9).
null
223
9,255
0
false
null
null
In most organisms, there are several copies of the rRNA transcription unit, and although as much as 11% sequence divergence has been observed between units within the same genome, the difference is usually less than 1% (9).
true
true
true
true
true
1,472
3
INTRODUCTION
0
null
null
17,452,365
null
In several cases, segments are also edited out of the transcribed rRNA.
null
71
9,256
0
false
null
null
In several cases, segments are also edited out of the transcribed rRNA.
true
true
true
true
true
1,472
3
INTRODUCTION
0
null
null
17,452,365
null
These segments may be introns that after splicing leave a continuous rRNA, or they can be intervening sequences (IVS) that leave a fragmented rRNA which is still functional within the ribosome structure (10).
null
208
9,257
0
false
null
null
These segments may be introns that after splicing leave a continuous rRNA, or they can be intervening sequences (IVS) that leave a fragmented rRNA which is still functional within the ribosome structure (10).
true
true
true
true
true
1,472
3
INTRODUCTION
0
null
null
17,452,365
null
Introns are most prevalent in eukaryotes and archaeas, while intervening sequences have been seen in eukaryotes and bacteria.
null
125
9,258
0
false
null
null
Introns are most prevalent in eukaryotes and archaeas, while intervening sequences have been seen in eukaryotes and bacteria.
true
true
true
true
true
1,472
3
INTRODUCTION
0
null
null
17,452,365
null
Introns are predominantly found within conserved sequences close to tRNA and mRNA-binding sites (10), whereas intervening sequences are ordinarily seen in hypervariable regions (11).
null
182
9,259
0
false
null
null
Introns are predominantly found within conserved sequences close to tRNA and mRNA-binding sites (10), whereas intervening sequences are ordinarily seen in hypervariable regions (11).
true
true
true
true
true
1,472
0
DISCUSSION
0
null
null
17,452,365
null
Our aim has been to enable high-throughput searches for rRNA while producing accurate and consistent predictions suitable for comparative analyses.
null
147
9,260
0
false
null
null
Our aim has been to enable high-throughput searches for rRNA while producing accurate and consistent predictions suitable for comparative analyses.
true
true
true
true
true
1,473
0
DISCUSSION
0
null
null
17,452,365
null
For this purpose, we have developed the RNAmmer package which relies on HMMs for both speed and accuracy.
null
105
9,261
0
false
null
null
For this purpose, we have developed the RNAmmer package which relies on HMMs for both speed and accuracy.
true
true
true
true
true
1,473
0
DISCUSSION
0
null
null
17,452,365
null
HMMs were made using HMMer (15), which from a multiple alignment produces an HMM where match states represent columns with a specific nucleotide distribution, corresponding deletion states represent the possibility of gaps, and insertion states represent columns with large numbers of gaps; transition probabilities betw...
null
378
9,262
0
false
null
null
HMMs were made using HMMer (15), which from a multiple alignment produces an HMM where match states represent columns with a specific nucleotide distribution, corresponding deletion states represent the possibility of gaps, and insertion states represent columns with large numbers of gaps; transition probabilities betw...
true
true
true
true
true
1,473
0
DISCUSSION
0
null
null
17,452,365
null
HMMs thus differ from sequence alignments in that the likelihood of insertions and deletions may vary along the sequence.
null
121
9,263
0
false
null
null
HMMs thus differ from sequence alignments in that the likelihood of insertions and deletions may vary along the sequence.
true
true
true
true
true
1,473
0
DISCUSSION
0
null
null
17,452,365
null
When searching a sequence with an HMM, the score indicates how well the sequence segment matches the model.
null
107
9,264
0
false
null
null
When searching a sequence with an HMM, the score indicates how well the sequence segment matches the model.
true
true
true
true
true
1,473
0
DISCUSSION
0
null
null
17,452,365
null
The information content of a position, which reflects the nucleotide distribution and the likelihood of gaps, indicates how well that position is conserved.
null
156
9,265
0
false
null
null
The information content of a position, which reflects the nucleotide distribution and the likelihood of gaps, indicates how well that position is conserved.
true
true
true
true
true
1,473
0
DISCUSSION
0
null
null
17,452,365
null
A good match to the HMM may come either from a highly conserved region which may well be short, or from a longer region with only weak conservation.
null
148
9,266
0
false
null
null
A good match to the HMM may come either from a highly conserved region which may well be short, or from a longer region with only weak conservation.
true
true
true
true
true
1,473
0
DISCUSSION
0
null
null
17,452,365
null
We find both these cases.
null
25
9,267
0
false
null
null
We find both these cases.
true
true
true
true
true
1,473
0
DISCUSSION
0
null
null
17,452,365
null
Bacterial 16S are detected despite almost half of the nucleotides being assigned to insert states, as other regions are highly conserved.
null
137
9,268
0
false
null
null
Bacterial 16S are detected despite almost half of the nucleotides being assigned to insert states, as other regions are highly conserved.
true
true
true
true
true
1,473
0
DISCUSSION
0
null
null
17,452,365
null
For archaeal 23S, however, the information content of each position is low, but the sequence is long and there are few allowed insert states.
null
141
9,269
0
false
null
null
For archaeal 23S, however, the information content of each position is low, but the sequence is long and there are few allowed insert states.
true
true
true
true
true
1,473
1
DISCUSSION
0
null
null
17,452,365
null
These aspects can also explain cases of poor performance, both of the full model and of the spotter model.
null
106
9,270
0
false
null
null
These aspects can also explain cases of poor performance, both of the full model and of the spotter model.
true
true
true
true
true
1,474
1
DISCUSSION
0
null
null
17,452,365
null
The low information content in the eukaryotic 5S and 18S alignments indicates that these sequences are more divergent than archaeal and bacterial 5S and
null
152
9,271
0
false
null
null
The low information content in the eukaryotic 5S and 18S alignments indicates that these sequences are more divergent than archaeal and bacterial 5S and
true
true
false
true
false
1,474
1
DISCUSSION
0
null
null
17,452,365
null
In addition, 40% of the 5S and 75% of the 18S alignment give rise to insert states in the HMM.
null
94
9,272
0
false
null
null
In addition, 40% of the 5S and 75% of the 18S alignment give rise to insert states in the HMM.
true
true
true
true
true
1,474
1
DISCUSSION
0
null
null
17,452,365
null
Thus, there is little for the HMM to recognize.
null
47
9,273
0
false
null
null
Thus, there is little for the HMM to recognize.
true
true
true
true
true
1,474
1
DISCUSSION
0
null
null
17,452,365
null
In addition, many of the missed 18S rRNAs were from Cryptophyta, a phylum which makes up only 0.6% of the alignment data.
null
121
9,274
0
false
null
null
In addition, many of the missed 18S rRNAs were from Cryptophyta, a phylum which makes up only 0.6% of the alignment data.
true
true
true
true
true
1,474
2
DISCUSSION
0
null
null
17,452,365
null
The archaeal 5S show the same characteristics as the eukaryotic 5S and 18S, which most likely explains the low performance for these rRNAs.
null
139
9,275
0
false
null
null
The archaeal 5S show the same characteristics as the eukaryotic 5S and 18S, which most likely explains the low performance for these rRNAs.
true
true
true
true
true
1,475
2
DISCUSSION
0
null
null
17,452,365
null
The score for archaeal 5S hits were generally low, and the spotter score comes only from a 75 nt part of the sequence giving it even lower score causing it to miss 12 of the full model hits.
null
190
9,276
0
false
null
null
The score for archaeal 5S hits were generally low, and the spotter score comes only from a 75 nt part of the sequence giving it even lower score causing it to miss 12 of the full model hits.
true
true
true
true
true
1,475
2
DISCUSSION
0
null
null
17,452,365
null
It is notable, however, that these were the only cases missed by the spotter model: with the exception of archaeal 5S, our analyses show that the spotter should be able to detect rRNAs unless they are much further diverged than what we find in our data.
null
253
9,277
0
false
null
null
It is notable, however, that these were the only cases missed by the spotter model: with the exception of archaeal 5S, our analyses show that the spotter should be able to detect rRNAs unless they are much further diverged than what we find in our data.
true
true
true
true
true
1,475
3
DISCUSSION
0
null
null
17,452,365
null
Columns at the beginning and end of the multiple alignments often have low conservation and many gaps.
null
102
9,278
0
false
null
null
Columns at the beginning and end of the multiple alignments often have low conservation and many gaps.
true
true
true
true
true
1,476
3
DISCUSSION
0
null
null
17,452,365
null
Such columns are generally accommodated into the HMM as insert states, but HMMer ignores them at the beginning and end of the alignment.
null
136
9,279
0
false
null
null
Such columns are generally accommodated into the HMM as insert states, but HMMer ignores them at the beginning and end of the alignment.
true
true
true
true
true
1,476
3
DISCUSSION
0
null
null
17,452,365
null
An example is the 5S, where match states stop around 10 columns from the end of the alignments effectively causing the HMM to predict the last conserved nucleotide of the consensus sequence rather than the stop of the rRNAs.
null
224
9,280
0
false
null
null
An example is the 5S, where match states stop around 10 columns from the end of the alignments effectively causing the HMM to predict the last conserved nucleotide of the consensus sequence rather than the stop of the rRNAs.
true
true
true
true
true
1,476
3
DISCUSSION
0
null
null
17,452,365
null
Hence, it is not uncommon for the stop position of the 5S to be predicted up to 10 nt downstream of the annotated stop position.
null
128
9,281
0
false
null
null
Hence, it is not uncommon for the stop position of the 5S to be predicted up to 10 nt downstream of the annotated stop position.
true
true
true
true
true
1,476
4
DISCUSSION
0
null
null
17,452,365
null
These effects can also explain the endpoint accuracy that was seen when we compared our results to experimentally determined 16S sequences.
null
139
9,282
0
false
null
null
These effects can also explain the endpoint accuracy that was seen when we compared our results to experimentally determined 16S sequences.
true
true
true
true
true
1,477
4
DISCUSSION
0
null
null
17,452,365
null
We tried to find sequences where the ends had been experimentally verified by RACE or PCR, but such rRNAs proved difficult to find.
null
131
9,283
0
false
null
null
We tried to find sequences where the ends had been experimentally verified by RACE or PCR, but such rRNAs proved difficult to find.
true
true
true
true
true
1,477
4
DISCUSSION
0
null
null
17,452,365
null
All the ones we selected were sequenced, but it is uncertain to what extent the authors had tried to determine the ends.
null
120
9,284
0
false
null
null
All the ones we selected were sequenced, but it is uncertain to what extent the authors had tried to determine the ends.
true
true
true
true
true
1,477
4
DISCUSSION
0
null
null
17,452,365
null
These experimentally found rRNAs did show better agreement with annotation than predictions in general, although this is not sufficient to conclude that our predictions are more accurate.
null
187
9,285
0
false
null
null
These experimentally found rRNAs did show better agreement with annotation than predictions in general, although this is not sufficient to conclude that our predictions are more accurate.
true
true
true
true
true
1,477
4
DISCUSSION
0
null
null
17,452,365
null
Our stop predictions were very accurate, but more deviation was seen in the start predictions.
null
94
9,286
0
false
null
null
Our stop predictions were very accurate, but more deviation was seen in the start predictions.
true
true
true
true
true
1,477
4
DISCUSSION
0
null
null
17,452,365
null
These results could reflect more variation in the beginning of the alignments, which as in the 5S case could effectively cause the HMM to predict the last conserved nucleotide of the consensus sequence rather than the end of the rRNAs.
null
235
9,287
0
false
null
null
These results could reflect more variation in the beginning of the alignments, which as in the 5S case could effectively cause the HMM to predict the last conserved nucleotide of the consensus sequence rather than the end of the rRNAs.
true
true
true
true
true
1,477
5
DISCUSSION
0
null
null
17,452,365
null
In some cases, larger endpoint deviations occur.
null
48
9,288
0
false
null
null
In some cases, larger endpoint deviations occur.
true
true
true
true
true
1,478
5
DISCUSSION
0
null
null
17,452,365
null
This can happen when one of the ends of the model finds a better match in a different part of the sequence.
null
107
9,289
0
false
null
null
This can happen when one of the ends of the model finds a better match in a different part of the sequence.
true
true
true
true
true
1,478
5
DISCUSSION
0
null
null
17,452,365
null
Insertion states sometimes allows the HMM to insert long gap regions and thus find a matching stop position far from the rest of the sequence.
null
142
9,290
0
false
null
null
Insertion states sometimes allows the HMM to insert long gap regions and thus find a matching stop position far from the rest of the sequence.
true
true
true
true
true
1,478
5
DISCUSSION
0
null
null
17,452,365
null
As shown for the bacterial 16S sequences that displayed this phenomenon, this is less of a problem when the spotter model is employed.
null
134
9,291
0
false
null
null
As shown for the bacterial 16S sequences that displayed this phenomenon, this is less of a problem when the spotter model is employed.
true
true
true
true
true
1,478
5
DISCUSSION
0
null
null
17,452,365
null
The window searched around the spotter hit would most likely be too short to accommodate such an insert, and the model would match with the proper sequence.
null
156
9,292
0
false
null
null
The window searched around the spotter hit would most likely be too short to accommodate such an insert, and the model would match with the proper sequence.
true
true
true
true
true
1,478
6
DISCUSSION
0
null
null
17,452,365
null
For fragmented rRNAs, long gap regions may be correctly predicted.
null
66
9,293
0
false
null
null
For fragmented rRNAs, long gap regions may be correctly predicted.
true
true
true
true
true
1,479
6
DISCUSSION
0
null
null
17,452,365
null
This was seen for Coxiella burnetii 23S where our prediction has the same start position as annotated, but where the predicted stop position is 1884 nt downstream of GenBank's stop position.
null
190
9,294
0
false
null
null
This was seen for Coxiella burnetii 23S where our prediction has the same start position as annotated, but where the predicted stop position is 1884 nt downstream of GenBank's stop position.
true
true
true
true
true
1,479
6
DISCUSSION
0
null
null
17,452,365
null
However, according to Entrez Gene, this rRNA appears in four pieces and with the same stop position as ours, suggesting that in some cases ‘too long’ predictions might actually be correct.
null
188
9,295
0
false
null
null
However, according to Entrez Gene, this rRNA appears in four pieces and with the same stop position as ours, suggesting that in some cases ‘too long’ predictions might actually be correct.
true
true
true
true
true
1,479
6
DISCUSSION
0
null
null
17,452,365
null
These cases should normally not be masked when using the spotter unless inserts between the fragments would make it exceed the window size.
null
139
9,296
0
false
null
null
These cases should normally not be masked when using the spotter unless inserts between the fragments would make it exceed the window size.
true
true
true
true
true
1,479
7
DISCUSSION
0
null
null
17,452,365
null
The HMM produced by HMMer requires time of order O(NM) to search a sequence of length N using a model with M states, M being proportional to the length of the multiple alignment.
null
178
9,297
0
false
null
null
The HMM produced by HMMer requires time of order O(NM) to search a sequence of length N using a model with M states, M being proportional to the length of the multiple alignment.
true
true
true
true
true
1,480
7
DISCUSSION
0
null
null
17,452,365
null
However, the speed is increased by using a 75 nt long spotter model to pre-screen the sequence, which requires time of order O(N), and then running the full HMM on windows around each spotter hit which requires time of order O(KM2) for K spotter hits, and window size proportional to M. The benefit of using the spotter ...
null
373
9,298
0
false
null
null
However, the speed is increased by using a 75 nt long spotter model to pre-screen the sequence, which requires time of order O(N), and then running the full HMM on windows around each spotter hit which requires time of order O(KM2) for K spotter hits, and window size proportional to M. The benefit of using the spotter ...
true
true
true
true
true
1,480
7
DISCUSSION
0
null
null
17,452,365
null
However, the time difference between the S. usitatus and the Sargasso Sea data searches shows that the spotter might lose its mission when dealing with many shorter sequences.
null
175
9,299
0
false
null
null
However, the time difference between the S. usitatus and the Sargasso Sea data searches shows that the spotter might lose its mission when dealing with many shorter sequences.
true
true
true
true
true
1,480