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Gene sequence signatures revealed by mining the UniGene affiliation network
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| Zeitschriftentitel: | Bioinformatics |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Bioinformatics, 22, 2006, 4, S. 385-391 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
| author_facet |
Zhang, Jiexin Zhang, Li Coombes, Kevin R. Zhang, Jiexin Zhang, Li Coombes, Kevin R. |
|---|---|
| author |
Zhang, Jiexin Zhang, Li Coombes, Kevin R. |
| spellingShingle |
Zhang, Jiexin Zhang, Li Coombes, Kevin R. Bioinformatics Gene sequence signatures revealed by mining the UniGene affiliation network Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| author_sort |
zhang, jiexin |
| spelling |
Zhang, Jiexin Zhang, Li Coombes, Kevin R. 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/bti796 <jats:title>Abstract</jats:title> <jats:p>Background: In the post-genomic era, developing tools to decode biological information from genomic sequences is important. Inspired by affiliation network theory, we investigated gene sequences of two kinds of UniGene clusters (UCs): narrowly expressed transcripts (NETs), whose expression is confined to a few tissues; and prevalently expressed transcripts (PETs) that are expressed in many tissues.</jats:p> <jats:p>Results: We explored the human and the mouse UniGene databases to compare NETs and PETs from different perspectives. We found that NETs were associated with smaller cluster size, shorter sequence length, a lower likelihood of having LocusLink annotations, and lower and more sporadic levels of expression. Significantly, the dinucleotide frequencies of NETs are similar to those of intergenic sequences in the genome, and they differ from those of PETs. We used these differences in dinucleotide frequencies to develop a discriminant analysis model to distinguish PETs from intergenic sequences.</jats:p> <jats:p>Conclusions: Our results show that most NETs resemble intergenic sequences, casting doubts on the quality of such UniGene clusters. However, we also noted that a fraction of NETs resemble PETs in terms of dinucleotide frequencies and other features. Such NETs may have fewer quality problems. This work may be helpful in the studies of non-coding RNAs and in the validation of gene sequence databases.</jats:p> <jats:p>Availability: </jats:p> <jats:p>Contact: kcoombes@mdanderson.org</jats:p> <jats:p>Supplementary information: </jats:p> Gene sequence signatures revealed by mining the UniGene affiliation network Bioinformatics |
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10.1093/bioinformatics/bti796 |
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| title |
Gene sequence signatures revealed by mining the UniGene affiliation network |
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Gene sequence signatures revealed by mining the UniGene affiliation network |
| title_full |
Gene sequence signatures revealed by mining the UniGene affiliation network |
| title_fullStr |
Gene sequence signatures revealed by mining the UniGene affiliation network |
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Gene sequence signatures revealed by mining the UniGene affiliation network |
| title_short |
Gene sequence signatures revealed by mining the UniGene affiliation network |
| title_sort |
gene sequence signatures revealed by mining the unigene affiliation network |
| topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| url |
http://dx.doi.org/10.1093/bioinformatics/bti796 |
| publishDate |
2006 |
| physical |
385-391 |
| description |
<jats:title>Abstract</jats:title>
<jats:p>Background: In the post-genomic era, developing tools to decode biological information from genomic sequences is important. Inspired by affiliation network theory, we investigated gene sequences of two kinds of UniGene clusters (UCs): narrowly expressed transcripts (NETs), whose expression is confined to a few tissues; and prevalently expressed transcripts (PETs) that are expressed in many tissues.</jats:p>
<jats:p>Results: We explored the human and the mouse UniGene databases to compare NETs and PETs from different perspectives. We found that NETs were associated with smaller cluster size, shorter sequence length, a lower likelihood of having LocusLink annotations, and lower and more sporadic levels of expression. Significantly, the dinucleotide frequencies of NETs are similar to those of intergenic sequences in the genome, and they differ from those of PETs. We used these differences in dinucleotide frequencies to develop a discriminant analysis model to distinguish PETs from intergenic sequences.</jats:p>
<jats:p>Conclusions: Our results show that most NETs resemble intergenic sequences, casting doubts on the quality of such UniGene clusters. However, we also noted that a fraction of NETs resemble PETs in terms of dinucleotide frequencies and other features. Such NETs may have fewer quality problems. This work may be helpful in the studies of non-coding RNAs and in the validation of gene sequence databases.</jats:p>
<jats:p>Availability: </jats:p>
<jats:p>Contact: kcoombes@mdanderson.org</jats:p>
<jats:p>Supplementary information: </jats:p> |
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| author | Zhang, Jiexin, Zhang, Li, Coombes, Kevin R. |
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| description | <jats:title>Abstract</jats:title> <jats:p>Background: In the post-genomic era, developing tools to decode biological information from genomic sequences is important. Inspired by affiliation network theory, we investigated gene sequences of two kinds of UniGene clusters (UCs): narrowly expressed transcripts (NETs), whose expression is confined to a few tissues; and prevalently expressed transcripts (PETs) that are expressed in many tissues.</jats:p> <jats:p>Results: We explored the human and the mouse UniGene databases to compare NETs and PETs from different perspectives. We found that NETs were associated with smaller cluster size, shorter sequence length, a lower likelihood of having LocusLink annotations, and lower and more sporadic levels of expression. Significantly, the dinucleotide frequencies of NETs are similar to those of intergenic sequences in the genome, and they differ from those of PETs. We used these differences in dinucleotide frequencies to develop a discriminant analysis model to distinguish PETs from intergenic sequences.</jats:p> <jats:p>Conclusions: Our results show that most NETs resemble intergenic sequences, casting doubts on the quality of such UniGene clusters. However, we also noted that a fraction of NETs resemble PETs in terms of dinucleotide frequencies and other features. Such NETs may have fewer quality problems. This work may be helpful in the studies of non-coding RNAs and in the validation of gene sequence databases.</jats:p> <jats:p>Availability: </jats:p> <jats:p>Contact: kcoombes@mdanderson.org</jats:p> <jats:p>Supplementary information: </jats:p> |
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| imprint | Oxford University Press (OUP), 2006 |
| imprint_str_mv | Oxford University Press (OUP), 2006 |
| institution | DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229 |
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| spelling | Zhang, Jiexin Zhang, Li Coombes, Kevin R. 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/bti796 <jats:title>Abstract</jats:title> <jats:p>Background: In the post-genomic era, developing tools to decode biological information from genomic sequences is important. Inspired by affiliation network theory, we investigated gene sequences of two kinds of UniGene clusters (UCs): narrowly expressed transcripts (NETs), whose expression is confined to a few tissues; and prevalently expressed transcripts (PETs) that are expressed in many tissues.</jats:p> <jats:p>Results: We explored the human and the mouse UniGene databases to compare NETs and PETs from different perspectives. We found that NETs were associated with smaller cluster size, shorter sequence length, a lower likelihood of having LocusLink annotations, and lower and more sporadic levels of expression. Significantly, the dinucleotide frequencies of NETs are similar to those of intergenic sequences in the genome, and they differ from those of PETs. We used these differences in dinucleotide frequencies to develop a discriminant analysis model to distinguish PETs from intergenic sequences.</jats:p> <jats:p>Conclusions: Our results show that most NETs resemble intergenic sequences, casting doubts on the quality of such UniGene clusters. However, we also noted that a fraction of NETs resemble PETs in terms of dinucleotide frequencies and other features. Such NETs may have fewer quality problems. This work may be helpful in the studies of non-coding RNAs and in the validation of gene sequence databases.</jats:p> <jats:p>Availability: </jats:p> <jats:p>Contact: kcoombes@mdanderson.org</jats:p> <jats:p>Supplementary information: </jats:p> Gene sequence signatures revealed by mining the UniGene affiliation network Bioinformatics |
| spellingShingle | Zhang, Jiexin, Zhang, Li, Coombes, Kevin R., Bioinformatics, Gene sequence signatures revealed by mining the UniGene affiliation network, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| title | Gene sequence signatures revealed by mining the UniGene affiliation network |
| title_full | Gene sequence signatures revealed by mining the UniGene affiliation network |
| title_fullStr | Gene sequence signatures revealed by mining the UniGene affiliation network |
| title_full_unstemmed | Gene sequence signatures revealed by mining the UniGene affiliation network |
| title_short | Gene sequence signatures revealed by mining the UniGene affiliation network |
| title_sort | gene sequence signatures revealed by mining the unigene affiliation network |
| title_unstemmed | Gene sequence signatures revealed by mining the UniGene affiliation network |
| topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| url | http://dx.doi.org/10.1093/bioinformatics/bti796 |