Abstract
Background: RNA metabolism, through 'combinatorial splicing', can generate enormous structural diversity in the proteome. Alternative domains may interact, however, with unpredictable phenotypic consequences, necessitating integrated RNA-level regulation of molecular composition. Splicing correlations within transcripts of single genes provide valuable clues to functional relationships among molecular domains as well as genomic targets for higher-order splicing regulation. Results: We present tools to visualize complex splicing patterns in full-length cDNA libraries. Developmental changes in pair-wise correlations are presented vectorially in 'clock plots' and linkage grids. Higher-order correlations are assessed statistically through Monte Carlo analysis of a log-linear model with an empirical-Bayes estimate of the true probabilities of observed and unobserved splice forms. Log-linear coefficients are visualized in a 'spliceprint,' a signature of splice correlations in the transcriptome. We present two novel metrics: the linkage change index, which measures the directional change in pair-wise correlation with tissue differentiation, and the accuracy index, a very simple goodness-of-fit metric that is more sensitive than the integrated squared error when applied to sparsely populated tables, and unlike chi-square, does not diverge at low variance. Considerable attention is given to sparse contingency tables, which are inherent to single-gene libraries. Conclusion: Patterns of splicing correlations are revealed, which span a broad range of interaction order and change in development. The methods have a broad scope of applicability, beyond the single gene - including, for example, multiple gene interactions in the complete transcriptome.
| Language | English (US) |
|---|---|
| Article number | 16 |
| Journal | BMC Bioinformatics |
| Volume | 8 |
| DOIs | |
| State | Published - 2007 |
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ASJC Scopus subject areas
- Medicine(all)
- Structural Biology
- Applied Mathematics
Cite this
Multivariate analysis and visualization of splicing correlations in single-gene transcriptomes. / Emerick, Mark C.; Parmigiani, Giovanni; Agnew, William.
In: BMC Bioinformatics, Vol. 8, 16, 2007.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Multivariate analysis and visualization of splicing correlations in single-gene transcriptomes
AU - Emerick, Mark C.
AU - Parmigiani, Giovanni
AU - Agnew, William
PY - 2007
Y1 - 2007
N2 - Background: RNA metabolism, through 'combinatorial splicing', can generate enormous structural diversity in the proteome. Alternative domains may interact, however, with unpredictable phenotypic consequences, necessitating integrated RNA-level regulation of molecular composition. Splicing correlations within transcripts of single genes provide valuable clues to functional relationships among molecular domains as well as genomic targets for higher-order splicing regulation. Results: We present tools to visualize complex splicing patterns in full-length cDNA libraries. Developmental changes in pair-wise correlations are presented vectorially in 'clock plots' and linkage grids. Higher-order correlations are assessed statistically through Monte Carlo analysis of a log-linear model with an empirical-Bayes estimate of the true probabilities of observed and unobserved splice forms. Log-linear coefficients are visualized in a 'spliceprint,' a signature of splice correlations in the transcriptome. We present two novel metrics: the linkage change index, which measures the directional change in pair-wise correlation with tissue differentiation, and the accuracy index, a very simple goodness-of-fit metric that is more sensitive than the integrated squared error when applied to sparsely populated tables, and unlike chi-square, does not diverge at low variance. Considerable attention is given to sparse contingency tables, which are inherent to single-gene libraries. Conclusion: Patterns of splicing correlations are revealed, which span a broad range of interaction order and change in development. The methods have a broad scope of applicability, beyond the single gene - including, for example, multiple gene interactions in the complete transcriptome.
AB - Background: RNA metabolism, through 'combinatorial splicing', can generate enormous structural diversity in the proteome. Alternative domains may interact, however, with unpredictable phenotypic consequences, necessitating integrated RNA-level regulation of molecular composition. Splicing correlations within transcripts of single genes provide valuable clues to functional relationships among molecular domains as well as genomic targets for higher-order splicing regulation. Results: We present tools to visualize complex splicing patterns in full-length cDNA libraries. Developmental changes in pair-wise correlations are presented vectorially in 'clock plots' and linkage grids. Higher-order correlations are assessed statistically through Monte Carlo analysis of a log-linear model with an empirical-Bayes estimate of the true probabilities of observed and unobserved splice forms. Log-linear coefficients are visualized in a 'spliceprint,' a signature of splice correlations in the transcriptome. We present two novel metrics: the linkage change index, which measures the directional change in pair-wise correlation with tissue differentiation, and the accuracy index, a very simple goodness-of-fit metric that is more sensitive than the integrated squared error when applied to sparsely populated tables, and unlike chi-square, does not diverge at low variance. Considerable attention is given to sparse contingency tables, which are inherent to single-gene libraries. Conclusion: Patterns of splicing correlations are revealed, which span a broad range of interaction order and change in development. The methods have a broad scope of applicability, beyond the single gene - including, for example, multiple gene interactions in the complete transcriptome.
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UR - http://www.scopus.com/inward/citedby.url?scp=33846962479&partnerID=8YFLogxK
U2 - 10.1186/1471-2105-8-16
DO - 10.1186/1471-2105-8-16
M3 - Article
VL - 8
JO - BMC Bioinformatics
T2 - BMC Bioinformatics
JF - BMC Bioinformatics
SN - 1471-2105
M1 - 16
ER -