Nucleic Acids Research Advance Access originally published online on July 24, 2008
Nucleic Acids Research 2008 36(15):4823-4832; doi:10.1093/nar/gkn463
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Nucleic Acids Research, 2008, Vol. 36, No. 15 4823-4832
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
Tissue-specific splicing factor gene expression signatures
1Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal, 2Department of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK and 3Department of Hematology, Erasmus Medical Center, 3015 GE Rotterdam, The Netherlands
*To whom correspondence should be addressed. Tel: +351 21 79 99 411; Fax: +351 21 79 99 412; Email: carmo.fonseca{at}fm.ul.pt
Received January 2, 2008. Revised June 29, 2008. Accepted July 3, 2008.
The alternative splicing code that controls and coordinates the transcriptome in complex multicellular organisms remains poorly understood. It has long been argued that regulation of alternative splicing relies on combinatorial interactions between multiple proteins, and that tissue-specific splicing decisions most likely result from differences in the concentration and/or activity of these proteins. However, large-scale data to systematically address this issue have just recently started to become available. Here we show that splicing factor gene expression signatures can be identified that reflect cell type and tissue-specific patterns of alternative splicing. We used a computational approach to analyze microarray-based gene expression profiles of splicing factors from mouse, chimpanzee and human tissues. Our results show that brain and testis, the two tissues with highest levels of alternative splicing events, have the largest number of splicing factor genes that are most highly differentially expressed. We further identified SR protein kinases and small nuclear ribonucleoprotein particle (snRNP) proteins among the splicing factor genes that are most highly differentially expressed in a particular tissue. These results indicate the power of generating signature-based predictions as an initial computational approach into a global view of tissue-specific alternative splicing regulation.
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