tailieunhanh - Báo cáo y học: " Studying alternative splicing regulatory networks through partial correlation analysis"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: Studying alternative splicing regulatory networks through partial correlation analysis. | Open Access Research Studying alternative splicing regulatory networks through partial correlation analysis Liang Chen and Sika Zheng Addresses Molecular and Computational Biology Department of Biological Sciences University of Southern California Los Angeles California 90089 USA. Howard Hughes Medical Institute University of California Los Angeles MRL 6-619 Los Angeles California 90095 USA. Correspondence Liang Chen. Email Published 9 January 2009 Genome Biology 2009 10 R3 doi gb-2009-l 0-l-r3 The electronic version of this article is the complete one and can be found online at http 2009 10 l R3 Received 19 November 2008 Revised 18 December 2008 Accepted 9 January 2009 2009 Chen and Zheng licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background Alternative pre-mRNA splicing is an important gene regulation mechanism for expanding proteomic diversity in higher eukaryotes. Each splicing regulator can potentially influence a large group of alternative exons. Meanwhile each alternative exon is controlled by multiple splicing regulators. The rapid accumulation of high-throughput data provides us with a unique opportunity to study the complicated alternative splicing regulatory network. Results We propose the use of partial correlation analysis to identify association links between exons and their upstream regulators or their downstream target genes exon-gene links and links between co-spliced exons exon-exon links . The partial correlation analysis avoids taking the ratio of two noisy random variables exon expression level and gene expression level so that it achieves a higher statistical power. We named this analysis procedure pCastNet partial Correlation analysis of .

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