• Inference of alternative splic...

*Result*: Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs.

Title:
Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs.
Authors:
LeGault LH; Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA., Dewey CN
Source:
Bioinformatics (Oxford, England) [Bioinformatics] 2013 Sep 15; Vol. 29 (18), pp. 2300-10. Date of Electronic Publication: 2013 Jul 11.
Publication Type:
Journal Article; Research Support, N.I.H., Extramural
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford : Oxford University Press, c1998-
MeSH Terms:
Alternative Splicing* , Models, Statistical*, Gene Expression Profiling/*methods , Sequence Analysis, RNA/*methods, Algorithms ; Animals ; Cell Line ; Drosophila/genetics ; Drosophila/metabolism ; Humans ; Software
References:
Nat Methods. 2010 Nov;7(11):909-12. (PMID: 20935650)
Cell. 2000 Jun 9;101(6):671-84. (PMID: 10892653)
Science. 2007 Jun 15;316(5831):1586-91. (PMID: 17569856)
Genome Biol. 2012 Jan 31;13(1):R4. (PMID: 22293517)
BMC Bioinformatics. 2012 Apr 19;13 Suppl 6:S11. (PMID: 22537040)
Nucleic Acids Res. 2010 Jun;38(10):e112. (PMID: 20150413)
Bioinformatics. 2002;18 Suppl 1:S181-8. (PMID: 12169546)
Bioinformatics. 2009 Dec 1;25(23):3056-9. (PMID: 19762346)
Nat Biotechnol. 2013 Jan;31(1):46-53. (PMID: 23222703)
Genome Biol. 2009;10(3):R25. (PMID: 19261174)
Nat Biotechnol. 2011 Jul 11;29(7):572-3. (PMID: 21747377)
Nat Biotechnol. 2010 May;28(5):511-5. (PMID: 20436464)
Bioinformatics. 2006 May 1;22(9):1036-46. (PMID: 16500937)
Bioinformatics. 2009 Apr 15;25(8):1026-32. (PMID: 19244387)
Bioinformatics. 2010 Feb 15;26(4):493-500. (PMID: 20022975)
Nat Biotechnol. 2011 May 15;29(7):644-52. (PMID: 21572440)
Nat Methods. 2010 Dec;7(12):1009-15. (PMID: 21057496)
Genome Biol. 2010;11(5):R50. (PMID: 20459815)
Nature. 2010 Apr 1;464(7289):773-7. (PMID: 20220756)
Nature. 2008 Nov 27;456(7221):470-6. (PMID: 18978772)
BMC Bioinformatics. 2011 May 16;12:162. (PMID: 21575225)
Nat Genet. 2004 Mar;36(3):240-6. (PMID: 14758360)
Proteins. 2006 Aug 1;64(2):320-42. (PMID: 16671074)
Mol Cell. 2005 Aug 5;19(3):393-404. (PMID: 16061185)
Nat Rev Genet. 2009 Jan;10(1):57-63. (PMID: 19015660)
Bioinformatics. 2009 May 1;25(9):1105-11. (PMID: 19289445)
Nat Biotechnol. 2010 May;28(5):503-10. (PMID: 20436462)
Nucleic Acids Res. 2010 Jul;38(12):e131. (PMID: 20395217)
J Comput Biol. 2011 Nov;18(11):1693-707. (PMID: 21951053)
Proc Natl Acad Sci U S A. 2011 Dec 13;108(50):19867-72. (PMID: 22135461)
Nature. 2011 Mar 24;471(7339):473-9. (PMID: 21179090)
Genome Biol. 2011;12(3):R22. (PMID: 21410973)
Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W444-7. (PMID: 16845045)
Mol Cell. 2006 Mar 17;21(6):849-59. (PMID: 16543153)
PLoS Biol. 2011 Apr;9(4):e1001046. (PMID: 21526222)
Genome Res. 2011 Feb;21(2):301-14. (PMID: 21177962)
Nat Rev Mol Cell Biol. 2005 May;6(5):386-98. (PMID: 15956978)
Bioinformatics. 2011 Oct 1;27(19):2633-40. (PMID: 21824971)
Nucleic Acids Res. 2006 Jun 06;34(10):3150-60. (PMID: 16757580)
Grant Information:
R01 HG005232 United States HG NHGRI NIH HHS; HG005232 United States HG NHGRI NIH HHS
Entry Date(s):
Date Created: 20130713 Date Completed: 20140214 Latest Revision: 20211021
Update Code:
20260130
PubMed Central ID:
PMC3753571
DOI:
10.1093/bioinformatics/btt396
PMID:
23846746
Database:
MEDLINE

*Further Information*

*Motivation: Alternative splicing and other processes that allow for different transcripts to be derived from the same gene are significant forces in the eukaryotic cell. RNA-Seq is a promising technology for analyzing alternative transcripts, as it does not require prior knowledge of transcript structures or genome sequences. However, analysis of RNA-Seq data in the presence of genes with large numbers of alternative transcripts is currently challenging due to efficiency, identifiability and representation issues.
Results: We present RNA-Seq models and associated inference algorithms based on the concept of probabilistic splice graphs, which alleviate these issues. We prove that our models are often identifiable and demonstrate that our inference methods for quantification and differential processing detection are efficient and accurate.
Availability: Software implementing our methods is available at http://deweylab.biostat.wisc.edu/psginfer.
Contact: cdewey@biostat.wisc.edu
Supplementary Information: Supplementary data are available at Bioinformatics online.*