Einzeltreffer — DigiBib

Background: The primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information.

Results: In this study, we propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, we can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then we should rely on the results from the pathway analysis tools, which is termed as significance in this paper. We realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. Our method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets.

Conclusions: Our novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at http://biohealth.snu.ac.kr/software/contextTRAP .

Titel:	Prioritizing biological pathways by recognizing context in time-series gene expression data.
Autor/in / Beteiligte Person:	Lee, J ; Jo, K ; Lee, S ; Kang, J ; Kim, S
Link:	Full Text Finder (Volltext)
Zeitschrift:	BMC bioinformatics, Jg. 17 (2016-12-23), Heft Suppl 17, S. 477
Veröffentlichung:	[London] : BioMed Central, 2000-, 2016
Medientyp:	academicJournal
ISSN:	1471-2105 (electronic)
DOI:	10.1186/s12859-016-1335-8
Schlagwort:	Algorithms Humans Transcriptome Computational Biology methods Gene Expression Metabolic Networks and Pathways Software
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [BMC Bioinformatics] 2016 Dec 23; Vol. 17 (Suppl 17), pp. 477. <i>Date of Electronic Publication: </i>2016 Dec 23. MeSH Terms: Gene Expression* ; Metabolic Networks and Pathways* ; Software* ; Computational Biology / *methods ; Algorithms ; Humans ; Transcriptome References: Nucleic Acids Res. 2000 Jan 1;28(1):27-30. (PMID: 10592173) ; Nucleic Acids Res. 2005 Jan 1;33(Database issue):D428-32. (PMID: 15608231) ; Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50. (PMID: 16199517) ; Nature. 2006 May 11;441(7090):235-8. (PMID: 16648838) ; Bioinformatics. 2006 Jul 15;22(14):e489-96. (PMID: 16873511) ; Proc Natl Acad Sci U S A. 2006 Sep 19;103(38):14194-9. (PMID: 16963558) ; Bioinformatics. 2007 Feb 1;23(3):306-13. (PMID: 17127676) ; Bioinformatics. 2007 Feb 15;23(4):401-7. (PMID: 17182697) ; Emerg Infect Dis. 2007 May;13(5):708-12. (PMID: 17553248) ; Arthritis Rheum. 2008 Jun;58(6):1619-29. (PMID: 18512782) ; Nucleic Acids Res. 2009 Jan;37(Database issue):D674-9. (PMID: 18832364) ; Nucleic Acids Res. 2009 Jan;37(1):1-13. (PMID: 19033363) ; Nat Protoc. 2009;4(1):44-57. (PMID: 19131956) ; J Cell Physiol. 2010 Jan;222(1):95-102. (PMID: 19746420) ; J Immunol. 2010 Apr 15;184(8):4295-306. (PMID: 20304828) ; Eur J Hum Genet. 2010 Sep;18(9):1045-53. (PMID: 20442747) ; Nat Rev Immunol. 2010 Jun;10(6):384. (PMID: 20514675) ; Virol J. 2010 Jul 23;7:170. (PMID: 20653952) ; Adv Genet. 2010;72:141-79. (PMID: 21029852) ; J Virol. 2011 Nov;85(21):10955-67. (PMID: 21865398) ; Prog Mol Biol Transl Sci. 2012;106:223-50. (PMID: 22340720) ; PLoS One. 2012;7(2):e31721. (PMID: 22359619) ; PLoS Comput Biol. 2012;8(2):e1002375. (PMID: 22383865) ; Nat Rev Genet. 2012 Jul 18;13(8):552-64. (PMID: 22805708) ; PLoS One. 2012;7(7):e41541. (PMID: 22848524) ; Nucleic Acids Res. 2013 Jan 7;41(1):e19. (PMID: 23002139) ; PLoS One. 2012;7(12):e53320. (PMID: 23285279) ; Nature. 2013 Apr 25;496(7446):461-8. (PMID: 23467089) ; Cell Death Dis. 2013 Mar 28;4:e562. (PMID: 23538443) ; Nucleic Acids Res. 2013 Jul;41(Web Server issue):W89-97. (PMID: 23666626) ; Bioinformatics. 2013 Jul 15;29(14):1776-85. (PMID: 23749957) ; Genome Res. 2013 Nov;23(11):1885-93. (PMID: 23934932) ; PLoS One. 2013 Nov 15;8(11):e79217. (PMID: 24260172) ; Methods. 2014 Jun 1;67(3):364-72. (PMID: 24518221) ; PLoS One. 2014 Jul 08;9(7):e101384. (PMID: 25003973) ; PLoS One. 2014 Jul 09;9(7):e100887. (PMID: 25007247) ; BMC Bioinformatics. 2014 Aug 03;15:260. (PMID: 25086605) ; Genomics Proteomics Bioinformatics. 2014 Oct;12(5):210-20. (PMID: 25462153) ; Database (Oxford). 2015 Jan 28;2015:null. (PMID: 25632107) ; Int J Obes (Lond). 2015 Aug;39(8):1241-8. (PMID: 25907313) ; Immunology. 2015 Oct;146(2):251-63. (PMID: 26095162) ; Nat Methods. 2015 Jul;12(7):615-621. (PMID: 26125594) ; Methods. 2016 Nov 1;110:81-89. (PMID: 27329435) ; PLoS One. 2016 Oct 19;11(10):e0164680. (PMID: 27760149) Contributed Indexing: Keywords: Literature information; Pathway; Pathway analysis; Pathway prioritization; Time series Entry Date(s): Date Created: 20170204 Date Completed: 20170825 Latest Revision: 20190109 Update Code: 20231215 PubMed Central ID: PMC5259824

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Prioritizing biological pathways by recognizing context in time-series gene expression data.