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Title:Gene set enrichment meta-learning analysis: next-generation sequencing versus microarrays
Authors:ID Štiglic, Gregor (Author)
ID Bajgot, Mateja (Author)
ID Kokol, Peter (Author)
Files:.pdf BMC_Bioinformatics_2010_Stiglic,_Bajgot,_Kokol_Gene_set_enrichment_meta-learning_analysis_next-_generation_sequencing_versus_microarrays.pdf (1,17 MB)
MD5: 7E939CB045B8A26AB3543F140592E280
PID: 20.500.12556/dkum/0dc73667-95f8-4f7e-9f34-90384dcd6634
 
URL http://www.biomedcentral.com/1471-2105/11/176
 
Language:English
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:FZV - Faculty of Health Sciences
Abstract:Background Reproducibility of results can have a significant impact on the acceptance of new technologies in gene expression analysis. With the recent introduction of the so-called next-generation sequencing (NGS) technology and established microarrays, one is able to choose between two completely different platforms for gene expression measurements. This study introduces a novel methodology for gene-ranking stability analysis that is applied to the evaluation of gene-ranking reproducibility on NGS and microarray data. Results The same data used in a well-known MicroArray Quality Control (MAQC) study was also used in this study to compare ranked lists of genes from MAQC samples A and B, obtained from Affymetrix HG-U133 Plus 2.0 and Roche 454 Genome Sequencer FLX platforms. An initial evaluation, where the percentage ofoverlapping genes was observed, demonstrates higher reproducibility on microarray data in 10 out of 11 gene-ranking methods. A gene set enrichment analysis shows similar enrichment of top gene sets when NGS is compared with microarrays on a pathway level. Our novel approach demonstrates high accuracy of decision trees when used for knowledge extraction from multiple bootstrapped gene set enrichment analysis runs. A comparison of the two approaches in sample preparation for high-throughput sequencing shows that alternating decision trees represent the optimal knowledge representation method in comparison with classical decision trees. Conclusions Usual reproducibility measurements are mostly based on statistical techniques that offer very limited biological insights into the studied gene expression data sets. This paper introduces the meta-learning-based gene set enrichment analysis that can be used to complement the analysis of gene-ranking stabilityestimation techniques such as percentage of overlapping genes or classic gene set enrichment analysis. It is useful and practical when reproducibility of gene ranking results or different gene selection techniquesis observed. The proposed method reveals very accurate descriptive models that capture the co-enrichment of gene sets which are differently enriched in the compared data sets.
Keywords:meta-learning, microarray, gene expression analysis
Publication status:Published
Publication version:Version of Record
Year of publishing:2010
Number of pages:str. 1-10
Numbering:Letn. 11
PID:20.500.12556/DKUM-30836 New window
ISSN:1471-2105
UDC:004.8
ISSN on article:1471-2105
COBISS.SI-ID:1592996 New window
DOI:10.1186/1471-2105-11-176 New window
NUK URN:URN:SI:UM:DK:HMHTHPG1
Publication date in DKUM:05.06.2012
Views:4040
Downloads:342
Metadata:XML DC-XML DC-RDF
Categories:Misc.
:
ŠTIGLIC, Gregor, BAJGOT, Mateja and KOKOL, Peter, 2010, Gene set enrichment meta-learning analysis: next-generation sequencing versus microarrays. BMC Bioinformatics [online]. 2010. Vol. 11, p. 1–10. [Accessed 26 April 2025]. DOI 10.1186/1471-2105-11-176. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=30836
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Record is a part of a journal

Title:BMC Bioinformatics
Publisher:BioMed Central
ISSN:1471-2105
COBISS.SI-ID:2433556 New window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:05.06.2012

Secondary language

Language:Slovenian
Keywords:meta-učenje, mikromreža, analiza izražanja genov


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