Globaltest and GOEAST: two different approaches for Gene Ontology analysis
Background Gene set analysis is a commonly used method for analysing microarray data by considering groups of functionally related genes instead of individual genes. Here we present the use of two gene set analysis approaches: Globaltest and GOEAST. Globaltest is a method for testing whether sets of...
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Gespeichert in:
| Autor*in: |
Hulsegge, Ina [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2009 |
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| Schlagwörter: |
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| Anmerkung: |
© Hulsegge et al; licensee BioMed Central Ltd. 2009 |
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| Übergeordnetes Werk: |
Enthalten in: BMC proceedings - London : BioMed Central, 2007, 3(2009), Suppl 4 vom: 16. Juli |
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| Übergeordnetes Werk: |
volume:3 ; year:2009 ; number:Suppl 4 ; day:16 ; month:07 |
| Links: |
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| DOI / URN: |
10.1186/1753-6561-3-S4-S10 |
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SPR028429699 |
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| 520 | |a Background Gene set analysis is a commonly used method for analysing microarray data by considering groups of functionally related genes instead of individual genes. Here we present the use of two gene set analysis approaches: Globaltest and GOEAST. Globaltest is a method for testing whether sets of genes are significantly associated with a variable of interest. GOEAST is a freely accessible web-based tool to test GO term enrichment within given gene sets. The two approaches were applied in the analysis of gene lists obtained from three different contrasts in a microarray experiment conducted to study the host reactions in broilers following Eimeria infection. Results The Globaltest identified significantly associated gene sets in one of the three contrasts made in the microarray experiment whereas the functional analysis of the differentially expressed genes using GOEAST revealed enriched GO terms in all three contrasts. Conclusion Globaltest and GOEAST gave different results, probably due to the different algorithms and the different criteria used for evaluating the significance of GO terms. | ||
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| 700 | 1 | |a Smits, Mari A |4 aut | |
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10.1186/1753-6561-3-S4-S10 doi (DE-627)SPR028429699 (SPR)1753-6561-3-S4-S10-e DE-627 ger DE-627 rakwb eng Hulsegge, Ina verfasserin aut Globaltest and GOEAST: two different approaches for Gene Ontology analysis 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hulsegge et al; licensee BioMed Central Ltd. 2009 Background Gene set analysis is a commonly used method for analysing microarray data by considering groups of functionally related genes instead of individual genes. Here we present the use of two gene set analysis approaches: Globaltest and GOEAST. Globaltest is a method for testing whether sets of genes are significantly associated with a variable of interest. GOEAST is a freely accessible web-based tool to test GO term enrichment within given gene sets. The two approaches were applied in the analysis of gene lists obtained from three different contrasts in a microarray experiment conducted to study the host reactions in broilers following Eimeria infection. Results The Globaltest identified significantly associated gene sets in one of the three contrasts made in the microarray experiment whereas the functional analysis of the differentially expressed genes using GOEAST revealed enriched GO terms in all three contrasts. Conclusion Globaltest and GOEAST gave different results, probably due to the different algorithms and the different criteria used for evaluating the significance of GO terms. Gene Ontology (dpeaa)DE-He213 False Discovery Rate (dpeaa)DE-He213 Microarray Experiment (dpeaa)DE-He213 Gene List (dpeaa)DE-He213 Gene Ontology Enrichment Analysis (dpeaa)DE-He213 Kommadath, Arun aut Smits, Mari A aut Enthalten in BMC proceedings London : BioMed Central, 2007 3(2009), Suppl 4 vom: 16. Juli (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:3 year:2009 number:Suppl 4 day:16 month:07 https://dx.doi.org/10.1186/1753-6561-3-S4-S10 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2009 Suppl 4 16 07 |
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10.1186/1753-6561-3-S4-S10 doi (DE-627)SPR028429699 (SPR)1753-6561-3-S4-S10-e DE-627 ger DE-627 rakwb eng Hulsegge, Ina verfasserin aut Globaltest and GOEAST: two different approaches for Gene Ontology analysis 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hulsegge et al; licensee BioMed Central Ltd. 2009 Background Gene set analysis is a commonly used method for analysing microarray data by considering groups of functionally related genes instead of individual genes. Here we present the use of two gene set analysis approaches: Globaltest and GOEAST. Globaltest is a method for testing whether sets of genes are significantly associated with a variable of interest. GOEAST is a freely accessible web-based tool to test GO term enrichment within given gene sets. The two approaches were applied in the analysis of gene lists obtained from three different contrasts in a microarray experiment conducted to study the host reactions in broilers following Eimeria infection. Results The Globaltest identified significantly associated gene sets in one of the three contrasts made in the microarray experiment whereas the functional analysis of the differentially expressed genes using GOEAST revealed enriched GO terms in all three contrasts. Conclusion Globaltest and GOEAST gave different results, probably due to the different algorithms and the different criteria used for evaluating the significance of GO terms. Gene Ontology (dpeaa)DE-He213 False Discovery Rate (dpeaa)DE-He213 Microarray Experiment (dpeaa)DE-He213 Gene List (dpeaa)DE-He213 Gene Ontology Enrichment Analysis (dpeaa)DE-He213 Kommadath, Arun aut Smits, Mari A aut Enthalten in BMC proceedings London : BioMed Central, 2007 3(2009), Suppl 4 vom: 16. Juli (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:3 year:2009 number:Suppl 4 day:16 month:07 https://dx.doi.org/10.1186/1753-6561-3-S4-S10 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2009 Suppl 4 16 07 |
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10.1186/1753-6561-3-S4-S10 doi (DE-627)SPR028429699 (SPR)1753-6561-3-S4-S10-e DE-627 ger DE-627 rakwb eng Hulsegge, Ina verfasserin aut Globaltest and GOEAST: two different approaches for Gene Ontology analysis 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hulsegge et al; licensee BioMed Central Ltd. 2009 Background Gene set analysis is a commonly used method for analysing microarray data by considering groups of functionally related genes instead of individual genes. Here we present the use of two gene set analysis approaches: Globaltest and GOEAST. Globaltest is a method for testing whether sets of genes are significantly associated with a variable of interest. GOEAST is a freely accessible web-based tool to test GO term enrichment within given gene sets. The two approaches were applied in the analysis of gene lists obtained from three different contrasts in a microarray experiment conducted to study the host reactions in broilers following Eimeria infection. Results The Globaltest identified significantly associated gene sets in one of the three contrasts made in the microarray experiment whereas the functional analysis of the differentially expressed genes using GOEAST revealed enriched GO terms in all three contrasts. Conclusion Globaltest and GOEAST gave different results, probably due to the different algorithms and the different criteria used for evaluating the significance of GO terms. Gene Ontology (dpeaa)DE-He213 False Discovery Rate (dpeaa)DE-He213 Microarray Experiment (dpeaa)DE-He213 Gene List (dpeaa)DE-He213 Gene Ontology Enrichment Analysis (dpeaa)DE-He213 Kommadath, Arun aut Smits, Mari A aut Enthalten in BMC proceedings London : BioMed Central, 2007 3(2009), Suppl 4 vom: 16. Juli (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:3 year:2009 number:Suppl 4 day:16 month:07 https://dx.doi.org/10.1186/1753-6561-3-S4-S10 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2009 Suppl 4 16 07 |
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10.1186/1753-6561-3-S4-S10 doi (DE-627)SPR028429699 (SPR)1753-6561-3-S4-S10-e DE-627 ger DE-627 rakwb eng Hulsegge, Ina verfasserin aut Globaltest and GOEAST: two different approaches for Gene Ontology analysis 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hulsegge et al; licensee BioMed Central Ltd. 2009 Background Gene set analysis is a commonly used method for analysing microarray data by considering groups of functionally related genes instead of individual genes. Here we present the use of two gene set analysis approaches: Globaltest and GOEAST. Globaltest is a method for testing whether sets of genes are significantly associated with a variable of interest. GOEAST is a freely accessible web-based tool to test GO term enrichment within given gene sets. The two approaches were applied in the analysis of gene lists obtained from three different contrasts in a microarray experiment conducted to study the host reactions in broilers following Eimeria infection. Results The Globaltest identified significantly associated gene sets in one of the three contrasts made in the microarray experiment whereas the functional analysis of the differentially expressed genes using GOEAST revealed enriched GO terms in all three contrasts. Conclusion Globaltest and GOEAST gave different results, probably due to the different algorithms and the different criteria used for evaluating the significance of GO terms. Gene Ontology (dpeaa)DE-He213 False Discovery Rate (dpeaa)DE-He213 Microarray Experiment (dpeaa)DE-He213 Gene List (dpeaa)DE-He213 Gene Ontology Enrichment Analysis (dpeaa)DE-He213 Kommadath, Arun aut Smits, Mari A aut Enthalten in BMC proceedings London : BioMed Central, 2007 3(2009), Suppl 4 vom: 16. Juli (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:3 year:2009 number:Suppl 4 day:16 month:07 https://dx.doi.org/10.1186/1753-6561-3-S4-S10 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2009 Suppl 4 16 07 |
| allfieldsSound |
10.1186/1753-6561-3-S4-S10 doi (DE-627)SPR028429699 (SPR)1753-6561-3-S4-S10-e DE-627 ger DE-627 rakwb eng Hulsegge, Ina verfasserin aut Globaltest and GOEAST: two different approaches for Gene Ontology analysis 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hulsegge et al; licensee BioMed Central Ltd. 2009 Background Gene set analysis is a commonly used method for analysing microarray data by considering groups of functionally related genes instead of individual genes. Here we present the use of two gene set analysis approaches: Globaltest and GOEAST. Globaltest is a method for testing whether sets of genes are significantly associated with a variable of interest. GOEAST is a freely accessible web-based tool to test GO term enrichment within given gene sets. The two approaches were applied in the analysis of gene lists obtained from three different contrasts in a microarray experiment conducted to study the host reactions in broilers following Eimeria infection. Results The Globaltest identified significantly associated gene sets in one of the three contrasts made in the microarray experiment whereas the functional analysis of the differentially expressed genes using GOEAST revealed enriched GO terms in all three contrasts. Conclusion Globaltest and GOEAST gave different results, probably due to the different algorithms and the different criteria used for evaluating the significance of GO terms. Gene Ontology (dpeaa)DE-He213 False Discovery Rate (dpeaa)DE-He213 Microarray Experiment (dpeaa)DE-He213 Gene List (dpeaa)DE-He213 Gene Ontology Enrichment Analysis (dpeaa)DE-He213 Kommadath, Arun aut Smits, Mari A aut Enthalten in BMC proceedings London : BioMed Central, 2007 3(2009), Suppl 4 vom: 16. Juli (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:3 year:2009 number:Suppl 4 day:16 month:07 https://dx.doi.org/10.1186/1753-6561-3-S4-S10 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2009 Suppl 4 16 07 |
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Globaltest and GOEAST: two different approaches for Gene Ontology analysis |
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Background Gene set analysis is a commonly used method for analysing microarray data by considering groups of functionally related genes instead of individual genes. Here we present the use of two gene set analysis approaches: Globaltest and GOEAST. Globaltest is a method for testing whether sets of genes are significantly associated with a variable of interest. GOEAST is a freely accessible web-based tool to test GO term enrichment within given gene sets. The two approaches were applied in the analysis of gene lists obtained from three different contrasts in a microarray experiment conducted to study the host reactions in broilers following Eimeria infection. Results The Globaltest identified significantly associated gene sets in one of the three contrasts made in the microarray experiment whereas the functional analysis of the differentially expressed genes using GOEAST revealed enriched GO terms in all three contrasts. Conclusion Globaltest and GOEAST gave different results, probably due to the different algorithms and the different criteria used for evaluating the significance of GO terms. © Hulsegge et al; licensee BioMed Central Ltd. 2009 |
| abstractGer |
Background Gene set analysis is a commonly used method for analysing microarray data by considering groups of functionally related genes instead of individual genes. Here we present the use of two gene set analysis approaches: Globaltest and GOEAST. Globaltest is a method for testing whether sets of genes are significantly associated with a variable of interest. GOEAST is a freely accessible web-based tool to test GO term enrichment within given gene sets. The two approaches were applied in the analysis of gene lists obtained from three different contrasts in a microarray experiment conducted to study the host reactions in broilers following Eimeria infection. Results The Globaltest identified significantly associated gene sets in one of the three contrasts made in the microarray experiment whereas the functional analysis of the differentially expressed genes using GOEAST revealed enriched GO terms in all three contrasts. Conclusion Globaltest and GOEAST gave different results, probably due to the different algorithms and the different criteria used for evaluating the significance of GO terms. © Hulsegge et al; licensee BioMed Central Ltd. 2009 |
| abstract_unstemmed |
Background Gene set analysis is a commonly used method for analysing microarray data by considering groups of functionally related genes instead of individual genes. Here we present the use of two gene set analysis approaches: Globaltest and GOEAST. Globaltest is a method for testing whether sets of genes are significantly associated with a variable of interest. GOEAST is a freely accessible web-based tool to test GO term enrichment within given gene sets. The two approaches were applied in the analysis of gene lists obtained from three different contrasts in a microarray experiment conducted to study the host reactions in broilers following Eimeria infection. Results The Globaltest identified significantly associated gene sets in one of the three contrasts made in the microarray experiment whereas the functional analysis of the differentially expressed genes using GOEAST revealed enriched GO terms in all three contrasts. Conclusion Globaltest and GOEAST gave different results, probably due to the different algorithms and the different criteria used for evaluating the significance of GO terms. © Hulsegge et al; licensee BioMed Central Ltd. 2009 |
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| score |
7.4003124 |