GO Trimming: Systematically reducing redundancy in large Gene Ontology datasets

<p<Abstract</p< <p<Background</p< <p<The increased accessibility of gene expression tools has enabled a wide variety of experiments utilizing transcriptomic analyses. As these tools increase in prevalence, the need for improved standardization in processing and presenta...
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Autor*in:	Koop Ben F [verfasserIn] Minkley David R [verfasserIn] Sutherland Ben JG [verfasserIn] Jantzen Stuart G [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Übergeordnetes Werk:	In: BMC Research Notes - BMC, 2008, 4(2011), 1, p 267
Übergeordnetes Werk:	volume:4 ; year:2011 ; number:1, p 267

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/1756-0500-4-267

Katalog-ID:	DOAJ039136507

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spelling	10.1186/1756-0500-4-267 doi (DE-627)DOAJ039136507 (DE-599)DOAJ4c31033f72fa4977a776d384f8fee5d5 DE-627 ger DE-627 rakwb eng QH301-705.5 Q1-390 Koop Ben F verfasserin aut GO Trimming: Systematically reducing redundancy in large Gene Ontology datasets 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The increased accessibility of gene expression tools has enabled a wide variety of experiments utilizing transcriptomic analyses. As these tools increase in prevalence, the need for improved standardization in processing and presentation of data increases, as does the need to guard against interpretation bias. Gene Ontology (GO) analysis is a powerful method of interpreting and summarizing biological functions. However, while there are many tools available to investigate GO enrichment, there remains a need for methods that directly remove redundant terms from enriched GO lists that often provide little, if any, additional information.</p< <p<Findings</p< <p<Here we present a simple yet novel method called GO Trimming that utilizes an algorithm designed to reduce redundancy in lists of enriched GO categories. Depending on the needs of the user, this method can be performed with variable stringency. In the example presented here, an initial list of 90 terms was reduced to 54, eliminating 36 largely redundant terms. We also compare this method to existing methods and find that GO Trimming, while simple, performs well to eliminate redundant terms in a large dataset throughout the depth of the GO hierarchy.</p< <p<Conclusions</p< <p<The GO Trimming method provides an alternative to other procedures, some of which involve removing large numbers of terms prior to enrichment analysis. This method should free up the researcher from analyzing overly large, redundant lists, and instead enable the concise presentation of manageable, informative GO lists. The implementation of this tool is freely available at: <url<http://lucy.ceh.uvic.ca/go_trimming/cbr_go_trimming.py</url<</p< Medicine R Biology (General) Science (General) Minkley David R verfasserin aut Sutherland Ben JG verfasserin aut Jantzen Stuart G verfasserin aut In BMC Research Notes BMC, 2008 4(2011), 1, p 267 (DE-627)559431805 (DE-600)2413336-X 17560500 nnns volume:4 year:2011 number:1, p 267 https://doi.org/10.1186/1756-0500-4-267 kostenfrei https://doaj.org/article/4c31033f72fa4977a776d384f8fee5d5 kostenfrei http://www.biomedcentral.com/1756-0500/4/267 kostenfrei https://doaj.org/toc/1756-0500 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 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 4 2011 1, p 267
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allfieldsGer	10.1186/1756-0500-4-267 doi (DE-627)DOAJ039136507 (DE-599)DOAJ4c31033f72fa4977a776d384f8fee5d5 DE-627 ger DE-627 rakwb eng QH301-705.5 Q1-390 Koop Ben F verfasserin aut GO Trimming: Systematically reducing redundancy in large Gene Ontology datasets 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The increased accessibility of gene expression tools has enabled a wide variety of experiments utilizing transcriptomic analyses. As these tools increase in prevalence, the need for improved standardization in processing and presentation of data increases, as does the need to guard against interpretation bias. Gene Ontology (GO) analysis is a powerful method of interpreting and summarizing biological functions. However, while there are many tools available to investigate GO enrichment, there remains a need for methods that directly remove redundant terms from enriched GO lists that often provide little, if any, additional information.</p< <p<Findings</p< <p<Here we present a simple yet novel method called GO Trimming that utilizes an algorithm designed to reduce redundancy in lists of enriched GO categories. Depending on the needs of the user, this method can be performed with variable stringency. In the example presented here, an initial list of 90 terms was reduced to 54, eliminating 36 largely redundant terms. We also compare this method to existing methods and find that GO Trimming, while simple, performs well to eliminate redundant terms in a large dataset throughout the depth of the GO hierarchy.</p< <p<Conclusions</p< <p<The GO Trimming method provides an alternative to other procedures, some of which involve removing large numbers of terms prior to enrichment analysis. This method should free up the researcher from analyzing overly large, redundant lists, and instead enable the concise presentation of manageable, informative GO lists. The implementation of this tool is freely available at: <url<http://lucy.ceh.uvic.ca/go_trimming/cbr_go_trimming.py</url<</p< Medicine R Biology (General) Science (General) Minkley David R verfasserin aut Sutherland Ben JG verfasserin aut Jantzen Stuart G verfasserin aut In BMC Research Notes BMC, 2008 4(2011), 1, p 267 (DE-627)559431805 (DE-600)2413336-X 17560500 nnns volume:4 year:2011 number:1, p 267 https://doi.org/10.1186/1756-0500-4-267 kostenfrei https://doaj.org/article/4c31033f72fa4977a776d384f8fee5d5 kostenfrei http://www.biomedcentral.com/1756-0500/4/267 kostenfrei https://doaj.org/toc/1756-0500 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 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 4 2011 1, p 267
allfieldsSound	10.1186/1756-0500-4-267 doi (DE-627)DOAJ039136507 (DE-599)DOAJ4c31033f72fa4977a776d384f8fee5d5 DE-627 ger DE-627 rakwb eng QH301-705.5 Q1-390 Koop Ben F verfasserin aut GO Trimming: Systematically reducing redundancy in large Gene Ontology datasets 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The increased accessibility of gene expression tools has enabled a wide variety of experiments utilizing transcriptomic analyses. As these tools increase in prevalence, the need for improved standardization in processing and presentation of data increases, as does the need to guard against interpretation bias. Gene Ontology (GO) analysis is a powerful method of interpreting and summarizing biological functions. However, while there are many tools available to investigate GO enrichment, there remains a need for methods that directly remove redundant terms from enriched GO lists that often provide little, if any, additional information.</p< <p<Findings</p< <p<Here we present a simple yet novel method called GO Trimming that utilizes an algorithm designed to reduce redundancy in lists of enriched GO categories. Depending on the needs of the user, this method can be performed with variable stringency. In the example presented here, an initial list of 90 terms was reduced to 54, eliminating 36 largely redundant terms. We also compare this method to existing methods and find that GO Trimming, while simple, performs well to eliminate redundant terms in a large dataset throughout the depth of the GO hierarchy.</p< <p<Conclusions</p< <p<The GO Trimming method provides an alternative to other procedures, some of which involve removing large numbers of terms prior to enrichment analysis. This method should free up the researcher from analyzing overly large, redundant lists, and instead enable the concise presentation of manageable, informative GO lists. The implementation of this tool is freely available at: <url<http://lucy.ceh.uvic.ca/go_trimming/cbr_go_trimming.py</url<</p< Medicine R Biology (General) Science (General) Minkley David R verfasserin aut Sutherland Ben JG verfasserin aut Jantzen Stuart G verfasserin aut In BMC Research Notes BMC, 2008 4(2011), 1, p 267 (DE-627)559431805 (DE-600)2413336-X 17560500 nnns volume:4 year:2011 number:1, p 267 https://doi.org/10.1186/1756-0500-4-267 kostenfrei https://doaj.org/article/4c31033f72fa4977a776d384f8fee5d5 kostenfrei http://www.biomedcentral.com/1756-0500/4/267 kostenfrei https://doaj.org/toc/1756-0500 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 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 4 2011 1, p 267
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title	GO Trimming: Systematically reducing redundancy in large Gene Ontology datasets
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title_full	GO Trimming: Systematically reducing redundancy in large Gene Ontology datasets
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title_sort	go trimming: systematically reducing redundancy in large gene ontology datasets
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title_auth	GO Trimming: Systematically reducing redundancy in large Gene Ontology datasets
abstract	<p<Abstract</p< <p<Background</p< <p<The increased accessibility of gene expression tools has enabled a wide variety of experiments utilizing transcriptomic analyses. As these tools increase in prevalence, the need for improved standardization in processing and presentation of data increases, as does the need to guard against interpretation bias. Gene Ontology (GO) analysis is a powerful method of interpreting and summarizing biological functions. However, while there are many tools available to investigate GO enrichment, there remains a need for methods that directly remove redundant terms from enriched GO lists that often provide little, if any, additional information.</p< <p<Findings</p< <p<Here we present a simple yet novel method called GO Trimming that utilizes an algorithm designed to reduce redundancy in lists of enriched GO categories. Depending on the needs of the user, this method can be performed with variable stringency. In the example presented here, an initial list of 90 terms was reduced to 54, eliminating 36 largely redundant terms. We also compare this method to existing methods and find that GO Trimming, while simple, performs well to eliminate redundant terms in a large dataset throughout the depth of the GO hierarchy.</p< <p<Conclusions</p< <p<The GO Trimming method provides an alternative to other procedures, some of which involve removing large numbers of terms prior to enrichment analysis. This method should free up the researcher from analyzing overly large, redundant lists, and instead enable the concise presentation of manageable, informative GO lists. The implementation of this tool is freely available at: <url<http://lucy.ceh.uvic.ca/go_trimming/cbr_go_trimming.py</url<</p<
abstractGer	<p<Abstract</p< <p<Background</p< <p<The increased accessibility of gene expression tools has enabled a wide variety of experiments utilizing transcriptomic analyses. As these tools increase in prevalence, the need for improved standardization in processing and presentation of data increases, as does the need to guard against interpretation bias. Gene Ontology (GO) analysis is a powerful method of interpreting and summarizing biological functions. However, while there are many tools available to investigate GO enrichment, there remains a need for methods that directly remove redundant terms from enriched GO lists that often provide little, if any, additional information.</p< <p<Findings</p< <p<Here we present a simple yet novel method called GO Trimming that utilizes an algorithm designed to reduce redundancy in lists of enriched GO categories. Depending on the needs of the user, this method can be performed with variable stringency. In the example presented here, an initial list of 90 terms was reduced to 54, eliminating 36 largely redundant terms. We also compare this method to existing methods and find that GO Trimming, while simple, performs well to eliminate redundant terms in a large dataset throughout the depth of the GO hierarchy.</p< <p<Conclusions</p< <p<The GO Trimming method provides an alternative to other procedures, some of which involve removing large numbers of terms prior to enrichment analysis. This method should free up the researcher from analyzing overly large, redundant lists, and instead enable the concise presentation of manageable, informative GO lists. The implementation of this tool is freely available at: <url<http://lucy.ceh.uvic.ca/go_trimming/cbr_go_trimming.py</url<</p<
abstract_unstemmed	<p<Abstract</p< <p<Background</p< <p<The increased accessibility of gene expression tools has enabled a wide variety of experiments utilizing transcriptomic analyses. As these tools increase in prevalence, the need for improved standardization in processing and presentation of data increases, as does the need to guard against interpretation bias. Gene Ontology (GO) analysis is a powerful method of interpreting and summarizing biological functions. However, while there are many tools available to investigate GO enrichment, there remains a need for methods that directly remove redundant terms from enriched GO lists that often provide little, if any, additional information.</p< <p<Findings</p< <p<Here we present a simple yet novel method called GO Trimming that utilizes an algorithm designed to reduce redundancy in lists of enriched GO categories. Depending on the needs of the user, this method can be performed with variable stringency. In the example presented here, an initial list of 90 terms was reduced to 54, eliminating 36 largely redundant terms. We also compare this method to existing methods and find that GO Trimming, while simple, performs well to eliminate redundant terms in a large dataset throughout the depth of the GO hierarchy.</p< <p<Conclusions</p< <p<The GO Trimming method provides an alternative to other procedures, some of which involve removing large numbers of terms prior to enrichment analysis. This method should free up the researcher from analyzing overly large, redundant lists, and instead enable the concise presentation of manageable, informative GO lists. The implementation of this tool is freely available at: <url<http://lucy.ceh.uvic.ca/go_trimming/cbr_go_trimming.py</url<</p<
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We also compare this method to existing methods and find that GO Trimming, while simple, performs well to eliminate redundant terms in a large dataset throughout the depth of the GO hierarchy.</p< <p<Conclusions</p< <p<The GO Trimming method provides an alternative to other procedures, some of which involve removing large numbers of terms prior to enrichment analysis. This method should free up the researcher from analyzing overly large, redundant lists, and instead enable the concise presentation of manageable, informative GO lists. 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GO Trimming: Systematically reducing redundancy in large Gene Ontology datasets

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