Conserved co-expression for candidate disease gene prioritization

Background Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably t...
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Autor*in:	Oti, Martin [verfasserIn] van Reeuwijk, Jeroen Huynen, Martijn A Brunner, Han G

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2008

Schlagwörter:	Disease Gene Evolutionary Conservation Candidate Disease Gene Total Expression Level Gene Atlas Expression

Anmerkung:	© Oti et al; licensee BioMed Central Ltd. 2008. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 9(2008), 1 vom: 23. Apr.
Übergeordnetes Werk:	volume:9 ; year:2008 ; number:1 ; day:23 ; month:04

Links:	Volltext

DOI / URN:	10.1186/1471-2105-9-208

Katalog-ID:	SPR026843897

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520			\|a Background Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone. Results We use co-expression data from yeast (S. cerevisiae), nematode worm (C. elegans), fruit fly (D. melanogaster), mouse and human and find that the use of evolutionary conservation can indeed improve the predictive value of co-expression. The effect that genes causing the same disease have higher co-expression than do other genes from their associated disease loci, is significantly enhanced when co-expression data are combined across evolutionarily distant species. We also find that performance can vary significantly depending on the co-expression datasets used, and just using more data does not necessarily lead to better prioritization. Instead, we find that dataset quality is more important than quantity, and using a consistent microarray platform per species leads to better performance than using more inclusive datasets pooled from various platforms. Conclusion We find that evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone, and provide the integrated data as a new resource for disease gene prioritization tools.
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allfields	10.1186/1471-2105-9-208 doi (DE-627)SPR026843897 (SPR)1471-2105-9-208-e DE-627 ger DE-627 rakwb eng Oti, Martin verfasserin aut Conserved co-expression for candidate disease gene prioritization 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Oti et al; licensee BioMed Central Ltd. 2008. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone. Results We use co-expression data from yeast (S. cerevisiae), nematode worm (C. elegans), fruit fly (D. melanogaster), mouse and human and find that the use of evolutionary conservation can indeed improve the predictive value of co-expression. The effect that genes causing the same disease have higher co-expression than do other genes from their associated disease loci, is significantly enhanced when co-expression data are combined across evolutionarily distant species. We also find that performance can vary significantly depending on the co-expression datasets used, and just using more data does not necessarily lead to better prioritization. Instead, we find that dataset quality is more important than quantity, and using a consistent microarray platform per species leads to better performance than using more inclusive datasets pooled from various platforms. Conclusion We find that evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone, and provide the integrated data as a new resource for disease gene prioritization tools. Disease Gene (dpeaa)DE-He213 Evolutionary Conservation (dpeaa)DE-He213 Candidate Disease Gene (dpeaa)DE-He213 Total Expression Level (dpeaa)DE-He213 Gene Atlas Expression (dpeaa)DE-He213 van Reeuwijk, Jeroen aut Huynen, Martijn A aut Brunner, Han G aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 23. Apr. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:23 month:04 https://dx.doi.org/10.1186/1471-2105-9-208 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2008 1 23 04
spelling	10.1186/1471-2105-9-208 doi (DE-627)SPR026843897 (SPR)1471-2105-9-208-e DE-627 ger DE-627 rakwb eng Oti, Martin verfasserin aut Conserved co-expression for candidate disease gene prioritization 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Oti et al; licensee BioMed Central Ltd. 2008. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone. Results We use co-expression data from yeast (S. cerevisiae), nematode worm (C. elegans), fruit fly (D. melanogaster), mouse and human and find that the use of evolutionary conservation can indeed improve the predictive value of co-expression. The effect that genes causing the same disease have higher co-expression than do other genes from their associated disease loci, is significantly enhanced when co-expression data are combined across evolutionarily distant species. We also find that performance can vary significantly depending on the co-expression datasets used, and just using more data does not necessarily lead to better prioritization. Instead, we find that dataset quality is more important than quantity, and using a consistent microarray platform per species leads to better performance than using more inclusive datasets pooled from various platforms. Conclusion We find that evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone, and provide the integrated data as a new resource for disease gene prioritization tools. Disease Gene (dpeaa)DE-He213 Evolutionary Conservation (dpeaa)DE-He213 Candidate Disease Gene (dpeaa)DE-He213 Total Expression Level (dpeaa)DE-He213 Gene Atlas Expression (dpeaa)DE-He213 van Reeuwijk, Jeroen aut Huynen, Martijn A aut Brunner, Han G aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 23. Apr. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:23 month:04 https://dx.doi.org/10.1186/1471-2105-9-208 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2008 1 23 04
allfields_unstemmed	10.1186/1471-2105-9-208 doi (DE-627)SPR026843897 (SPR)1471-2105-9-208-e DE-627 ger DE-627 rakwb eng Oti, Martin verfasserin aut Conserved co-expression for candidate disease gene prioritization 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Oti et al; licensee BioMed Central Ltd. 2008. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone. Results We use co-expression data from yeast (S. cerevisiae), nematode worm (C. elegans), fruit fly (D. melanogaster), mouse and human and find that the use of evolutionary conservation can indeed improve the predictive value of co-expression. The effect that genes causing the same disease have higher co-expression than do other genes from their associated disease loci, is significantly enhanced when co-expression data are combined across evolutionarily distant species. We also find that performance can vary significantly depending on the co-expression datasets used, and just using more data does not necessarily lead to better prioritization. Instead, we find that dataset quality is more important than quantity, and using a consistent microarray platform per species leads to better performance than using more inclusive datasets pooled from various platforms. Conclusion We find that evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone, and provide the integrated data as a new resource for disease gene prioritization tools. Disease Gene (dpeaa)DE-He213 Evolutionary Conservation (dpeaa)DE-He213 Candidate Disease Gene (dpeaa)DE-He213 Total Expression Level (dpeaa)DE-He213 Gene Atlas Expression (dpeaa)DE-He213 van Reeuwijk, Jeroen aut Huynen, Martijn A aut Brunner, Han G aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 23. Apr. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:23 month:04 https://dx.doi.org/10.1186/1471-2105-9-208 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2008 1 23 04
allfieldsGer	10.1186/1471-2105-9-208 doi (DE-627)SPR026843897 (SPR)1471-2105-9-208-e DE-627 ger DE-627 rakwb eng Oti, Martin verfasserin aut Conserved co-expression for candidate disease gene prioritization 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Oti et al; licensee BioMed Central Ltd. 2008. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone. Results We use co-expression data from yeast (S. cerevisiae), nematode worm (C. elegans), fruit fly (D. melanogaster), mouse and human and find that the use of evolutionary conservation can indeed improve the predictive value of co-expression. The effect that genes causing the same disease have higher co-expression than do other genes from their associated disease loci, is significantly enhanced when co-expression data are combined across evolutionarily distant species. We also find that performance can vary significantly depending on the co-expression datasets used, and just using more data does not necessarily lead to better prioritization. Instead, we find that dataset quality is more important than quantity, and using a consistent microarray platform per species leads to better performance than using more inclusive datasets pooled from various platforms. Conclusion We find that evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone, and provide the integrated data as a new resource for disease gene prioritization tools. Disease Gene (dpeaa)DE-He213 Evolutionary Conservation (dpeaa)DE-He213 Candidate Disease Gene (dpeaa)DE-He213 Total Expression Level (dpeaa)DE-He213 Gene Atlas Expression (dpeaa)DE-He213 van Reeuwijk, Jeroen aut Huynen, Martijn A aut Brunner, Han G aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 23. Apr. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:23 month:04 https://dx.doi.org/10.1186/1471-2105-9-208 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2008 1 23 04
allfieldsSound	10.1186/1471-2105-9-208 doi (DE-627)SPR026843897 (SPR)1471-2105-9-208-e DE-627 ger DE-627 rakwb eng Oti, Martin verfasserin aut Conserved co-expression for candidate disease gene prioritization 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Oti et al; licensee BioMed Central Ltd. 2008. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone. Results We use co-expression data from yeast (S. cerevisiae), nematode worm (C. elegans), fruit fly (D. melanogaster), mouse and human and find that the use of evolutionary conservation can indeed improve the predictive value of co-expression. The effect that genes causing the same disease have higher co-expression than do other genes from their associated disease loci, is significantly enhanced when co-expression data are combined across evolutionarily distant species. We also find that performance can vary significantly depending on the co-expression datasets used, and just using more data does not necessarily lead to better prioritization. Instead, we find that dataset quality is more important than quantity, and using a consistent microarray platform per species leads to better performance than using more inclusive datasets pooled from various platforms. Conclusion We find that evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone, and provide the integrated data as a new resource for disease gene prioritization tools. Disease Gene (dpeaa)DE-He213 Evolutionary Conservation (dpeaa)DE-He213 Candidate Disease Gene (dpeaa)DE-He213 Total Expression Level (dpeaa)DE-He213 Gene Atlas Expression (dpeaa)DE-He213 van Reeuwijk, Jeroen aut Huynen, Martijn A aut Brunner, Han G aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 23. Apr. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:23 month:04 https://dx.doi.org/10.1186/1471-2105-9-208 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2008 1 23 04
language	English
source	Enthalten in BMC bioinformatics 9(2008), 1 vom: 23. Apr. volume:9 year:2008 number:1 day:23 month:04
sourceStr	Enthalten in BMC bioinformatics 9(2008), 1 vom: 23. Apr. volume:9 year:2008 number:1 day:23 month:04
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Disease Gene Evolutionary Conservation Candidate Disease Gene Total Expression Level Gene Atlas Expression
isfreeaccess_bool	false
container_title	BMC bioinformatics
authorswithroles_txt_mv	Oti, Martin @@aut@@ van Reeuwijk, Jeroen @@aut@@ Huynen, Martijn A @@aut@@ Brunner, Han G @@aut@@
publishDateDaySort_date	2008-04-23T00:00:00Z
hierarchy_top_id	326644814
id	SPR026843897
language_de	englisch
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author	Oti, Martin
spellingShingle	Oti, Martin misc Disease Gene misc Evolutionary Conservation misc Candidate Disease Gene misc Total Expression Level misc Gene Atlas Expression Conserved co-expression for candidate disease gene prioritization
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topic_title	Conserved co-expression for candidate disease gene prioritization Disease Gene (dpeaa)DE-He213 Evolutionary Conservation (dpeaa)DE-He213 Candidate Disease Gene (dpeaa)DE-He213 Total Expression Level (dpeaa)DE-He213 Gene Atlas Expression (dpeaa)DE-He213
topic	misc Disease Gene misc Evolutionary Conservation misc Candidate Disease Gene misc Total Expression Level misc Gene Atlas Expression
topic_unstemmed	misc Disease Gene misc Evolutionary Conservation misc Candidate Disease Gene misc Total Expression Level misc Gene Atlas Expression
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title	Conserved co-expression for candidate disease gene prioritization
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title_sort	conserved co-expression for candidate disease gene prioritization
title_auth	Conserved co-expression for candidate disease gene prioritization
abstract	Background Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone. Results We use co-expression data from yeast (S. cerevisiae), nematode worm (C. elegans), fruit fly (D. melanogaster), mouse and human and find that the use of evolutionary conservation can indeed improve the predictive value of co-expression. The effect that genes causing the same disease have higher co-expression than do other genes from their associated disease loci, is significantly enhanced when co-expression data are combined across evolutionarily distant species. We also find that performance can vary significantly depending on the co-expression datasets used, and just using more data does not necessarily lead to better prioritization. Instead, we find that dataset quality is more important than quantity, and using a consistent microarray platform per species leads to better performance than using more inclusive datasets pooled from various platforms. Conclusion We find that evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone, and provide the integrated data as a new resource for disease gene prioritization tools. © Oti et al; licensee BioMed Central Ltd. 2008. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	Background Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone. Results We use co-expression data from yeast (S. cerevisiae), nematode worm (C. elegans), fruit fly (D. melanogaster), mouse and human and find that the use of evolutionary conservation can indeed improve the predictive value of co-expression. The effect that genes causing the same disease have higher co-expression than do other genes from their associated disease loci, is significantly enhanced when co-expression data are combined across evolutionarily distant species. We also find that performance can vary significantly depending on the co-expression datasets used, and just using more data does not necessarily lead to better prioritization. Instead, we find that dataset quality is more important than quantity, and using a consistent microarray platform per species leads to better performance than using more inclusive datasets pooled from various platforms. Conclusion We find that evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone, and provide the integrated data as a new resource for disease gene prioritization tools. © Oti et al; licensee BioMed Central Ltd. 2008. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	Background Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone. Results We use co-expression data from yeast (S. cerevisiae), nematode worm (C. elegans), fruit fly (D. melanogaster), mouse and human and find that the use of evolutionary conservation can indeed improve the predictive value of co-expression. The effect that genes causing the same disease have higher co-expression than do other genes from their associated disease loci, is significantly enhanced when co-expression data are combined across evolutionarily distant species. We also find that performance can vary significantly depending on the co-expression datasets used, and just using more data does not necessarily lead to better prioritization. Instead, we find that dataset quality is more important than quantity, and using a consistent microarray platform per species leads to better performance than using more inclusive datasets pooled from various platforms. Conclusion We find that evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone, and provide the integrated data as a new resource for disease gene prioritization tools. © Oti et al; licensee BioMed Central Ltd. 2008. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
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title_short	Conserved co-expression for candidate disease gene prioritization
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Conserved co-expression for candidate disease gene prioritization

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