A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis

<p<Abstract</p< <p<Background</p< <p<Several computational candidate gene selection and prioritization methods have recently been developed. These <it<in silico </it<selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known.</p< <p<Results</p< <p<The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (<80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (<url<http://main.g2.bx.psu.edu/</url<). Nine genes (<it<APLN</it<, <it<ZC4H2</it<, <it<MAGED4</it<, <it<MAGED4B</it<, <it<RAP2C</it<, <it<FAM156A</it<, <it<FAM156B</it<, <it<TBL1X</it<, and <it<UXT</it<) were highlighted as highly-ranked XLMR methods.</p< <p<Conclusions</p< <p<The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR.</p< <p<<it<Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi).</it<</p< Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Lombard Zané [verfasserIn] Park Chungoo [verfasserIn] Makova Kateryna D [verfasserIn] Ramsay Michèle [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Übergeordnetes Werk:	In: Biology Direct - BMC, 2006, 6(2011), 1, p 30
Übergeordnetes Werk:	volume:6 ; year:2011 ; number:1, p 30

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/1745-6150-6-30

Katalog-ID:	DOAJ008744246

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allfields	10.1186/1745-6150-6-30 doi (DE-627)DOAJ008744246 (DE-599)DOAJb644dc46cdea459ca4ebaec2f11fd044 DE-627 ger DE-627 rakwb eng QH301-705.5 Lombard Zané verfasserin aut A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Several computational candidate gene selection and prioritization methods have recently been developed. These <it<in silico </it<selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known.</p< <p<Results</p< <p<The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (<80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (<url<http://main.g2.bx.psu.edu/</url<). Nine genes (<it<APLN</it<, <it<ZC4H2</it<, <it<MAGED4</it<, <it<MAGED4B</it<, <it<RAP2C</it<, <it<FAM156A</it<, <it<FAM156B</it<, <it<TBL1X</it<, and <it<UXT</it<) were highlighted as highly-ranked XLMR methods.</p< <p<Conclusions</p< <p<The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR.</p< <p<<it<Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi).</it<</p< Biology (General) Park Chungoo verfasserin aut Makova Kateryna D verfasserin aut Ramsay Michèle verfasserin aut In Biology Direct BMC, 2006 6(2011), 1, p 30 (DE-627)507522516 (DE-600)2221028-3 17456150 nnns volume:6 year:2011 number:1, p 30 https://doi.org/10.1186/1745-6150-6-30 kostenfrei https://doaj.org/article/b644dc46cdea459ca4ebaec2f11fd044 kostenfrei http://www.biology-direct.com/content/6/1/30 kostenfrei https://doaj.org/toc/1745-6150 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_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 6 2011 1, p 30
spelling	10.1186/1745-6150-6-30 doi (DE-627)DOAJ008744246 (DE-599)DOAJb644dc46cdea459ca4ebaec2f11fd044 DE-627 ger DE-627 rakwb eng QH301-705.5 Lombard Zané verfasserin aut A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Several computational candidate gene selection and prioritization methods have recently been developed. These <it<in silico </it<selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known.</p< <p<Results</p< <p<The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (<80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (<url<http://main.g2.bx.psu.edu/</url<). Nine genes (<it<APLN</it<, <it<ZC4H2</it<, <it<MAGED4</it<, <it<MAGED4B</it<, <it<RAP2C</it<, <it<FAM156A</it<, <it<FAM156B</it<, <it<TBL1X</it<, and <it<UXT</it<) were highlighted as highly-ranked XLMR methods.</p< <p<Conclusions</p< <p<The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR.</p< <p<<it<Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi).</it<</p< Biology (General) Park Chungoo verfasserin aut Makova Kateryna D verfasserin aut Ramsay Michèle verfasserin aut In Biology Direct BMC, 2006 6(2011), 1, p 30 (DE-627)507522516 (DE-600)2221028-3 17456150 nnns volume:6 year:2011 number:1, p 30 https://doi.org/10.1186/1745-6150-6-30 kostenfrei https://doaj.org/article/b644dc46cdea459ca4ebaec2f11fd044 kostenfrei http://www.biology-direct.com/content/6/1/30 kostenfrei https://doaj.org/toc/1745-6150 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_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 6 2011 1, p 30
allfields_unstemmed	10.1186/1745-6150-6-30 doi (DE-627)DOAJ008744246 (DE-599)DOAJb644dc46cdea459ca4ebaec2f11fd044 DE-627 ger DE-627 rakwb eng QH301-705.5 Lombard Zané verfasserin aut A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Several computational candidate gene selection and prioritization methods have recently been developed. These <it<in silico </it<selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known.</p< <p<Results</p< <p<The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (<80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (<url<http://main.g2.bx.psu.edu/</url<). Nine genes (<it<APLN</it<, <it<ZC4H2</it<, <it<MAGED4</it<, <it<MAGED4B</it<, <it<RAP2C</it<, <it<FAM156A</it<, <it<FAM156B</it<, <it<TBL1X</it<, and <it<UXT</it<) were highlighted as highly-ranked XLMR methods.</p< <p<Conclusions</p< <p<The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR.</p< <p<<it<Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi).</it<</p< Biology (General) Park Chungoo verfasserin aut Makova Kateryna D verfasserin aut Ramsay Michèle verfasserin aut In Biology Direct BMC, 2006 6(2011), 1, p 30 (DE-627)507522516 (DE-600)2221028-3 17456150 nnns volume:6 year:2011 number:1, p 30 https://doi.org/10.1186/1745-6150-6-30 kostenfrei https://doaj.org/article/b644dc46cdea459ca4ebaec2f11fd044 kostenfrei http://www.biology-direct.com/content/6/1/30 kostenfrei https://doaj.org/toc/1745-6150 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_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 6 2011 1, p 30
allfieldsGer	10.1186/1745-6150-6-30 doi (DE-627)DOAJ008744246 (DE-599)DOAJb644dc46cdea459ca4ebaec2f11fd044 DE-627 ger DE-627 rakwb eng QH301-705.5 Lombard Zané verfasserin aut A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Several computational candidate gene selection and prioritization methods have recently been developed. These <it<in silico </it<selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known.</p< <p<Results</p< <p<The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (<80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (<url<http://main.g2.bx.psu.edu/</url<). Nine genes (<it<APLN</it<, <it<ZC4H2</it<, <it<MAGED4</it<, <it<MAGED4B</it<, <it<RAP2C</it<, <it<FAM156A</it<, <it<FAM156B</it<, <it<TBL1X</it<, and <it<UXT</it<) were highlighted as highly-ranked XLMR methods.</p< <p<Conclusions</p< <p<The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR.</p< <p<<it<Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi).</it<</p< Biology (General) Park Chungoo verfasserin aut Makova Kateryna D verfasserin aut Ramsay Michèle verfasserin aut In Biology Direct BMC, 2006 6(2011), 1, p 30 (DE-627)507522516 (DE-600)2221028-3 17456150 nnns volume:6 year:2011 number:1, p 30 https://doi.org/10.1186/1745-6150-6-30 kostenfrei https://doaj.org/article/b644dc46cdea459ca4ebaec2f11fd044 kostenfrei http://www.biology-direct.com/content/6/1/30 kostenfrei https://doaj.org/toc/1745-6150 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_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 6 2011 1, p 30
allfieldsSound	10.1186/1745-6150-6-30 doi (DE-627)DOAJ008744246 (DE-599)DOAJb644dc46cdea459ca4ebaec2f11fd044 DE-627 ger DE-627 rakwb eng QH301-705.5 Lombard Zané verfasserin aut A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Several computational candidate gene selection and prioritization methods have recently been developed. These <it<in silico </it<selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known.</p< <p<Results</p< <p<The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (<80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (<url<http://main.g2.bx.psu.edu/</url<). Nine genes (<it<APLN</it<, <it<ZC4H2</it<, <it<MAGED4</it<, <it<MAGED4B</it<, <it<RAP2C</it<, <it<FAM156A</it<, <it<FAM156B</it<, <it<TBL1X</it<, and <it<UXT</it<) were highlighted as highly-ranked XLMR methods.</p< <p<Conclusions</p< <p<The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR.</p< <p<<it<Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi).</it<</p< Biology (General) Park Chungoo verfasserin aut Makova Kateryna D verfasserin aut Ramsay Michèle verfasserin aut In Biology Direct BMC, 2006 6(2011), 1, p 30 (DE-627)507522516 (DE-600)2221028-3 17456150 nnns volume:6 year:2011 number:1, p 30 https://doi.org/10.1186/1745-6150-6-30 kostenfrei https://doaj.org/article/b644dc46cdea459ca4ebaec2f11fd044 kostenfrei http://www.biology-direct.com/content/6/1/30 kostenfrei https://doaj.org/toc/1745-6150 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_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 6 2011 1, p 30
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title	A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis
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title_full	A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis
author_sort	Lombard Zané
journal	Biology Direct
journalStr	Biology Direct
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author_browse	Lombard Zané Park Chungoo Makova Kateryna D Ramsay Michèle
container_volume	6
class	QH301-705.5
format_se	Elektronische Aufsätze
author-letter	Lombard Zané
doi_str_mv	10.1186/1745-6150-6-30
author2-role	verfasserin
title_sort	computational approach to candidate gene prioritization for x-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis
callnumber	QH301-705.5
title_auth	A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis
abstract	<p<Abstract</p< <p<Background</p< <p<Several computational candidate gene selection and prioritization methods have recently been developed. These <it<in silico </it<selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known.</p< <p<Results</p< <p<The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (<80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (<url<http://main.g2.bx.psu.edu/</url<). Nine genes (<it<APLN</it<, <it<ZC4H2</it<, <it<MAGED4</it<, <it<MAGED4B</it<, <it<RAP2C</it<, <it<FAM156A</it<, <it<FAM156B</it<, <it<TBL1X</it<, and <it<UXT</it<) were highlighted as highly-ranked XLMR methods.</p< <p<Conclusions</p< <p<The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR.</p< <p<<it<Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi).</it<</p<
abstractGer	<p<Abstract</p< <p<Background</p< <p<Several computational candidate gene selection and prioritization methods have recently been developed. These <it<in silico </it<selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known.</p< <p<Results</p< <p<The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (<80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (<url<http://main.g2.bx.psu.edu/</url<). Nine genes (<it<APLN</it<, <it<ZC4H2</it<, <it<MAGED4</it<, <it<MAGED4B</it<, <it<RAP2C</it<, <it<FAM156A</it<, <it<FAM156B</it<, <it<TBL1X</it<, and <it<UXT</it<) were highlighted as highly-ranked XLMR methods.</p< <p<Conclusions</p< <p<The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR.</p< <p<<it<Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi).</it<</p<
abstract_unstemmed	<p<Abstract</p< <p<Background</p< <p<Several computational candidate gene selection and prioritization methods have recently been developed. These <it<in silico </it<selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known.</p< <p<Results</p< <p<The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (<80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (<url<http://main.g2.bx.psu.edu/</url<). Nine genes (<it<APLN</it<, <it<ZC4H2</it<, <it<MAGED4</it<, <it<MAGED4B</it<, <it<RAP2C</it<, <it<FAM156A</it<, <it<FAM156B</it<, <it<TBL1X</it<, and <it<UXT</it<) were highlighted as highly-ranked XLMR methods.</p< <p<Conclusions</p< <p<The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR.</p< <p<<it<Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi).</it<</p<
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title_short	A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis
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