Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data

Affinity purification followed by mass spectrometry (AP‐MS) has become a common approach for identifying protein–protein interactions (PPIs) and complexes. However, data analysis and visualization often rely on generic approaches that do not take advantage of the quantitative nature of AP‐MS. We pre...
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Autor*in:	Hyungwon Choi [verfasserIn] Sinae Kim [verfasserIn] Anne‐Claude Gingras [verfasserIn] Alexey I Nesvizhskii [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	clustering mass spectrometry protein complexes protein‐protein interaction spectral counts

Übergeordnetes Werk:	In: Molecular Systems Biology - Wiley, 2005, 6(2010), 1, Seite n/a-n/a
Übergeordnetes Werk:	volume:6 ; year:2010 ; number:1 ; pages:n/a-n/a

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1038/msb.2010.41

Katalog-ID:	DOAJ098508385

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520			\|a Affinity purification followed by mass spectrometry (AP‐MS) has become a common approach for identifying protein–protein interactions (PPIs) and complexes. However, data analysis and visualization often rely on generic approaches that do not take advantage of the quantitative nature of AP‐MS. We present a novel computational method, nested clustering, for biclustering of label‐free quantitative AP‐MS data. Our approach forms bait clusters based on the similarity of quantitative interaction profiles and identifies submatrices of prey proteins showing consistent quantitative association within bait clusters. In doing so, nested clustering effectively addresses the problem of overrepresentation of interactions involving baits proteins as compared with proteins only identified as preys. The method does not require specification of the number of bait clusters, which is an advantage against existing model‐based clustering methods. We illustrate the performance of the algorithm using two published intermediate scale human PPI data sets, which are representative of the AP‐MS data generated from mammalian cells. We also discuss general challenges of analyzing and interpreting clustering results in the context of AP‐MS data.
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allfields	10.1038/msb.2010.41 doi (DE-627)DOAJ098508385 (DE-599)DOAJ94203032d6a04b99bffb2fbad7abe8c7 DE-627 ger DE-627 rakwb eng QH301-705.5 R5-920 Hyungwon Choi verfasserin aut Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Affinity purification followed by mass spectrometry (AP‐MS) has become a common approach for identifying protein–protein interactions (PPIs) and complexes. However, data analysis and visualization often rely on generic approaches that do not take advantage of the quantitative nature of AP‐MS. We present a novel computational method, nested clustering, for biclustering of label‐free quantitative AP‐MS data. Our approach forms bait clusters based on the similarity of quantitative interaction profiles and identifies submatrices of prey proteins showing consistent quantitative association within bait clusters. In doing so, nested clustering effectively addresses the problem of overrepresentation of interactions involving baits proteins as compared with proteins only identified as preys. The method does not require specification of the number of bait clusters, which is an advantage against existing model‐based clustering methods. We illustrate the performance of the algorithm using two published intermediate scale human PPI data sets, which are representative of the AP‐MS data generated from mammalian cells. We also discuss general challenges of analyzing and interpreting clustering results in the context of AP‐MS data. clustering mass spectrometry protein complexes protein‐protein interaction spectral counts Biology (General) Medicine (General) Sinae Kim verfasserin aut Anne‐Claude Gingras verfasserin aut Alexey I Nesvizhskii verfasserin aut In Molecular Systems Biology Wiley, 2005 6(2010), 1, Seite n/a-n/a (DE-627)490536905 (DE-600)2193510-5 17444292 nnns volume:6 year:2010 number:1 pages:n/a-n/a https://doi.org/10.1038/msb.2010.41 kostenfrei https://doaj.org/article/94203032d6a04b99bffb2fbad7abe8c7 kostenfrei https://doi.org/10.1038/msb.2010.41 kostenfrei https://doaj.org/toc/1744-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2010 1 n/a-n/a
spelling	10.1038/msb.2010.41 doi (DE-627)DOAJ098508385 (DE-599)DOAJ94203032d6a04b99bffb2fbad7abe8c7 DE-627 ger DE-627 rakwb eng QH301-705.5 R5-920 Hyungwon Choi verfasserin aut Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Affinity purification followed by mass spectrometry (AP‐MS) has become a common approach for identifying protein–protein interactions (PPIs) and complexes. However, data analysis and visualization often rely on generic approaches that do not take advantage of the quantitative nature of AP‐MS. We present a novel computational method, nested clustering, for biclustering of label‐free quantitative AP‐MS data. Our approach forms bait clusters based on the similarity of quantitative interaction profiles and identifies submatrices of prey proteins showing consistent quantitative association within bait clusters. In doing so, nested clustering effectively addresses the problem of overrepresentation of interactions involving baits proteins as compared with proteins only identified as preys. The method does not require specification of the number of bait clusters, which is an advantage against existing model‐based clustering methods. We illustrate the performance of the algorithm using two published intermediate scale human PPI data sets, which are representative of the AP‐MS data generated from mammalian cells. We also discuss general challenges of analyzing and interpreting clustering results in the context of AP‐MS data. clustering mass spectrometry protein complexes protein‐protein interaction spectral counts Biology (General) Medicine (General) Sinae Kim verfasserin aut Anne‐Claude Gingras verfasserin aut Alexey I Nesvizhskii verfasserin aut In Molecular Systems Biology Wiley, 2005 6(2010), 1, Seite n/a-n/a (DE-627)490536905 (DE-600)2193510-5 17444292 nnns volume:6 year:2010 number:1 pages:n/a-n/a https://doi.org/10.1038/msb.2010.41 kostenfrei https://doaj.org/article/94203032d6a04b99bffb2fbad7abe8c7 kostenfrei https://doi.org/10.1038/msb.2010.41 kostenfrei https://doaj.org/toc/1744-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2010 1 n/a-n/a
allfields_unstemmed	10.1038/msb.2010.41 doi (DE-627)DOAJ098508385 (DE-599)DOAJ94203032d6a04b99bffb2fbad7abe8c7 DE-627 ger DE-627 rakwb eng QH301-705.5 R5-920 Hyungwon Choi verfasserin aut Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Affinity purification followed by mass spectrometry (AP‐MS) has become a common approach for identifying protein–protein interactions (PPIs) and complexes. However, data analysis and visualization often rely on generic approaches that do not take advantage of the quantitative nature of AP‐MS. We present a novel computational method, nested clustering, for biclustering of label‐free quantitative AP‐MS data. Our approach forms bait clusters based on the similarity of quantitative interaction profiles and identifies submatrices of prey proteins showing consistent quantitative association within bait clusters. In doing so, nested clustering effectively addresses the problem of overrepresentation of interactions involving baits proteins as compared with proteins only identified as preys. The method does not require specification of the number of bait clusters, which is an advantage against existing model‐based clustering methods. We illustrate the performance of the algorithm using two published intermediate scale human PPI data sets, which are representative of the AP‐MS data generated from mammalian cells. We also discuss general challenges of analyzing and interpreting clustering results in the context of AP‐MS data. clustering mass spectrometry protein complexes protein‐protein interaction spectral counts Biology (General) Medicine (General) Sinae Kim verfasserin aut Anne‐Claude Gingras verfasserin aut Alexey I Nesvizhskii verfasserin aut In Molecular Systems Biology Wiley, 2005 6(2010), 1, Seite n/a-n/a (DE-627)490536905 (DE-600)2193510-5 17444292 nnns volume:6 year:2010 number:1 pages:n/a-n/a https://doi.org/10.1038/msb.2010.41 kostenfrei https://doaj.org/article/94203032d6a04b99bffb2fbad7abe8c7 kostenfrei https://doi.org/10.1038/msb.2010.41 kostenfrei https://doaj.org/toc/1744-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2010 1 n/a-n/a
allfieldsGer	10.1038/msb.2010.41 doi (DE-627)DOAJ098508385 (DE-599)DOAJ94203032d6a04b99bffb2fbad7abe8c7 DE-627 ger DE-627 rakwb eng QH301-705.5 R5-920 Hyungwon Choi verfasserin aut Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Affinity purification followed by mass spectrometry (AP‐MS) has become a common approach for identifying protein–protein interactions (PPIs) and complexes. However, data analysis and visualization often rely on generic approaches that do not take advantage of the quantitative nature of AP‐MS. We present a novel computational method, nested clustering, for biclustering of label‐free quantitative AP‐MS data. Our approach forms bait clusters based on the similarity of quantitative interaction profiles and identifies submatrices of prey proteins showing consistent quantitative association within bait clusters. In doing so, nested clustering effectively addresses the problem of overrepresentation of interactions involving baits proteins as compared with proteins only identified as preys. The method does not require specification of the number of bait clusters, which is an advantage against existing model‐based clustering methods. We illustrate the performance of the algorithm using two published intermediate scale human PPI data sets, which are representative of the AP‐MS data generated from mammalian cells. We also discuss general challenges of analyzing and interpreting clustering results in the context of AP‐MS data. clustering mass spectrometry protein complexes protein‐protein interaction spectral counts Biology (General) Medicine (General) Sinae Kim verfasserin aut Anne‐Claude Gingras verfasserin aut Alexey I Nesvizhskii verfasserin aut In Molecular Systems Biology Wiley, 2005 6(2010), 1, Seite n/a-n/a (DE-627)490536905 (DE-600)2193510-5 17444292 nnns volume:6 year:2010 number:1 pages:n/a-n/a https://doi.org/10.1038/msb.2010.41 kostenfrei https://doaj.org/article/94203032d6a04b99bffb2fbad7abe8c7 kostenfrei https://doi.org/10.1038/msb.2010.41 kostenfrei https://doaj.org/toc/1744-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2010 1 n/a-n/a
allfieldsSound	10.1038/msb.2010.41 doi (DE-627)DOAJ098508385 (DE-599)DOAJ94203032d6a04b99bffb2fbad7abe8c7 DE-627 ger DE-627 rakwb eng QH301-705.5 R5-920 Hyungwon Choi verfasserin aut Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Affinity purification followed by mass spectrometry (AP‐MS) has become a common approach for identifying protein–protein interactions (PPIs) and complexes. However, data analysis and visualization often rely on generic approaches that do not take advantage of the quantitative nature of AP‐MS. We present a novel computational method, nested clustering, for biclustering of label‐free quantitative AP‐MS data. Our approach forms bait clusters based on the similarity of quantitative interaction profiles and identifies submatrices of prey proteins showing consistent quantitative association within bait clusters. In doing so, nested clustering effectively addresses the problem of overrepresentation of interactions involving baits proteins as compared with proteins only identified as preys. The method does not require specification of the number of bait clusters, which is an advantage against existing model‐based clustering methods. We illustrate the performance of the algorithm using two published intermediate scale human PPI data sets, which are representative of the AP‐MS data generated from mammalian cells. We also discuss general challenges of analyzing and interpreting clustering results in the context of AP‐MS data. clustering mass spectrometry protein complexes protein‐protein interaction spectral counts Biology (General) Medicine (General) Sinae Kim verfasserin aut Anne‐Claude Gingras verfasserin aut Alexey I Nesvizhskii verfasserin aut In Molecular Systems Biology Wiley, 2005 6(2010), 1, Seite n/a-n/a (DE-627)490536905 (DE-600)2193510-5 17444292 nnns volume:6 year:2010 number:1 pages:n/a-n/a https://doi.org/10.1038/msb.2010.41 kostenfrei https://doaj.org/article/94203032d6a04b99bffb2fbad7abe8c7 kostenfrei https://doi.org/10.1038/msb.2010.41 kostenfrei https://doaj.org/toc/1744-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2010 1 n/a-n/a
language	English
source	In Molecular Systems Biology 6(2010), 1, Seite n/a-n/a volume:6 year:2010 number:1 pages:n/a-n/a
sourceStr	In Molecular Systems Biology 6(2010), 1, Seite n/a-n/a volume:6 year:2010 number:1 pages:n/a-n/a
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	clustering mass spectrometry protein complexes protein‐protein interaction spectral counts Biology (General) Medicine (General)
isfreeaccess_bool	true
container_title	Molecular Systems Biology
authorswithroles_txt_mv	Hyungwon Choi @@aut@@ Sinae Kim @@aut@@ Anne‐Claude Gingras @@aut@@ Alexey I Nesvizhskii @@aut@@
publishDateDaySort_date	2010-01-01T00:00:00Z
hierarchy_top_id	490536905
id	DOAJ098508385
language_de	englisch
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callnumber-first	Q - Science
author	Hyungwon Choi
spellingShingle	Hyungwon Choi misc QH301-705.5 misc R5-920 misc clustering misc mass spectrometry misc protein complexes misc protein‐protein interaction misc spectral counts misc Biology (General) misc Medicine (General) Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data
authorStr	Hyungwon Choi
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topic_title	QH301-705.5 R5-920 Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data clustering mass spectrometry protein complexes protein‐protein interaction spectral counts
topic	misc QH301-705.5 misc R5-920 misc clustering misc mass spectrometry misc protein complexes misc protein‐protein interaction misc spectral counts misc Biology (General) misc Medicine (General)
topic_unstemmed	misc QH301-705.5 misc R5-920 misc clustering misc mass spectrometry misc protein complexes misc protein‐protein interaction misc spectral counts misc Biology (General) misc Medicine (General)
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title	Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data
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title_full	Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data
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title_sort	analysis of protein complexes through model‐based biclustering of label‐free quantitative ap‐ms data
callnumber	QH301-705.5
title_auth	Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data
abstract	Affinity purification followed by mass spectrometry (AP‐MS) has become a common approach for identifying protein–protein interactions (PPIs) and complexes. However, data analysis and visualization often rely on generic approaches that do not take advantage of the quantitative nature of AP‐MS. We present a novel computational method, nested clustering, for biclustering of label‐free quantitative AP‐MS data. Our approach forms bait clusters based on the similarity of quantitative interaction profiles and identifies submatrices of prey proteins showing consistent quantitative association within bait clusters. In doing so, nested clustering effectively addresses the problem of overrepresentation of interactions involving baits proteins as compared with proteins only identified as preys. The method does not require specification of the number of bait clusters, which is an advantage against existing model‐based clustering methods. We illustrate the performance of the algorithm using two published intermediate scale human PPI data sets, which are representative of the AP‐MS data generated from mammalian cells. We also discuss general challenges of analyzing and interpreting clustering results in the context of AP‐MS data.
abstractGer	Affinity purification followed by mass spectrometry (AP‐MS) has become a common approach for identifying protein–protein interactions (PPIs) and complexes. However, data analysis and visualization often rely on generic approaches that do not take advantage of the quantitative nature of AP‐MS. We present a novel computational method, nested clustering, for biclustering of label‐free quantitative AP‐MS data. Our approach forms bait clusters based on the similarity of quantitative interaction profiles and identifies submatrices of prey proteins showing consistent quantitative association within bait clusters. In doing so, nested clustering effectively addresses the problem of overrepresentation of interactions involving baits proteins as compared with proteins only identified as preys. The method does not require specification of the number of bait clusters, which is an advantage against existing model‐based clustering methods. We illustrate the performance of the algorithm using two published intermediate scale human PPI data sets, which are representative of the AP‐MS data generated from mammalian cells. We also discuss general challenges of analyzing and interpreting clustering results in the context of AP‐MS data.
abstract_unstemmed	Affinity purification followed by mass spectrometry (AP‐MS) has become a common approach for identifying protein–protein interactions (PPIs) and complexes. However, data analysis and visualization often rely on generic approaches that do not take advantage of the quantitative nature of AP‐MS. We present a novel computational method, nested clustering, for biclustering of label‐free quantitative AP‐MS data. Our approach forms bait clusters based on the similarity of quantitative interaction profiles and identifies submatrices of prey proteins showing consistent quantitative association within bait clusters. In doing so, nested clustering effectively addresses the problem of overrepresentation of interactions involving baits proteins as compared with proteins only identified as preys. The method does not require specification of the number of bait clusters, which is an advantage against existing model‐based clustering methods. We illustrate the performance of the algorithm using two published intermediate scale human PPI data sets, which are representative of the AP‐MS data generated from mammalian cells. We also discuss general challenges of analyzing and interpreting clustering results in the context of AP‐MS data.
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title_short	Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data
url	https://doi.org/10.1038/msb.2010.41 https://doaj.org/article/94203032d6a04b99bffb2fbad7abe8c7 https://doaj.org/toc/1744-4292
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Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data

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