Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets

Background Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene onto...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Klus, Petr [verfasserIn] Ponti, Riccardo Delli Livi, Carmen Maria Tartaglia, Gian Gaetano

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Protein classification Physico-chemical properties Gene ontology Solubility RNA-binding ability

Anmerkung:	© Klus et al. 2015

Übergeordnetes Werk:	Enthalten in: BMC genomics - London : BioMed Central, 2000, 16(2015), 1 vom: 16. Dez.
Übergeordnetes Werk:	volume:16 ; year:2015 ; number:1 ; day:16 ; month:12

Links:	Volltext

DOI / URN:	10.1186/s12864-015-2280-z

Katalog-ID:	SPR027114392

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520			\|a Background Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities. Results We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis. Conclusions In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal. Each of the pages contains links to the corresponding documentation and tutorial.
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allfields	10.1186/s12864-015-2280-z doi (DE-627)SPR027114392 (SPR)s12864-015-2280-z-e DE-627 ger DE-627 rakwb eng Klus, Petr verfasserin aut Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Klus et al. 2015 Background Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities. Results We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis. Conclusions In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal. Each of the pages contains links to the corresponding documentation and tutorial. Protein classification (dpeaa)DE-He213 Physico-chemical properties (dpeaa)DE-He213 Gene ontology (dpeaa)DE-He213 Solubility (dpeaa)DE-He213 RNA-binding ability (dpeaa)DE-He213 Ponti, Riccardo Delli aut Livi, Carmen Maria aut Tartaglia, Gian Gaetano aut Enthalten in BMC genomics London : BioMed Central, 2000 16(2015), 1 vom: 16. Dez. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:16 year:2015 number:1 day:16 month:12 https://dx.doi.org/10.1186/s12864-015-2280-z kostenfrei 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_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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 16 12
spelling	10.1186/s12864-015-2280-z doi (DE-627)SPR027114392 (SPR)s12864-015-2280-z-e DE-627 ger DE-627 rakwb eng Klus, Petr verfasserin aut Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Klus et al. 2015 Background Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities. Results We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis. Conclusions In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal. Each of the pages contains links to the corresponding documentation and tutorial. Protein classification (dpeaa)DE-He213 Physico-chemical properties (dpeaa)DE-He213 Gene ontology (dpeaa)DE-He213 Solubility (dpeaa)DE-He213 RNA-binding ability (dpeaa)DE-He213 Ponti, Riccardo Delli aut Livi, Carmen Maria aut Tartaglia, Gian Gaetano aut Enthalten in BMC genomics London : BioMed Central, 2000 16(2015), 1 vom: 16. Dez. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:16 year:2015 number:1 day:16 month:12 https://dx.doi.org/10.1186/s12864-015-2280-z kostenfrei 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_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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 16 12
allfields_unstemmed	10.1186/s12864-015-2280-z doi (DE-627)SPR027114392 (SPR)s12864-015-2280-z-e DE-627 ger DE-627 rakwb eng Klus, Petr verfasserin aut Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Klus et al. 2015 Background Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities. Results We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis. Conclusions In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal. Each of the pages contains links to the corresponding documentation and tutorial. Protein classification (dpeaa)DE-He213 Physico-chemical properties (dpeaa)DE-He213 Gene ontology (dpeaa)DE-He213 Solubility (dpeaa)DE-He213 RNA-binding ability (dpeaa)DE-He213 Ponti, Riccardo Delli aut Livi, Carmen Maria aut Tartaglia, Gian Gaetano aut Enthalten in BMC genomics London : BioMed Central, 2000 16(2015), 1 vom: 16. Dez. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:16 year:2015 number:1 day:16 month:12 https://dx.doi.org/10.1186/s12864-015-2280-z kostenfrei 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_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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 16 12
allfieldsGer	10.1186/s12864-015-2280-z doi (DE-627)SPR027114392 (SPR)s12864-015-2280-z-e DE-627 ger DE-627 rakwb eng Klus, Petr verfasserin aut Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Klus et al. 2015 Background Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities. Results We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis. Conclusions In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal. Each of the pages contains links to the corresponding documentation and tutorial. Protein classification (dpeaa)DE-He213 Physico-chemical properties (dpeaa)DE-He213 Gene ontology (dpeaa)DE-He213 Solubility (dpeaa)DE-He213 RNA-binding ability (dpeaa)DE-He213 Ponti, Riccardo Delli aut Livi, Carmen Maria aut Tartaglia, Gian Gaetano aut Enthalten in BMC genomics London : BioMed Central, 2000 16(2015), 1 vom: 16. Dez. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:16 year:2015 number:1 day:16 month:12 https://dx.doi.org/10.1186/s12864-015-2280-z kostenfrei 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_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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 16 12
allfieldsSound	10.1186/s12864-015-2280-z doi (DE-627)SPR027114392 (SPR)s12864-015-2280-z-e DE-627 ger DE-627 rakwb eng Klus, Petr verfasserin aut Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Klus et al. 2015 Background Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities. Results We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis. Conclusions In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal. Each of the pages contains links to the corresponding documentation and tutorial. Protein classification (dpeaa)DE-He213 Physico-chemical properties (dpeaa)DE-He213 Gene ontology (dpeaa)DE-He213 Solubility (dpeaa)DE-He213 RNA-binding ability (dpeaa)DE-He213 Ponti, Riccardo Delli aut Livi, Carmen Maria aut Tartaglia, Gian Gaetano aut Enthalten in BMC genomics London : BioMed Central, 2000 16(2015), 1 vom: 16. Dez. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:16 year:2015 number:1 day:16 month:12 https://dx.doi.org/10.1186/s12864-015-2280-z kostenfrei 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_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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 16 12
language	English
source	Enthalten in BMC genomics 16(2015), 1 vom: 16. Dez. volume:16 year:2015 number:1 day:16 month:12
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authorswithroles_txt_mv	Klus, Petr @@aut@@ Ponti, Riccardo Delli @@aut@@ Livi, Carmen Maria @@aut@@ Tartaglia, Gian Gaetano @@aut@@
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author	Klus, Petr
spellingShingle	Klus, Petr misc Protein classification misc Physico-chemical properties misc Gene ontology misc Solubility misc RNA-binding ability Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets
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topic_title	Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets Protein classification (dpeaa)DE-He213 Physico-chemical properties (dpeaa)DE-He213 Gene ontology (dpeaa)DE-He213 Solubility (dpeaa)DE-He213 RNA-binding ability (dpeaa)DE-He213
topic	misc Protein classification misc Physico-chemical properties misc Gene ontology misc Solubility misc RNA-binding ability
topic_unstemmed	misc Protein classification misc Physico-chemical properties misc Gene ontology misc Solubility misc RNA-binding ability
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title	Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets
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title_full	Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets
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author_browse	Klus, Petr Ponti, Riccardo Delli Livi, Carmen Maria Tartaglia, Gian Gaetano
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title_sort	protein aggregation, structural disorder and rna-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets
title_auth	Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets
abstract	Background Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities. Results We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis. Conclusions In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal. Each of the pages contains links to the corresponding documentation and tutorial. © Klus et al. 2015
abstractGer	Background Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities. Results We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis. Conclusions In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal. Each of the pages contains links to the corresponding documentation and tutorial. © Klus et al. 2015
abstract_unstemmed	Background Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities. Results We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis. Conclusions In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal. Each of the pages contains links to the corresponding documentation and tutorial. © Klus et al. 2015
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title_short	Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets
url	https://dx.doi.org/10.1186/s12864-015-2280-z
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author2	Ponti, Riccardo Delli Livi, Carmen Maria Tartaglia, Gian Gaetano
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Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities. Results We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis. Conclusions In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal. 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Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets

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