Mapping OMIM Disease–Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes

Human genome resequencing projects provide an unprecedented amount of data about single-nucleotide variations occurring in protein-coding regions and often leading to observable changes in the covalent structure of gene products. For many of these variations, links to Online Mendelian Inheritance in Man (OMIM) genetic diseases are available and are reported in many databases that are collecting human variation data such as Humsavar. However, the current knowledge on the molecular mechanisms that are leading to diseases is, in many cases, still limited. For understanding the complex mechanisms behind disease insurgence, the identification of putative models, when considering the protein structure and chemico-physical features of the variations, can be useful in many contexts, including early diagnosis and prognosis. In this study, we investigate the occurrence and distribution of human disease–related variations in the context of Pfam domains. The aim of this study is the identification and characterization of Pfam domains that are statistically more likely to be associated with disease-related variations. The study takes into consideration 2,513 human protein sequences with 22,763 disease-related variations. We describe patterns of disease-related variation types in biunivocal relation with Pfam domains, which are likely to be possible markers for linking Pfam domains to OMIM diseases. Furthermore, we take advantage of the specific association between disease-related variation types and Pfam domains for clustering diseases according to the Human Disease Ontology, and we establish a relation among variation types, Pfam domains, and disease classes. We find that Pfam models are specific markers of patterns of variation types and that they can serve to bridge genes, diseases, and disease classes. Data are available as Supplementary Material for 1,670 Pfam models, including 22,763 disease-related variations associated to 3,257 OMIM diseases. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Castrense Savojardo [verfasserIn] Giulia Babbi [verfasserIn] Pier Luigi Martelli [verfasserIn] Rita Casadio [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	protein variations protein structure protein domain variation type disease-related variations disease variant databases

Übergeordnetes Werk:	In: Frontiers in Molecular Biosciences - Frontiers Media S.A., 2015, 8(2021)
Übergeordnetes Werk:	volume:8 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fmolb.2021.617016

Katalog-ID:	DOAJ053494016

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spelling	10.3389/fmolb.2021.617016 doi (DE-627)DOAJ053494016 (DE-599)DOAJ1fb6bbd532dc400483a5893932992746 DE-627 ger DE-627 rakwb eng QH301-705.5 Castrense Savojardo verfasserin aut Mapping OMIM Disease–Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Human genome resequencing projects provide an unprecedented amount of data about single-nucleotide variations occurring in protein-coding regions and often leading to observable changes in the covalent structure of gene products. For many of these variations, links to Online Mendelian Inheritance in Man (OMIM) genetic diseases are available and are reported in many databases that are collecting human variation data such as Humsavar. However, the current knowledge on the molecular mechanisms that are leading to diseases is, in many cases, still limited. For understanding the complex mechanisms behind disease insurgence, the identification of putative models, when considering the protein structure and chemico-physical features of the variations, can be useful in many contexts, including early diagnosis and prognosis. In this study, we investigate the occurrence and distribution of human disease–related variations in the context of Pfam domains. The aim of this study is the identification and characterization of Pfam domains that are statistically more likely to be associated with disease-related variations. The study takes into consideration 2,513 human protein sequences with 22,763 disease-related variations. We describe patterns of disease-related variation types in biunivocal relation with Pfam domains, which are likely to be possible markers for linking Pfam domains to OMIM diseases. Furthermore, we take advantage of the specific association between disease-related variation types and Pfam domains for clustering diseases according to the Human Disease Ontology, and we establish a relation among variation types, Pfam domains, and disease classes. We find that Pfam models are specific markers of patterns of variation types and that they can serve to bridge genes, diseases, and disease classes. Data are available as Supplementary Material for 1,670 Pfam models, including 22,763 disease-related variations associated to 3,257 OMIM diseases. protein variations protein structure protein domain variation type disease-related variations disease variant databases Biology (General) Giulia Babbi verfasserin aut Pier Luigi Martelli verfasserin aut Rita Casadio verfasserin aut Rita Casadio verfasserin aut In Frontiers in Molecular Biosciences Frontiers Media S.A., 2015 8(2021) (DE-627)820039691 (DE-600)2814330-9 2296889X nnns volume:8 year:2021 https://doi.org/10.3389/fmolb.2021.617016 kostenfrei https://doaj.org/article/1fb6bbd532dc400483a5893932992746 kostenfrei https://www.frontiersin.org/articles/10.3389/fmolb.2021.617016/full kostenfrei https://doaj.org/toc/2296-889X 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 8 2021
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allfieldsGer	10.3389/fmolb.2021.617016 doi (DE-627)DOAJ053494016 (DE-599)DOAJ1fb6bbd532dc400483a5893932992746 DE-627 ger DE-627 rakwb eng QH301-705.5 Castrense Savojardo verfasserin aut Mapping OMIM Disease–Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Human genome resequencing projects provide an unprecedented amount of data about single-nucleotide variations occurring in protein-coding regions and often leading to observable changes in the covalent structure of gene products. For many of these variations, links to Online Mendelian Inheritance in Man (OMIM) genetic diseases are available and are reported in many databases that are collecting human variation data such as Humsavar. However, the current knowledge on the molecular mechanisms that are leading to diseases is, in many cases, still limited. For understanding the complex mechanisms behind disease insurgence, the identification of putative models, when considering the protein structure and chemico-physical features of the variations, can be useful in many contexts, including early diagnosis and prognosis. In this study, we investigate the occurrence and distribution of human disease–related variations in the context of Pfam domains. The aim of this study is the identification and characterization of Pfam domains that are statistically more likely to be associated with disease-related variations. The study takes into consideration 2,513 human protein sequences with 22,763 disease-related variations. We describe patterns of disease-related variation types in biunivocal relation with Pfam domains, which are likely to be possible markers for linking Pfam domains to OMIM diseases. Furthermore, we take advantage of the specific association between disease-related variation types and Pfam domains for clustering diseases according to the Human Disease Ontology, and we establish a relation among variation types, Pfam domains, and disease classes. We find that Pfam models are specific markers of patterns of variation types and that they can serve to bridge genes, diseases, and disease classes. Data are available as Supplementary Material for 1,670 Pfam models, including 22,763 disease-related variations associated to 3,257 OMIM diseases. protein variations protein structure protein domain variation type disease-related variations disease variant databases Biology (General) Giulia Babbi verfasserin aut Pier Luigi Martelli verfasserin aut Rita Casadio verfasserin aut Rita Casadio verfasserin aut In Frontiers in Molecular Biosciences Frontiers Media S.A., 2015 8(2021) (DE-627)820039691 (DE-600)2814330-9 2296889X nnns volume:8 year:2021 https://doi.org/10.3389/fmolb.2021.617016 kostenfrei https://doaj.org/article/1fb6bbd532dc400483a5893932992746 kostenfrei https://www.frontiersin.org/articles/10.3389/fmolb.2021.617016/full kostenfrei https://doaj.org/toc/2296-889X 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 8 2021
allfieldsSound	10.3389/fmolb.2021.617016 doi (DE-627)DOAJ053494016 (DE-599)DOAJ1fb6bbd532dc400483a5893932992746 DE-627 ger DE-627 rakwb eng QH301-705.5 Castrense Savojardo verfasserin aut Mapping OMIM Disease–Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Human genome resequencing projects provide an unprecedented amount of data about single-nucleotide variations occurring in protein-coding regions and often leading to observable changes in the covalent structure of gene products. For many of these variations, links to Online Mendelian Inheritance in Man (OMIM) genetic diseases are available and are reported in many databases that are collecting human variation data such as Humsavar. However, the current knowledge on the molecular mechanisms that are leading to diseases is, in many cases, still limited. For understanding the complex mechanisms behind disease insurgence, the identification of putative models, when considering the protein structure and chemico-physical features of the variations, can be useful in many contexts, including early diagnosis and prognosis. In this study, we investigate the occurrence and distribution of human disease–related variations in the context of Pfam domains. The aim of this study is the identification and characterization of Pfam domains that are statistically more likely to be associated with disease-related variations. The study takes into consideration 2,513 human protein sequences with 22,763 disease-related variations. We describe patterns of disease-related variation types in biunivocal relation with Pfam domains, which are likely to be possible markers for linking Pfam domains to OMIM diseases. Furthermore, we take advantage of the specific association between disease-related variation types and Pfam domains for clustering diseases according to the Human Disease Ontology, and we establish a relation among variation types, Pfam domains, and disease classes. We find that Pfam models are specific markers of patterns of variation types and that they can serve to bridge genes, diseases, and disease classes. Data are available as Supplementary Material for 1,670 Pfam models, including 22,763 disease-related variations associated to 3,257 OMIM diseases. protein variations protein structure protein domain variation type disease-related variations disease variant databases Biology (General) Giulia Babbi verfasserin aut Pier Luigi Martelli verfasserin aut Rita Casadio verfasserin aut Rita Casadio verfasserin aut In Frontiers in Molecular Biosciences Frontiers Media S.A., 2015 8(2021) (DE-627)820039691 (DE-600)2814330-9 2296889X nnns volume:8 year:2021 https://doi.org/10.3389/fmolb.2021.617016 kostenfrei https://doaj.org/article/1fb6bbd532dc400483a5893932992746 kostenfrei https://www.frontiersin.org/articles/10.3389/fmolb.2021.617016/full kostenfrei https://doaj.org/toc/2296-889X 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 8 2021
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callnumber-first	Q - Science
author	Castrense Savojardo
spellingShingle	Castrense Savojardo misc QH301-705.5 misc protein variations misc protein structure misc protein domain misc variation type misc disease-related variations misc disease variant databases misc Biology (General) Mapping OMIM Disease–Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes
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topic_title	QH301-705.5 Mapping OMIM Disease–Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes protein variations protein structure protein domain variation type disease-related variations disease variant databases
topic	misc QH301-705.5 misc protein variations misc protein structure misc protein domain misc variation type misc disease-related variations misc disease variant databases misc Biology (General)
topic_unstemmed	misc QH301-705.5 misc protein variations misc protein structure misc protein domain misc variation type misc disease-related variations misc disease variant databases misc Biology (General)
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title	Mapping OMIM Disease–Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes
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title_full	Mapping OMIM Disease–Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes
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title_sort	mapping omim disease–related variations on protein domains reveals an association among variation type, pfam models, and disease classes
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title_auth	Mapping OMIM Disease–Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes
abstract	Human genome resequencing projects provide an unprecedented amount of data about single-nucleotide variations occurring in protein-coding regions and often leading to observable changes in the covalent structure of gene products. For many of these variations, links to Online Mendelian Inheritance in Man (OMIM) genetic diseases are available and are reported in many databases that are collecting human variation data such as Humsavar. However, the current knowledge on the molecular mechanisms that are leading to diseases is, in many cases, still limited. For understanding the complex mechanisms behind disease insurgence, the identification of putative models, when considering the protein structure and chemico-physical features of the variations, can be useful in many contexts, including early diagnosis and prognosis. In this study, we investigate the occurrence and distribution of human disease–related variations in the context of Pfam domains. The aim of this study is the identification and characterization of Pfam domains that are statistically more likely to be associated with disease-related variations. The study takes into consideration 2,513 human protein sequences with 22,763 disease-related variations. We describe patterns of disease-related variation types in biunivocal relation with Pfam domains, which are likely to be possible markers for linking Pfam domains to OMIM diseases. Furthermore, we take advantage of the specific association between disease-related variation types and Pfam domains for clustering diseases according to the Human Disease Ontology, and we establish a relation among variation types, Pfam domains, and disease classes. We find that Pfam models are specific markers of patterns of variation types and that they can serve to bridge genes, diseases, and disease classes. Data are available as Supplementary Material for 1,670 Pfam models, including 22,763 disease-related variations associated to 3,257 OMIM diseases.
abstractGer	Human genome resequencing projects provide an unprecedented amount of data about single-nucleotide variations occurring in protein-coding regions and often leading to observable changes in the covalent structure of gene products. For many of these variations, links to Online Mendelian Inheritance in Man (OMIM) genetic diseases are available and are reported in many databases that are collecting human variation data such as Humsavar. However, the current knowledge on the molecular mechanisms that are leading to diseases is, in many cases, still limited. For understanding the complex mechanisms behind disease insurgence, the identification of putative models, when considering the protein structure and chemico-physical features of the variations, can be useful in many contexts, including early diagnosis and prognosis. In this study, we investigate the occurrence and distribution of human disease–related variations in the context of Pfam domains. The aim of this study is the identification and characterization of Pfam domains that are statistically more likely to be associated with disease-related variations. The study takes into consideration 2,513 human protein sequences with 22,763 disease-related variations. We describe patterns of disease-related variation types in biunivocal relation with Pfam domains, which are likely to be possible markers for linking Pfam domains to OMIM diseases. Furthermore, we take advantage of the specific association between disease-related variation types and Pfam domains for clustering diseases according to the Human Disease Ontology, and we establish a relation among variation types, Pfam domains, and disease classes. We find that Pfam models are specific markers of patterns of variation types and that they can serve to bridge genes, diseases, and disease classes. Data are available as Supplementary Material for 1,670 Pfam models, including 22,763 disease-related variations associated to 3,257 OMIM diseases.
abstract_unstemmed	Human genome resequencing projects provide an unprecedented amount of data about single-nucleotide variations occurring in protein-coding regions and often leading to observable changes in the covalent structure of gene products. For many of these variations, links to Online Mendelian Inheritance in Man (OMIM) genetic diseases are available and are reported in many databases that are collecting human variation data such as Humsavar. However, the current knowledge on the molecular mechanisms that are leading to diseases is, in many cases, still limited. For understanding the complex mechanisms behind disease insurgence, the identification of putative models, when considering the protein structure and chemico-physical features of the variations, can be useful in many contexts, including early diagnosis and prognosis. In this study, we investigate the occurrence and distribution of human disease–related variations in the context of Pfam domains. The aim of this study is the identification and characterization of Pfam domains that are statistically more likely to be associated with disease-related variations. The study takes into consideration 2,513 human protein sequences with 22,763 disease-related variations. We describe patterns of disease-related variation types in biunivocal relation with Pfam domains, which are likely to be possible markers for linking Pfam domains to OMIM diseases. Furthermore, we take advantage of the specific association between disease-related variation types and Pfam domains for clustering diseases according to the Human Disease Ontology, and we establish a relation among variation types, Pfam domains, and disease classes. We find that Pfam models are specific markers of patterns of variation types and that they can serve to bridge genes, diseases, and disease classes. Data are available as Supplementary Material for 1,670 Pfam models, including 22,763 disease-related variations associated to 3,257 OMIM diseases.
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title_short	Mapping OMIM Disease–Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes
url	https://doi.org/10.3389/fmolb.2021.617016 https://doaj.org/article/1fb6bbd532dc400483a5893932992746 https://www.frontiersin.org/articles/10.3389/fmolb.2021.617016/full https://doaj.org/toc/2296-889X
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We describe patterns of disease-related variation types in biunivocal relation with Pfam domains, which are likely to be possible markers for linking Pfam domains to OMIM diseases. Furthermore, we take advantage of the specific association between disease-related variation types and Pfam domains for clustering diseases according to the Human Disease Ontology, and we establish a relation among variation types, Pfam domains, and disease classes. We find that Pfam models are specific markers of patterns of variation types and that they can serve to bridge genes, diseases, and disease classes. 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Mapping OMIM Disease–Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes

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