ArticleFrontiers in molecular biosciences2021
Mapping OMIM Disease-Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes.
Article in Frontiers in molecular biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
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- Interrelational Proteomic Sequence Features Enhance Predictive Modeling: Application to COVID-19 Severity.Biomedicines · 2026Article
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- Functional genome analysis reveals that serine carboxypeptidaseFrontiers in plant science · 2025Article
- Variant Effect Prediction in the Age of Machine Learning.Cold Spring Harbor perspectives in biology · 2024Review
- Most protein domains exist as variants with distinct functions across cells, tissues and diseases.NAR genomics and bioinformatics · 2023Article
- Pathogenic variation types in human genes relate to diseases through Pfam and InterPro mapping.Frontiers in molecular biosciences · 2022Article
- A Glance into MTHFR Deficiency at a Molecular Level.International journal of molecular sciences · 2021Article
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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.
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