Evidence map›Paper›PMID 34026820›Full record

ArticleFrontiers in molecular biosciences2021

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

Castrense Savojardo, Giulia Babbi, Pier Luigi Martelli, Rita Casadio

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Variant Effect Prediction in the Age of Machine Learning.Cold Spring Harbor perspectives in biology · 2024
    Review
  8. Article
  9. Article
  10. A Glance into MTHFR Deficiency at a Molecular Level.International journal of molecular sciences · 2021
    Article
4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Castrense SavojardoBiocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Giulia BabbiBiocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Pier Luigi MartelliBiocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Rita CasadioBiocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

disease-related variationsdisease variant databasesPfam-disease associationprotein domainprotein structureprotein variationsvariation type

Identifiers

PMID34026820
PMCPMC8138129

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.