The morbid cutaneous anatomy of the human genome revealed by a bioinformatic approach.
Bayesian network
Bioinformatics
Classification
Genetic diseases
Genodermatosis
Prioritization methods
Skin
Journal
Genomics
ISSN: 1089-8646
Titre abrégé: Genomics
Pays: United States
ID NLM: 8800135
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
13
09
2019
revised:
28
03
2020
accepted:
02
07
2020
pubmed:
11
7
2020
medline:
16
9
2021
entrez:
11
7
2020
Statut:
ppublish
Résumé
Computational approaches have been developed to prioritize candidate genes in disease gene identification. They are based on different pieces of evidences associating each gene with the given disease. In this study, 648 genes underlying genodermatoses have been compared to 1808 genes involved in other genetic diseases using a bioinformatic approach. These genes were studied at the structural, evolutionary and functional levels. Results show that genes underlying genodermatoses present longer CDS and have more exons. Significant differences were observed in nucleotide motif and amino-acid compositions. Evolutionary conservation analysis revealed that genodermatoses genes have less paralogs, more orthologs in Mouse and Dog and are less conserved. Functional analysis revealed that genodermatosis genes seem to be involved in immune system and skin layers. The Bayesian network model returned a rate of good classification of around 80%. This computational approach could help investigators working in the field of dermatology by prioritizing positional candidate genes for mutation screening.
Identifiants
pubmed: 32650097
pii: S0888-7543(19)30663-9
doi: 10.1016/j.ygeno.2020.07.009
pii:
doi:
Substances chimiques
Proteins
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4232-4241Informations de copyright
Copyright © 2020 Elsevier Inc. All rights reserved.