Functional Pathway and Process Enrichment Analysis of Genes Associated With Morphological Abnormalities of the Outer Ear

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Congenital anomalies of the outer ear are common birth defects, including a variety of congenital deformities or malformations ranging from mild structural anomalies to total absence of the ear. Despite its high incidence and detrimental impact on patients, the etiology of outer ear abnormalities remains poorly understood. The goal of this study was to summarize the related genes and improve our understanding of the genetic etiology of morphological abnormalities of the outer ear. Human Phenotype Ontology (HPO) database, Mouse Genome Informatics (MGI) database, and PubMed search engine were used to acquire the genes associated with abnormal human or mouse outer ear. Metascape was employed on the genes above to conduct functional annotation, pathway and process enrichment analysis, protein-protein interaction network analysis, and MCODE component analysis. After a comprehensive review of the databases and literature, we identified 394 human genes and 148 mouse genes that have been associated with abnormal phenotypes of the outer ear, and we identified several biological pathways for human and mouse respectively. Especially, the analysis of common genes shared by human and mouse emphasized the importance of certain genes ( PAX6, PBX1, HOXA1, HOXA2, TBX1, TBX15, PRRX1, and HMX1) in the embryonic development of the external ear. Through our analysis of genes associated with morphological abnormalities of the outer ear, the authors have shown that embryonic development pathways take important roles in the morphogenesis of abnormal external ear and highlighted some potential genetic drivers.

Related collections

Most cited references 41

Record: found
Abstract: found
Article: not found

Gene Ontology: tool for the unification of biology

Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

0 comments Cited 15853 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

KEGG: kyoto encyclopedia of genes and genomes.

M Kanehisa (2000)

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).

0 comments Cited 7945 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Damian Szklarczyk, Annika L Gable, David Lyon … (2018)

Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.