Investigation Of Rare Disease Mechanism Based On Multi-omics Data Integration And Network Analysis

Rare diseases affect over a hundred million people worldwide,while most of these patients are not accurately diagnosed and effectively treated.Recently,with the continuous development of biomedical research,the concept of "precise medicine" has gradually aroused people’s attention in the field of biomedicine.In this perspective,rare diseases usually have extremely few cases,which belongs to the typical category of precision medicine.Therefore,the research of rare diseases tends to focus on molecular and individual level of each case.However,in the study of the molecular mechanisms of rare diseases,methods that are traditional queue-based or that require large-scale clinical and molecular-level samples are hampered by the scarcity of cases,scattered distribution,and low data sharing rates and availability.To this end,this paper proposes a research pipeline for studying molecular mechanisms of rare diseases based on multi-omics networks.This program aims to establish a standardized knowledge base of rare diseases by integrating multi-omics data,to overcome the problems in molecular mechanisms and clinical diagnosis and treatment of rare diseases caused by limited cases and lack of information on disease groups.Multi-omics disease annotations in the knowledge base help establish disease networks,explore the molecular mechanisms of rare diseases and predict potential pathogenic genes in rare diseases,meanwhile,it provides new clues for elucidating the molecular mechanisms of rare diseases and discovering potentially treatments for rare diseases.To achieve the mission,we integrate the multi-omics data of rare diseases including genomics and phenotypes,and expand a large number of rare disease-phenotype associations by text mining in nearly 10 million articles in the MEDLINE database.The eRAM(http://www.unimd.org/eram/),a standardized knowledge base for rare diseases,was constructed.Next,we quantified the association between diseases based on gene and phenotypic data,respectively.We elaborated the role of disease networks in the study of disease molecular mechanisms,disease gene prediction,and clinical diagnostic assistance;finally,considering the shortcoming that phenotype-based disease are sometimes phenotypesthemselves,this study quantifies the similarity of disease phenotype and molecular function based on gene ontology,and establishes a phenotypic network based on molecular function.This network provides new insights into the molecular mechanisms of complex diseases such as syndromes.