In bioinformatics, a Gene Disease Database is a systematized collection of data, typically structured to model aspects of reality, in a way to comprehend the underlying mechanisms of complex diseases, by understanding multiple composite interactions between phenotype-genotype relationships and gene-disease mechanisms.A. Bauer-Mehren, "Gene-Disease network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental diseases," PLOS One, pp. 1-3, 2011. Gene Disease Databases integrate human gene-disease associations from various expert curated databases and text mining derived associations including Mendelian, complex and environmental diseases.
Introduction
Experts in different areas of biology and bioinformatics have been trying to comprehend the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some illnesses, it has become apparent that it is the right amount of animosity is made for not enough to obtain an index of the disease-related genes but to uncover how disruptions of molecular grids in the cell give rise to disease phenotypes. Moreover, even with the unprecedented wealth of information available, obtaining such catalogues is extremely difficult.
Genetic Broadly speaking, genetic diseases are caused by aberrations in genes or chromosomes. Many genetic diseases are developed from before birth. Genetic disorders account for a significant number of the health care problems in our society. Advances in the understanding of this diseases have increased both the life span and quality of life for many of those affected by genetic disorders. Recent developments in bioinformatics and laboratory genetics have made possible the better delineation of certain malformation and mental retardation syndromes, so that their mode of inheritance can be understood. This information enables the genetic counselor to predict the risk for occurrence of a large number of genetic disorders. Most genetic counseling is done, however, only after the birth of at least one affected individual has alerted the family to their predilection for having children with a genetic disorder. The association of a single gene to a disease is rare and a genetic disease may or may not be a transmissible disorder. Some genetic diseases are inherited from the parent's genes, but others are caused by new mutations or changes to the DNA. In other occurrences, the same disease, for instance, some forms of carcinoma or melanoma, may stem from an inbred condition in some people, from new changes in other people, and from non-genetic causes in still other individuals.
There are more than six thousand known single-gene disorders (monogenic), which occur in about 1 out of every 200 births. As their term suggests, these diseases are caused by a mutation in one gene. By contrast, polygenic disorders are caused by several genes, regularly in combination with environmental factors. Examples of genetic phenotypes include Alzheimer's disease, breast cancer, leukemia, Down syndrome, heart defects, and deafness; therefore, cataloguing to sort out all the diseases related to genes is needed.
Challenges with creation
At different stages of any gene disease project, molecular biologists need to choose, even after careful statistical data analysis, which genes or proteins to investigate further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets, such as expression data, sequence information, functional annotation and the biomedical literature, allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers. So one of the main concerns in biological and biomedical research is to recognise the underlying mechanisms behind this intricate genetic phenotypes. Great effort has been spent on finding the genes related to diseases
However, increasingly evidences point out that most human diseases cannot be attributed to a single gene but arise due to complex interactions among multiple genetic variants and environmental risk factors. Several databases have been developed storing associations between genes and diseases such as the Comparative Toxicogenomics Database (CTD), Online Mendelian Inheritance in Man (OMIM), the genetic Association Database (GAD) or the Disease genetic Association Database (DisGeNET). Each of these databases focuses on different aspects of the phenotype-genotype relationship, and due to the nature of the database curation process, they are not complete, but in a way they are fully complementary between each other.
Types of databases
Essentially, there are four types of databases: curated databases, predictive databases, literature databases and integrative databases
Curated databases The term curated data refers to information, that may comprise the most sophisticated computational formats for structured data, scientific updates, and curated knowledge, that has been composed and prepared under the regulation of one or more experts considered to be qualified to engage in such an activity The implication is that the resulting database is of high quality. The contrast is with data which may have been gathered through some automated process or using particularly low or inexpert unsupported data quality and possibly untrustworthy. Some of the most common examples include: CTD and UNIPROT.
The Comparative Toxicogenomics Database (CTD) The Comparative Toxicogenomics Database, helps to understand about the effects of environmental compounds on human health by integrating data from curated scientific literature to describe biochemical interactions with genes and proteins, and links between diseases and chemicals, and diseases and genes or proteins. CTD contains curated data defining cross-species chemical–gene/protein interactions and chemical– and gene–disease associations to illuminate molecular mechanisms underlying variable susceptibility and environmentally influenced diseases. These data deliver insights into complex chemical–gene and protein interaction networks. One of the main sources in this Database is curated information from OMIM.
CTD is a unique resource where bioinformatics specialists read the scientific literature and manually curate four types of core data:
Chemical-gene interactions Chemical-disease associations Gene-disease associations Chemical-phenotype associations
The Universal Protein Resource (UNIPROT) The Universal Protein Resource (UniProt) is an inclusive resource for protein sequence and annotation data. It is a comprehensive, first-class and freely accessible database of protein sequence and functional information, that has many entries being derived from genome sequencing projects. It contains a large amount of information about the biological function of proteins derived from the study literature, which can hint to a direct connection between gene-protein-disease.
Predictive databases A predictive database is one based on statistical inference. One particular approach to such inference is known as predictive inference, but the prediction can be undertaken within any of the several approaches to statistical inference. Indeed, one description of biostatistics is that it provides a means of transferring knowledge about a sample of a genetic population to the whole population (genomics), and to other related genes or genomes, which the same as prediction over time is not necessarily.S. Hunter and P. Jones, "InterPro in 2011: new developments in the family and domain prediction database," Nucleic Acids Research, vol. 10, no. 1, pp. 12-22, 2011 When information is transferred across time, often to specific points in time, the process is known as forecasting. Three of the main examples of databases that can be considered in this category include: The Mouse genome Database (MGD), The Rat genome Database (RGD), OMIM and the SIFT Tool from Ensembl.
The Mouse genome Database (MGD) The Mouse genome Database (MGD) is the international community resource for integrated genetic, genomic and biological data about the laboratory mouse. MGD provides full annotation of phenotypes and human disease associations for mouse models (genotypes) using terms from the Mammalian Phenotype Ontology and disease names from OMIM.C. Bult and J. Eppig, "The Mouse genome Database (MGD): mouse biology and model systems," Nucleic Acids Research, vol. 36, no. 1, pp. 724-728, 2007
The Rat Genome Database (RGD)
The Rat Genome Database (RGD) began as a collaborative effort between leading research institutions involved in rat genetic and genomic research. The rat continues to be extensively used by researchers as a model organism for investigating the biology and pathophysiology of disease. In the past several years, there has been a rapid increase in rat genetic and genomic data.M. Dwinell, E. Worthey and S. M, "The Rat genome Database 2009: variation, ontologies and pathways," Nucleic Acids Research, vol. 37, no. 1, pp. 744-749, 2009 This explosion of information highlighted the need for a centralized database to efficiently and effectively collect, manage, and distribute a rat-centric view of this data to researchers around the world. The Rat Genome Database was created to serve as a repository of rat genetic and genomic data, as well as mapping, strain, and physiological information. It also facilitates investigators research efforts by providing tools to search, mine, and predict this data.
Data at RGD that is useful for researchers investigating disease genes include disease annotations for rat, mouse and human genes. Annotations are manually curated from the literature, or downloaded via automated pipelines from other disease-related databases. Downloaded annotations are mapped to the same disease vocabulary used for manual annotations to provide consistency across the dataset. RGD also maintains disease-related quantitative phenotype data for the rat (PhenoMiner).
The Online Mendelian Inheritance in Man (OMIM)
Supported by the NCBI, The Online Mendelian Inheritance in Man (OMIM) is a database that catalogues all the known diseases with a genetic component, and predicts their relationship to relevant genes in the human genome and provides references for further research and tools for genomic analysis of a catalogued gene.A. Homosh, "Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders," Nucleic Acids Research, vol. 33, no. 1, pp. 514-517, 2005 OMIM is a comprehensive, authoritative compendium of human genes and genetic phenotypes that is freely available and updated daily. The database has been used as a resource for predicting relevant information to inherited conditions.
Ensembl SIFT tool