Gene Disease Database - Knowledge (XXG)

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that overlap the variation. Then it uses a rule-based approach to predict the effects that each allele of the variation may have on the transcript. The set of consequence terms, defined by the Sequence Ontology (SO) can be currently assigned to each combination of an allele and a transcript. Each allele of each variation may have a different effect in different transcripts. A variety of different tools are used to predict human mutations in the Ensembl database, one of the most widely used is SIFT, that predicts whether an amino acid substitution is likely to affect protein function based on sequence homology and the physic-chemical similarity between the alternate amino acids. The data provided for each amino acid substitution is a score and a qualitative prediction (either 'tolerated' or 'deleterious'). The score is the normalized probability that the amino acid change is tolerated so scores near 0 are more likely to be deleterious. The qualitative prediction is derived from this score such that substitutions with a score < 0.05 are called 'deleterious' and all others are called 'tolerated'. SIFT can be applied to naturally occurring nonsynonymous polymorphisms and laboratory-induced missense mutations, that will lead to build relationships in phenotype characteristics,

121:. 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 1822:

approaches to respond to new techniques, such as next-generation sequencing technologies. For instance, the availability of large numbers of individual human genomes will promote the development of computational analyses of rare variants, including the statistical mining of their relations to lifestyles, drug interactions and other factors. Biomedical research will also be driven by our ability to efficiently mine the large body of existing and continuously generated biomedical data. Text-mining techniques, in particular, when combined with other molecular data, can provide information about gene mutations and interactions and will become crucial to stay ahead of the exponential growth of data generated in biomedical research. Another field that is benefiting from the advances in mining and integration of molecular, clinical and drug analysis is pharmacogenomics.

696:(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. 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. 1141:

that leads to disease) or is a biomarker for a disease. #Genetic Variation Association: Used when a sequence variation (a mutation, a SNP) is associated to the disease phenotype, but there is still no evidence to say that the variation causes the disease. In some cases the presence of the variants increase the susceptibility to the disease. In general, the NCBI SNP identifiers are provided. #Altered Expression Association: Alterations in the function of the protein by means of altered expression of the gene are associated with the disease phenotype. #Post-translational Modification Association: Alterations in the function of the protein by means of post-translational modifications (methylation or phosphorylation of the protein) are associated with the disease phenotype.

210:, 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. 1124:

assess the concept of modularity of human diseases, this database performs a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including Mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. Obtaining similar results when studying

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multiple diseases. The next step is to produce a complete picture of the mechanistic aspects of the diseases and the design of drugs against them. For that, a combination of two approaches will be needed: a systematic search and in-depth study of each gene. The future of the field will be defined by new techniques to integrate large bodies of data from different sources and to incorporate functional information into the analysis of large-scale data generated by bioinformatics studies.

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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.

559:), and to other related genes or genomes, which the same as prediction over time is not necessarily. 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. 1839: 1133: 1818:. Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties, or differences between populations. In a less formal way, bioinformatics also tries to understand the organisational principles within nucleic acid and protein sequences. 198:

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.

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The response of bioinformatics to new experimental techniques brings a new perspective into the analysis of the experimental data, as demonstrated by the advances in the analysis of information from gene disease databases and other technologies. It is expected that this trend will continue with novel

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The description of each association type in this ontology is: #Therapeutic Association: The gene/protein has a therapeutic role in the amelioration of the disease. #Biomarker Association: The gene/protein either plays a role in the etiology of the disease (e.g. participates in the molecular mechanism

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is one of several well-known genome browsers for the retrieval of genomic-disease information. Ensembl imports variation data from a variety of different sources, Ensembl predicts the effects of variants. For each variation that is mapped to the reference genome, each Ensembl transcript is identified

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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

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Bioinformatics is both a term for the body of biological gene disease studies that use computer programming as part of their methodology, as well as a reference to specific analysis pipelines that are repeatedly used, particularly in the fields of genetics and genomics. Common uses of bioinformatics

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This sort of databases include Mendelian, compound and environmental diseases in an integrated gene-disease association archive and show that the concept of modularity applies for all of them They provide a functional analysis of diseases in case of important new biological insights, which might not

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The literature-derived human gene-disease network (LHGDN) is a text mining derived database with focus on extracting and classifying gene-disease associations with respect to several biomolecular conditions. It uses a machine learning based algorithm to extract semantic gene-disease relations from a

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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

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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

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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

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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

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studies of the relationships between human variations and their effect on diseases will be key to the development of personalized medicine. In summary, Gene Disease Databases have already transformed the search for disease genes and has the potential to become a crucial component of other areas of

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The completion of the human genome has changed the way the search for disease genes is performed. In the past, the approach was to focus on one or a few genes at a time. Now, projects like the DisGeNET exemplify the efforts to systematically analyze all the gene alterations involved in a single or

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of diseases, the findings in this database suggest that related diseases might arise due to dysfunction of common biological processes in the cell. The network analysis of this integrated database points out that data integration is needed to obtain a comprehensive view of the genetic landscape of

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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

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is a comprehensive gene-disease association database that integrates associations from several sources that covers different biomedical aspects of diseases. In particular, it is focused on the current knowledge of human genetic diseases including Mendelian, complex and environmental diseases. To

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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

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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

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The Genetic Association Database is an archive of human genetic association studies of complex diseases. GAD is primarily focused on archiving information on common complex human disease rather than rare Mendelian disorders as found in the OMIM. It includes curated summary data extracted from

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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

160:: Typical lists come from linkage regions, chromosomal aberrations, association study loci, deferentially expressed gene lists or genes identified by sequencing variants. Alternatively, the complete genome can be prioritized, but substantially more false positives would then be expected. 1654:

Gu, Ying; Liu, Guang-Hui; Plongthongkum, Nongluk; Benner, Christopher; Yi, Fei; Qu, Jing; Suzuki, Keiichiro; Yang, Jiping; Zhang, Weiqi; Li, Mo; Montserrat, Nuria; Crespo, Isaac; Del Sol, Antonio; Esteban, Concepcion Rodriguez; Zhang, Kun; Izpisua Belmonte, Juan Carlos (2014).

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be discovered when considering each of the gene-disease associations independently. Hence, they present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. The best example for this sort of database is DisGeNET.

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This one of the largest resources available for all genomic and genetic studies, it provides a centralized resource for geneticists, molecular biologists and other researchers studying the genomes of our own species and other vertebrates and model disease organisms.

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Extracts gene-disease associations from MEDLINE abstract using the BeFree system. BeFree is composed of a biomedical Named Entity Recognition (BioNER) module to detect diseases and genes and a relation extraction module based on morphosyntactic information.

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catalogued gene. 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.

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Showing the concept that diseases have large association with a variety of genes, a mean pathway homogeneity values of single diseases and random controls are plotted for four networks binned by the number of associated gene products per disease.

239:) 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 85:

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

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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.

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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.

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Kaikkonen, Minna U.; Niskanen, Henri; Romanoski, Casey E.; Kansanen, Emilia; Kivelä, Annukka M.; Laitalainen, Jarkko; Heinz, Sven; Benner, Christopher; Glass, Christopher K.; Ylä-Herttuala, Seppo (2014).

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This sort of databases summarize books, articles, book reviews, dissertations, and annotations about gene-disease databases. Some of the following are examples of this type: GAD, LGHDN and BeFree Data.

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Lee, In-Hee; Lee, Kyungjoon; Hsing, Michael; Choe, Yongjoon; Park, Jin-Ho; Kim, Shu Hee; Bohn, Justin M.; Neu, Matthew B.; Hwang, Kyu-Baek; Green, Robert C.; Kohane, Isaac S.; Kong, Sek Won (2014).

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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

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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).

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This graph shows how difficult is to correlate a bigger number of diseases vs concordance in 4 different databases, hence Gene Disease Databases test these relationships

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Koczor, Christopher A.; Lee, Eva K.; Torres, Rebecca A.; Boyd, Amy; Vega, J. David; Uppal, Karan; Yuan, Fan; Fields, Earl J.; Samarel, Allen M.; Lewis, William (2013).

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research is to recognise the underlying mechanisms behind this intricate genetic phenotypes. Great effort has been spent on finding the genes related to diseases

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Grosdidier, Solène; Ferrer, Antoni; Faner, Rosa; Piñero, Janet; Roca, Josep; Cosío, Borja; Agustí, Alvar; Gea, Joaquim; Sanz, Ferran; Furlong, Laura I. (2014).

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Gallagher, Suzanne Renick; Dombrower, Micah; Goldberg, Debra S. (2014). "Using 2-node hypergraph clustering coefficients to analyze disease-gene networks".

1933: 2798: 145:, breast cancer, leukemia, Down syndrome, heart defects, and deafness; therefore, cataloguing to sort out all the diseases related to genes is needed. 2016:

Botstein, D (2003). "Discovering genotypes underlying human phenotypes: past successes for Mendelian disease, future approaches for complex disease".

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relationships and gene-disease mechanisms. Gene Disease Databases integrate human gene-disease associations from various expert curated databases and

2218:"Text Mining Effectively Scores and Ranks the Literature for Improving Chemical-Gene-Disease Curation at the Comparative Toxicogenomics Database" 137:, may stem from an inbred condition in some people, from new changes in other people, and from non-genetic causes in still other individuals. 1435:

Santiago, Jose A.; Potashkin, Judith A. (2014). "System-based approaches to decode the molecular links in Parkinson's disease and diabetes".

1613:"Detection of differentially methylated gene promoters in failing and nonfailing human left ventricle myocardium using computation analysis" 207: 189:

Essentially, there are four types of databases: curated databases, predictive databases, literature databases and integrative databases

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CTD is a unique resource where bioinformatics specialists read the scientific literature and manually curate four types of core data:

2814:"Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research" 1277:

Cristiano, Francesca; Veltri, Pierangelo (2014). "An R-based tool for miRNA data analysis and correlation with clinical ontologies".

1888: 1329: 1294: 876: 315: 311: 1700:"Integrated analysis of transcript-level regulation of metabolism reveals disease-relevant nodes of the human metabolic network" 1923: 871: 354: 949: 545:

The curated data may comprise a process from practical experience and literature review to web publication of the database

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Yes – manual and automatic. Rules for automatic annotation generated by database curators and computational algorithms.

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Tieri, Paolo; Termanini, Alberto; Bellavista, Elena; Salvioli, Stefano; Capri, Miriam; Franceschi, Claudio (2012).

1948: 1873: 1392:"Molecularly and clinically related drugs and diseases are enriched in phenotypically similar drug-disease pairs" 1314:

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics - BCB '14

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Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics - BCB '14

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A. Homosh, "Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders,"

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Galhardo, Mafalda; Sinkkonen, Lasse; Berninger, Philipp; Lin, Jake; Sauter, Thomas; Heinäniemi, Merja (2014).

1474:"Prioritizing Disease-Linked Variants, Genes, and Pathways with an Interactive Whole-Genome Analysis Pipeline" 2401:"Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical–gene–disease networks" 2558:

S. Hunter and P. Jones, "InterPro in 2011: new developments in the family and domain prediction database,"

1878: 684: 350: 142: 1349:"Organ system heterogeneity DB: A database for the visualization of phenotypes at the organ system level" 1868: 468: 2325:"Systematic analysis of gene properties influencing organ system phenotypes in mammalian perturbations" 1150:

Some of the most interesting cases using Gene-Disease Databases can be found in the following papers:

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M. Dwinell, E. Worthey and S. M, "The Rat genome Database 2009: variation, ontologies and pathways,"

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human diseases and that the genetic origin of complex diseases is much more common than expected.

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published papers in peer reviewed journals on candidate gene and genome Wide Association Studies (

2149: 2041: 1898: 1460: 1335: 1300: 2868:"DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes" 2284:"DisGeNET: a Cytoscape plugin to visualize, integrate, search and analyze gene–disease networks" 1568:

Zhao, Yilei; Wang, Chen; Wu, Jianwei; Wang, Yan; Zhu, Wenliang; Zhang, Yong; Du, Zhimin (2013).

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textual source of interest. It is part of the Linked Life Data, of the LMU in Munchen, Germany.

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means of transferring knowledge about a sample of a genetic population to the whole population (

2946: 2897: 2845: 2792: 2754: 2697: 2632: 2528: 2479: 2430: 2356: 2305: 2257: 2198: 2141: 2082: 2033: 1782: 1729: 1686: 1642: 1599: 1556: 1503: 1452: 1423: 1378: 1325: 1290: 1265: 1220: 1176: 1064: 765: 649: 165: 2936: 2928: 2887: 2879: 2835: 2825: 2744: 2687: 2679: 2622: 2614: 2603:"The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease" 2518: 2510: 2469: 2461: 2420: 2412: 2346: 2336: 2295: 2247: 2237: 2188: 2180: 2131: 2072: 2025: 1772: 1762: 1719: 1711: 1676: 1668: 1632: 1624: 1589: 1581: 1546: 1536: 1493: 1485: 1444: 1413: 1403: 1368: 1360: 1317: 1282: 1255: 1245: 1210: 1202: 1168: 1156: 490: 485: 153: 2571:

C. Bult and J. Eppig, "The Mouse genome Database (MGD): mouse biology and model systems,"

1918: 1125: 1035: 863: 750: 616: 462: 306: 1657:"Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes" 2233: 1758: 1532: 1191:"Control of VEGF-A transcriptional programs by pausing and genomic compartmentalization" 2941: 2916: 2892: 2867: 2840: 2813: 2627: 2602: 2523: 2498: 2474: 2449: 2425: 2400: 2351: 2324: 2252: 2217: 2193: 2168: 1844: 1777: 1742: 1724: 1699: 1681: 1656: 1637: 1628: 1612: 1594: 1569: 1551: 1516: 1498: 1473: 1418: 1391: 1373: 1348: 1260: 1233: 1215: 1190: 107: 78: 41: 2964: 2692: 2667: 2136: 2119: 1943: 1159:(2014). "A network approach to clinical intervention in neurodegenerative diseases". 2341: 2300: 2283: 2153: 2077: 2060: 2045: 1464: 1339: 1304: 537: 808: 2242: 1767: 1541: 1517:"A Computational Framework to Infer Human Disease-Associated Long Noncoding RNAs" 1172: 1863: 424: 340: 118: 95: 1838: 2883: 2830: 1995:

A. Bauer-Mehren, "Gene-Disease network Analysis Reveals Functional Modules in

1834: 1672: 1570:"Choline Protects Against Cardiac Hypertrophy Induced by Increased After-load" 1448: 1408: 1250: 927: 174: 98:

derived associations including Mendelian, complex and environmental diseases.

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need to choose, even after careful statistical data analysis, which genes or

2932: 1996: 1938: 1321: 1286: 952:). The GAD was frozen as of 09/01/2014 but is still available for download. 439: 130: 122: 87: 51: 2950: 2901: 2849: 2758: 2701: 2636: 2532: 2483: 2434: 2360: 2309: 2261: 2202: 2145: 2086: 2037: 1786: 1733: 1690: 1646: 1603: 1560: 1515:

Liu, Ming-Xi; Chen, Xing; Chen, Geng; Cui, Qing-Hua; Yan, Gui-Ying (2014).

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Broadly speaking, genetic diseases are caused by aberrations in genes or

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Mannil, Deepthi; Vogt, Ingo; Prinz, Jeanette; Campillos, Monica (2015).

2749: 2732: 1489: 922: 472: 269: 236: 169: 1585: 129:. In other occurrences, the same disease, for instance, some forms of 240: 134: 1132: 785: 2499:"Ongoing and future developments at the Universal Protein Resource" 2029: 400: 1798: 1131: 807: 536: 346: 330:

Ongoing and future developments at the Universal Protein Resource

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include the identification of candidate genes and nucleotides,

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environmental factors. Examples of genetic phenotypes include

126: 2917:"Predicting disease genes using protein-protein interactions" 272:

data created by combining the Swiss-Prot, TrEMBL and PIR-PSD

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Research Programme on Biomedical Informatics (GRIB) IMIM-UPF

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OMIM is a compendium of human genes and genetic phenotypes.

2103:. American Medical Informatics Association. Archived from 907: 2545:

K. Brown, "Online Predicted human Interaction Database,"

2101:"American Medical Informatics Association Strategic Plan" 412: 370: 2601:

Shimoyama M, De Pons J, Hayman GT, et al. (2015).

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Vogt, Ingo; Prinz, Jeanette; Campillos, Mónica (2014).

2169:"The Comparative Toxicogenomics Database: update 2011" 669: 1056:

Ferran Sanz and Laura I. Furlong (Pinero et al, 2015)

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Literature-derived human gene-disease network (LHGDN)

1234:"Network medicine analysis of COPD multimorbidities" 1110: 1100: 1095: 1079: 1074: 1060: 1052: 1044: 1034: 1029: 1015: 1005: 997: 992: 902: 897: 885: 862: 857: 847: 842: 780: 775: 761: 749: 744: 730: 722: 717: 680: 664: 659: 645: 637: 627: 615: 610: 597: 586: 581: 525: 517: 507: 497: 484: 479: 461: 456: 423: 395: 365: 339: 334: 326: 305: 300: 290: 280: 257: 252: 67: 57: 47: 37: 1795:Remarks about the future in Gene Disease Databases 531:Yes – both individual protein entries and searches 164:At different stages of any gene disease project, 542:The process of database compilation and curation 1914:International Society for Computational Biology 983:The Gene Disease Associations Database DisGeNET 158:Gene prioritization workflow of human diseases 202:The Comparative Toxicogenomics Database (CTD) 8: 2061:"Databases, data tombs and dust in the wind" 1991: 1989: 1987: 1985: 1743:"Charting the NF-κB Pathway Interactome Map" 1574:International Journal of Biological Sciences 987: 828: 712: 641:Mary E. Shimoyama, PhD; Howard J. Jacob, PhD 576: 247: 32: 2277: 2275: 2273: 2271: 2011: 2009: 1983: 1981: 1979: 1977: 1975: 1973: 1971: 1969: 1967: 1965: 1934:List of open-source bioinformatics software 1137:DisGeNET gene-disease association ontology 2583: 2581: 2450:"The Universal Protein Resource (UniProt)" 2372: 2370: 1001:Integrates human gene-disease associations 986: 827: 711: 575: 246: 106:Experts in different areas of biology and 2940: 2891: 2861: 2859: 2839: 2829: 2748: 2717:P. Flicek and M. Ridwan, "Ensembl 2012," 2713: 2711: 2691: 2648: 2646: 2626: 2522: 2473: 2424: 2394: 2392: 2377:Buneman, P. (2008). "Curated Databases". 2350: 2340: 2299: 2251: 2241: 2192: 2135: 2076: 1776: 1766: 1723: 1680: 1636: 1593: 1550: 1540: 1497: 1417: 1407: 1372: 1259: 1249: 1214: 704:The Online Mendelian Inheritance in Man ( 410:& for downloading complete data sets 2120:"The modular nature of genetic diseases" 1048:Integrative Biomedical Informatics Group 231:The Universal Protein Resource (UNIPROT) 152: 23:For broader coverage of this topic, see 2666:Hubbard T, et al. (January 2002). 2282:Bauer-Mehren, A.; Rautscha, M. (2010). 1961: 713:The Online Mendelian Inheritance in Man 2797:: CS1 maint: archived copy as title ( 2790: 2549:, vol. 21, no. 9, pp. 2076-2082, 2005. 568:Ontology and disease names from OMIM. 475:O, bulk retrieval/download, ID mapping 31: 2668:"The Ensembl genome database project" 813:Pathway Hogeneity vs Associated Genes 7: 829:The Ensembl genome database project. 2656:, vol. 33, no. 1, pp. 514-517, 2005 2591:, vol. 37, no. 1, pp. 744-749, 2009 2575:, vol. 36, no. 1, pp. 724-728, 2007 632:Human Molecular and Genetics Center 208:Comparative Toxicogenomics Database 2733:"The genetic Association Database" 1629:10.1152/physiolgenomics.00013.2013 943:Genetic Association Database (GAD) 268:resource, a central repository of 16:For bioinformatics databases, see 14: 2721:, vol. 40, no. 1, pp. 84-90, 2012 2562:, vol. 10, no. 1, pp. 12-22, 2011 1889:European Bioinformatics Institute 877:European Bioinformatics Institute 2137:10.1111/j.1399-0004.2006.00708.x 1837: 834: 235:The Universal Protein Resource ( 2731:Becker, K.; Barnes, K. (2004). 2216:Davis, A.; Wiegers, T. (2013). 1924:List of bioinformatics journals 872:Wellcome Trust Sanger Institute 563:The Mouse genome Database (MGD) 226:Chemical-phenotype associations 2812:Bravo, A; et al. (2014). 2399:Murphy, C.; Davis, A. (2009). 1804:Relationships in Gene Diseases 1: 2509:(Database issue): D214–D219. 2342:10.1093/bioinformatics/btu487 2301:10.1093/bioinformatics/btq538 2078:10.1093/bioinformatics/btn464 1359:(Database issue): D900–D906. 572:The Rat Genome Database (RGD) 220:Chemical-disease associations 2866:Piñero; et al. (2015). 2448:Uniprot, Consortium (2008). 2243:10.1371/journal.pone.0058201 2167:Davis, A.; King, B. (2011). 1999:, Complex and Environmental 1929:List of biological databases 1768:10.1371/journal.pone.0032678 1542:10.1371/journal.pone.0084408 1173:10.1016/j.molmed.2014.10.002 1161:Trends in Molecular Medicine 622:Medical College of Wisconsin 18:List of biological databases 2613:(Database issue): D743–50. 2059:Wren JD, Bateman A (2008). 2003:," PLOS One, pp. 1-3, 2011. 1884:Disease gene identification 2987: 1909:Integrative bioinformatics 217:Chemical-gene interactions 22: 15: 2831:10.1186/s12859-015-0472-9 1949:Structural bioinformatics 1874:Computational biomodeling 1673:10.1007/s13238-013-0016-x 1449:10.1016/j.nbd.2014.03.019 1409:10.1186/s13073-014-0052-z 1251:10.1186/s12931-014-0111-4 833: 223:Gene-disease associations 1859:Bioinformatics companies 1854:Biodiversity informatics 149:Challenges with creation 2933:10.1136/jmg.2006.041376 2884:10.1093/database/bav028 1437:Neurobiology of Disease 1322:10.1145/2649387.2660817 1287:10.1145/2649387.2660847 2719:Nucleic Acids Research 2672:Nucleic Acids Research 2654:Nucleic Acids Research 2607:Nucleic Acids Research 2589:Nucleic Acids Research 2573:Nucleic Acids Research 2560:Nucleic Acids Research 2503:Nucleic Acids Research 2454:Nucleic Acids Research 1879:Computational genomics 1806: 1704:Nucleic Acids Research 1617:Physiological Genomics 1353:Nucleic Acids Research 1195:Nucleic Acids Research 1142: 820: 546: 161: 1869:Computational biology 1802: 1135: 1011:Associations Database 974:Integrative databases 811: 540: 156: 83:Gene Disease Database 33:Gene Disease Database 2497:Uniprot, C. (2010). 1904:Human Genome Project 1316:. pp. 647–648. 1281:. pp. 768–773. 1238:Respiratory Research 1157:Potashkin, Judith A. 934:Literature databases 550:Predictive databases 493:Attribution-NoDerivs 166:molecular biologists 68:Subtype of Databases 2779:on 24 February 2021 2684:10.1093/nar/30.1.38 2619:10.1093/nar/gku1026 2515:10.1093/nar/gkq1020 2234:2013PLoSO...858201D 2107:on 26 October 2009. 1894:Functional genomics 1759:2012PLoSO...732678T 1533:2014PLoSO...984408L 1207:10.1093/nar/gku1036 1201:(20): 12570–12584. 1155:Santiago, Jose A.; 989: 830: 714: 694:Rat Genome Database 592:Rat Genome Database 578: 249: 143:Alzheimer's disease 34: 25:Biological database 2971:Genetics databases 2818:BMC Bioinformatics 2750:10.1038/ng0504-431 2466:10.1093/nar/gkm895 2417:10.1093/nar/gkn580 2185:10.1093/nar/gkq813 1899:Health informatics 1827:medical research. 1807: 1716:10.1093/nar/gkt989 1661:Protein & Cell 1490:10.1002/humu.22520 1365:10.1093/nar/gku948 1143: 821: 547: 345:Custom flat file, 286:Protein annotation 185:Types of databases 162: 125:or changes to the 2335:(21): 3093–3100. 2323:Vogt, I. (2014). 2294:(22): 2924–2926. 2173:Nucleic Acids Res 2124:Clinical Genetics 1586:10.7150/ijbs.5976 1118: 1117: 918: 917: 824:Ensembl SIFT tool 802: 801: 690: 689: 604:Rattus norvegicus 535: 534: 467:Advanced search, 266:universal protein 193:Curated databases 75: 74: 58:Type of Databases 48:Subclassification 2978: 2955: 2954: 2944: 2912: 2906: 2905: 2895: 2863: 2854: 2853: 2843: 2833: 2809: 2803: 2802: 2796: 2788: 2786: 2784: 2775:. Archived from 2769: 2763: 2762: 2752: 2728: 2722: 2715: 2706: 2705: 2695: 2663: 2657: 2650: 2641: 2640: 2630: 2598: 2592: 2585: 2576: 2569: 2563: 2556: 2550: 2543: 2537: 2536: 2526: 2494: 2488: 2487: 2477: 2445: 2439: 2438: 2428: 2396: 2387: 2386: 2374: 2365: 2364: 2354: 2344: 2320: 2314: 2313: 2303: 2279: 2266: 2265: 2255: 2245: 2213: 2207: 2206: 2196: 2179:(1): 1067–1072. 2164: 2158: 2157: 2139: 2115: 2109: 2108: 2097: 2091: 2090: 2080: 2056: 2050: 2049: 2013: 2004: 1993: 1847: 1842: 1841: 1790: 1780: 1770: 1737: 1727: 1710:(3): 1474–1496. 1694: 1684: 1650: 1640: 1607: 1597: 1564: 1554: 1544: 1511: 1501: 1468: 1431: 1421: 1411: 1386: 1376: 1343: 1308: 1273: 1263: 1253: 1228: 1218: 1184: 1091: 1088: 1086: 1061:Primary citation 990: 914: 911: 909: 886:Primary citation 838: 831: 798: 795: 793: 791: 789: 787: 762:Primary citation 715: 685:RGD Data Release 676: 673: 671: 646:Primary citation 579: 491:Creative Commons 452: 442: 419: 416: 414: 409: 406: 404: 402: 391: 388: 386: 384: 382: 377: 374: 372: 327:Primary citation 250: 35: 2986: 2985: 2981: 2980: 2979: 2977: 2976: 2975: 2961: 2960: 2959: 2958: 2915:Oti, M (2006). 2914: 2913: 2909: 2865: 2864: 2857: 2811: 2810: 2806: 2789: 2782: 2780: 2773:"Archived copy" 2771: 2770: 2766: 2737:Nature Genetics 2730: 2729: 2725: 2716: 2709: 2665: 2664: 2660: 2651: 2644: 2600: 2599: 2595: 2586: 2579: 2570: 2566: 2557: 2553: 2544: 2540: 2496: 2495: 2491: 2447: 2446: 2442: 2398: 2397: 2390: 2376: 2375: 2368: 2322: 2321: 2317: 2281: 2280: 2269: 2215: 2214: 2210: 2166: 2165: 2161: 2118:Oti, M (2007). 2117: 2116: 2112: 2099: 2098: 2094: 2058: 2057: 2053: 2018:Nature Genetics 2015: 2014: 2007: 1994: 1963: 1958: 1953: 1919:Jumping library 1843: 1836: 1833: 1797: 1740: 1697: 1653: 1623:(14): 597–605. 1610: 1567: 1514: 1471: 1434: 1396:Genome Medicine 1389: 1346: 1332: 1311: 1297: 1276: 1231: 1187: 1167:(12): 694–703. 1154: 1148: 1139: 1102: 1083: 1036:Research center 1007: 985: 976: 967: 958: 945: 936: 906: 881: 864:Research center 826: 784: 751:Research center 710: 668: 617:Research center 574: 565: 552: 544: 527: 518:Curation policy 509: 448: 438: 411: 399: 379: 378: 369: 318:, Switzerland; 307:Research center 282: 233: 204: 195: 187: 151: 104: 28: 21: 12: 11: 5: 2984: 2982: 2974: 2973: 2963: 2962: 2957: 2956: 2927:(8): 691–698. 2907: 2855: 2804: 2764: 2743:(5): 431–432. 2723: 2707: 2658: 2642: 2593: 2577: 2564: 2551: 2547:Bioinformatics 2538: 2489: 2460:(1): 190–195. 2440: 2411:(1): 786–792. 2405:Bioinformatics 2388: 2366: 2329:Bioinformatics 2315: 2288:Bioinformatics 2267: 2208: 2159: 2110: 2092: 2071:(19): 2127–8. 2065:Bioinformatics 2051: 2030:10.1038/ng1090 2024:(1): 228–237. 2005: 1960: 1959: 1957: 1954: 1952: 1951: 1946: 1941: 1936: 1931: 1926: 1921: 1916: 1911: 1906: 1901: 1896: 1891: 1886: 1881: 1876: 1871: 1866: 1861: 1856: 1850: 1849: 1848: 1845:Biology portal 1832: 1829: 1796: 1793: 1792: 1791: 1738: 1695: 1651: 1608: 1580:(3): 295–302. 1565: 1512: 1484:(5): 537–547. 1478:Human Mutation 1469: 1432: 1387: 1344: 1330: 1309: 1295: 1274: 1229: 1185: 1147: 1146:Some use cases 1144: 1116: 1115: 1112: 1108: 1107: 1104: 1098: 1097: 1093: 1092: 1081: 1077: 1076: 1072: 1071: 1062: 1058: 1057: 1054: 1050: 1049: 1046: 1042: 1041: 1038: 1032: 1031: 1027: 1026: 1019: 1013: 1012: 1009: 1003: 1002: 999: 995: 994: 984: 981: 975: 972: 966: 963: 957: 954: 944: 941: 935: 932: 930:and genomics. 916: 915: 904: 900: 899: 895: 894: 887: 883: 882: 880: 879: 874: 868: 866: 860: 859: 855: 854: 849: 845: 844: 840: 839: 825: 822: 800: 799: 782: 778: 777: 773: 772: 763: 759: 758: 753: 747: 746: 742: 741: 734: 728: 727: 724: 720: 719: 709: 702: 688: 687: 682: 678: 677: 666: 662: 661: 657: 656: 647: 643: 642: 639: 635: 634: 629: 625: 624: 619: 613: 612: 608: 607: 601: 595: 594: 588: 584: 583: 573: 570: 564: 561: 551: 548: 533: 532: 529: 523: 522: 519: 515: 514: 511: 505: 504: 501: 495: 494: 488: 482: 481: 477: 476: 465: 459: 458: 454: 453: 428: 421: 420: 397: 393: 392: 367: 363: 362: 343: 337: 336: 332: 331: 328: 324: 323: 309: 303: 302: 298: 297: 294: 288: 287: 284: 278: 277: 259: 255: 254: 232: 229: 228: 227: 224: 221: 218: 203: 200: 194: 191: 186: 183: 150: 147: 108:bioinformatics 103: 100: 79:bioinformatics 73: 72: 69: 65: 64: 59: 55: 54: 49: 45: 44: 42:Bioinformatics 39: 38:Classification 13: 10: 9: 6: 4: 3: 2: 2983: 2972: 2969: 2968: 2966: 2952: 2948: 2943: 2938: 2934: 2930: 2926: 2922: 2921:J. Med. Genet 2918: 2911: 2908: 2903: 2899: 2894: 2889: 2885: 2881: 2877: 2873: 2869: 2862: 2860: 2856: 2851: 2847: 2842: 2837: 2832: 2827: 2823: 2819: 2815: 2808: 2805: 2800: 2794: 2778: 2774: 2768: 2765: 2760: 2756: 2751: 2746: 2742: 2738: 2734: 2727: 2724: 2720: 2714: 2712: 2708: 2703: 2699: 2694: 2689: 2685: 2681: 2677: 2673: 2669: 2662: 2659: 2655: 2649: 2647: 2643: 2638: 2634: 2629: 2624: 2620: 2616: 2612: 2608: 2604: 2597: 2594: 2590: 2584: 2582: 2578: 2574: 2568: 2565: 2561: 2555: 2552: 2548: 2542: 2539: 2534: 2530: 2525: 2520: 2516: 2512: 2508: 2504: 2500: 2493: 2490: 2485: 2481: 2476: 2471: 2467: 2463: 2459: 2455: 2451: 2444: 2441: 2436: 2432: 2427: 2422: 2418: 2414: 2410: 2406: 2402: 2395: 2393: 2389: 2385:(1): 152–162. 2384: 2380: 2379:Bibliometrics 2373: 2371: 2367: 2362: 2358: 2353: 2348: 2343: 2338: 2334: 2330: 2326: 2319: 2316: 2311: 2307: 2302: 2297: 2293: 2289: 2285: 2278: 2276: 2274: 2272: 2268: 2263: 2259: 2254: 2249: 2244: 2239: 2235: 2231: 2227: 2223: 2219: 2212: 2209: 2204: 2200: 2195: 2190: 2186: 2182: 2178: 2174: 2170: 2163: 2160: 2155: 2151: 2147: 2143: 2138: 2133: 2129: 2125: 2121: 2114: 2111: 2106: 2102: 2096: 2093: 2088: 2084: 2079: 2074: 2070: 2066: 2062: 2055: 2052: 2047: 2043: 2039: 2035: 2031: 2027: 2023: 2019: 2012: 2010: 2006: 2002: 1998: 1992: 1990: 1988: 1986: 1984: 1982: 1980: 1978: 1976: 1974: 1972: 1970: 1968: 1966: 1962: 1955: 1950: 1947: 1945: 1944:Phylogenetics 1942: 1940: 1937: 1935: 1932: 1930: 1927: 1925: 1922: 1920: 1917: 1915: 1912: 1910: 1907: 1905: 1902: 1900: 1897: 1895: 1892: 1890: 1887: 1885: 1882: 1880: 1877: 1875: 1872: 1870: 1867: 1865: 1862: 1860: 1857: 1855: 1852: 1851: 1846: 1840: 1835: 1830: 1828: 1825: 1819: 1817: 1811: 1805: 1801: 1794: 1788: 1784: 1779: 1774: 1769: 1764: 1760: 1756: 1753:(3): e32678. 1752: 1748: 1744: 1739: 1735: 1731: 1726: 1721: 1717: 1713: 1709: 1705: 1701: 1696: 1692: 1688: 1683: 1678: 1674: 1670: 1666: 1662: 1658: 1652: 1648: 1644: 1639: 1634: 1630: 1626: 1622: 1618: 1614: 1609: 1605: 1601: 1596: 1591: 1587: 1583: 1579: 1575: 1571: 1566: 1562: 1558: 1553: 1548: 1543: 1538: 1534: 1530: 1527:(1): e84408. 1526: 1522: 1518: 1513: 1509: 1505: 1500: 1495: 1491: 1487: 1483: 1479: 1475: 1470: 1466: 1462: 1458: 1454: 1450: 1446: 1442: 1438: 1433: 1429: 1425: 1420: 1415: 1410: 1405: 1401: 1397: 1393: 1388: 1384: 1380: 1375: 1370: 1366: 1362: 1358: 1354: 1350: 1345: 1341: 1337: 1333: 1331:9781450328944 1327: 1323: 1319: 1315: 1310: 1306: 1302: 1298: 1296:9781450328944 1292: 1288: 1284: 1280: 1275: 1271: 1267: 1262: 1257: 1252: 1247: 1243: 1239: 1235: 1230: 1226: 1222: 1217: 1212: 1208: 1204: 1200: 1196: 1192: 1186: 1182: 1178: 1174: 1170: 1166: 1162: 1158: 1153: 1152: 1151: 1145: 1138: 1134: 1130: 1127: 1122: 1113: 1109: 1105: 1099: 1096:Miscellaneous 1094: 1090: 1082: 1078: 1073: 1070: 1066: 1063: 1059: 1055: 1051: 1047: 1043: 1039: 1037: 1033: 1028: 1024: 1020: 1018: 1014: 1010: 1004: 1000: 996: 991: 982: 980: 973: 971: 964: 962: 955: 953: 951: 942: 940: 933: 931: 929: 924: 913: 905: 901: 896: 892: 888: 884: 878: 875: 873: 870: 869: 867: 865: 861: 856: 853: 850: 846: 841: 837: 832: 823: 819: 814: 810: 806: 797: 783: 779: 774: 771: 767: 764: 760: 757: 754: 752: 748: 743: 739: 735: 733: 729: 725: 721: 716: 707: 703: 701: 697: 695: 686: 683: 679: 675: 667: 663: 658: 655: 651: 648: 644: 640: 636: 633: 630: 626: 623: 620: 618: 614: 609: 605: 602: 600: 596: 593: 589: 585: 580: 571: 569: 562: 560: 558: 549: 543: 539: 530: 524: 520: 516: 512: 506: 502: 500: 496: 492: 489: 487: 483: 480:Miscellaneous 478: 474: 470: 466: 464: 460: 455: 451: 446: 441: 436: 433: 429: 426: 422: 418: 408: 398: 394: 390: 376: 368: 364: 360: 356: 352: 348: 344: 342: 338: 333: 329: 325: 321: 317: 313: 310: 308: 304: 299: 295: 293: 289: 285: 279: 275: 271: 267: 263: 260: 256: 251: 245: 242: 238: 230: 225: 222: 219: 216: 215: 214: 211: 209: 201: 199: 192: 190: 184: 182: 178: 176: 171: 167: 159: 155: 148: 146: 144: 138: 136: 132: 128: 124: 120: 116: 112: 109: 101: 99: 97: 93: 89: 84: 80: 70: 66: 63: 60: 56: 53: 50: 46: 43: 40: 36: 30: 26: 19: 2924: 2920: 2910: 2875: 2871: 2821: 2817: 2807: 2781:. Retrieved 2777:the original 2767: 2740: 2736: 2726: 2718: 2678:(1): 38–41. 2675: 2671: 2661: 2653: 2610: 2606: 2596: 2588: 2572: 2567: 2559: 2554: 2546: 2541: 2506: 2502: 2492: 2457: 2453: 2443: 2408: 2404: 2382: 2378: 2332: 2328: 2318: 2291: 2287: 2225: 2221: 2211: 2176: 2172: 2162: 2127: 2123: 2113: 2105:the original 2095: 2068: 2064: 2054: 2021: 2017: 1823: 1820: 1812: 1808: 1803: 1750: 1746: 1707: 1703: 1667:(1): 59–68. 1664: 1660: 1620: 1616: 1577: 1573: 1524: 1520: 1481: 1477: 1440: 1436: 1399: 1395: 1356: 1352: 1313: 1278: 1241: 1237: 1198: 1194: 1164: 1160: 1149: 1136: 1119: 1101:Data release 1022: 977: 968: 959: 946: 937: 919: 890: 851: 817: 812: 803: 737: 698: 691: 681:Download URL 603: 591: 566: 553: 541: 526:Bookmarkable 508:Data release 396:Download URL 265: 261: 234: 212: 205: 196: 188: 179: 163: 157: 139: 113: 105: 102:Introduction 82: 76: 71:Gene-Disease 29: 2783:18 November 2228:(4): 1–29. 2130:(1): 1–11. 1864:Biomedicine 998:Description 965:BeFree Data 848:Description 723:Description 587:Description 425:Web service 341:Data format 258:Description 119:chromosomes 96:text mining 2878:: bav028. 1956:References 1244:(1): 111. 1045:Laboratory 1023:H. Sapiens 1006:Data types 928:proteomics 738:H. Sapiens 628:Laboratory 499:Versioning 407:/downloads 281:Data types 175:biomedical 62:Biological 2824:(1): 55. 1997:Mendelian 1939:Pathology 1824:In silico 1443:: 84–91. 1402:(7): 52. 1103:frequency 1087:.disgenet 1017:Organisms 889:Hubbard, 732:Organisms 599:Organisms 510:frequency 447:see info 437:see info 292:Organisms 274:databases 131:carcinoma 123:mutations 88:phenotype 52:Databases 2965:Category 2951:16611749 2902:25877637 2872:Database 2850:25886734 2793:cite web 2759:15118671 2702:11752248 2637:25355511 2533:21051339 2484:18045787 2435:18782832 2361:25061072 2310:20861032 2262:23613709 2222:PLOS ONE 2203:20864448 2154:24615025 2146:17204041 2087:18819940 2046:10599219 2038:12610532 2001:diseases 1831:See also 1787:22403694 1747:PLOS ONE 1734:24198249 1691:24474197 1647:23695888 1604:23493786 1561:24392133 1521:PLOS ONE 1508:24478219 1465:41944859 1457:24718034 1428:25276232 1383:25313158 1340:30593231 1305:17123912 1270:25248857 1225:25352550 1181:25455073 1126:clusters 1121:DisGeNET 1069:25877637 1008:captured 988:DisGeNET 910:.ensembl 770:25398906 654:25355511 557:genomics 528:entities 415:.uniprot 403:.uniprot 383:.uniprot 373:.uniprot 312:EMBL-EBI 283:captured 170:proteins 135:melanoma 92:genotype 2942:2564594 2893:4397996 2841:4466840 2628:4383884 2524:3013648 2475:1669721 2426:2686584 2352:4609011 2253:3629079 2230:Bibcode 2194:3013756 1778:3293857 1755:Bibcode 1725:3919568 1682:3938846 1638:3727018 1595:3596715 1552:3879311 1529:Bibcode 1499:4130156 1419:4165361 1374:4384019 1261:4177421 1216:4227755 1111:Version 1080:Website 1053:Authors 1030:Contact 1021:Human ( 993:Content 923:Ensembl 903:Website 858:Contact 852:Ensembl 843:Content 781:Website 745:Contact 736:Human ( 718:Content 665:Website 638:Authors 611:Contact 582:Content 513:4 weeks 486:License 473:Clustal 366:Website 301:Contact 270:protein 264:is the 262:UniProt 253:Content 248:UniProt 237:UniProt 115:Genetic 2949: 2939: 2900: 2890: 2848: 2838: 2757: 2700: 2690: 2635: 2625: 2531: 2521: 2482: 2472: 2433: 2423: 2359: 2349: 2308: 2260: 2250: 2201: 2191: 2152: 2144: 2085: 2044: 2036: 1785: 1775: 1732: 1722: 1689: 1679: 1645: 1635: 1602: 1592: 1559: 1549: 1506: 1496: 1463: 1455: 1426: 1416: 1381: 1371: 1338: 1328: 1303: 1293: 1268: 1258: 1223: 1213: 1179: 1106:annual 1075:Access 1067: 898:Access 893:(2002) 891:et al. 776:Access 768: 660:Access 652: 443:& 430:Yes – 335:Access 314:, UK; 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