Human variation databases.

More than 100,000 human genetic variations have been described in various genes that are associated with a wide variety of diseases. Such data provides invaluable information for both clinical medicine and basic science. A number of locus-specific databases have been developed to exploit this huge amount of data. However, the scope, format and content of these databases differ strongly and as no standard for variation databases has yet been adopted, the way data is presented varies enormously. This review aims to give an overview of current resources for human variation data in public and commercial resources.

Background

Over the recent years the cloning of genes involved in complex diseases such as cancer as well as the development of new high throughput techniques like single nucleotide polymorphism (SNP) arrays has made enormous progress. This resulted in more than 100 000 human genetic variations which have been described in various genes associated with a wide variety of diseases (1–3). Somatic variations in cancer are used in clinical studies and molecular pathology to characterize tumor types, to improve the best suited treatment choice, and to predict response to treatment. Thus, mutation analysis can play an important role in drug discovery and drug development. Identification of genetic variants will yield new drug targets and biomarkers.

Cancer, as a disease of genome alterations, arises through the sporadic acquisition of multiple somatic variations (4). However, not all mutations contribute equally to the cancer type in which they are found. The proportion of mutations causally implicated in cancer is still unknown especially due to the high number of variations between different tumors (5–9) Although the number of unique variations for each cancer genome can be very high (10,11), only a few somatic variations will be critical for the development of the tumor. These causative variations, the so-called ‘drivers’, are emerging because of selective pressure during tumorigenesis, whereas many mutations are only incidental or caused by genome instabilities, so-called ‘passengers’ (12). The differentiation of disease causing driver mutations from the passenger variations is a challenge for mutation analysis (13).

Usefulness of mutation analysis

Analysis of mutations is useful in many ways: the study of cancer-prone DNA repair diseases (Xeroderma pigmentosum, Ataxia telangiectasia, Fanconi’s anemia, Bloom’s syndrome and others) has given valuable insights in the type and function of genes responsible for maintaining DNA integrity (14–18). Mutation analysis can help to predict the risk for developing certain types of cancer, BRCA1 and BRCA2 (increased breast cancer risk) (19) and APC (increased risk for colon cancer) (20) being among the best known so far.

Mutations can also influence the response of patients to cancer drugs, e.g. the KRAS (21,22) or BRAF (23,24) mutations. The presence of certain mutations can also influence progression free or overall survival rates of patients (22,25).

Germline versus somatic mutations

In general, mutations can be grouped into two different categories: germline and somatic. Germline mutations are variations found in all cells of an organism including germ line cells. They play an important role in evolution by giving every human its genetic individuality (see SNPs) but also give a rise to hereditary diseases like sickle-cell anemia or phenylketonuria. Germline mutations can also lead to increased risk for developing cancer, like BRCA1 and BRCA2 gene mutations which are associated with an increased risk for breast and ovarian cancer (26–28). Other examples of familial cancer syndromes include von Hippel–Lindau syndrome (caused by mutations in VHL) (29), Peutz–Jeghers syndrome (caused by mutations in LKB1) (30) and Li–Fraumeni syndrome (caused by mutations in TP53) (31).

Detection of germline mutations with current technologies is state of the art but time-consuming. Usually a large amount of genetic material of good quality can be extracted from blood cells. However, in addition to the mutation detection, the differentiation of disease causing and neutral germline mutations having no effect on the phenotype is an important but non-trivial task. Currently, no generally applicable solution for this problem exists and this question often remains unsolved.

Somatic mutations are not inherited but acquired during lifetime in somatic cells of an organism and might cause tissue specific tumors. An important problem with somatic mutations is the difficulty of their detection. Tumor samples can be very heterogeneous and are very often ‘contaminated’ with normal cells, such as stromal cells. However, since somatic mutations are identified through a comparison of a tumor sample with a normal sample of the same organism the identification of the mutation is unambiguous. Also for somatic mutations the differentiation between drivers and passengers is an important but still unsolved problem. In contrast to germline mutations however, all somatic mutations are tumor associated. Therefore, all non-silent somatic mutations are potential candidates for biomarker development.

Mutation types

Genome alterations are typically classified by the mutation type. The different databases characterize all variations first by the effect on the nucleotide sequence: deletions, insertions and single nucleotide variations. Mutations occurring in the coding region of a gene can also be classified by their effect on the amino acid sequence. A variation of the coding sequence without any change of the amino acid sequence of the protein is called silent mutation. Single nucleotide mutations causing the substitution of a different amino acid are called missense mutation. A frameshift mutation is an insertion or deletion in the coding sequence which changes the reading frame resulting in a different translation of the subsequent sequence. Nonsense mutations generate a premature stop codon and often a non-functional truncated protein product.

SNPs versus germline mutations

Single nucleotide germline mutations and SNP are often used as synonyms, since both describe variations of single nucleotides, which are inherited and not tumor-associated per se. However, concerning the databases presented here these synonyms are used in two different meanings: SNPs as presented in public databases like dbSNP (32,33) or HapMap (34) are germline variations for which at most population frequencies are known. In literature it is usually assumed that the variation should be found in more than 1% of the population in order to be called a SNP. Such information is very useful for biomarker development since it describes the prevalence of the mutation in different populations. However, it is normally not possible to get additional information (like gender, age, or disease status) on the individuals having the SNP, only the population a person belongs to is given. Since it is not known if the information comes from a tumor or normal sample, a correlation between diseases and SNPs cannot be calculated.

In contrast, germline mutations presented in cancer or disease mutation databases like ‘The Cancer Genome Atlas (TCGA)’ (35) are usually connected with additional sample information like patient gender, age, histology or tissue. Germline mutations are found in the normal as well as the tumor sample. Hence, the sample information allows for further analyses of associations between germline mutations and diseases.

Standardization efforts

A standard problem occurring in every field where huge amounts of data are generated is standardization. Without standardization the task to identify and integrate the data is very complicated, laborious, error-prone and time-consuming. Although databases may have different scope and aims it is important to standardize content and annotation. The Human Genome Variation Society (HGVS) has proposed a recommendation for the nomenclature of genetic variations and content of mutation databases and scientific publications (36). This naming of mutations has now become widely accepted. Some journals (e.g. Human Mutation) already accept only publications with mutation notation following the HGVS proposed recommendations. If more publishers should follow this trend it would have a very positive effect on the usability of mutation databases including an increase of the quality and amount of their content.

HGVS and members have published number of recommendations e.g. for the collection of somatic mutations, sharing data, etc. There are also projects at European Bioinformatics Institute (EBI) and National Center for Biotechnology Information (NCBI) to develop reference sequences, locus reference genomics (LRGs) (http://www.lrg-sequence.org) and RefSeqGenes (http://www.ncbi.nlm.nih.gov/projects/RefSeq/RSG/), respectively. In addition, the Gen2Phen (http://www.gen2phen.org) project works on data models and standards for a number of aspects related to variation data description, storage and integration in databases.

Except for the already widely accepted naming recommendations of mutations by the HGVS, a promising standardization effort for integrating all cancer genome data is still missing.

Structure and accessibility

Historically, mutations and variations in human have been reported only in the published literature. Mutation descriptions were often not precise, no standard notation existed, and the sequence of the reference gene under study was almost never indicated. To this end a sophisticated mutation analysis was mostly unfeasible. However, with the explosion of large-scale cancer genome sequencing (35,37–40) more and more information on genetic variations has been captured over the last years in publicly available databases that can be used by clinicians or scientists as a research tool. These databases are widespread and their scope, format and content can be very different. Current data related to somatic mutations is mostly buried in journals or scattered between several locus-specific databases (LSDBs) and general databases that have no or very limited connections between them.

Only a few large public resources exist that comprehensively compile data on somatic gene alterations in cancer: International Agency for Research on Cancer (IARC) TP53 Database (41), Catalog of Somatic Mutations in Cancer (COSMIC) (42), TCGA (35), Roche Cancer Genome Database (RCGDB) and Human Gene Mutation Database (HGMD®) (43).

The LSDBs often originate as loosely organized compilation of data. Since no standard system similar to the HGVS recommendation for mutation notation has yet been established, the presentation of the data in LSDBs varies enormously. The data is mostly presented in flat files, plain text databases, or Microsoft Excel spreadsheets making it easy to collect and store the information, but nearly impossible to search or retrieve specific data. More ambitious databases use open source database management software (DBMS)—like MySQL or PostgreSQL—whereas only a minority of curators use specialized software such as the UMD (44), the Mutation Storage and Retrieval Program (MuStaR) (45), or the Leiden Open Source Variation Database (LOVD) (46). The use of such relational DBMSs allows to specify complex queries and specific analyses of customized subsets of the database.

Cancer variation databases

Currently, the best-known publicly available primary database on somatic mutations in human cancer is the ‘COSMIC’(42) hosted at the Wellcome Trust Sanger institute in Cambridge. The data is gathered from scientific publications and genome-wide screens from the Cancer Genome Project (CGP) at the same institute. The project has been continuously updated and improved for over 9 years and currently contains more than 108 773 mutations in >13 500 different genes observed in over 449 676 different tumor samples. The curation process in COSMIC is largely manual resulting in a very high quality of the data. For each mutation all details on the sample like patient age, gender, histology and tissue are available. COSMIC uses its own internal classification system to provide tissue and histology consistency within the database and to reduce redundancy. All tissue and histology information from scientific publications is translated using this classification system. In addition, for each study the project offers the information which genes where actually screened, since published studies often focus on mutation hot spots, for example KRAS (47), BRAF (48) or TP53 (49). This information enables frequency data to be calculated for mutations in various genes and different cancer types. COSMIC offers also somatic mutations found in cell-lines including the NCI-60 (50). The website of COSMIC has a clear structure and is easy to use. The interface allows to browse by gene, or search by phenotype. Summary information on mutation counts and frequencies are presented graphically for a better understanding. In addition, all information can be downloaded as txt files, or as an Oracle dump file.

Another large mutation data source is ‘TCGA’ (35), a project at the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). The main goal of TCGA is to understand the molecular basis of cancer through the application of genome analysis techniques, including large-scale genome sequencing and SNP analysis. For each patient a whole genome analysis of a normal, a tumor and control samples (a second normal and tumor sample as control) is performed enabling researchers to distinguish between somatic and germline mutations. All mutations found are publicly available in a special Mutation Analysis file Format (MAF) and can be downloaded via the TCGA Data Portal. This portal contains all TCGA data concerning to clinical information associated with tumors and human subjects, genomic characterization, and high-throughput sequencing analysis of the tumor genomes. However, no advanced search interface or graphical visualization of the mutation data is available. In the starting phase the project focused on only two cancer types: brain cancer (glioblastoma multiforme) and ovarian serous adenocarcinoma. After the pilot phase, which was completed in 2009, TCGA matured to a full project and is now dealing with more than 20 types of cancer.

Another concept focusing on the integration of heterogeneous mutation data sources is pursued by the RCGDB (51), developed at Roche Pharma Research. The freely available warehouse system integrates somatic and germline mutations gathered by manual curation from scientific publications and public cancer mutation databases (COSMIC, TCGA, etc.). In addition, these mutations are enriched by SNP data from the HapMap (34) project. Updates are provided on a regular basis depending on the update frequency of the external data sources (approximately every 3 months). Access to the RCGDB is offered via a publicly available web interface. A major aspect in designing the user interface was that users should be able to search and view mutations in an intuitive and straight-forward manner, without having to understand the architectural details of the warehouse system. Therefore, the database offers a Google-like web interface to search for cancer genome information on a single gene, sample or cell-line, and on multiple genes, samples or cell-lines. As a special feature the search is supported by an auto-suggestion functionality allowing to search by NCBI GeneIDs, names, or synonyms.

The HGMD® (43) at the Institute of Medical Genetics in Cardiff is a commercial mutation database providing information on somatic and germline mutations. Furthermore, the database offers a less up-to-date public version which is freely available only to registered users from academic institutions or non-profit organizations, respectively. The data is gathered from scientific publications and from publicly available LSDBs. The project claims to include all mutations causing or associated with human inherited disease, plus disease-associated/functional polymorphisms reported in the literature. Currently, HGMD provides information on 96 631 mutations in 3611 genes under the professional license and 69 660 mutation in 2572 genes in the public version of the database. The website of HGMD allows to search by gene, publication or mutation id and presents the results in a table view. A downloadable version of HGMD is only available under the professional license.

In addition to multi-gene LSDBs, various single-gene LSDBs are publicly available. The largest and best-known single-gene LSDB is the TP53 mutation database from the IARC (41), with all TP53 gene variations identified in human populations and tumor samples since 1989. The database contains information on somatic as well as germline mutations of TP53 in patient samples, human cell-lines, and mouse models. This data is compiled from the peer-reviewed literature and from generalist databases. The website offers different sophisticated interfaces for searching and mining the database by multiple criteria. Furthermore, all information can be downloaded in tab-delimited txt-files. A large number of other single gene databases exists like the L1CAM mutation database from the university of Groningen (52) containing single gene somatic mutations. Most of these LSDBs are small containing mostly <500 variants.

For a detailed list of cancer mutation databases see Table 1.

Table 1.

Cancer variation database: a list of available cancer variation databases including web links

Database	URL	Gene(s)	Mutation type	Remark
BLMbase	http://bioinf.uta.fi/BLMbase	BLM	Germline
CASP10base	http://bioinf.uta.fi/CASP10base	CASP10	Germline
CASP8base	http://bioinf.uta.fi/CASP8base	CASP8	Germline
Catalog of Somatic Mutations in Cancer (COSMIC)	http://www.sanger.ac.uk/genetics/CGP/cosmic/	>13 500	Somatic
Fanconi Anemia Mutation Database	http://www.rockefeller.edu/fanconi/mutate/	FANCA, FANCB, FANCC, FANCD1, FANCD2, FANCE, FANCF, FANCG, FANCI, FANCJ, FANCL, FANCM, FANCN
Genetic Alterations in Cancer DB (GAC)	http://www.niehs.nih.gov/research/resources/databases/gac/	32 cancer genes	Somatic, germline
HNPCC database	http://www.insight-group.org/	APC, EPCAM, MUTYH, MSH2, MSH6, MLH1, MLH3, PMS1, PMS2
Human Genome Variation and Genotype/Phenotype Database (HGVbaseG2 P)	http://www.hgvbaseg2p.org/index		Germline
IARC TP53 database	http://www-p53.iarc.fr/	TP53	Somatic, germline
International HapMap Project	http://www.hapmap.org/		SNP
KinMutBase	http://bioinf.uta.fi/KinMutBase/	Protein kinases	Germline
LOVD-ATM	http://www.LOVD.nl/ATM	ATM	Germline	Uses Leiden Open Variation Database
LOVD-B3GALTL	http://www.LOVD.nl/B3GALTL	B3GALTL	Germline	Uses Leiden Open Variation Database
LOVD-BRCA2	http://www.LOVD.nl/BRCA2	BRCA2	Germline	Uses Leiden Open Variation Database
LOVD-FANCA	http://www.LOVD.nl/FANCA	FANCA	Germline	Uses Leiden Open Variation Database
LOVD-FANCB	http://www.LOVD.nl/FANCB	FANCB	Germline	Uses Leiden Open Variation Database
LOVD-FANCC	http://www.LOVD.nl/FANCC	FANCC	Germline	Uses Leiden Open Variation Database
LOVD-FANCD2	http://www.LOVD.nl/FANCD2	FANCD2	Germline	Uses Leiden Open Variation Database
LOVD-FANCE	http://www.LOVD.nl/FANCE	FANCE	Germline	Uses Leiden Open Variation Database
LOVD-FANCF	http://www.LOVD.nl/FANCF	FANCF	Germline	Uses Leiden Open Variation Database
LOVD-FANCG	http://www.LOVD.nl/FANCG	FANCG	Germline	Uses Leiden Open Variation Database
LOVD-FANCL	http://www.LOVD.nl/FANCL	FANCL	Germline	Uses Leiden Open Variation Database
LOVD-MUTYH	http://www.LOVD.nl/MUTYH	MUTYH	Germline	Uses Leiden Open Variation Database
LOVD-NOTCH3	http://www.LOVD.nl/NOTCH3	NOTCH3	Germline	Uses Leiden Open Variation Database
LOVD-NROB1	http://www.LOVD.nl/NROB1	NROB1	Germline	Uses Leiden Open Variation Database
LOVD-OTC	http://www.LOVD.nl/OTC	OTC	Germline	Uses Leiden Open Variation Database
LOVD-TSC1	http://www.LOVD.nl/TSC1	TSC1	Germline	Uses Leiden Open Variation Database
LOVD-TSC2	http://www.LOVD.nl/TSC2	TSC2	Germline	Uses Leiden Open Variation Database
MDL EGFR Mutation Database	http://www.egfr.org/	EGFR	Somatic, germline
Mismatch Repair Genes Variant Database	http://www.med.mun.ca/mmrvariants/	MSH2,MSH6,MLH1,PMS2	Germline
NCBI dbSNP	http://www.ncbi.nlm.nih.gov/projects/SNP/		SNP
Online Mendelian Inheritance in Man (OMIM)	http://www.ncbi.nlm.nih.gov/sites/entrez?db=omim	All genes	Germline
PTCH Mutation Database	http://www.cybergene.se/PTCH/	PTCH	Somatic, germline
Roche Cancer Genome Database (RCGDB)	http://rcgdb.bioinf.uni-sb.de/MutomeWeb	>10 000	Somatic, SNP
The Cancer Genome Atlas (TCGA)	http://cancergenome.nih.gov/	>400	Somatic, germline	Login for TCGA Data Portal necessary, more disease to come
The Human Gene Mutation Database (HGMD)	http://www.hgmd.cf.ac.uk/ac/index.php	2572	Somatic, germline	Commercial license: 3611 genes
The TP53 database	http://p53.free.fr/	TP53	Somatic, germline
TSH Receptor mutation database	http://www.uni-leipzig.de/innere/tsh/	TSHR	Somatic, germline
UMD-APC	http://www.umd.be/APC/	APC	Germline
UMD-BRCA1	http://www.umd.be/BRCA1/	BRCA1	Germline	Restricted access
UMD-BRCA2	http://www.umd.be/BRCA2/	BRCA2	Germline	Restricted access
UMD-MEN1	http://www.umd.be/MEN1/	MEN1	Germline
UMD-VHL	http://www.umd.be/VHL/	VHL	Germline
UML-TGFBR2	http://194.167.35.228/TGFBR2/	TGFBR2	Germline
University of Groningen L1CAM Mutation DB	http://www.l1cammutationdatabase.info/	L1CAM	Somatic
WASbase	http://bioinf.uta.fi/WASbase	WASP	Germline
Werner Syndrome Mutational Database	http://www.pathology.washington.edu/research/werner/database/	WRN	Germline

For each database the type of mutations as well as the genes have been covered.

Disease variation databases

In addition to the Cancer variation database a large number of publicly available databases focuses on disease specific variations. An overview on such disease variation databases can be found in Table 2.

Table 2.

Disease variation databases: a list of available disease variation databases including web links

Database	URL	Gene(s)	Diseases	Remark
ADAbase	http://bioinf.uta.fi/ADAbase	ADA	Adenosine deaminase deficiency
AICDAbase	http://bioinf.uta.fi/AICDAbase	AICDA	Non-X-linked hyper-IgM syndrome
AIREbase	http://bioinf.uta.fi/AIREbase	AIRE	Autoimmune polyendocrinopathy with candidiasis and ectodermal dystrophy (APECED)
Albinism Database (CHS)	http://albinismdb.med.umn.edu/chs1mut.html	LYST	Chediak–Higashi Syndrome
Albinism Database (HPS2)	http://albinismdb.med.umn.edu/hps2mut.htm	AP3B1	Hermansky–Pudlak syndrome 2
ALPSbase (II)	http://research.nhgri.nih.gov/ALPS/alpsII_mut.shtml	CASP10	Autoimmune lymphoproliferative syndrome, type II
ALPSbase (Ia)	http://research.nhgri.nih.gov/ALPS/alpsIa_mut.shtml	FAS	Autoimmune lymphoproliferative syndrome, type Ia
AP3B1base	http://bioinf.uta.fi/AP3B1base	AP3B1	Hermansky–Pudlak syndrome 2
BIRC4base	http://bioinf.uta.fi/BIRC4base	BIRC4	X-linked lymphoproliferative syndrome
BLMbase	http://bioinf.uta.fi/BLMbase	BLM	Bloom syndrome
BLNKbase	http://bioinf.uta.fi/BLNKbase	BLNK	BLNK deficiency
BTKbase	http://bioinf.uta.fi/BTKbase	BTK	X-linked agammaglobulinemia (XLA)
C1QAbase	http://bioinf.uta.fi/C1QAbase	C1QA	C1q α polypeptide deficiency
C1QBbase	http://bioinf.uta.fi/C1QBbase	C1QB	C1q β polypeptide deficiency
C1QCbase	http://bioinf.uta.fi/C1QCbase	C1QC	C1q γ-polypeptide deficiency
C1Sbase	http://bioinf.uta.fi/C1Sbase	C1 S	C1 s deficiency
C2base	http://bioinf.uta.fi/C2base	C2	C2 deficiency
C3base	http://bioinf.uta.fi/C3base	C3	C3 deficiency
C5base	http://bioinf.uta.fi/C5base	C5	C5 deficiency
C6base	http://bioinf.uta.fi/C6base	C6	C6 deficiency
C7base	http://bioinf.uta.fi/C7base	C7	C7 deficiency
C8Bbase	http://bioinf.uta.fi/C8Bbase	C8B	C8B deficiency
C9base	http://bioinf.uta.fi/C9base	C9	C9 deficiency
CA2base	http://bioinf.uta.fi/CA2base	CA2	Osteopetrosis with renal tubular acidosis
CASP10base	http://bioinf.uta.fi/CASP10base	CASP10	Autoimmune lymphoproliferative syndrome, type II
CASP8base	http://bioinf.uta.fi/CASP8base	CASP8	Caspase 8 deficiency
Catalogue of Somatic Mutations in Cancer (COSMIC)	http://www.sanger.ac.uk/genetics/CGP/cosmic/	>13 500	multiple tissues and histologies
CD19base	http://bioinf.uta.fi/CD19base	CD19	CD19 deficiency
CD247base	http://bioinf.uta.fi/CD247base	CD247	CD3ζ deficiency
CD3Dbase	http://bioinf.uta.fi/CD3Dbase	CD3D	CD3δ deficiency
CD3Ebase	http://bioinf.uta.fi/CD3Ebase	CD3 E	CD3ε deficiency
CD3Gbase	http://bioinf.uta.fi/CD3Gbase	CD3 G	CD3γ deficiency
CD40base	http://bioinf.uta.fi/CD40base	CD40	CD40 deficiency
CD40Lbase	http://bioinf.uta.fi/CD40Lbase	CD40 L	X-linked Hyper-IgM syndrome (XHIM)
CD55base	http://bioinf.uta.fi/CD55base	CD55	Decay-accelerating factor (CD55) deficiency
CD59base	http://bioinf.uta.fi/CD59base	CD59	CD59 deficiency
CD79Abase	http://bioinf.uta.fi/CD79Abase	CD79 A	Igα deficiency
CD79Bbase	http://bioinf.uta.fi/CD79Bbase	CD79B	Igβ deficiency
CD8Abase	http://bioinf.uta.fi/CD8Abase	CD8 A	CD8α deficiency
CEBPEbase	http://bioinf.uta.fi/CEBPEbase	CEBPE	Neutrophil-specific granule deficiency
CFDbase	http://bioinf.uta.fi/CFDbase	CFD	Factor D deficiency
CFHbase	http://bioinf.uta.fi/CFHbase	CFH	Factor H deficiency
CFIbase	http://bioinf.uta.fi/CFIbase	CFI	Complement factor I deficiency
CFPbase	http://bioinf.uta.fi/CFPbase	CFP	Properdin deficiency
CIITAbase	http://bioinf.uta.fi/CIITAbase	CIITA	MHCII transactivating protein deficiency
CLCN7base	http://bioinf.uta.fi/CLCN7base	CLCN7	Autosomal dominant osteopetrosis, type 2
CTSCbase	http://bioinf.uta.fi/CTSCbase	CTSC	Papillon-Lefevre syndrome
CXCR4base	http://bioinf.uta.fi/CXCR4base	CXCR4	WHIM syndrome
CYBAbase	http://bioinf.uta.fi/CYBAbase	CYBA	Autosomal recessive p22phox deficiency
CYBBbase	http://bioinf.uta.fi/CYBBbase	CYBB	X-linked chronic granulomatous disease (XCGD)
DCLRE1Cbase	http://bioinf.uta.fi/DCLRE1Cbase	DCLRE1 C	Artemis deficiency
DKC1base	http://bioinf.uta.fi/DKC1base	DKC1	Hoyeraal-Hreidarsson syndrome
DNMT3Bbase	http://bioinf.uta.fi/DNMT3Bbase	DNMT3B	ICF syndrome
ELA2base	http://bioinf.uta.fi/ELA2base	ELA2	Cyclic neutropenia; Congenital neutropenia
F12base	http://bioinf.uta.fi/F12base	F12	Hereditary angioedema type III
Fanconi Anemia Mutation Database	http://www.rockefeller.edu/fanconi/mutate/jumpa.html	FANCA, FANCB, FANCC, FANCD2, FANCE, FANCF, FANCG, FANCL	Fanconi anemia
FASLGbase	http://bioinf.uta.fi/FASLGbase	FASLG	Autoimmune lymphoproliferative syndrome, type 1B (ALPS1B)
FCGR1Abase	http://bioinf.uta.fi/FCGR1Abase	FCGR1 A	CD64 deficiency
FCGR3Abase	http://bioinf.uta.fi/FCGR3Abase	FCGR3 A	Natural killer cell deficiency
FH aHUS Mutation Database	http://www.fh-hus.org/	CFH	Hemolytic uraemic syndrome (HUS)
FOXN1base	http://bioinf.uta.fi/FOXN1base	FOXN1	T-cell immunodeficiency, congenital alopecia, and nail dystrophy
FOXP3base	http://bioinf.uta.fi/FOXP3base	FOXP3	Immunodysregulation, polyendocrinopathy, and enteropathy, X-linked; IPEX
GFI1base	http://bioinf.uta.fi/GFI1base	GFI1	Severe congenital neutropenia (SCN); Nonimmune chronic idiopathic neutropenia of adults (NI-CINA)
HAEdb	http://hae.enzim.hu/	SERPING1	Hereditary angioedema
HAX1base	http://bioinf.uta.fi/HAX1base	HAX1	Severe congenital neutropenia (Kostmann disease)
ICOSbase	http://bioinf.uta.fi/ICOSbase	ICOS	ICOS deficiency
IFNGR1base	http://bioinf.uta.fi/IFNGR1base	IFNGR1	IFNγ1-receptor deficiency
IFNGR2base	http://bioinf.uta.fi/IFNGR2base	IFNGR2	IFNγ2-receptor deficiency
IGHG2base	http://bioinf.uta.fi/IGHG2base	IGHG2	IgG2 deficiency
IGHMbase	http://bioinf.uta.fi/IGHMbase	IGHM	μ heavy chain deficiency
IGLL1base	http://bioinf.uta.fi/IGLL1base	IGLL1	λ5surrogate light-chain deficiency
IKBKGbase	http://bioinf.uta.fi/IKBKGbase	IKBKG	Nemo deficiency
IL12Bbase	http://bioinf.uta.fi/IL12Bbase	IL12B	Interleukin-12 (IL12) p40 deficiency
IL12RB1base	http://bioinf.uta.fi/IL12RB1base	IL12RB1	Interleukin-12 receptor β1 deficiency
IL2RAbase	http://bioinf.uta.fi/IL2RAbase	IL2RA	Interleukin-2 receptor α deficiency
IL2RGbase	http://research.nhgri.nih.gov/scid/	IL2RG	X-linked SCID
IL7Rbase	http://bioinf.uta.fi/IL7Rbase	IL7 R	Interleukin-7 receptor α deficiency
Infevers	http://fmf.igh.cnrs.fr/ISSAID/infevers/	LPIN2	Majeed syndrome
Infevers	http://fmf.igh.cnrs.fr/ISSAID/infevers/	MEFV	Familial Mediterranean fever
Infevers	http://fmf.igh.cnrs.fr/ISSAID/infevers/	MVK	Hyper IgD Syndrome and periodic fever
Infevers	http://fmf.igh.cnrs.fr/ISSAID/infevers/	NLRP3	Familial cold autoinflammatory syndrome, Muckle-Wells syndrome and chronic infantile neurological cutaneous and articular syndrome
Infevers	http://fmf.igh.cnrs.fr/ISSAID/infevers/	NLRP7	Recurrent Hydatidiform moles and reproductive wastage
Infevers	http://fmf.igh.cnrs.fr/ISSAID/infevers/	NOD2	Blau syndrome, Chrohn's disease, early onset sarcoidosis
Infevers	http://fmf.igh.cnrs.fr/ISSAID/infevers/	PSTPIP1	Pyogenic sterile arthritis, pyoderma gangrenosum and acne syndrome
Infevers	http://fmf.igh.cnrs.fr/ISSAID/infevers/	TNFRSF1 A	Tumor necrosis factor receptor-associated periodic syndrome
IRAK4base	http://bioinf.uta.fi/IRAK4base	IRAK4	IRAK4 deficiency
ITGB2base	http://bioinf.uta.fi/ITGB2base	ITGB2	Leukocyte adhesion deficiency I (LAD-I)
JAK3base	http://bioinf.uta.fi/JAK3base	JAK3	Jak3 deficiency
LIG1base	http://bioinf.uta.fi/LIG1base	LIG1	DNA ligase I deficiency
LIG4base	http://bioinf.uta.fi/LIG4base	LIG4	LIG4 syndrome
LRRC8Abase	http://bioinf.uta.fi/LRRC8Abase	LRRC8 A	Non-Bruton type autosomal dominant agammaglobulinemia
LYSTbase	http://bioinf.uta.fi/LYSTbase	LYST	Chediak–Higashi syndrome
MAPBPIPbase	http://bioinf.uta.fi/MAPBPIPbase	MAPBPIP	Endosomal adaptor protein p14 deficiency
MASP2base	http://bioinf.uta.fi/MASP2base	MASP2	MASP2 deficiency
MLPHbase	http://bioinf.uta.fi/MLPHbase	MLPH	Griscelli syndrome, type 3 (GS3)
MPObase	http://bioinf.uta.fi/MPObase	MPO	Myeloperoxidase deficiency
MRE11Abase	http://bioinf.uta.fi/MRE11Abase	MRE11 A	Ataxia-telangiectasia-like disorder (ATLD)
Mutation Database - Papillon Lefevre Syndrome	http://www.genetics.pitt.edu/mutation/pls/	CTSC	Papillon Lefevre syndrome
MYO5Abase	http://bioinf.uta.fi/MYO5Abase	MYO5 A	Griscelli syndrome, type 1 (GS1)
NCF1base	http://bioinf.uta.fi/NCF1base	NCF1	Autosomal recessive p47phox deficiency
NCF2base	http://bioinf.uta.fi/NCF2base	NCF2	Autosomal recessive p67phox deficiency
NFKBIAbase	http://bioinf.uta.fi/NFKBIAbase	NFKBIA	Autosomal dominant anhidrotic ectodermal dysplasia and T-cell immunodeficiency
NHEJ1base	http://bioinf.uta.fi/NHEJ1base	NHEJ1	Combined immunodeficiency (CID) associated with microcephaly and increased cellular sensitivity to IR
NPbase	http://bioinf.uta.fi/Npbase	NP	PNP deficiency
NRASbase	http://bioinf.uta.fi/NRASbase	NRAS	Autoimmune lymphoproliferative syndrome type IV
ORAI1base	http://bioinf.uta.fi/ORAI1base	ORAI1	Severe combined immunodeficiency
OSTM1base	http://bioinf.uta.fi/OSTM1base	OSTM1	Autosomal recessive osteopetrosis
PIK3R1base	http://bioinf.uta.fi/PIK3R1base	PIK3R1	Pathogenic mutations in the p85α SH2 domain
PRF1base	http://bioinf.uta.fi/PRF1base	PRF1	Familial haemophagocytic lymphohistiocytosis, type II (FHL2)
PTPN11base	http://bioinf.uta.fi/PTPN11base	PTPN11	Pathogenic mutations in the SHP-2 SH2 domain
PTPRCbase	http://bioinf.uta.fi/PTPRCbase	PTPRC	CD45 deficiency
RAB27Abase	http://bioinf.uta.fi/RAB27Abase	RAB27 A	Griscelli syndrome, type 2 (GS2)
RAC2base	http://bioinf.uta.fi/RAC2base	RAC2	Neutrophil immunodeficiency syndrome
RAG1base	http://bioinf.uta.fi/RAG1base	RAG1	RAG1 deficiency
RAG2base	http://bioinf.uta.fi/RAG2base	RAG2	RAG2 deficiency
RASA1base	http://bioinf.uta.fi/RASA1base	RASA1	Pathogenic mutations in the RasGAP SH2 domain
RASGRP2base	http://bioinf.uta.fi/RASGRP2base	RASGRP2	Leukocyte adhesion deficiency III
RFX5base	http://bioinf.uta.fi/RFX5base	RFX5	MHCII promoter X box regulatory factor 5 deficiency
RFXANKbase	http://bioinf.uta.fi/RFXANKbase	RFXANK	Ankyrin repeat containing regulatory factor X-associated protein deficiency
RFXAPbase	http://bioinf.uta.fi/RFXAPbase	RFXAP	Regulatory factor X-associated protein deficiency
Roche Cancer Genome Database (RCGDB)	http://rcgdb.bioinf.uni-sb.de/MutomeWeb	>10 000	multiple tissues and histologies
SBDSbase	http://bioinf.uta.fi/SBDSbase	SBDS	Shwachman–Diamond syndrome
SERPING1base	http://bioinf.uta.fi/SERPING1base	SERPING1	Hereditary angioedema
SH2base	http://bioinf.uta.fi/SH2base	SH2	Pathogenic SH2 domain mutations
SH2D1Abase	http://bioinf.uta.fi/SH2D1Abase	SH2D1 A	X-linked lymphoproliferative syndrome (XLP)
SLC35C1base	http://bioinf.uta.fi/SLC35C1base	SLC35C1	Leukocyte adhesion deficiency I I (LAD-II)
SMARCAL1base	http://bioinf.uta.fi/SMARCAL1base	SMARCAL1	Schimke immuno-osseous dysplasia
SP110base	http://bioinf.uta.fi/SP110base	SP110	Hepatic veno-occlusive disease with immunodeficiency syndrome (VODI)
SPINK5base	http://bioinf.uta.fi/SPINK5base	SPINK5	Netherton syndrome
STAT1base	http://bioinf.uta.fi/STAT1base	STAT1	STAT1 deficiency
STAT3base	http://bioinf.uta.fi/STAT3base	STAT3	Hyper-IgE syndrome
STAT5Bbase	http://bioinf.uta.fi/STAT5Bbase	STAT5B	Growth hormone insensitivity with immunodeficiency
STX11base	http://bioinf.uta.fi/STX11base	STX11	Familial haemophagocytic lymphohistiocytosis 4
TAP1base	http://bioinf.uta.fi/TAP1base	TAP1	TAP1 deficiency
TAP2base	http://bioinf.uta.fi/TAP2base	TAP2	TAP2 deficiency
TAPBPbase	http://bioinf.uta.fi/TAPBPbase	TAPBP	Tapasin deficiency
TAZbase	http://bioinf.uta.fi/TAZbase	TAZ	Barth syndrome
TCIRG1base	http://bioinf.uta.fi/TCIRG1base	TCIRG1	Autosomal recessive osteopetrosis (arOP)
TCN2base	http://bioinf.uta.fi/TCN2base	TCN2	Transcobalamin II deficiency
The Cancer Genome Atlas (TCGA)	http://cancergenome.nih.gov/	>400	Brain (glioblastoma multiforme), ovarian (serous cystadenocarcinoma)	Login for TCGA Data Portal necessary, more disease to come
TLR3base	http://bioinf.uta.fi/TLR3base	TLR3	Influenza-associated encephalopathy
TMC6base	http://bioinf.uta.fi/TMC6base	TMC6	Epidermodysplasia verruciformis
TMC8base	http://bioinf.uta.fi/TMC8base	TMC8	Epidermodysplasia verruciformis
TNFRSF13Bbase	http://bioinf.uta.fi/TNFRSF13Bbase	TNFRSF13B	TACI deficiency
TYK2base	http://bioinf.uta.fi/TYK2base	TYK2	TYK2 deficiency
UMD-ATP7B	http://www.umd.be/ATP7B/	ATPase, Cu++ transporting, beta polypetide	Wilson disease
UMD-COL3A1	http://www.umd.be/COL3A1/	COL3A1	COL3A1 deficiency	Restricted access
UMD-CSA	http://www.umd.be/CSA/	ERCC8	ERCC8 deficiency
UMD-CSB	http://www.umd.be/CSB/	ERCC6	ERCC6 deficiency
UMD-DFNB1-GJB2	http://www.umd.be/DFNB1-GJB2/	DFNB1, GJB2	DFNB1 deficiency	Restricted access
UMD-DMD	http://www.umd.be/DMD/	DMD	DMD deficiency
UMD-DPYD	http://www.umd.be/DPYD/	DPYD	Dihydropyrimidine dehydrogenase disease	Restricted access
UMD-EMD	http://www.umd.be/EMD/	EMD	EMD deficiency
UMD-FBN1	http://www.umd.be/FBN1/	FBN1	Marfan syndrome and related disorders
UMD-FBN2	http://194.167.35.168/FBN2/	FBN2	Congenital contractural arachnodactyly
UMD-LDLR	http://www.umd.be/LDLR/	LDLR	Familial hypercholesterolemia (FH)
UMD-LMNA	http://www.umd.be/LMNA/	LMNA	LMNA deficiency
UMD-TGFBR1	http://www.umd.be/LSDB.html	TGFBR1	TGFBR1 deficiency	Restricted access
UMD-TGFBR2	http://www.umd.be/TGFBR2/	TGFBR2	Marfan syndrome, Loeys–Dietz syndome, Familial thoracic aortic anezrysms and dissections
UMD-USHbases	http://www.umd.be/usher.html		Usher syndrome
UNC13Dbase	http://bioinf.uta.fi/UNC13Dbase	UNC13D	Familial hemophagocytic lymphohistiocytosis 3
UNC93B1base	http://bioinf.uta.fi/UNC93B1base	UNC93B1	UNC93B deficiency (Herpes simplex encephalitis)
UNGbase	http://bioinf.uta.fi/UNGbase	UNG	UNG deficiency
WASbase	http://bioinf.uta.fi/WASbase	WAS	Wiskott–Aldrich syndrome (WAS)
WASPbase	http://homepage.mac.com/kohsukeimai/wasp/WASPbase.html	WASP	Wiskott–Aldrich syndrome
ZAP70base	http://bioinf.uta.fi/ZAP70base	ZAP70	ZAP70 deficiency

For each database the disease as well as the genes have been covered.

Prominent disease mutation databases are the public IDbases (53) maintained at the Institute of Medical Technology, University of Tampere. The IDbases are LSDBs for immunodeficiency-causing mutations. The project maintains 122 different IDBases containing altogether data for 5359 patients. In addition to gene mutations, IDbases provide information about clinical presentation. All information has been collected from the literature as well as directly from researchers. The databases do not provide any sophisticated search interface and allow to download the data as a txt-file.

Conclusion

All databases presented are good starting points to retrieve human variation data for certain use cases depending on the provided interfaces. However, as soon as a query gets more complicated, an integrative approach will be necessary. Unfortunately, the diversity of current mutation information systems and the underlying data models make it difficult to mine human variation databases in an integrative approach. Currently, researchers typically have to browse and search several databases to obtain the required information. No unified access to all different cancer genome related data sources exists resulting in a need for more efficient integrative systems. With COSMIC, which is currently integrating TCGA and IARC TP53 information, and the RCGDB, which already integrates most of in this review presented data sources, two promising integrative data resources are available. Nevertheless, the standardization and virtual consolidation of existing databases will be one major challenge for future developments. Although these problems have already been discussed in previous publications (54–56), the current situation concerning mutation databases and their heterogeneity is still an acute problem due to the exponential growth of data generated by genome sequencing. This review is meant to provide an overview on the current status of mutation data in public resources to overcome the difficulties for users to know where to find the information they are looking for.

Funding

This work was supported by the Roche Postdoc Fellowship Program.

Conflict of interest. None declared.